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/srv/reproducible-results/rbuild-debian/tmp.FTzzuDJkiI/dbd-tmp-gqMLHgR/diffoscope_1mz05yxs/tmps7w_13tl/8e5010e4-eaf1-4a21-82fa-15b7b04bdbec vs.

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"replace": {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Least squares fitting of models to data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is a quick introduction to `statsmodels` for physical scientists (e.g. physicists, astronomers) or engineers.\n",
"\n",
"Why is this needed?\n",
"\n",
"Because most of `statsmodels` was written by statisticians and they use a different terminology and sometimes methods, making it hard to know which classes and functions are relevant and what their inputs and outputs mean."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
" from pandas.core import datetools\n"
]
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import statsmodels.api as sm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Linear models"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Assume you have data points with measurements `y` at positions `x` as well as measurement errors `y_err`.\n",
"\n",
"How can you use `statsmodels` to fit a straight line model to this data?\n",
"\n",
"For an extensive discussion see [Hogg et al. (2010), \"Data analysis recipes: Fitting a model to data\"](http://arxiv.org/abs/1008.4686) ... we'll use the example data given by them in Table 1.\n",
"\n",
"So the model is `f(x) = a * x + b` and on Figure 1 they print the result we want to reproduce ... the best-fit parameter and the parameter errors for a \"standard weighted least-squares fit\" for this data are:\n",
"* `a = 2.24 +- 0.11`\n",
"* `b = 34 +- 18`"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>x</th>\n",
" <th>y</th>\n",
" <th>y_err</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>201.0</td>\n",
" <td>592.0</td>\n",
" <td>61.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>244.0</td>\n",
" <td>401.0</td>\n",
" <td>25.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>47.0</td>\n",
" <td>583.0</td>\n",
" <td>38.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>287.0</td>\n",
" <td>402.0</td>\n",
" <td>15.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>203.0</td>\n",
" <td>495.0</td>\n",
" <td>21.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" x y y_err\n",
"0 201.0 592.0 61.0\n",
"1 244.0 401.0 25.0\n",
"2 47.0 583.0 38.0\n",
"3 287.0 402.0 15.0\n",
"4 203.0 495.0 21.0"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = \"\"\"\n",
" x y y_err\n",
"201 592 61\n",
"244 401 25\n",
" 47 583 38\n",
"287 402 15\n",
"203 495 21\n",
" 58 173 15\n",
"210 479 27\n",
"202 504 14\n",
"198 510 30\n",
"158 416 16\n",
"165 393 14\n",
"201 442 25\n",
"157 317 52\n",
"131 311 16\n",
"166 400 34\n",
"160 337 31\n",
"186 423 42\n",
"125 334 26\n",
"218 533 16\n",
"146 344 22\n",
"\"\"\"\n",
"try:\n",
" from StringIO import StringIO\n",
"except ImportError:\n",
" from io import StringIO\n",
"data = pd.read_csv(StringIO(data), delim_whitespace=True).astype(float)\n",
"\n",
"# Note: for the results we compare with the paper here, they drop the first four points\n",
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To fit a straight line use the weighted least squares class [WLS](http://www.statsmodels.org/devel/generated/statsmodels.regression.linear_model.WLS.html) ... the parameters are called:\n",
"* `exog` = `sm.add_constant(x)`\n",
"* `endog` = `y`\n",
"* `weights` = `1 / sqrt(y_err)`\n",
"\n",
"Note that `exog` must be a 2-dimensional array with `x` as a column and an extra column of ones. Adding this column of ones means you want to fit the model `y = a * x + b`, leaving it off means you want to fit the model `y = a * x`.\n",
"\n",
"And you have to use the option `cov_type='fixed scale'` to tell `statsmodels` that you really have measurement errors with an absolute scale. If you don't, `statsmodels` will treat the weights as relative weights between the data points and internally re-scale them so that the best-fit model will have `chi**2 / ndf = 1`."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" WLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: y R-squared: 0.400\n",
"Model: WLS Adj. R-squared: 0.367\n",
"Method: Least Squares F-statistic: 193.5\n",
"Date: Sat, 10 Apr 2021 Prob (F-statistic): 4.52e-11\n",
"Time: 01:00:04 Log-Likelihood: -119.06\n",
"No. Observations: 20 AIC: 242.1\n",
"Df Residuals: 18 BIC: 244.1\n",
"Df Model: 1 \n",
"Covariance Type: fixed scale \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const 213.2735 14.394 14.817 0.000 185.062 241.485\n",
"x 1.0767 0.077 13.910 0.000 0.925 1.228\n",
"==============================================================================\n",
"Omnibus: 0.943 Durbin-Watson: 2.901\n",
"Prob(Omnibus): 0.624 Jarque-Bera (JB): 0.181\n",
"Skew: -0.205 Prob(JB): 0.914\n",
"Kurtosis: 3.220 Cond. No. 575.\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors are based on fixed scale\n"
]
}
],
"source": [
"exog = sm.add_constant(data['x'])\n",
"endog = data['y']\n",
"weights = 1. / (data['y_err'] ** 2)\n",
"wls = sm.WLS(endog, exog, weights)\n",
"results = wls.fit(cov_type='fixed scale')\n",
"print(results.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Check against scipy.optimize.curve_fit"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"a = 1.077 +- 0.077\n",
"b = 213.273 +- 14.394\n"
]
}
],
"source": [
"# You can use `scipy.optimize.curve_fit` to get the best-fit parameters and parameter errors.\n",
"from scipy.optimize import curve_fit\n",
"\n",
"def f(x, a, b):\n",
" return a * x + b\n",
"\n",
"xdata = data['x']\n",
"ydata = data['y']\n",
"p0 = [0, 0] # initial parameter estimate\n",
"sigma = data['y_err']\n",
"popt, pcov = curve_fit(f, xdata, ydata, p0, sigma, absolute_sigma=True)\n",
"perr = np.sqrt(np.diag(pcov))\n",
"print('a = {0:10.3f} +- {1:10.3f}'.format(popt[0], perr[0]))\n",
"print('b = {0:10.3f} +- {1:10.3f}'.format(popt[1], perr[1]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Check against self-written cost function"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"a = 1.077\n",
"b = 213.274\n"
]
}
],
"source": [
"# You can also use `scipy.optimize.minimize` and write your own cost function.\n",
"# This doesn't give you the parameter errors though ... you'd have\n",
"# to estimate the HESSE matrix separately ...\n",
"from scipy.optimize import minimize\n",
"\n",
"def chi2(pars):\n",
" \"\"\"Cost function.\n",
" \"\"\"\n",
" y_model = pars[0] * data['x'] + pars[1]\n",
" chi = (data['y'] - y_model) / data['y_err']\n",
" return np.sum(chi ** 2)\n",
"\n",
"result = minimize(fun=chi2, x0=[0, 0])\n",
"popt = result.x\n",
"print('a = {0:10.3f}'.format(popt[0]))\n",
"print('b = {0:10.3f}'.format(popt[1]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Non-linear models"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# TODO: we could use the examples from here:\n",
"# http://probfit.readthedocs.org/en/latest/api.html#probfit.costfunc.Chi2Regression"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
}

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/srv/reproducible-results/rbuild-debian/tmp.FTzzuDJkiI/dbd-tmp-gqMLHgR/diffoscope_1mz05yxs/tmpbdj3qmw7/65593ce8-0cc2-4f1c-a73d-1f6527a32b4f vs.

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Differences: {
"replace": {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Discrete Choice Models"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Fair's Affair data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A survey of women only was conducted in 1974 by *Redbook* asking about extramarital affairs."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
" from pandas.core import datetools\n"
]
}
],
"source": [
"%matplotlib inline\n",
"\n",
"from __future__ import print_function\n",
"import numpy as np\n",
"from scipy import stats\n",
"import matplotlib.pyplot as plt\n",
"import statsmodels.api as sm\n",
"from statsmodels.formula.api import logit, probit, poisson, ols"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Fair, Ray. 1978. \"A Theory of Extramarital Affairs,\" `Journal of Political\n",
"Economy`, February, 45-61.\n",
"\n",
"The data is available at http://fairmodel.econ.yale.edu/rayfair/pdf/2011b.htm\n",
"\n"
]
}
],
"source": [
"print(sm.datasets.fair.SOURCE)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"::\n",
"\n",
" Number of observations: 6366\n",
" Number of variables: 9\n",
" Variable name definitions:\n",
"\n",
" rate_marriage : How rate marriage, 1 = very poor, 2 = poor, 3 = fair,\n",
" 4 = good, 5 = very good\n",
" age : Age\n",
" yrs_married : No. years married. Interval approximations. See\n",
" original paper for detailed explanation.\n",
" children : No. children\n",
" religious : How relgious, 1 = not, 2 = mildly, 3 = fairly,\n",
" 4 = strongly\n",
" educ : Level of education, 9 = grade school, 12 = high\n",
" school, 14 = some college, 16 = college graduate,\n",
" 17 = some graduate school, 20 = advanced degree\n",
" occupation : 1 = student, 2 = farming, agriculture; semi-skilled,\n",
" or unskilled worker; 3 = white-colloar; 4 = teacher\n",
" counselor social worker, nurse; artist, writers;\n",
" technician, skilled worker, 5 = managerial,\n",
" administrative, business, 6 = professional with\n",
" advanced degree\n",
" occupation_husb : Husband's occupation. Same as occupation.\n",
" affairs : measure of time spent in extramarital affairs\n",
"\n",
" See the original paper for more details.\n",
"\n"
]
}
],
"source": [
"print( sm.datasets.fair.NOTE)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dta = sm.datasets.fair.load_pandas().data"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" rate_marriage age yrs_married children religious educ occupation \\\n",
"0 3.0 32.0 9.0 3.0 3.0 17.0 2.0 \n",
"1 3.0 27.0 13.0 3.0 1.0 14.0 3.0 \n",
"2 4.0 22.0 2.5 0.0 1.0 16.0 3.0 \n",
"3 4.0 37.0 16.5 4.0 3.0 16.0 5.0 \n",
"4 5.0 27.0 9.0 1.0 1.0 14.0 3.0 \n",
"5 4.0 27.0 9.0 0.0 2.0 14.0 3.0 \n",
"6 5.0 37.0 23.0 5.5 2.0 12.0 5.0 \n",
"7 5.0 37.0 23.0 5.5 2.0 12.0 2.0 \n",
"8 3.0 22.0 2.5 0.0 2.0 12.0 3.0 \n",
"9 3.0 27.0 6.0 0.0 1.0 16.0 3.0 \n",
"\n",
" occupation_husb affairs affair \n",
"0 5.0 0.111111 1.0 \n",
"1 4.0 3.230769 1.0 \n",
"2 5.0 1.400000 1.0 \n",
"3 5.0 0.727273 1.0 \n",
"4 4.0 4.666666 1.0 \n",
"5 4.0 4.666666 1.0 \n",
"6 4.0 0.852174 1.0 \n",
"7 3.0 1.826086 1.0 \n",
"8 3.0 4.799999 1.0 \n",
"9 5.0 1.333333 1.0 \n"
]
}
],
"source": [
"dta['affair'] = (dta['affairs'] > 0).astype(float)\n",
"print(dta.head(10))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" rate_marriage age yrs_married children religious \\\n",
"count 6366.000000 6366.000000 6366.000000 6366.000000 6366.000000 \n",
"mean 4.109645 29.082862 9.009425 1.396874 2.426170 \n",
"std 0.961430 6.847882 7.280120 1.433471 0.878369 \n",
"min 1.000000 17.500000 0.500000 0.000000 1.000000 \n",
"25% 4.000000 22.000000 2.500000 0.000000 2.000000 \n",
"50% 4.000000 27.000000 6.000000 1.000000 2.000000 \n",
"75% 5.000000 32.000000 16.500000 2.000000 3.000000 \n",
"max 5.000000 42.000000 23.000000 5.500000 4.000000 \n",
"\n",
" educ occupation occupation_husb affairs affair \n",
"count 6366.000000 6366.000000 6366.000000 6366.000000 6366.000000 \n",
"mean 14.209865 3.424128 3.850141 0.705374 0.322495 \n",
"std 2.178003 0.942399 1.346435 2.203374 0.467468 \n",
"min 9.000000 1.000000 1.000000 0.000000 0.000000 \n",
"25% 12.000000 3.000000 3.000000 0.000000 0.000000 \n",
"50% 14.000000 3.000000 4.000000 0.000000 0.000000 \n",
"75% 16.000000 4.000000 5.000000 0.484848 1.000000 \n",
"max 20.000000 6.000000 6.000000 57.599991 1.000000 \n"
]
}
],
"source": [
"print(dta.describe())"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimization terminated successfully.\n",
" Current function value: 0.545314\n",
" Iterations 6\n"
]
}
],
"source": [
"affair_mod = logit(\"affair ~ occupation + educ + occupation_husb\" \n",
" \"+ rate_marriage + age + yrs_married + children\"\n",
" \" + religious\", dta).fit()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Logit Regression Results \n",
"==============================================================================\n",
"Dep. Variable: affair No. Observations: 6366\n",
"Model: Logit Df Residuals: 6357\n",
"Method: MLE Df Model: 8\n",
"Date: Sat, 10 Apr 2021 Pseudo R-squ.: 0.1327\n",
"Time: 01:00:11 Log-Likelihood: -3471.5\n",
"converged: True LL-Null: -4002.5\n",
" LLR p-value: 5.807e-224\n",
"===================================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"-----------------------------------------------------------------------------------\n",
"Intercept 3.7257 0.299 12.470 0.000 3.140 4.311\n",
"occupation 0.1602 0.034 4.717 0.000 0.094 0.227\n",
"educ -0.0392 0.015 -2.533 0.011 -0.070 -0.009\n",
"occupation_husb 0.0124 0.023 0.541 0.589 -0.033 0.057\n",
"rate_marriage -0.7161 0.031 -22.784 0.000 -0.778 -0.655\n",
"age -0.0605 0.010 -5.885 0.000 -0.081 -0.040\n",
"yrs_married 0.1100 0.011 10.054 0.000 0.089 0.131\n",
"children -0.0042 0.032 -0.134 0.893 -0.066 0.058\n",
"religious -0.3752 0.035 -10.792 0.000 -0.443 -0.307\n",
"===================================================================================\n"
]
}
],
"source": [
"print(affair_mod.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"How well are we predicting?"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[3882., 431.],\n",
" [1326., 727.]])"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"affair_mod.pred_table()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The coefficients of the discrete choice model do not tell us much. What we're after is marginal effects."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Logit Marginal Effects \n",
"=====================================\n",
"Dep. Variable: affair\n",
"Method: dydx\n",
"At: overall\n",
"===================================================================================\n",
" dy/dx std err z P>|z| [0.025 0.975]\n",
"-----------------------------------------------------------------------------------\n",
"occupation 0.0293 0.006 4.744 0.000 0.017 0.041\n",
"educ -0.0072 0.003 -2.538 0.011 -0.013 -0.002\n",
"occupation_husb 0.0023 0.004 0.541 0.589 -0.006 0.010\n",
"rate_marriage -0.1308 0.005 -26.891 0.000 -0.140 -0.121\n",
"age -0.0110 0.002 -5.937 0.000 -0.015 -0.007\n",
"yrs_married 0.0201 0.002 10.327 0.000 0.016 0.024\n",
"children -0.0008 0.006 -0.134 0.893 -0.012 0.011\n",
"religious -0.0685 0.006 -11.119 0.000 -0.081 -0.056\n",
"===================================================================================\n"
]
}
],
"source": [
"mfx = affair_mod.get_margeff()\n",
"print(mfx.summary())"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"rate_marriage 4.000000\n",
"age 37.000000\n",
"yrs_married 23.000000\n",
"children 3.000000\n",
"religious 3.000000\n",
"educ 12.000000\n",
"occupation 3.000000\n",
"occupation_husb 4.000000\n",
"affairs 0.521739\n",
"affair 1.000000\n",
"Name: 1000, dtype: float64\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib/python3/dist-packages/ipykernel_launcher.py:1: DeprecationWarning: \n",
".ix is deprecated. Please use\n",
".loc for label based indexing or\n",
".iloc for positional indexing\n",
"\n",
"See the documentation here:\n",
"http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
" \"\"\"Entry point for launching an IPython kernel.\n"
]
}
],
"source": [
"respondent1000 = dta.ix[1000]\n",
"print(respondent1000)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{1: 3.0, 2: 12.0, 3: 4.0, 4: 4.0, 5: 37.0, 6: 23.0, 7: 3.0, 8: 3.0, 0: 1}\n"
]
}
],
"source": [
"resp = dict(zip(range(1,9), respondent1000[[\"occupation\", \"educ\", \n",
" \"occupation_husb\", \"rate_marriage\", \n",
" \"age\", \"yrs_married\", \"children\", \n",
" \"religious\"]].tolist()))\n",
"resp.update({0 : 1})\n",
"print(resp)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Logit Marginal Effects \n",
"=====================================\n",
"Dep. Variable: affair\n",
"Method: dydx\n",
"At: overall\n",
"===================================================================================\n",
" dy/dx std err z P>|z| [0.025 0.975]\n",
"-----------------------------------------------------------------------------------\n",
"occupation 0.0400 0.008 4.711 0.000 0.023 0.057\n",
"educ -0.0098 0.004 -2.537 0.011 -0.017 -0.002\n",
"occupation_husb 0.0031 0.006 0.541 0.589 -0.008 0.014\n",
"rate_marriage -0.1788 0.008 -22.743 0.000 -0.194 -0.163\n",
"age -0.0151 0.003 -5.928 0.000 -0.020 -0.010\n",
"yrs_married 0.0275 0.003 10.256 0.000 0.022 0.033\n",
"children -0.0011 0.008 -0.134 0.893 -0.017 0.014\n",
"religious -0.0937 0.009 -10.722 0.000 -0.111 -0.077\n",
"===================================================================================\n"
]
}
],
"source": [
"mfx = affair_mod.get_margeff(atexog=resp)\n",
"print(mfx.summary())"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'pd' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-14-13d03df9f844>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maffair_mod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrespondent1000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# patsy requires a DataFrame, not a Series\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined"
]
}
],
"source": [
"affair_mod.predict(pd.DataFrame(respondent1000).T) # patsy requires a DataFrame, not a Series"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.07516159285055579"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"affair_mod.fittedvalues[1000]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.5187815572121453"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"affair_mod.model.cdf(affair_mod.fittedvalues[1000])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The \"correct\" model here is likely the Tobit model. We have an work in progress branch \"tobit-model\" on github, if anyone is interested in censored regression models."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise: Logit vs Probit"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12,8))\n",
"ax = fig.add_subplot(111)\n",
"support = np.linspace(-6, 6, 1000)\n",
"ax.plot(support, stats.logistic.cdf(support), 'r-', label='Logistic')\n",
"ax.plot(support, stats.norm.cdf(support), label='Probit')\n",
"ax.legend();"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12,8))\n",
"ax = fig.add_subplot(111)\n",
"support = np.linspace(-6, 6, 1000)\n",
"ax.plot(support, stats.logistic.pdf(support), 'r-', label='Logistic')\n",
"ax.plot(support, stats.norm.pdf(support), label='Probit')\n",
"ax.legend();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compare the estimates of the Logit Fair model above to a Probit model. Does the prediction table look better? Much difference in marginal effects?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Genarlized Linear Model Example"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Jeff Gill's `Generalized Linear Models: A Unified Approach`\n",
"\n",
"http://jgill.wustl.edu/research/books.html\n",
"\n"
]
}
],
"source": [
"print(sm.datasets.star98.SOURCE)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"This data is on the California education policy and outcomes (STAR program\n",
"results for 1998. The data measured standardized testing by the California\n",
"Department of Education that required evaluation of 2nd - 11th grade students\n",
"by the the Stanford 9 test on a variety of subjects. This dataset is at\n",
"the level of the unified school district and consists of 303 cases. The\n",
"binary response variable represents the number of 9th graders scoring\n",
"over the national median value on the mathematics exam.\n",
"\n",
"The data used in this example is only a subset of the original source.\n",
"\n"
]
}
],
"source": [
"print(sm.datasets.star98.DESCRLONG)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"::\n",
"\n",
" Number of Observations - 303 (counties in California).\n",
"\n",
" Number of Variables - 13 and 8 interaction terms.\n",
"\n",
" Definition of variables names::\n",
"\n",
" NABOVE - Total number of students above the national median for the\n",
" math section.\n",
" NBELOW - Total number of students below the national median for the\n",
" math section.\n",
" LOWINC - Percentage of low income students\n",
" PERASIAN - Percentage of Asian student\n",
" PERBLACK - Percentage of black students\n",
" PERHISP - Percentage of Hispanic students\n",
" PERMINTE - Percentage of minority teachers\n",
" AVYRSEXP - Sum of teachers' years in educational service divided by the\n",
" number of teachers.\n",
" AVSALK - Total salary budget including benefits divided by the number\n",
" of full-time teachers (in thousands)\n",
" PERSPENK - Per-pupil spending (in thousands)\n",
" PTRATIO - Pupil-teacher ratio.\n",
" PCTAF - Percentage of students taking UC/CSU prep courses\n",
" PCTCHRT - Percentage of charter schools\n",
" PCTYRRND - Percentage of year-round schools\n",
"\n",
" The below variables are interaction terms of the variables defined\n",
" above.\n",
"\n",
" PERMINTE_AVYRSEXP\n",
" PEMINTE_AVSAL\n",
" AVYRSEXP_AVSAL\n",
" PERSPEN_PTRATIO\n",
" PERSPEN_PCTAF\n",
" PTRATIO_PCTAF\n",
" PERMINTE_AVTRSEXP_AVSAL\n",
" PERSPEN_PTRATIO_PCTAF\n",
"\n"
]
}
],
"source": [
"print(sm.datasets.star98.NOTE)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['NABOVE', 'NBELOW', 'LOWINC', 'PERASIAN', 'PERBLACK', 'PERHISP',\n",
" 'PERMINTE', 'AVYRSEXP', 'AVSALK', 'PERSPENK', 'PTRATIO', 'PCTAF',\n",
" 'PCTCHRT', 'PCTYRRND', 'PERMINTE_AVYRSEXP', 'PERMINTE_AVSAL',\n",
" 'AVYRSEXP_AVSAL', 'PERSPEN_PTRATIO', 'PERSPEN_PCTAF', 'PTRATIO_PCTAF',\n",
" 'PERMINTE_AVYRSEXP_AVSAL', 'PERSPEN_PTRATIO_PCTAF'],\n",
" dtype='object')\n"
]
}
],
"source": [
"dta = sm.datasets.star98.load_pandas().data\n",
"print(dta.columns)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" NABOVE NBELOW LOWINC PERASIAN PERBLACK PERHISP PERMINTE\n",
"0 452.0 355.0 34.39730 23.299300 14.235280 11.411120 15.918370\n",
"1 144.0 40.0 17.36507 29.328380 8.234897 9.314884 13.636360\n",
"2 337.0 234.0 32.64324 9.226386 42.406310 13.543720 28.834360\n",
"3 395.0 178.0 11.90953 13.883090 3.796973 11.443110 11.111110\n",
"4 8.0 57.0 36.88889 12.187500 76.875000 7.604167 43.589740\n",
"5 1348.0 899.0 20.93149 28.023510 4.643221 13.808160 15.378490\n",
"6 477.0 887.0 53.26898 8.447858 19.374830 37.905330 25.525530\n",
"7 565.0 347.0 15.19009 3.665781 2.649680 13.092070 6.203008\n",
"8 205.0 320.0 28.21582 10.430420 6.786374 32.334300 13.461540\n",
"9 469.0 598.0 32.77897 17.178310 12.484930 28.323290 27.259890\n"
]
}
],
"source": [
"print(dta[['NABOVE', 'NBELOW', 'LOWINC', 'PERASIAN', 'PERBLACK', 'PERHISP', 'PERMINTE']].head(10))"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" AVYRSEXP AVSALK PERSPENK PTRATIO PCTAF PCTCHRT PCTYRRND\n",
"0 14.70646 59.15732 4.445207 21.71025 57.03276 0.0 22.222220\n",
"1 16.08324 59.50397 5.267598 20.44278 64.62264 0.0 0.000000\n",
"2 14.59559 60.56992 5.482922 18.95419 53.94191 0.0 0.000000\n",
"3 14.38939 58.33411 4.165093 21.63539 49.06103 0.0 7.142857\n",
"4 13.90568 63.15364 4.324902 18.77984 52.38095 0.0 0.000000\n",
"5 14.97755 66.97055 3.916104 24.51914 44.91578 0.0 2.380952\n",
"6 14.67829 57.62195 4.270903 22.21278 32.28916 0.0 12.121210\n",
"7 13.66197 63.44740 4.309734 24.59026 30.45267 0.0 0.000000\n",
"8 16.41760 57.84564 4.527603 21.74138 22.64574 0.0 0.000000\n",
"9 12.51864 57.80141 4.648917 20.26010 26.07099 0.0 0.000000\n"
]
}
],
"source": [
"print(dta[['AVYRSEXP', 'AVSALK', 'PERSPENK', 'PTRATIO', 'PCTAF', 'PCTCHRT', 'PCTYRRND']].head(10))"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"formula = 'NABOVE + NBELOW ~ LOWINC + PERASIAN + PERBLACK + PERHISP + PCTCHRT '\n",
"formula += '+ PCTYRRND + PERMINTE*AVYRSEXP*AVSALK + PERSPENK*PTRATIO*PCTAF'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Aside: Binomial distribution"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Toss a six-sided die 5 times, what's the probability of exactly 2 fours?"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.16075102880658435"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stats.binom(5, 1./6).pmf(2)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib/python3/dist-packages/ipykernel_launcher.py:2: DeprecationWarning: `comb` is deprecated!\n",
"Importing `comb` from scipy.misc is deprecated in scipy 1.0.0. Use `scipy.special.comb` instead.\n",
" \n"
]
},
{
"data": {
"text/plain": [
"0.1607510288065844"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from scipy.misc import comb\n",
"comb(5,2) * (1/6.)**2 * (5/6.)**3"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from statsmodels.formula.api import glm\n",
"glm_mod = glm(formula, dta, family=sm.families.Binomial()).fit()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Generalized Linear Model Regression Results \n",
"================================================================================\n",
"Dep. Variable: ['NABOVE', 'NBELOW'] No. Observations: 303\n",
"Model: GLM Df Residuals: 282\n",
"Model Family: Binomial Df Model: 20\n",
"Link Function: logit Scale: 1.0\n",
"Method: IRLS Log-Likelihood: -2998.6\n",
"Date: Sat, 10 Apr 2021 Deviance: 4078.8\n",
"Time: 01:00:13 Pearson chi2: 9.60\n",
"No. Iterations: 5 \n",
"============================================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"--------------------------------------------------------------------------------------------\n",
"Intercept 2.9589 1.547 1.913 0.056 -0.073 5.990\n",
"LOWINC -0.0168 0.000 -38.749 0.000 -0.018 -0.016\n",
"PERASIAN 0.0099 0.001 16.505 0.000 0.009 0.011\n",
"PERBLACK -0.0187 0.001 -25.182 0.000 -0.020 -0.017\n",
"PERHISP -0.0142 0.000 -32.818 0.000 -0.015 -0.013\n",
"PCTCHRT 0.0049 0.001 3.921 0.000 0.002 0.007\n",
"PCTYRRND -0.0036 0.000 -15.878 0.000 -0.004 -0.003\n",
"PERMINTE 0.2545 0.030 8.498 0.000 0.196 0.313\n",
"AVYRSEXP 0.2407 0.057 4.212 0.000 0.129 0.353\n",
"PERMINTE:AVYRSEXP -0.0141 0.002 -7.391 0.000 -0.018 -0.010\n",
"AVSALK 0.0804 0.014 5.775 0.000 0.053 0.108\n",
"PERMINTE:AVSALK -0.0040 0.000 -8.450 0.000 -0.005 -0.003\n",
"AVYRSEXP:AVSALK -0.0039 0.001 -4.059 0.000 -0.006 -0.002\n",
"PERMINTE:AVYRSEXP:AVSALK 0.0002 2.99e-05 7.428 0.000 0.000 0.000\n",
"PERSPENK -1.9522 0.317 -6.162 0.000 -2.573 -1.331\n",
"PTRATIO -0.3341 0.061 -5.453 0.000 -0.454 -0.214\n",
"PERSPENK:PTRATIO 0.0917 0.015 6.321 0.000 0.063 0.120\n",
"PCTAF -0.1690 0.033 -5.169 0.000 -0.233 -0.105\n",
"PERSPENK:PCTAF 0.0490 0.007 6.574 0.000 0.034 0.064\n",
"PTRATIO:PCTAF 0.0080 0.001 5.362 0.000 0.005 0.011\n",
"PERSPENK:PTRATIO:PCTAF -0.0022 0.000 -6.445 0.000 -0.003 -0.002\n",
"============================================================================================\n"
]
}
],
"source": [
"print(glm_mod.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The number of trials "
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 807.0\n",
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" ... \n",
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"300 595.0\n",
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"302 156.0\n",
"Length: 303, dtype: float64"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"glm_mod.model.data.orig_endog.sum(1)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
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"29 1408.837645\n",
" ... \n",
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"Length: 303, dtype: float64"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"glm_mod.fittedvalues * glm_mod.model.data.orig_endog.sum(1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First differences: We hold all explanatory variables constant at their means and manipulate the percentage of low income households to assess its impact\n",
"on the response variables:"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"exog = glm_mod.model.data.orig_exog # get the dataframe"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Intercept 1.000000\n",
"LOWINC 41.409877\n",
"PERASIAN 5.896335\n",
"PERBLACK 5.636808\n",
"PERHISP 34.398080\n",
"PCTCHRT 1.175909\n",
"PCTYRRND 11.611905\n",
"PERMINTE 14.694747\n",
"AVYRSEXP 14.253875\n",
"PERMINTE:AVYRSEXP 209.018700\n",
"AVSALK 58.640258\n",
"PERMINTE:AVSALK 879.979883\n",
"AVYRSEXP:AVSALK 839.718173\n",
"PERMINTE:AVYRSEXP:AVSALK 12585.266464\n",
"PERSPENK 4.320310\n",
"PTRATIO 22.464250\n",
"PERSPENK:PTRATIO 96.295756\n",
"PCTAF 33.630593\n",
"PERSPENK:PCTAF 147.235740\n",
"PTRATIO:PCTAF 747.445536\n",
"PERSPENK:PTRATIO:PCTAF 3243.607568\n",
"dtype: float64\n"
]
}
],
"source": [
"means25 = exog.mean()\n",
"print(means25)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Intercept 1.000000\n",
"LOWINC 26.683040\n",
"PERASIAN 5.896335\n",
"PERBLACK 5.636808\n",
"PERHISP 34.398080\n",
"PCTCHRT 1.175909\n",
"PCTYRRND 11.611905\n",
"PERMINTE 14.694747\n",
"AVYRSEXP 14.253875\n",
"PERMINTE:AVYRSEXP 209.018700\n",
"AVSALK 58.640258\n",
"PERMINTE:AVSALK 879.979883\n",
"AVYRSEXP:AVSALK 839.718173\n",
"PERMINTE:AVYRSEXP:AVSALK 12585.266464\n",
"PERSPENK 4.320310\n",
"PTRATIO 22.464250\n",
"PERSPENK:PTRATIO 96.295756\n",
"PCTAF 33.630593\n",
"PERSPENK:PCTAF 147.235740\n",
"PTRATIO:PCTAF 747.445536\n",
"PERSPENK:PTRATIO:PCTAF 3243.607568\n",
"dtype: float64\n"
]
}
],
"source": [
"means25['LOWINC'] = exog['LOWINC'].quantile(.25)\n",
"print(means25)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Intercept 1.000000\n",
"LOWINC 55.460075\n",
"PERASIAN 5.896335\n",
"PERBLACK 5.636808\n",
"PERHISP 34.398080\n",
"PCTCHRT 1.175909\n",
"PCTYRRND 11.611905\n",
"PERMINTE 14.694747\n",
"AVYRSEXP 14.253875\n",
"PERMINTE:AVYRSEXP 209.018700\n",
"AVSALK 58.640258\n",
"PERMINTE:AVSALK 879.979883\n",
"AVYRSEXP:AVSALK 839.718173\n",
"PERMINTE:AVYRSEXP:AVSALK 12585.266464\n",
"PERSPENK 4.320310\n",
"PTRATIO 22.464250\n",
"PERSPENK:PTRATIO 96.295756\n",
"PCTAF 33.630593\n",
"PERSPENK:PCTAF 147.235740\n",
"PTRATIO:PCTAF 747.445536\n",
"PERSPENK:PTRATIO:PCTAF 3243.607568\n",
"dtype: float64\n"
]
}
],
"source": [
"means75 = exog.mean()\n",
"means75['LOWINC'] = exog['LOWINC'].quantile(.75)\n",
"print(means75)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'pd' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-36-8a9e3216b7f0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mresp25\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mglm_mod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmeans25\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# patsy requires a DataFrame, not a Series\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mresp75\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mglm_mod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmeans75\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mdiff\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresp75\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mresp25\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined"
]
}
],
"source": [
"resp25 = glm_mod.predict(pd.DataFrame(means25).T) # patsy requires a DataFrame, not a Series\n",
"resp75 = glm_mod.predict(pd.DataFrame(means75).T)\n",
"diff = resp75 - resp25"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The interquartile first difference for the percentage of low income households in a school district is:"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'diff' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-37-f46d4183d9e3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"%2.4f%%\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdiff\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'diff' is not defined"
]
}
],
"source": [
"print(\"%2.4f%%\" % (diff[0]*100))"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"nobs = glm_mod.nobs\n",
"y = glm_mod.model.endog\n",
"yhat = glm_mod.mu"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from statsmodels.graphics.api import abline_plot\n",
"fig = plt.figure(figsize=(12,8))\n",
"ax = fig.add_subplot(111, ylabel='Observed Values', xlabel='Fitted Values')\n",
"ax.scatter(yhat, y)\n",
"y_vs_yhat = sm.OLS(y, sm.add_constant(yhat, prepend=True)).fit()\n",
"fig = abline_plot(model_results=y_vs_yhat, ax=ax)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Plot fitted values vs Pearson residuals"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pearson residuals are defined to be \n",
"\n",
"$$\\frac{(y - \\mu)}{\\sqrt{(var(\\mu))}}$$\n",
"\n",
"where var is typically determined by the family. E.g., binomial variance is $np(1 - p)$"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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bAQCIFcE0gNKdNTsz1u0AAMSKYBpA6bZuXq+Z6alVt81MT2nr5vWBSgQAQDYMQARQus4gQ2bzAABUHcE0gCC2bJgjeAYAVB5pHgAAAEBGBNMAAABARgTTAAAAQEYE0wAAAEBGBNMAAABARgTTAAAAQEYE0wAAAEBGBNMAAABARizaAgAACrOw2GK1U9QawTQAACjEwmJLV1x/m5ZXDkuSWkvLuuL62ySJgBq1QTANAACGytq7vGPngaOBdMfyymHt2HmAYBq1QTANAAAGmqR3+e6l5bFuB6oo2ABEMzvHzG4ys8+Y2X4ze3mosgAAgP6G9S6PctbszFi3A1UUcjaPQ5Iud/fzJT1J0svM7PyA5QEAAD0m6V3eunm9ZqanVt02Mz2lrZvX51I2IAbBgml3/7K7fyr5+9uSPiuJBCoAACIySe/ylg1zuvrSx2hudkYmaW52Rldf+hjypVErUeRMm9l5kjZI+tc+910m6TJJOvfcc0stF1AnTE8FIIutm9evypmWxutd3rJhjmMNai34oi1mdqqk90p6hbt/q/d+d7/G3efdfX7t2rXlFxCogc4AotbSslzHBhAtLLZCFw1A5OhdBoYL2jNtZtNqB9LvcPfrQ5YFqDOmpwIwCXqXgcFCzuZhkv5S0mfd/fWhygE0AdNTAQBQjJBpHhsl/XdJm8xsX/LzMwHLA9QW01MBAFCMkLN5fMLdzd0f6+6PT34+FKo8QJ0xPRUAAMWIYjYPAMXq5DoymwcAAPkimAYaggFEAADkj2A6cswNDABIizYDKB/BdMQ6cwN3pjTrzA0siYMjAGAV2gwgjOCLtmCwYXMDA0AWC4stbdy+S+u23aiN23excE+N0GYAYdAzHTHmBgaQJ3ou6402AwiDnumIMTcwgDzRc1lvtBlAGATTEWNuYAB5ouey3mgzgDBI84gYcwMDyNNZszNq9Qmc6bmsB9oMIAxz99BlSG1+ft737NkTuhgAUEm9OdNSu+fy6ksfQ8AFAD3MbK+7z496HD3TANAQ9FwCQP5GBtNm9hxJ/+Du3zaz10h6gqTfdfdPFV46AECuWAkTAPKVZgDibyaB9P8labOkv5H0Z8UWCwAAAIhfmmC6k1x3kaQ/c/f3S3pAcUUCAAAAqiFNznTLzN4s6ack/b6ZnSim1ENNLCy2yB8FAACZpQmmnyvpGZL+wN2XzOwHJG0ttlhA8VgNDkAROEkHmmVgD7OZnWFmZ0g6SdLNkr6e/H+/JOanQ+WxGhyAvHVO0ltLy3IdO0lfWGyFLhqAggzrmd4rySVZ1+8Ol/SwAssFFI7V4ADkbdhJOr3TQD0NDKbdfV2ZBQHKxmpwmASX8tEPJ+lA86RatMXMTpf0SLVTPiRJ7v6xogoFlGHr5vV9V4Pbunl9wFKhCsi3P4aTitU4SQeaZ+SsHGb2S5I+JmmnpNcmv68qtlhA8bZsmNPVlz5Gc7MzMklzszMsq4xUyLdvIz/4eFs3r9fM9NSq2zhJB+otTc/0yyX9sKRb3P1pZvYotYNqoPJYDQ5ZcCm/jfzg47FkO9A8aYLp77r7d81MZnaiu3/OzDjFBiLAJfYwuJTfxknF8dgngeZJs/jKl8xsVtKCpA+b2fsl3V1ssRCLhcWWNm7fpXXbbtTG7bsaffk2NlxiD4dL+W2DTh6adlLRwT4JNNPIYNrdn+XuS+5+laTflPSXkrYUXTCER8MQN/J2wyHfvi22k4rQJ//sk0AzjUzzMLNzu/69Pfn9EEl3FlIiRIN8yLhxiT0s8u3jyg+OYYYV9kmERIpROGlypm/UsUVbTpK0TtIBSRcUWC5EgIYhbuTtIgaxnFTEcPLPPolQYjiZbLI0aR6PcffHJr8fKelHJH2i+KIhNPIh4xbbJXYgpBhO/tknEQopRmGlGYC4irt/StJ8AWVBZGgY4kbeLnDM7MnTY91eBPZJhBLDyWSTpcmZ/vWuf9dIeoKkrxVWIkSjNx9y9uRpuUuvvHafduw8UEo+Fjlgw8VyiR0IzX2824vCPokQSDEKK03P9GldPyeqnUP9s0UWCvHYsmFOu7dt0hue93h9d+WIlpZXSpvZg9lEkLfQsz2gON9cXhnrdqBOuJIc1sieaXdntUMEGdwTw4Ai1EcRA3S4chIPeubQZDHNrNNEA4NpM/uA2rN49OXulxRSIkQpRD4WOWDIU94nZ4yej8vWzetXfR8SPXNoFlKMwhmW5vEHkv5Q7bmllyW9Jfm5V9K/F180xCTEzB7MJoI85X1yxuj5uDD4D0AoA3um3f2jkmRmv+PuP9511wfM7GOFlwxRCdHrQ08T8pR3GgBXTuJDzxyAENIMQFxrZg/r/GNm6yStLa5IiFGIXh96mpCnvAfocOUEZWLwLBAv8xHzBpnZMyRdI+kLaq+C+FBJ/8PddxZfvNXm5+d9z549Zb8tgJrIc8Bgb8601A7OOeGDxLZWZQwsRoeZ7XX3kWurjAymkxc7UdKjkn8/5+73T1i+TAimAcSkzo3uqM9W588+qbyD343bd/VNUZqbndHubZsmKitW48QF3dIG08Nm89jk7rvM7NKeux5uZnL36ycuJQBUWF1zdEfNVMJMJsPlPXMM+fnlYUpWZDEsZ/opye+L+/w8s+ByAQACGTVTCTOZDJd38Et+fnk4cUEWw2bzuDL5/YvlFQcAENqogIKAY7i8Z45hZqPysPgPshg5m4eZvdzMHmhtf2FmnzKzp5dROABA+Ub1hNJTOlzeM8cUObMRs4SsxrLcyGLkcuKSXuzuf2xmmyU9WNIvSvorSf9YaMkA1AaD1aplVE8oPaXDFbG0cx75+b374dMetVbv3dsi970Ly3IjizRT433a3R9rZn8s6WZ3f5+ZLbr7hnKKeAyzeQDV0Wm4W0vLMkndRxpGx8eP2Tzqpd8sFb37ZQezhABtuU2NZ2Z/JWlO0jpJj5M0pXZQfWEeBR0HwXQz0EhXX7+GuxcNNlCeQdPr9WOSbt9+UbEFAipg4qnxurxE0uMlfcHd7zOz71M71QPIHVNu1UO/2R56MVgNKM84+xu578B40iwn7pLOl/Rryf+nSDqpsBKh0Zhyqx7SNNw02EB5Bu1v1vM/ue/A+NIE038q6cmSXpD8/21JbyqsRGg0ptyqh1GBMg12sZihAb0GzVLxwiedW8gsIUCTpEnzeKK7P8HMFiXJ3e8xswcUXC40VB3n+GxiDni/2R46g53mGlIHoZAqhX6YpQIoTppgesXMppQM+jWztZKO5PHmZvZWtVdT/Kq7PzqP10S11W3KraYGNjTcoxV1ksVyyBgkj+n1ABwvTTD9J5LeJ+nBZvY6Sc+W9Jqc3v+vJb1R0ttyej1UXN2CsCYHNjTcgxV5kkWqVDqhrxiFfn8A+RkZTLv7O8xsr6SfUPtK7RZ3/2web+7uHzOz8/J4LdRHnYIwAhv0U+RJVh1TpfIW+opR6PcHkK80AxDl7p9z9ze5+xslfdnMXl1wuY4ys8vMbI+Z7Tl48GBZbwvkgmWX0U+ak6ysgwjrshxykYMoQ88aFPr9AeRrYDBtZueY2TVm9kEz+yUzO9nM/lDS59VeVrwU7n6Nu8+7+/zatWvLelsgF3UJbJCvUSdZnZ7L1tKyXMd6LtMElFs2zOnqSx9T6RkaJvn8aYS+YhT6/YGQ6jjb0LA0j7dJ+qik90p6hqRbJO2X9Fh3/0oJZQMqry454OR35mvUQNtJ00CqnipV9FiD0Kkwod+/KThuxaeuKU7Dgukz3P2q5O+dZvZfkn7Y3e8vvlhA9Qw6cFc9sKnrwS+kUSdZg5Z9bkrPZdE9t6FnDQr9/k1QxeNWE4L/ug7KHzoA0cxO17EFkr4i6WQzO0WS3P0bk765mb1T0lMlnWlmX5J0pbv/5aSvC5StyAN36ANsXQ9+oQ06yVpYbB2dk7tXU3oui+65DX3FKPT7N0HVjltVDP6zqGuK07Bg+kGS9mr1aqOfSn67pIdN+ubu/oLRj0IWoQOwpinqwB3DAbauB79Y7dh5oG8gbVJjei7L6LkNfcUo9PvXXdWOW1UL/rOqa4rTwGDa3c8rsRyVUJUANYYArGmKOnDHcICt68EvVoO2GVf199+0x1B6bjGpqh23ygz+Q8YydU1xSrNoC1StADWGAKwssZzgFHXgjqF3pa4Hv1gN2pbmIg0C0hr3GErPLSZRteNWWcF/6FimrifKqeaZRrXmBY0hACtD0dNnjaOoKfBimKe6DlOtVUns0ylmndaqSsdQVF/Vjltl7fcx7IdbNsxp97ZNun37Rdq9bVO038k46JlOqUoBatUub2XtXU7bA19G73VRZ9ux9K6M20sYyxWDIhX1GWPuuZmkV6tKx1DUQ5WubpS137MfFiNVMG1mU5K+v/vx7n5nUYWKUZUC1FgCsDT6Nc6vvHafXnHtPs2NOJikXUVunMZ/kgCpiAN3zIHVIKEvI5ah6M8YaxAwSQpZlY6hQAhl7Pfsh8UYmeZhZr8q6b8kfVjSjcnPBwsuV3Riv/TarUqXt/o1zp2ZDEalbaRJgRjU+L/2A/uPe15MaSPdqnZJLIbLiEVrwmfsZ5JerSodQ4G6Yj8sRpqe6ZdLWu/uXy+6MDGrWg9hrD1bvUY1wsN6vdL0wA96/XvuW9HCYmvV6zZp4GaRmnAZsQmfsZ9JerWqdgwF6oj9sBhpgum7JH2z6IJUQVUC1CoZ1Dh3GxSgpDkoDHv93iC5qQFS3ppwGbEJn7GfSVPIYjmGViGnvwplRDXFsh/WSZpg+guSbjazGyUdXUrc3V9fWKnQGP0a517DApRRB4Wtm9frFdfu63tfb5Dc1AApb8MCrjoECAuLLd33vUPH3d6ES6V16NWqQk5/FcoI4Jg0wfSdyc8Dkh8gN92Nc2tp+bhllMcJUAYFalfdsF9LyyvHPb43SK7SwM2YDQq4JFU+QOgNcjpMq3OmY/k8RZy8VL1Xq+x0rizfQVVTzupwsgxkMTKYdvfXSpKZnZr8f2/RhUKzdDfOWQ/Gw3pyrrrkglRBcky9blVvlPoFXBu376pkgNCtX5AjHT9oVgofUNO72V/ZK81l+Q6qmHLG9oYmGxlMm9mjJb1d0hnJ/1+T9H+7+/HTIQATytrrNawnZ/e2TUcfk2YZ49AH/ro2SlUMEHqlKWssJwhV7d0sWpnpXFm/gyqmnLG9ocnSpHlcI+nX3f0mSTKzp0p6i6QfLbBcKFFevaAhe1NHBWoxBMlp1bVRqmKA0CvNgFkpjhOEOpy8FKHMdK6s30EVU85i2d6qflUP1ZRmOfFTOoG0JLn7zZJOKaxEKFVecyuHnqN5kmW3sy6PXJRYGqW81WF+036foZ8YThDyWoo+tv0jq87neOW1+3TiCWt0+snThc/Dn/U7qNJaAR15bW+TCN0OobnSBNNfMLPfNLPzkp/XqD3DB2ogr8UnQi9ikTVQi/HgG0OjVIQqBgi9ej/D6SdPa3qNrXpMLCcIeZy8xLh/ZNH7OZaWV/TdlSN6w/MeX+hCSJN8B1VbrCmGk+XQ7RCaK02ax4slvVbS9cn/H09uQw3k1Qsaujc16+DBGFMqqniJN60qpdsM0vsZYr2snMeA2hD7RxH1GWo/j2lQc9Fi+Kyh2yE0V5rZPO6R9GuSZGZTaqd9fKvogqEceeWxxpAPmyVQi/HgG0OjhPRiPkGYtGxl7x9FDb4NuZ/HvH3kLfRnjaEdQjONTPMws78zswea2SmS9ks6YGZbiy8aypDXpbkYLvFlEWtKRdUu8aKeyt4/irpMH+t+jnxVtR1C9aXJmT4/6YneIulDks6V9N8LLRVKk1cea1XzYZt68K3LoDIUq+z9o6ge5Kbu501T1XYI1W+T0uRMT5vZtNrB9BvdfcXMfNSTUB15XZoLcYlv0vzKJqZU1HUe67oKmZNd9v5R1GX6Ju7nTRU61QTjq0ObZO7D42Iz+1VJ2yTdKukitXum/9bdf6z44q02Pz/ve/bsKfttEal+SzvPTE9VuieijMBp4/ZdfQOWudmZowvc1FmsAwb7KXIbj7Ee6rhPAxgu5jbJzPa6+/yoxw3tmTazNZL+y93num67U9LTJi8iMJkYZ+KYRFln5zEOuixL1XpAitrGY60HepAxjhhPCDG+OrRJQ4Npdz9iZr8i6bqfG6pHAAAgAElEQVSu21zSoaILBoxShx2wW1knB00e8V50HefduBe1jcd8IlrkZXqCr/qI9YQQ46tDm5RmAOKHzexVZnaOmZ3R+Sm8ZMAIdRuhX9bJQZMHYxVZx0UscFLUNp62Hqo+KKhbXRagQRsLtNRHHdqkNMH0iyW9TNLHJO1NfkhcRnB12AG7lXVy0OQR70XWcZ6NeyeIbS0ty3ruy2MbT1MPdQs+Cb7qpW5XJpusDm1SmkVb1pVREGBcdcuvLHPlw7qPeB90Ob/IOs6rce+9fO2SLPk9l9M2nqYeYk4FyYLgq17qkBqAY6reJqWZGk9m9mhJ50s6qXObu7+tqEIBaVVtBxyWsxn65KAu+aRpcimL+Jx5Ne79gthOIN09sn2S7ytNPdQt+CT4qpcyOx+AUUYG02Z2paSnqh1Mf0jST0v6hCSCaWAMaYK8UCcHdRrMM6pHtag6zqtxTxPE5vF9DauHhcWW1pjpcJ+pU6safBJ81UvozgegW5qe6WdLepykRXf/RTP7fkl/UWyxgPqJ+bJ5zGUbV9k9qt09xLMnT+vEE9bom8srmRv3ND2oRX5fnUC9XyAtSU971NqJXj8Ugq/6qdqVSdRXmmB6OZki75CZPVDSVyU9rOByAbUT82XzmMs2rjIv5/f2EN9z34pmpqf0huc9PnMjn6YHtcjvq1+g3u2mzx0c+zVjSSEi+AJQhDSzeewxs1lJb1F7Jo9PSfq3QksF1FDMU/nFXLZxlTnLSxEzRKQZ2T7p9zVsyrtRAXnWAZV1mRUEQHGqOh1nmtk8/p/kzz83s3+Q9EB3/3SxxQLqJ+aczZjLNq4yL+cX1UM8qgd1ku9rVL71oJ79jjwGVFY1hQiDxXL1oSh1/3wxqPLYnTQDEE3SCyU9zN1/28zONbMfcXd6p4ExxJyzGXPZ0grR2GVJKcmjnP2+r6c9aq127DygV167b+jrXnXD/qHBbb9AvaOoAZUoR1H7SJWDoDTq/vliUeUT7zQ5038q6YikTZJ+W9K3Jb1X0g8XWC6glmLO2Yy5bKOEauzG7SHOs5zd31fa111YbGlpeaXv63WC2+5AvbW0rKlkVo+0c1z3BmyzJ0/rnvuOf8/eEw56/opV5D5S5SAojbp/vlhU+cQ7TTD9RHd/gpktSpK732NmDyi4XACQWqjGbtwe/aLKmfZ1h+Vydwe3WU6sFhZbeu0H9q8KnFtLy5peY5qeMq0cPjY7SO8JBz1/xStyH6lyEJRG3T9fLKo8F3yaYHrFzKbUXjdAZrZW7Z5q1Bw9RaiChcXWwBzfMhq7cQLPohrltK877H0myY/vDYa7rRxxzc5M65QTTxh4LKHnr3hFBoRVDoKk0W1d1T9fVVR57E6aYPpPJL1P0oPN7HVqzzv9mkJLheDoKRqMk4x4dLbTQWJr7IpqlNO+7qDHnX7ydO49492+ubyifVc+feD9aReqYb/LrsiAsMpBUJq2rsqfr0qqPHZn5NR47v4OSb8h6WpJX5a0xd3fXXTBEFYRU37VAdN8xWVUEPed+w9F9d0UNW1f2tcd9LgrL75govcf1bs5KmAbNdUf+93kipwyMs10jrFK09ZV+fNVzZYNc9q9bZNu336Rdm/bVJk6HtgzbWYnSXqppEdIuk3Sm939UFkFQ1jkiPXH5ei4jNoel5ZXorqiUlTPS9rXLer9h02nlyZgG9Xzx343uaJ7/ao6gDltW1fVz4dyDEvz+BtJK5I+LumnJf2QpFeUUSiER45Yf5xkHBPDZfdRcyJL8QVdRTXKaV+3iPcfNJ3e7My0rrrkgpHvNyrQq9N+F3K/ISA8Hm0d8jAsmD7f3R8jSWb2l2LVw0YhR6y/qh54827AY8mpHzYncrdBQdc49RLDyUOs8uj1HBboVXW/6xXLfoNjaOuQh2HB9NH5jdz9UHvtFjRFlQcCFKmKB94iGvBYLrv3bqdrkjmRez1oZvq428apl6KCoBgD9KxlKrLXs4r7XT+x7DfdYtwGy0RbhzyY92l4JMnMDkv6TudfSTOS7kv+dnd/YCkl7DI/P+979uwp+23RIGkalqo1Phu37+rbqzc3O6Pd2zZles11225UvyOHSbp9+0WZXjMPC4stbX33rVo5srp001OmHc9+3KrvaZx6KaIO+00nNzM9FXRgU4xl6qjaftdPbPtNzN83EAMz2+vu86MeN7Bn2t2nBt0H1EFv4/y0R63Ve/e2RvY+Vi3vsIh801gvu2/ZMHfcwiGStHLYj+v9G6deiqjDGHspYyxTR9X2u35i22+K/L7rcPIDpDVyajygjvpNtfWOW+6s5XSAo6Ydy6LIabYmtdRn6Wrp+MB3nHoZ57ELiy1t3L5L67bdqI3bdw2cvi3GQXUxlmlSab+PMsS23xT1fTOVIZqGYBqN1K9Hpn/CU7UDCamYBjzmeVfTBr7j1Evax44TRBRxkjOpGMs0idiCutj2m6K+b9YpQNOkWQERiN64uc6DAud+qhpIdIwaYBPjgLNJpB2sNs7Ao7SPHeey+SSD6oq6hF6XgX4dMaatxLTfFPV91/EKBzAMwTQqL81MC/0G2vRjWt1DHVsgkXfgW8epurZsmNOeL35D7/zXu3TYXVNm+rkL+3/+cQKbNI8dJ4jIOotAXt/ZsG2pLrmuBHXDlb2IT9U7JoBBCKZReWl6n0YtOy21A+efu3BON33uYJSBRJ2nuMvTwmJL793bOjpF3mF3vXdvS/MPPaPwzzRuEJGllzKP72zUtlTV774XQd1oZS3iE1vHBJCnoMG0mT1D0h9LmpL0F+6+PWR5UE1pep+G9USZFF3g3K3Tg9gvKJg08K1jz13IE4Qygog8vrNx6ii2WRnGKU+sQV1sdZq3ul3hAEYJFkyb2ZSkN0n6KUlfkvRJM7vB3T8TqkyopjS9T4MeM+48wWU3gmnSU+o4xd0kQp4glBFE5PGdpa2jPK+G5LHv9CvPK67dp9d+YL+uvPj4ZctjDOrqmFrVT52ucKCt7ieBkxi4aEvhb2z2ZElXufvm5P8rJMndrx70nNNOO80vvPDCkkqIqvjavffrCwe/oyNd2/IaMz1s7Sk689QTUz8mj/fJ2+KdS7r/0PD0lBNPmNKGc2czvX6Iz1S0QXU2ST11+9q99+uubyzr/kOHdeIJUzrnjJlS6yqP7yxtHeVVl3ltZ8P2h6pst0VvnyhP6GNBmerYVqTx0Y9+dLJFW0owJ+murv+/JOmJvQ8ys8skXSZJJ55Y3y8M2XV25GEHtTSPGeWubyyvOpBI0hF33fWN5cIOJqMCaUk654zsvch51Etszjljpu9Bf5J66uhtUO4/dFhfONheKLasOsvjO0tbR4O2vzTbZbe89p1h79vv9WIMdvKq06qL8bsZRwzHgjKFaP+qJPoBiO5+jaRrpPZy4jfffHPYAqGxhi0FfHNBSwEPWsa64/STp7X4W08v5L2rrKjLkRu379KD+3wfD5qd0c0ZlxUPZVgdde57yIBtb27Mz5vXvjNqf+h+vdcs3KZ33HKnTu+6//D0lF4ReD70YUvTV20byqqT6nJ6V/paDN/NOOp0LEgjRPsXAzNL9biQwXRL0jld/5+d3IYaStNwx56HFSK/uN8Aqo6Z6SldefEFhb13lRWVr1mnAZtpp0vslWUAX177zrD9ofv1FhZbesctdx7X+McwU02sgyLLVIdZhOp0LEijjuNr8hQymP6kpEea2Tq1g+jnS/pvAcuDggwbcCOplME4eQTsIRrB7gFUraVlTZnpsLvmIj7pCKWMk7IQDUrZJ5vDppHMut3lte903veqG/ZraXn1svHdr7dj54FoVzSNcVBkEYZtt3UIRJsWXHISOFywYNrdD5nZr0jaqfbUeG919/2hylOGqvTA5m3U0rJF91DkNXp+kkZwku+eUfGjlTVDQtkNSoiZHwYFNCaNNfNNtzwDyM7+kCVYk+IIdsrYp0O2N6O22zoEok971Fr97S139r29jppyEphV0Jxpd/+QpA+FLENZmjIdUj9ZeiHy7KHI85Jilkawyd99Wcq6bFx2gxLicnhRgU7eAeSw1xv0GUxqRE9a6GPOqO22Dr2cN33u4Fi31wEdO4NFPwCxLuqQI5bVqMa56B6K0JcUB333l193q1557T7O8HMwzrzJkwbCZTYoIbbdOgQ6/T6DSXrhk86t/H6WZhsO3d6M2m7r0MsZul1BXAimS9LkHW9U41x0wx36kuKg77iz3DU91ZNL8x2H7q3LYpJtN+uJQx0CnaI/Q6gUirTbcOj2Js12W/VeztDtCuKyJnQBmmLQDtaEHW/LhjldfeljNDc7I1N7ENPVyRRIw+7Ly9bN6zUzPbXqtjJ72tJ8x9055Bhfv+9Ykr5z/yEtLLYnCRqVux+jrNtuJ+hqLS3LdSzo6tTFKFs2zGn3tk26fftF2r1tUyWDnqI+w6R1O4m023Do9ib0MbcMTfiMSI+e6ZLU4dLpJIb1QhTdQxG6p23UdF4dTbhKUZTOd/naD+zXPfcdm+VhaXnlaM/doPptLS1r3bYbo+yBzbrtTnKZv6kDpdMKmUKRtsc5dHsT+phbhiZ8RqRHMF0SdrwwegODNzzv8aXXee93vyaZ3q5XE65SFGnLhjnt2HlgVTAtHQt0Bl2WlbSqh7HzWrHIcrKZ9TJ/FVNhRsnj5KD7NUJOuZc2tSCG9qbqaRxpNOEzIh2C6RKx45UrpsCg+7vvtyhG2kv3eTeOaV9z3PcO1bs5LIh8w/MeP/IKQV0GBWfN5ww9cC1veRwDRi1i01HGyfA4Pc60N0B5yJlGNBYWW9q4fZfWbbtRG7fvmjgHMdYc2Sx54kXkaaZ9zXHfO2RO6bBc0d56H6QO6TZZ8zlDD1zLWx7HgGGL2HSUlUJRxhgTAOOjZxpRKKIXuU6BQRE9hmlfc9z3Dtm7Oarnrru3buP2XbUdjZ/1Mn/dZigYdgxIe/Vk2PHCpNJTKOhxBuJDMI0oFBGAxRoYZDlxKOLEIO1rjvveIU9ixgkiQw/SKlqWoKtudTLoGPCgmenU++Cg15ibncm8IiSAeiHNA1EoIgCLdeqiLJeei5jqKu1rjvveoaflSjslGpfMjxdLneSV8jXoGGCm1PtgjMeRvFPiAEyGnmlEIUsv8qjLtHmNaM97MF2WE4ciegzTvuY4772w2NJ93zt03O2hg49BuGR+vNB1kmfK16BjwCuv3df38d37YPd+/6CZaZ00vUZL960En4kppoHVANoIplGYcYLQcYPFtA3KpIFBEQ1XlhOHIqa6SvuaaR83aNaD2ZlpXXXJBTT0XYqe7aTKc0XnnfLV7xiwY+eBoftg77a8tLyimempIFNr9qrbjCtAHRBMY2L9Gm5JYwWh4waLZTUoRbxP1l7mInoM075mmscNmvXglBNPoJHvUnTPYtV7LsvIuR+1D8YcsNZpYDVQFwTTmMighvuk6TVjN0bjBItlNShFvM+gEwepPcNEFXsTpeErDC4stir1WYpUdKAWcyCYRhkDh0edvMccsMY6sBpoMoJpTGRQwz1oXta8GqOyGpSi3qf3xKHqvYnS4LqSVMhnqWoqQ9GBWsyBYBplzSgy7OQ95oC1bjOuAHXAbB4NUOTI73Eb6Lwao7JG2Jf1PrEuMDOOfnXVkfdnCbkwzKSKnu0k9Gwqk4phRpEYZ/DoiKF+isAMJagyeqZrrugez0E9OLMz07r/0JHCek+KGJAX8n2q3psoHaurV6SYKWFSVU5lGNazmEdv+6ieyyr06IeeUaSs/T6r0PWTtzpcmUOzEUzXXNFBx6CG+6pLLjj6/nk2RiECgTIarrIvKxdVj1s2zI2cKSEPVT75GJYzPyygSPudDQsECVrS65eKVeUxDTGr8skxIBFM196wQWF5GNWDk9fsBJ0AzSR5cnudAoEy8yCLDqjy+izDgseYc1rT6HeCtnH7rqGpPuPOjtPv9iYFLXmeMHISUqwqnxwDEjnTtTcouDApt5y0tCvOZdGdGysdC6Q7qpZXPEiZeZBF52fn8VlG5UTHnNOa1bCAIq/vrClBS9459XUY0xCzquf5A/RM11xnta/eINSlSvRGDZq7uFtdAoGy8iDLCKgm/SyjelBjz2nNYlhve17fWdV69LP2LufdA9+Uk5BQmKEEVUcwXXNbNsyVMiCsKGnKGGsg0BHbgK8qBFRpgpc8Tz5i+I6GBRR55aHnEbSUVVeTpFbkHfxWYZ+psjqeHKNZCKYbYK7CDcGwuYul+HsvQuZaDgp6qtALVGbwEks+7KiAIo/vbNKgpcy6mqR3Oe/tpwr7TNXVbYYSNAvBdI1UOXgapF/ZO4MQ5yrQexFqwFeaoCfmXqAyt9mYBuUNCihGzdAxznc5SdBSZl1N0ruc9/SDVdhnAIRDMF0TZQZPC4stvfYD+3XPfSuS2nNKX3XJBYU0LFVvxELlWqbJOY65Dsv83quSD9vvO0vbU5xXakaZdTVJ73LW6QdHvWbM+wyAcAima6Ks4GlhsaWt77lVK4ePDWlcWl7R1nffKqmYy+JVbsRC5VpWJUAcJuv3Pm7gWPR3VGSOcZqe4jxTM8rcnie9OjHu9INVPcYACI+p8WqirOBpx84DqwLpjpUjzjRRfYSawq2pU01lmRKtyO+o6GXP0+z3eU7rVub2XMR0kaPqq2lLWjft8wJFoWe6JsrqMRoWnFep17MsodJUqpwnP4ksOb1FfkdF5xin2e/zPNEue3vO+6rUsPoqenBlDDPG9JYnhoG3QB0QTNdEWcHTsNk16t7rmVWINJWq55pnlTVwLOo7KuKKUXdQNnvytKbXmFaOHLta1Lvf532iXeW0q1HTDxZ14hNj4BrTwFug6gima6Ks4Gnr5vXH5UxL0vQaq32vZ9VUOegZpkrLjOddnt6g7J77VjQ9ZZqdmdY3l1f67vdNvUrRz7Dj5CsLnI8/xsC1DuMqgFgQTNdIGcFT5/XLms0D6Daqhy+2wDHv8vQLylYOu0458QTtu/LpfZ8T21WK0OkOg46TRZ6IxRi4xnbiCVQZwTTGVtceT8SvasuM512eItJYygxuY0x36CjyRCzGwDW2E0+gygimGyx0DxHKVYfvu+xlxvOQZ3mKThvJM7jtt73FmO7QUeSJWIyBa2wnnkCVEUw3VMw9RMhfDN93HsF8jD18ZSojbSSP4HbQ9tb7Xh2x5OkWdSIWa+Aa24knUFUE0w0Vcw8R8hf6+84rmI+xh2+UPK8IxJI2Msqg7W3KTIf9+Hnq8zwZivUKDIErUF8E0w0V44AYFCfv73thsaWrbtivpeX2INTTT57WlRcPHoSaVzAfaw/fIEVcEYg5baRj0HZ12F0z01OFnQzFcAUGQPOwAmJDNXWFvKbK8/teWGxp67tvPRpIS+0p2ra+59aBK6jlvXDI7m2bdPv2i7R726aog6RBJxGXX3drFKvOFbWi4aDtqrOKYZ6rGnbLc7VHAEiLnumGGna5PNbLpFmU9Vlir7M80yN27DywapGQjpXDPrCnuam5zsN6aKXwPadF9fQP296KTHfgihvyFPtxHfEgmG6oQY2opNpcJi3rkm8VLi2nDZrSNB5ZlpSfJJivcoM2bMXQjtBjFYoIbkOl41TxpK3K23edVeG4jniY9xkMEqv5+Xnfs2dP6GLU2sbtu/o2RnOzM9q9bVOAEmVX1mepS531Nh5SO+DtvQw/6PNKwz9zlqAhbZli1a/8/Zik27dfVE6haqxffZskV3vbjC1Qrfr2XWd1Oa5jMma2193nRz2OnmmsUqfLpGV9lrrUWdpBgls3r9fWd996XKrH9NTwJeWz9ICGnoVkUr09tGtKmM0iRmX1vnbXd2tp+WggLcXZs1j17bvO6nJcRzkIprFKFS+TDlLWZ6lLnaVtPDqNfJrZPCYNourQoHWfRAzqiQw1tV8ZQW7Zl8s79d2vZzG2QLUO23dd1eW4jnIwmwdWKWp0fwhlfZa61Nk4M35s2TCnfVc+XXdsv0h3bL9Ii7/19L6B9BXX36bW0rJcx4KocWavqNusM1s2zBU6m8U48vh+0gg1w0aaQHVhsaWN23cFm1mlbtt3ndTluI5y0DONVao2j+8wZX2WqtTZqF7IGFfXy1Km2Ad0xbJ4R1kpBqF6X0f1LI7bY17EdlXFRYiaoirHdcSBYBrHiaWxz0NZnyX2OksTOMS4ut64ZWIEfnplBbkhLpcvLLZ03/cOHXd7d6A6zslEUdtVmQFb7CeZMYr9uI54EEwDDZA2cMir8VhYbOU22G6cMjGgK72ygtyye18HzaAyOzOtqy45ltc/zslEkdtVGQEbJ5lAsciZBhqgzEvtnYa7XyBd9CXs2AZ05ZGTW1Reb1k5oWXnifcLfCXplBNPWPWe4+Qrx7ZdjYuVIYFi0TMNNECZl9oHBTNTZoUPtotpBH4evYFF9iiWmWJQ5uXytIHvOD3mMW1XWVT9ZACIHT3TQAOUOTJ9UAN9xL3wgCqmEfh59AYW3aO4ZcOcdm/bpNu3X6Td2zbV4pJ/2h7ncXrMY9qusmDWEKBYQXqmzew5kq6S9EOSfsTdWdYQKFCZvZAhe/FiGoGfR28gPYrHpB1AN06Pc9oe85i2qyyYNQQoVqg0j3+XdKmkNwd6f6BxyrrUHrrhjmUEfh4nFVVPL8jLOOkuRQW+sWxXWVT9ZACIXZBg2t0/K0lmFuLtARQodMMdyxRgeZxUhD4x6RWqbsedTaPKgW9RqBOgONEPQDSzyyRdJknnnntu4NIASCNUwx3TFGB5nFSEPjHpFrJuSXcBELPCgmkz+4ikh/S569Xu/v60r+Pu10i6RpLm5+ePn2sLwEix9NYWLbZ5pvM4qYilRzFk3ZLuAiBmhQXT7v6TRb02gPRi6q0tGj2Y/U1yMtV5br9gViqnbmNLdwGAbkyNB9RckxZsYAqw43VOplpLy3IdO5lKs/hL93MHKWuWljIXfgGAcYSaGu9Zkv63pLWSbjSzfe6+OURZ0F9T0gLKELoum9RbSw/m8SZJzxi0AE9HE2dpQX5CHxuBvISazeN9kt4X4r0xWpPSAooWQ102Kd80pgF7sZjkZGrYY+ao20aYNOAd9PwYjo1AXqKfzQPli20QV5XFUJdN662teg9m3r11k5xMDXrulBmBdANMGvAOe34Mx0YgL+RM19jCYksbt+/Sum03auP2XalyJKVmpQUULYa6JN+0OibJbx5kkqWw+z1Xkg67T1wuxG/S8RbDnh/DsRHICz3TNTVJj0KT0gKKFktdVr23timK6K2bJPWl85jLr7tVh331zKR59SKSNxuvSQPeYc+P5dgI5IGe6ZqapEdhkp4srLZ183pNr1m90uf0GqMu0VdRvXVbNsxp97ZNun37Rdq9bdPYC8cc8f5T/E9ariJ64pGfSWfHGfZ82hnUCcF0TU3SKJMWkDMb8T+QiHVqv6LK1aRpG6to0oB32PNpZ1AnpHnU1KSX0EgLyMeOnQe0cnh1r97KYWeQDfqKdbBolnKlSd8gbzZuk86OM+r5tDPNVMfULoLpmoq1UW4aggWMI9ap/cYtV9oxG+TNxm/SgJeAGd3qOiUiwXRNxdooNw3BAsYVa/AxTrnSDqTkpB9olrpOiUgwXWOxNspNElOwUMdLayhX2m0o7RUZTvqBZqnr1VqCaaBAsQQLdb20VrQmnoDksWLdOFdkOOkHmqOuV2sJpoGCxRAs1PXSWpGaeAKS14p1MV2RARCPuh4bCKaBBqjrpbUiNfEEJK8V62K5IjOJJl6VAIpWh2NDPwTTQAPU9dJakZp4ApLninUxXJHJKsRVCYJ3NEWVjw2DsGgL0ACsNja+WBdQKRIr1rWVvZgMK0EC1UYwDTTAOKuNLSy2tHH7Lq3bdqM2bt/V2Aa9ScFjByvWtZV9VYKVIIFqI80DaIg0l9aaOOhukLrm9g3DinVtZadFNTGlCKgTgmkARzVx0N0wsQSPZebTxvKZQyp7xgHGNADVRpoHgKPoIYsP+bTlKzulpYkpRUCd0DMN4Ch6yOLD1YIwyuyhb2JKEVAnBNMAjqrrhPpVxtWCZiC9BqgugmmgIsrIm6WHLD5cLQCAuBFMAxVQ5iwb9JDFhasFABA3BiACFcA8tM3VpPmdAaCK6JkGKoC82WbjakH5sqRVsSQ40EwE00AFxJQ3S8BQjCrUaxXKmIcsaVUseAQ0F2keQAXEMg8tcx4Xowr1WoUy5iVLWhWpWEBzEUwDFRBL3myVA4aFxZY2bt+lddtu1Mbtu6IKAqtQr1UoY16ypFWRigU0F2keQEXEkDdb1YAh9kvwVajXKpQxL1nSqgY9Z42Z1m27sdZpMUDT0TMNILVBwUTscx5fdcP+qHtVq1CvVShjXrKkVfV7jiQddq99WgzQdATTAEbqpEi0lpZlPffFPufxwmJLS8srfe+LpVc1lpz4YapQxrxkSavqfc6U9e4pcZ3AAcgPaR4AhupNkXBJlvyeq8Cl62HBSyy9qlVYebIKZcxTlrSq7ues23Zj38fEcgIHID8E0wCG6jfwrBNI7962KUyhxjAseImpVzWGnPhRqlDGWMQ0nSWAYpHmAWCoqg88GxS8nH7yNIEhCtOktBig6QimAQxV9YFng4KaKy++IFCJ0ASxTGcJoHikeQAYaGGxpe/cf+i426vUw9a0XF/Eg7QYoBkIpgH01TvwsOP0k6d15cUXVCpIIKgBVmvK0vBAGQimgQopswHsN/BQkk5+wAk0ukCFxb6IEVA1BNNARZTdAFZ94CHKR29nNQxbGp7vCxgfAxCBihjWABah6gMPUa7OyV5raZkV/yLHiTKQL4JpoCLKbgCZ2gvjKPtkD9lxogzki2AaqIiyG0Cm9sI46O2sDk6UgXyRMw1UxNbN64+bXaPoBpBZMJAWK/5VB9NFAvkimAYqggYwfwyYy0+Ikz1kx4kykB+CaaBCaADzw/Rg+eJkD0BTEUwDaCSmB8sfJ3sAmogBiAAaiQFzAIA8EEwDaCSmB1SJL1cAAAvASURBVAMA5IFgGkAjMT0YACAP5EwDaCQGzAEA8hAkmDazHZIulvQ9Sf8p6RfdfSlEWQA0FwPmAACTCpXm8WFJj3b3x0r6vKQrApUDAAqzsNjSxu27tG7bjdq4fZcWFluhiwQAyFmQYNrd/9HdDyX/3iLp7BDlAICidOaxbi0ty3VsHmsCagColxgGIL5Y0t8PutPMLjOzPWa25+DBgyUWCwCyGzaPNQCgPgrLmTazj0h6SJ+7Xu3u708e82pJhyS9Y9DruPs1kq6RpPn5eS+gqACQO+axBoBmKCyYdvefHHa/mb1I0jMl/YS7EyQDqJWzZmfU6hM4M481ANRLkDQPM3uGpN+QdIm73xeiDABQJOaxBoBmCDXP9BslnSjpw2YmSbe4+0sDlQUAcsc81gDQDEGCaXd/RIj3BYAyMY81ANRfDLN5AAAAAJVEMA0AAABkFCpnGgCAyltYbJEXDzQcwTQAABl0VrnsLM7TWeVSEgE10CCkeQAAkAGrXAKQCKYBAMiEVS4BSATTAABkMmg1S1a5BJqFYBoAgAxY5RKAxABEAAAyYZVLABLBNAAAmbHKJQDSPAAAAICMCKYBAACAjAimAQAAgIwIpgEAAICMGIAIAAByt7DYYqYTNALBNABkRLAA9Lew2NIV1992dLn11tKyrrj+NkliH0HtkOYBABl0goXW0rJcx4KFhcVW6KIBwe3YeeBoIN2xvHJYO3YeCFQioDgE0wCQAcECMNjdS8tj3Q5UGWkeQAZc3gfBAjDYWbMzavXZF86anQlQGqBY9EwDY+LyPqTBQQHBAiBt3bxeM9NTq26bmZ7S1s3rA5UIKA7BNDAmLu9DIlgAhtmyYU5XX/oYzc3OyCTNzc7o6ksfwxU81BJpHsCYuLwP6diMBKT7AP1t2TDH/oBGIJgGxkQuIDoIFgAApHkAY+LyPgAA6KBnGhgTl/cBAEAHwTSQAZf3AQCARJoHAAAAkBnBNAAAAJARwTQAAACQEcE0AAAAkBHBNAAAAJARwTQAAACQEcE0AAAAkBHBNAAAAJARwTQAAACQEcE0AAAAkBHBNAAAAJARwTQAAACQEcE0AAAAkBHBNAAAAJARwTQAAACQkbl76DKkZmYHJX0xdDm6nCnpa6ELUUHUW3bUXTbUWzbUWzbUW3bUXTbUWzaj6u2h7r521ItUKpiOjZntcff50OWoGuotO+ouG+otG+otG+otO+ouG+otm7zqjTQPAAAAICOCaQAAACAjgunJXBO6ABVFvWVH3WVDvWVDvWVDvWVH3WVDvWWTS72RMw0AAABkRM80AAAAkBHBdApm9gwzO2Bm/2Fm2/rc/+Nm9ikzO2Rmzw5RxhilqLdfN7PPmNmnzeyfzOyhIcoZmxT19lIzu83M9pnZJ8zs/BDljNGouut63M+ZmZsZo9+Vapt7kZkdTLa5fWb2SyHKGZs025uZPTc5zu03s78ru4wxSrG9vaFrW/u8mS2FKGdsUtTbuWZ2k5ktJu3qz4QoZ4xS1N1Dkzjk02Z2s5mdPdYbuDs/Q34kTUn6T0kPk/QASbdKOr/nMedJeqykt0l6dugyx/CTst6eJunk5O//Kena0OUO/ZOy3h7Y9fclkv4hdLlj+ElTd8njTpP0MUm3SJoPXe7QPym3uRdJemPossb0k7LeHilpUdLpyf8PDl3u0D9p99Oux/+qpLeGLnfon5Tb2zWS/mfy9/mS7ghd7hh+UtbduyX9QvL3JklvH+c96Jke7Uck/Ye7f8HdvyfpXZJ+tvsB7n6Hu39a0pEQBYxUmnq7yd3vS/69RdJ4Z4L1lKbevtX17ymSGPjQNrLuEr8j6fclfbfMwkUsbb1htTT19suS3uTu90iSu3+15DLGaNzt7QWS3llKyeKWpt5c0gOTvx8k6e4SyxezNHV3vqRdyd839bl/KILp0eYk3dX1/5eS2zDcuPX2Ekl/X2iJqiFVvZnZy8zsPyX9f5J+raSyxW5k3ZnZEySd4+43llmwyKXdV38uuQT6HjM7p5yiRS1Nvf2gpB80s91mdouZPaO00sUrdduQpP6t07Egp8nS1NtVkn7ezL4k6UNq9+ojXd3dKunS5O9nSTrNzL4v7RsQTCM4M/t5SfOSdoQuS1W4+5vc/eGS/pek14QuTxWY2RpJr5d0eeiyVNAHJJ3n7o+V9GFJfxO4PFVxgtqpHk9Vu4f1LWY2G7RE1fJ8Se9x98OhC1IRL5D01+5+tqSfkfT25LiH0V4l6SlmtijpKZJaklJvd1TyaC1J3b0wZye3YbhU9WZmPynp1ZIucff7SypbzMbd3t4laUuhJaqOUXV3mqRHS7rZzO6Q9CRJNzAIcfQ25+5f79o//0LShSWVLWZp9tUvSbrB3Vfc/XZJn1c7uG6ycY5xzxcpHh1p6u0lkq6TJHf/F0knSTqzlNLFLc0x7m53v9TdN6gdk8jdUw98JZge7ZOSHmlm68zsAWrv3DcELlMVjKw3M9sg6c1qB9LkEralqbfuxvgiSf+nxPLFbGjdufs33f1Mdz/P3c9TO0//EnffE6a40Uizzf1A17+XSPpsieWLVZq2YUHtXmmZ2Zlqp318ocxCRihVm2pmj5J0uqR/Kbl8sUpTb3dK+glJMrMfUjuYPlhqKeOU5hh3Zlcv/hWS3jrOGxBMj+DuhyT9iqSdajcg17n7fjP7bTO7RJLM7IeTHKXnSHqzme0PV+I4pKk3tdM6TpX07mQKpMafpKSst19JptnaJ+nXJf1CoOJGJWXdoUfKevu1ZJu7Ve0c/ReFKW08UtbbTklfN7PPqD2oaau7fz1MieMwxn76fEnv8mR6haZLWW+XS/rlZD99p6QXUX+p6+6pkg6Y2eclfb+k143zHqyACAAAAGREzzQAAACQEcE0AAAAkBHBNAAAAJARwTQAAACQEcE0AAAAkBHBNAAUzMwOJ9M/dn7OM7N5M/uT5P6nmtmPdj1+i5mdn+F97u1z201mtrnntleY2Z+N+1oAgOOdELoAANAAy+7++J7b7pDUWTDmqZLulfTPyf9bJH1Q0mdyeO93qj1n786u254v6TdyeG0AaDx6pgEggKQ3+oNmdp6kl0p6ZdJr/RS1Vxnckfz/8OTnH8xsr5l9PFkdTsmKXv9iZp80s98Z8FbvkXRRsvKXkvc7S9LHzexUM/snM/uUmd1mZj87qJxd/7/RzF6U/H2hmX00KdfOzkqJZvZrZvYZM/u0mb0rlwoDgEjRMw0AxZtJVqyUpNvd/VmdO9z9DjP7c0n3uvsfSFKyGugH3f09yf//JOml7v5/zOyJkv5U0iZJfyzpz9z9bWb2sn5v7O7fMLN/k/TTkt6vdq/0de7uZvZdSc9y928ly13fYmY3pFk1zcymJf1vST/r7gfN7Hlqrxr2YknbJK1z9/vNbHbcygKAKiGYBoDi9UvzSMXMTpX0o5LebWadm09Mfm+U9HPJ32+X9PsDXqaT6tEJpl/SeXlJv2dmPy7piKQ5tZfS/UqKoq2X9GhJH07KNSXpy8l9n5b0DjNbkLSQ4rUAoLIIpgEgbmskLQ0Jxkf2IqsdRL/BzJ4g6WR335vc/kJJayVd6O4rZnaHpJN6nntIq1MCO/ebpP3u/uQ+73eRpB9XO13lN83sAnc/lKKcAFA55EwDQHjflnRav//d/VuSbjez50iStT0uedxutXuapXZg3Je73yvpJklvVbuXuuNBkr6aBNJPk/TQPk//oqTzzezEJGXjJ5LbD0haa2ZPTso1bWYXmNkaSee4+01qD3KclXRqmkoAgCoimAaA8D4g6VnJgMMfk/QuSVvNbNHMHq52oPwSM7tV0n5JnYGCL5f0MjP7pNqB8TDvlPQ4rQ6m3yFp3sz2JO/xud4nuftdkq5TO3Xj7ZIWk9u/J+nZkn4/Kdc+tdNRpiT9rZndljz2De6+NFZtAECFWIpxJgAAAAD6oGcaAAAAyIhgGgAAAMiIYBoAAADIiGAaAAAAyIhgGgAAAMiIYBoAAADIiGAaAAAAyIhgGgAAAMjo/wefey6vbR9jvQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12,8))\n",
"ax = fig.add_subplot(111, title='Residual Dependence Plot', xlabel='Fitted Values',\n",
" ylabel='Pearson Residuals')\n",
"ax.scatter(yhat, stats.zscore(glm_mod.resid_pearson))\n",
"ax.axis('tight')\n",
"ax.plot([0.0, 1.0],[0.0, 0.0], 'k-');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Histogram of standardized deviance residuals with Kernel Density Estimate overlayed"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The definition of the deviance residuals depends on the family. For the Binomial distribution this is \n",
"\n",
"$$r_{dev} = sign\\left(Y-\\mu\\right)*\\sqrt{2n(Y\\log\\frac{Y}{\\mu}+(1-Y)\\log\\frac{(1-Y)}{(1-\\mu)}}$$\n",
"\n",
"They can be used to detect ill-fitting covariates"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib/python3/dist-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
" return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
]
}
],
"source": [
"resid = glm_mod.resid_deviance\n",
"resid_std = stats.zscore(resid) \n",
"kde_resid = sm.nonparametric.KDEUnivariate(resid_std)\n",
"kde_resid.fit()"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib/python3/dist-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \n",
"The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
" alternative=\"'density'\", removal=\"3.1\")\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12,8))\n",
"ax = fig.add_subplot(111, title=\"Standardized Deviance Residuals\")\n",
"ax.hist(resid_std, bins=25, normed=True);\n",
"ax.plot(kde_resid.support, kde_resid.density, 'r');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### QQ-plot of deviance residuals"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12,8))\n",
"ax = fig.add_subplot(111)\n",
"fig = sm.graphics.qqplot(resid, line='r', ax=ax)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
}

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/srv/reproducible-results/rbuild-debian/tmp.FTzzuDJkiI/dbd-tmp-gqMLHgR/diffoscope_1mz05yxs/tmpgtutwdo4/43edefb5-a3f2-4896-b2d7-5784ba0d182a vs.

Similarity: 0.0%
Differences: {
"replace": {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Discrete Choice Models Overview"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
" from pandas.core import datetools\n"
]
}
],
"source": [
"from __future__ import print_function\n",
"import numpy as np\n",
"import statsmodels.api as sm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data\n",
"\n",
"Load data from Spector and Mazzeo (1980). Examples follow Greene's Econometric Analysis Ch. 21 (5th Edition)."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/datasets/utils.py:100: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n",
" exog = np.column_stack(data[field] for field in exog_name)\n"
]
}
],
"source": [
"spector_data = sm.datasets.spector.load()\n",
"spector_data.exog = sm.add_constant(spector_data.exog, prepend=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Inspect the data:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 2.66 20. 0. 1. ]\n",
" [ 2.89 22. 0. 1. ]\n",
" [ 3.28 24. 0. 1. ]\n",
" [ 2.92 12. 0. 1. ]\n",
" [ 4. 21. 0. 1. ]]\n",
"[0. 0. 0. 0. 1.]\n"
]
}
],
"source": [
"print(spector_data.exog[:5,:])\n",
"print(spector_data.endog[:5])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Linear Probability Model (OLS)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Parameters: [0.46385168 0.01049512 0.37855479]\n"
]
}
],
"source": [
"lpm_mod = sm.OLS(spector_data.endog, spector_data.exog)\n",
"lpm_res = lpm_mod.fit()\n",
"print('Parameters: ', lpm_res.params[:-1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Logit Model"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Parameters: [ 2.82611259 0.09515766 2.37868766 -13.02134686]\n"
]
}
],
"source": [
"logit_mod = sm.Logit(spector_data.endog, spector_data.exog)\n",
"logit_res = logit_mod.fit(disp=0)\n",
"print('Parameters: ', logit_res.params)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Marginal Effects"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Logit Marginal Effects \n",
"=====================================\n",
"Dep. Variable: y\n",
"Method: dydx\n",
"At: overall\n",
"==============================================================================\n",
" dy/dx std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"x1 0.3626 0.109 3.313 0.001 0.148 0.577\n",
"x2 0.0122 0.018 0.686 0.493 -0.023 0.047\n",
"x3 0.3052 0.092 3.304 0.001 0.124 0.486\n",
"==============================================================================\n"
]
}
],
"source": [
"margeff = logit_res.get_margeff()\n",
"print(margeff.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As in all the discrete data models presented below, we can print a nice summary of results:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Logit Regression Results \n",
"==============================================================================\n",
"Dep. Variable: y No. Observations: 32\n",
"Model: Logit Df Residuals: 28\n",
"Method: MLE Df Model: 3\n",
"Date: Sat, 10 Apr 2021 Pseudo R-squ.: 0.3740\n",
"Time: 01:00:04 Log-Likelihood: -12.890\n",
"converged: True LL-Null: -20.592\n",
" LLR p-value: 0.001502\n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"x1 2.8261 1.263 2.238 0.025 0.351 5.301\n",
"x2 0.0952 0.142 0.672 0.501 -0.182 0.373\n",
"x3 2.3787 1.065 2.234 0.025 0.292 4.465\n",
"const -13.0213 4.931 -2.641 0.008 -22.687 -3.356\n",
"==============================================================================\n"
]
}
],
"source": [
"print(logit_res.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Probit Model "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimization terminated successfully.\n",
" Current function value: 0.400588\n",
" Iterations 6\n",
"Parameters: [ 1.62581004 0.05172895 1.42633234 -7.45231965]\n",
"Marginal effects: \n",
" Probit Marginal Effects \n",
"=====================================\n",
"Dep. Variable: y\n",
"Method: dydx\n",
"At: overall\n",
"==============================================================================\n",
" dy/dx std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"x1 0.3608 0.113 3.182 0.001 0.139 0.583\n",
"x2 0.0115 0.018 0.624 0.533 -0.025 0.048\n",
"x3 0.3165 0.090 3.508 0.000 0.140 0.493\n",
"==============================================================================\n"
]
}
],
"source": [
"probit_mod = sm.Probit(spector_data.endog, spector_data.exog)\n",
"probit_res = probit_mod.fit()\n",
"probit_margeff = probit_res.get_margeff()\n",
"print('Parameters: ', probit_res.params)\n",
"print('Marginal effects: ')\n",
"print(probit_margeff.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Multinomial Logit"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load data from the American National Election Studies:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/datasets/utils.py:100: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n",
" exog = np.column_stack(data[field] for field in exog_name)\n"
]
}
],
"source": [
"anes_data = sm.datasets.anes96.load()\n",
"anes_exog = anes_data.exog\n",
"anes_exog = sm.add_constant(anes_exog, prepend=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Inspect the data:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[-2.30258509 7. 36. 3. 1. ]\n",
" [ 5.24755025 3. 20. 4. 1. ]\n",
" [ 3.43720782 2. 24. 6. 1. ]\n",
" [ 4.4200447 3. 28. 6. 1. ]\n",
" [ 6.46162441 5. 68. 6. 1. ]]\n",
"[6. 1. 1. 1. 0.]\n"
]
}
],
"source": [
"print(anes_data.exog[:5,:])\n",
"print(anes_data.endog[:5])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Fit MNL model:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimization terminated successfully.\n",
" Current function value: 1.548647\n",
" Iterations 7\n",
"[[-1.15359746e-02 -8.87506530e-02 -1.05966699e-01 -9.15567017e-02\n",
" -9.32846040e-02 -1.40880692e-01]\n",
" [ 2.97714352e-01 3.91668642e-01 5.73450508e-01 1.27877179e+00\n",
" 1.34696165e+00 2.07008014e+00]\n",
" [-2.49449954e-02 -2.28978371e-02 -1.48512069e-02 -8.68134503e-03\n",
" -1.79040689e-02 -9.43264870e-03]\n",
" [ 8.24914421e-02 1.81042758e-01 -7.15241904e-03 1.99827955e-01\n",
" 2.16938850e-01 3.21925702e-01]\n",
" [ 5.19655317e-03 4.78739761e-02 5.75751595e-02 8.44983753e-02\n",
" 8.09584122e-02 1.08894083e-01]\n",
" [-3.73401677e-01 -2.25091318e+00 -3.66558353e+00 -7.61384309e+00\n",
" -7.06047825e+00 -1.21057509e+01]]\n"
]
}
],
"source": [
"mlogit_mod = sm.MNLogit(anes_data.endog, anes_exog)\n",
"mlogit_res = mlogit_mod.fit()\n",
"print(mlogit_res.params)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Poisson\n",
"\n",
"Load the Rand data. Note that this example is similar to Cameron and Trivedi's `Microeconometrics` Table 20.5, but it is slightly different because of minor changes in the data. "
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/datasets/utils.py:100: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n",
" exog = np.column_stack(data[field] for field in exog_name)\n"
]
}
],
"source": [
"rand_data = sm.datasets.randhie.load()\n",
"rand_exog = rand_data.exog.view(float).reshape(len(rand_data.exog), -1)\n",
"rand_exog = sm.add_constant(rand_exog, prepend=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Fit Poisson model: "
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimization terminated successfully.\n",
" Current function value: 3.091609\n",
" Iterations 12\n",
" Poisson Regression Results \n",
"==============================================================================\n",
"Dep. Variable: y No. Observations: 20190\n",
"Model: Poisson Df Residuals: 20180\n",
"Method: MLE Df Model: 9\n",
"Date: Sat, 10 Apr 2021 Pseudo R-squ.: 0.06343\n",
"Time: 01:00:05 Log-Likelihood: -62420.\n",
"converged: True LL-Null: -66647.\n",
" LLR p-value: 0.000\n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"x1 -0.0525 0.003 -18.216 0.000 -0.058 -0.047\n",
"x2 -0.2471 0.011 -23.272 0.000 -0.268 -0.226\n",
"x3 0.0353 0.002 19.302 0.000 0.032 0.039\n",
"x4 -0.0346 0.002 -21.439 0.000 -0.038 -0.031\n",
"x5 0.2717 0.012 22.200 0.000 0.248 0.296\n",
"x6 0.0339 0.001 60.098 0.000 0.033 0.035\n",
"x7 -0.0126 0.009 -1.366 0.172 -0.031 0.005\n",
"x8 0.0541 0.015 3.531 0.000 0.024 0.084\n",
"x9 0.2061 0.026 7.843 0.000 0.155 0.258\n",
"const 0.7004 0.011 62.741 0.000 0.678 0.722\n",
"==============================================================================\n"
]
}
],
"source": [
"poisson_mod = sm.Poisson(rand_data.endog, rand_exog)\n",
"poisson_res = poisson_mod.fit(method=\"newton\")\n",
"print(poisson_res.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Negative Binomial\n",
"\n",
"The negative binomial model gives slightly different results. "
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
" \"Check mle_retvals\", ConvergenceWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" NegativeBinomial Regression Results \n",
"==============================================================================\n",
"Dep. Variable: y No. Observations: 20190\n",
"Model: NegativeBinomial Df Residuals: 20180\n",
"Method: MLE Df Model: 9\n",
"Date: Sat, 10 Apr 2021 Pseudo R-squ.: 0.01845\n",
"Time: 01:00:07 Log-Likelihood: -43384.\n",
"converged: False LL-Null: -44199.\n",
" LLR p-value: 0.000\n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"x1 -0.0580 0.006 -9.517 0.000 -0.070 -0.046\n",
"x2 -0.2678 0.023 -11.802 0.000 -0.312 -0.223\n",
"x3 0.0412 0.004 9.937 0.000 0.033 0.049\n",
"x4 -0.0381 0.003 -11.219 0.000 -0.045 -0.031\n",
"x5 0.2690 0.030 8.981 0.000 0.210 0.328\n",
"x6 0.0382 0.001 26.081 0.000 0.035 0.041\n",
"x7 -0.0441 0.020 -2.200 0.028 -0.083 -0.005\n",
"x8 0.0172 0.036 0.477 0.633 -0.054 0.088\n",
"x9 0.1780 0.074 2.397 0.017 0.032 0.324\n",
"const 0.6636 0.025 26.787 0.000 0.615 0.712\n",
"alpha 1.2930 0.019 69.477 0.000 1.256 1.329\n",
"==============================================================================\n"
]
}
],
"source": [
"mod_nbin = sm.NegativeBinomial(rand_data.endog, rand_exog)\n",
"res_nbin = mod_nbin.fit(disp=False)\n",
"print(res_nbin.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Alternative solvers\n",
"\n",
"The default method for fitting discrete data MLE models is Newton-Raphson. You can use other solvers by using the ``method`` argument: "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Warning: Maximum number of iterations has been exceeded.\n",
" Current function value: 1.548650\n",
" Iterations: 100\n",
" Function evaluations: 106\n",
" Gradient evaluations: 106\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" MNLogit Regression Results \n",
"==============================================================================\n",
"Dep. Variable: y No. Observations: 944\n",
"Model: MNLogit Df Residuals: 908\n",
"Method: MLE Df Model: 30\n",
"Date: Sat, 10 Apr 2021 Pseudo R-squ.: 0.1648\n",
"Time: 01:00:07 Log-Likelihood: -1461.9\n",
"converged: False LL-Null: -1750.3\n",
" LLR p-value: 1.827e-102\n",
"==============================================================================\n",
" y=1 coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"x1 -0.0116 0.034 -0.338 0.735 -0.079 0.056\n",
"x2 0.2973 0.094 3.175 0.001 0.114 0.481\n",
"x3 -0.0250 0.007 -3.825 0.000 -0.038 -0.012\n",
"x4 0.0821 0.074 1.116 0.264 -0.062 0.226\n",
"x5 0.0052 0.018 0.294 0.769 -0.029 0.040\n",
"const -0.3689 0.630 -0.586 0.558 -1.603 0.866\n",
"------------------------------------------------------------------------------\n",
" y=2 coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"x1 -0.0888 0.039 -2.269 0.023 -0.166 -0.012\n",
"x2 0.3913 0.108 3.615 0.000 0.179 0.603\n",
"x3 -0.0229 0.008 -2.897 0.004 -0.038 -0.007\n",
"x4 0.1808 0.085 2.120 0.034 0.014 0.348\n",
"x5 0.0478 0.022 2.145 0.032 0.004 0.091\n",
"const -2.2451 0.763 -2.942 0.003 -3.741 -0.749\n",
"------------------------------------------------------------------------------\n",
" y=3 coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"x1 -0.1062 0.057 -1.861 0.063 -0.218 0.006\n",
"x2 0.5730 0.159 3.614 0.000 0.262 0.884\n",
"x3 -0.0149 0.011 -1.313 0.189 -0.037 0.007\n",
"x4 -0.0075 0.126 -0.060 0.952 -0.255 0.240\n",
"x5 0.0575 0.034 1.711 0.087 -0.008 0.123\n",
"const -3.6592 1.156 -3.164 0.002 -5.926 -1.393\n",
"------------------------------------------------------------------------------\n",
" y=4 coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"x1 -0.0914 0.044 -2.085 0.037 -0.177 -0.005\n",
"x2 1.2826 0.129 9.937 0.000 1.030 1.536\n",
"x3 -0.0085 0.008 -1.008 0.314 -0.025 0.008\n",
"x4 0.2012 0.094 2.136 0.033 0.017 0.386\n",
"x5 0.0850 0.026 3.240 0.001 0.034 0.136\n",
"const -7.6589 0.960 -7.982 0.000 -9.540 -5.778\n",
"------------------------------------------------------------------------------\n",
" y=5 coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"x1 -0.0934 0.039 -2.374 0.018 -0.170 -0.016\n",
"x2 1.3451 0.117 11.485 0.000 1.116 1.575\n",
"x3 -0.0180 0.008 -2.362 0.018 -0.033 -0.003\n",
"x4 0.2161 0.085 2.542 0.011 0.049 0.383\n",
"x5 0.0808 0.023 3.517 0.000 0.036 0.126\n",
"const -7.0401 0.844 -8.344 0.000 -8.694 -5.387\n",
"------------------------------------------------------------------------------\n",
" y=6 coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"x1 -0.1409 0.042 -3.345 0.001 -0.224 -0.058\n",
"x2 2.0686 0.143 14.433 0.000 1.788 2.349\n",
"x3 -0.0095 0.008 -1.164 0.244 -0.025 0.006\n",
"x4 0.3216 0.091 3.532 0.000 0.143 0.500\n",
"x5 0.1087 0.025 4.299 0.000 0.059 0.158\n",
"const -12.0913 1.059 -11.415 0.000 -14.167 -10.015\n",
"==============================================================================\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
" \"Check mle_retvals\", ConvergenceWarning)\n"
]
}
],
"source": [
"mlogit_res = mlogit_mod.fit(method='bfgs', maxiter=100)\n",
"print(mlogit_res.summary())"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
}

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"replace": {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Formulas: Fitting models using R-style formulas"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since version 0.5.0, ``statsmodels`` allows users to fit statistical models using R-style formulas. Internally, ``statsmodels`` uses the [patsy](http://patsy.readthedocs.org/) package to convert formulas and data to the matrices that are used in model fitting. The formula framework is quite powerful; this tutorial only scratches the surface. A full description of the formula language can be found in the ``patsy`` docs: \n",
"\n",
"* [Patsy formula language description](http://patsy.readthedocs.org/)\n",
"\n",
"## Loading modules and functions"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
" from pandas.core import datetools\n"
]
}
],
"source": [
"from __future__ import print_function\n",
"import numpy as np\n",
"import statsmodels.api as sm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Import convention"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can import explicitly from statsmodels.formula.api"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from statsmodels.formula.api import ols"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Alternatively, you can just use the `formula` namespace of the main `statsmodels.api`."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<bound method Model.from_formula of <class 'statsmodels.regression.linear_model.OLS'>>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sm.formula.ols"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Or you can use the following conventioin"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import statsmodels.formula.api as smf"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These names are just a convenient way to get access to each model's `from_formula` classmethod. See, for instance"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<bound method Model.from_formula of <class 'statsmodels.regression.linear_model.OLS'>>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sm.OLS.from_formula"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"All of the lower case models accept ``formula`` and ``data`` arguments, whereas upper case ones take ``endog`` and ``exog`` design matrices. ``formula`` accepts a string which describes the model in terms of a ``patsy`` formula. ``data`` takes a [pandas](http://pandas.pydata.org/) data frame or any other data structure that defines a ``__getitem__`` for variable names like a structured array or a dictionary of variables. \n",
"\n",
"``dir(sm.formula)`` will print a list of available models. \n",
"\n",
"Formula-compatible models have the following generic call signature: ``(formula, data, subset=None, *args, **kwargs)``"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## OLS regression using formulas\n",
"\n",
"To begin, we fit the linear model described on the [Getting Started](gettingstarted.html) page. Download the data, subset columns, and list-wise delete to remove missing observations:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dta = sm.datasets.get_rdataset(\"Guerry\", \"HistData\", cache=True)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Lottery</th>\n",
" <th>Literacy</th>\n",
" <th>Wealth</th>\n",
" <th>Region</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>41</td>\n",
" <td>37</td>\n",
" <td>73</td>\n",
" <td>E</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>38</td>\n",
" <td>51</td>\n",
" <td>22</td>\n",
" <td>N</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>66</td>\n",
" <td>13</td>\n",
" <td>61</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>80</td>\n",
" <td>46</td>\n",
" <td>76</td>\n",
" <td>E</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>79</td>\n",
" <td>69</td>\n",
" <td>83</td>\n",
" <td>E</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Lottery Literacy Wealth Region\n",
"0 41 37 73 E\n",
"1 38 51 22 N\n",
"2 66 13 61 C\n",
"3 80 46 76 E\n",
"4 79 69 83 E"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = dta.data[['Lottery', 'Literacy', 'Wealth', 'Region']].dropna()\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Fit the model:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: Lottery R-squared: 0.338\n",
"Model: OLS Adj. R-squared: 0.287\n",
"Method: Least Squares F-statistic: 6.636\n",
"Date: Sat, 10 Apr 2021 Prob (F-statistic): 1.07e-05\n",
"Time: 01:00:05 Log-Likelihood: -375.30\n",
"No. Observations: 85 AIC: 764.6\n",
"Df Residuals: 78 BIC: 781.7\n",
"Df Model: 6 \n",
"Covariance Type: nonrobust \n",
"===============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"-------------------------------------------------------------------------------\n",
"Intercept 38.6517 9.456 4.087 0.000 19.826 57.478\n",
"Region[T.E] -15.4278 9.727 -1.586 0.117 -34.793 3.938\n",
"Region[T.N] -10.0170 9.260 -1.082 0.283 -28.453 8.419\n",
"Region[T.S] -4.5483 7.279 -0.625 0.534 -19.039 9.943\n",
"Region[T.W] -10.0913 7.196 -1.402 0.165 -24.418 4.235\n",
"Literacy -0.1858 0.210 -0.886 0.378 -0.603 0.232\n",
"Wealth 0.4515 0.103 4.390 0.000 0.247 0.656\n",
"==============================================================================\n",
"Omnibus: 3.049 Durbin-Watson: 1.785\n",
"Prob(Omnibus): 0.218 Jarque-Bera (JB): 2.694\n",
"Skew: -0.340 Prob(JB): 0.260\n",
"Kurtosis: 2.454 Cond. No. 371.\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
]
}
],
"source": [
"mod = ols(formula='Lottery ~ Literacy + Wealth + Region', data=df)\n",
"res = mod.fit()\n",
"print(res.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Categorical variables\n",
"\n",
"Looking at the summary printed above, notice that ``patsy`` determined that elements of *Region* were text strings, so it treated *Region* as a categorical variable. `patsy`'s default is also to include an intercept, so we automatically dropped one of the *Region* categories.\n",
"\n",
"If *Region* had been an integer variable that we wanted to treat explicitly as categorical, we could have done so by using the ``C()`` operator: "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Intercept 38.651655\n",
"C(Region)[T.E] -15.427785\n",
"C(Region)[T.N] -10.016961\n",
"C(Region)[T.S] -4.548257\n",
"C(Region)[T.W] -10.091276\n",
"Literacy -0.185819\n",
"Wealth 0.451475\n",
"dtype: float64\n"
]
}
],
"source": [
"res = ols(formula='Lottery ~ Literacy + Wealth + C(Region)', data=df).fit()\n",
"print(res.params)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Patsy's mode advanced features for categorical variables are discussed in: [Patsy: Contrast Coding Systems for categorical variables](contrasts.html)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Operators\n",
"\n",
"We have already seen that \"~\" separates the left-hand side of the model from the right-hand side, and that \"+\" adds new columns to the design matrix. \n",
"\n",
"### Removing variables\n",
"\n",
"The \"-\" sign can be used to remove columns/variables. For instance, we can remove the intercept from a model by: "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"C(Region)[C] 38.651655\n",
"C(Region)[E] 23.223870\n",
"C(Region)[N] 28.634694\n",
"C(Region)[S] 34.103399\n",
"C(Region)[W] 28.560379\n",
"Literacy -0.185819\n",
"Wealth 0.451475\n",
"dtype: float64\n"
]
}
],
"source": [
"res = ols(formula='Lottery ~ Literacy + Wealth + C(Region) -1 ', data=df).fit()\n",
"print(res.params)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Multiplicative interactions\n",
"\n",
"\":\" adds a new column to the design matrix with the interaction of the other two columns. \"*\" will also include the individual columns that were multiplied together:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Literacy:Wealth 0.018176\n",
"dtype: float64 \n",
"\n",
"Literacy 0.427386\n",
"Wealth 1.080987\n",
"Literacy:Wealth -0.013609\n",
"dtype: float64\n"
]
}
],
"source": [
"res1 = ols(formula='Lottery ~ Literacy : Wealth - 1', data=df).fit()\n",
"res2 = ols(formula='Lottery ~ Literacy * Wealth - 1', data=df).fit()\n",
"print(res1.params, '\\n')\n",
"print(res2.params)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Many other things are possible with operators. Please consult the [patsy docs](https://patsy.readthedocs.org/en/latest/formulas.html) to learn more."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Functions\n",
"\n",
"You can apply vectorized functions to the variables in your model: "
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Intercept 115.609119\n",
"np.log(Literacy) -20.393959\n",
"dtype: float64\n"
]
}
],
"source": [
"res = smf.ols(formula='Lottery ~ np.log(Literacy)', data=df).fit()\n",
"print(res.params)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define a custom function:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Intercept 136.003079\n",
"log_plus_1(Literacy) -20.393959\n",
"dtype: float64\n"
]
}
],
"source": [
"def log_plus_1(x):\n",
" return np.log(x) + 1.\n",
"res = smf.ols(formula='Lottery ~ log_plus_1(Literacy)', data=df).fit()\n",
"print(res.params)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Any function that is in the calling namespace is available to the formula."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Using formulas with models that do not (yet) support them\n",
"\n",
"Even if a given `statsmodels` function does not support formulas, you can still use `patsy`'s formula language to produce design matrices. Those matrices \n",
"can then be fed to the fitting function as `endog` and `exog` arguments. \n",
"\n",
"To generate ``numpy`` arrays: "
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Lottery\n",
"0 41.0\n",
"1 38.0\n",
"2 66.0\n",
"3 80.0\n",
"4 79.0\n",
" Intercept Literacy Wealth Literacy:Wealth\n",
"0 1.0 37.0 73.0 2701.0\n",
"1 1.0 51.0 22.0 1122.0\n",
"2 1.0 13.0 61.0 793.0\n",
"3 1.0 46.0 76.0 3496.0\n",
"4 1.0 69.0 83.0 5727.0\n"
]
}
],
"source": [
"import patsy\n",
"f = 'Lottery ~ Literacy * Wealth'\n",
"y,X = patsy.dmatrices(f, df, return_type='dataframe')\n",
"print(y[:5])\n",
"print(X[:5])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To generate pandas data frames: "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Lottery\n",
"0 41.0\n",
"1 38.0\n",
"2 66.0\n",
"3 80.0\n",
"4 79.0\n",
" Intercept Literacy Wealth Literacy:Wealth\n",
"0 1.0 37.0 73.0 2701.0\n",
"1 1.0 51.0 22.0 1122.0\n",
"2 1.0 13.0 61.0 793.0\n",
"3 1.0 46.0 76.0 3496.0\n",
"4 1.0 69.0 83.0 5727.0\n"
]
}
],
"source": [
"f = 'Lottery ~ Literacy * Wealth'\n",
"y,X = patsy.dmatrices(f, df, return_type='dataframe')\n",
"print(y[:5])\n",
"print(X[:5])"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: Lottery R-squared: 0.309\n",
"Model: OLS Adj. R-squared: 0.283\n",
"Method: Least Squares F-statistic: 12.06\n",
"Date: Sat, 10 Apr 2021 Prob (F-statistic): 1.32e-06\n",
"Time: 01:00:06 Log-Likelihood: -377.13\n",
"No. Observations: 85 AIC: 762.3\n",
"Df Residuals: 81 BIC: 772.0\n",
"Df Model: 3 \n",
"Covariance Type: nonrobust \n",
"===================================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"-----------------------------------------------------------------------------------\n",
"Intercept 38.6348 15.825 2.441 0.017 7.149 70.121\n",
"Literacy -0.3522 0.334 -1.056 0.294 -1.016 0.312\n",
"Wealth 0.4364 0.283 1.544 0.126 -0.126 0.999\n",
"Literacy:Wealth -0.0005 0.006 -0.085 0.933 -0.013 0.012\n",
"==============================================================================\n",
"Omnibus: 4.447 Durbin-Watson: 1.953\n",
"Prob(Omnibus): 0.108 Jarque-Bera (JB): 3.228\n",
"Skew: -0.332 Prob(JB): 0.199\n",
"Kurtosis: 2.314 Cond. No. 1.40e+04\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"[2] The condition number is large, 1.4e+04. This might indicate that there are\n",
"strong multicollinearity or other numerical problems.\n"
]
}
],
"source": [
"print(sm.OLS(y, X).fit().summary())"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
}

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/srv/reproducible-results/rbuild-debian/tmp.FTzzuDJkiI/dbd-tmp-gqMLHgR/diffoscope_1mz05yxs/tmpbum2ja01/adb37b2a-ecc0-453b-8f34-7eb00ff3fa9f vs.

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Differences: {
"replace": {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Maximum Likelihood Estimation (Generic models)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This tutorial explains how to quickly implement new maximum likelihood models in `statsmodels`. We give two examples: \n",
"\n",
"1. Probit model for binary dependent variables\n",
"2. Negative binomial model for count data\n",
"\n",
"The `GenericLikelihoodModel` class eases the process by providing tools such as automatic numeric differentiation and a unified interface to ``scipy`` optimization functions. Using ``statsmodels``, users can fit new MLE models simply by \"plugging-in\" a log-likelihood function. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Example 1: Probit model"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
" from pandas.core import datetools\n"
]
}
],
"source": [
"from __future__ import print_function\n",
"import numpy as np\n",
"from scipy import stats\n",
"import statsmodels.api as sm\n",
"from statsmodels.base.model import GenericLikelihoodModel"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The ``Spector`` dataset is distributed with ``statsmodels``. You can access a vector of values for the dependent variable (``endog``) and a matrix of regressors (``exog``) like this:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"::\n",
"\n",
" Number of Observations - 32\n",
"\n",
" Number of Variables - 4\n",
"\n",
" Variable name definitions::\n",
"\n",
" Grade - binary variable indicating whether or not a student's grade\n",
" improved. 1 indicates an improvement.\n",
" TUCE - Test score on economics test\n",
" PSI - participation in program\n",
" GPA - Student's grade point average\n",
"\n",
" GPA TUCE PSI\n",
"0 2.66 20.0 0.0\n",
"1 2.89 22.0 0.0\n",
"2 3.28 24.0 0.0\n",
"3 2.92 12.0 0.0\n",
"4 4.00 21.0 0.0\n"
]
}
],
"source": [
"data = sm.datasets.spector.load_pandas()\n",
"exog = data.exog\n",
"endog = data.endog\n",
"print(sm.datasets.spector.NOTE)\n",
"print(data.exog.head())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Them, we add a constant to the matrix of regressors:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"exog = sm.add_constant(exog, prepend=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To create your own Likelihood Model, you simply need to overwrite the loglike method."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"class MyProbit(GenericLikelihoodModel):\n",
" def loglike(self, params):\n",
" exog = self.exog\n",
" endog = self.endog\n",
" q = 2 * endog - 1\n",
" return stats.norm.logcdf(q*np.dot(exog, params)).sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Estimate the model and print a summary:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimization terminated successfully.\n",
" Current function value: 0.400588\n",
" Iterations: 292\n",
" Function evaluations: 494\n",
" MyProbit Results \n",
"==============================================================================\n",
"Dep. Variable: GRADE Log-Likelihood: -12.819\n",
"Model: MyProbit AIC: 33.64\n",
"Method: Maximum Likelihood BIC: 39.50\n",
"Date: Sat, 10 Apr 2021 \n",
"Time: 01:00:12 \n",
"No. Observations: 32 \n",
"Df Residuals: 28 \n",
"Df Model: 3 \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const -7.4523 2.542 -2.931 0.003 -12.435 -2.469\n",
"GPA 1.6258 0.694 2.343 0.019 0.266 2.986\n",
"TUCE 0.0517 0.084 0.617 0.537 -0.113 0.216\n",
"PSI 1.4263 0.595 2.397 0.017 0.260 2.593\n",
"==============================================================================\n"
]
}
],
"source": [
"sm_probit_manual = MyProbit(endog, exog).fit()\n",
"print(sm_probit_manual.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compare your Probit implementation to ``statsmodels``' \"canned\" implementation:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimization terminated successfully.\n",
" Current function value: 0.400588\n",
" Iterations 6\n"
]
}
],
"source": [
"sm_probit_canned = sm.Probit(endog, exog).fit()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"const -7.452320\n",
"GPA 1.625810\n",
"TUCE 0.051729\n",
"PSI 1.426332\n",
"dtype: float64\n",
"[-7.45233176 1.62580888 0.05172971 1.42631954]\n"
]
}
],
"source": [
"print(sm_probit_canned.params)\n",
"print(sm_probit_manual.params)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" const GPA TUCE PSI\n",
"const 6.464166 -1.169668 -0.101173 -0.594792\n",
"GPA -1.169668 0.481473 -0.018914 0.105439\n",
"TUCE -0.101173 -0.018914 0.007038 0.002472\n",
"PSI -0.594792 0.105439 0.002472 0.354070\n",
"[[ 6.46416774e+00 -1.16966617e+00 -1.01173183e-01 -5.94788997e-01]\n",
" [-1.16966617e+00 4.81472112e-01 -1.89134583e-02 1.05438224e-01]\n",
" [-1.01173183e-01 -1.89134583e-02 7.03758398e-03 2.47189248e-03]\n",
" [-5.94788997e-01 1.05438224e-01 2.47189248e-03 3.54069513e-01]]\n"
]
}
],
"source": [
"print(sm_probit_canned.cov_params())\n",
"print(sm_probit_manual.cov_params())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice that the ``GenericMaximumLikelihood`` class provides automatic differentiation, so we didn't have to provide Hessian or Score functions in order to calculate the covariance estimates."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"## Example 2: Negative Binomial Regression for Count Data\n",
"\n",
"Consider a negative binomial regression model for count data with\n",
"log-likelihood (type NB-2) function expressed as:\n",
"\n",
"$$\n",
" \\mathcal{L}(\\beta_j; y, \\alpha) = \\sum_{i=1}^n y_i ln \n",
" \\left ( \\frac{\\alpha exp(X_i'\\beta)}{1+\\alpha exp(X_i'\\beta)} \\right ) -\n",
" \\frac{1}{\\alpha} ln(1+\\alpha exp(X_i'\\beta)) + ln \\Gamma (y_i + 1/\\alpha) - ln \\Gamma (y_i+1) - ln \\Gamma (1/\\alpha)\n",
"$$\n",
"\n",
"with a matrix of regressors $X$, a vector of coefficients $\\beta$,\n",
"and the negative binomial heterogeneity parameter $\\alpha$. \n",
"\n",
"Using the ``nbinom`` distribution from ``scipy``, we can write this likelihood\n",
"simply as:\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"from scipy.stats import nbinom"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def _ll_nb2(y, X, beta, alph):\n",
" mu = np.exp(np.dot(X, beta))\n",
" size = 1/alph\n",
" prob = size/(size+mu)\n",
" ll = nbinom.logpmf(y, size, prob)\n",
" return ll"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### New Model Class\n",
"\n",
"We create a new model class which inherits from ``GenericLikelihoodModel``:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from statsmodels.base.model import GenericLikelihoodModel"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"class NBin(GenericLikelihoodModel):\n",
" def __init__(self, endog, exog, **kwds):\n",
" super(NBin, self).__init__(endog, exog, **kwds)\n",
" \n",
" def nloglikeobs(self, params):\n",
" alph = params[-1]\n",
" beta = params[:-1]\n",
" ll = _ll_nb2(self.endog, self.exog, beta, alph)\n",
" return -ll \n",
" \n",
" def fit(self, start_params=None, maxiter=10000, maxfun=5000, **kwds):\n",
" # we have one additional parameter and we need to add it for summary\n",
" self.exog_names.append('alpha')\n",
" if start_params == None:\n",
" # Reasonable starting values\n",
" start_params = np.append(np.zeros(self.exog.shape[1]), .5)\n",
" # intercept\n",
" start_params[-2] = np.log(self.endog.mean())\n",
" return super(NBin, self).fit(start_params=start_params, \n",
" maxiter=maxiter, maxfun=maxfun, \n",
" **kwds) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Two important things to notice: \n",
"\n",
"+ ``nloglikeobs``: This function should return one evaluation of the negative log-likelihood function per observation in your dataset (i.e. rows of the endog/X matrix). \n",
"+ ``start_params``: A one-dimensional array of starting values needs to be provided. The size of this array determines the number of parameters that will be used in optimization.\n",
" \n",
"That's it! You're done!\n",
"\n",
"### Usage Example\n",
"\n",
"The [Medpar](http://vincentarelbundock.github.com/Rdatasets/doc/COUNT/medpar.html)\n",
"dataset is hosted in CSV format at the [Rdatasets repository](http://vincentarelbundock.github.com/Rdatasets). We use the ``read_csv``\n",
"function from the [Pandas library](http://pandas.pydata.org) to load the data\n",
"in memory. We then print the first few columns: \n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import statsmodels.api as sm"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>los</th>\n",
" <th>hmo</th>\n",
" <th>white</th>\n",
" <th>died</th>\n",
" <th>age80</th>\n",
" <th>type</th>\n",
" <th>type1</th>\n",
" <th>type2</th>\n",
" <th>type3</th>\n",
" <th>provnum</th>\n",
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" <td>9</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>30001</td>\n",
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" <th>2</th>\n",
" <td>3</td>\n",
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" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>30001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>9</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>30001</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" los hmo white died age80 type type1 type2 type3 provnum\n",
"0 4 0 1 0 0 1 1 0 0 30001\n",
"1 9 1 1 0 0 1 1 0 0 30001\n",
"2 3 1 1 1 1 1 1 0 0 30001\n",
"3 9 0 1 0 0 1 1 0 0 30001\n",
"4 1 0 1 1 1 1 1 0 0 30001"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"medpar = sm.datasets.get_rdataset(\"medpar\", \"COUNT\", cache=True).data\n",
"\n",
"medpar.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The model we are interested in has a vector of non-negative integers as\n",
"dependent variable (``los``), and 5 regressors: ``Intercept``, ``type2``,\n",
"``type3``, ``hmo``, ``white``.\n",
"\n",
"For estimation, we need to create two variables to hold our regressors and the outcome variable. These can be ndarrays or pandas objects."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y = medpar.los\n",
"X = medpar[[\"type2\", \"type3\", \"hmo\", \"white\"]].copy()\n",
"X[\"constant\"] = 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then, we fit the model and extract some information: "
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimization terminated successfully.\n",
" Current function value: 3.209014\n",
" Iterations: 805\n",
" Function evaluations: 1238\n"
]
}
],
"source": [
"mod = NBin(y, X)\n",
"res = mod.fit()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Extract parameter estimates, standard errors, p-values, AIC, etc.:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Parameters: [ 0.2212642 0.70613942 -0.06798155 -0.12903932 2.31026565 0.44575147]\n",
"Standard errors: [0.05059259 0.07613045 0.05326096 0.06854109 0.06794668 0.01981542]\n",
"P-values: [1.22297610e-005 1.76974607e-020 2.01819059e-001 5.97469448e-002\n",
" 2.14170707e-253 4.62695738e-112]\n",
"AIC: 9604.953205830161\n"
]
}
],
"source": [
"print('Parameters: ', res.params)\n",
"print('Standard errors: ', res.bse)\n",
"print('P-values: ', res.pvalues)\n",
"print('AIC: ', res.aic)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As usual, you can obtain a full list of available information by typing\n",
"``dir(res)``.\n",
"We can also look at the summary of the estimation results."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" NBin Results \n",
"==============================================================================\n",
"Dep. Variable: los Log-Likelihood: -4797.5\n",
"Model: NBin AIC: 9605.\n",
"Method: Maximum Likelihood BIC: 9632.\n",
"Date: Sat, 10 Apr 2021 \n",
"Time: 01:00:13 \n",
"No. Observations: 1495 \n",
"Df Residuals: 1490 \n",
"Df Model: 4 \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"type2 0.2213 0.051 4.373 0.000 0.122 0.320\n",
"type3 0.7061 0.076 9.275 0.000 0.557 0.855\n",
"hmo -0.0680 0.053 -1.276 0.202 -0.172 0.036\n",
"white -0.1290 0.069 -1.883 0.060 -0.263 0.005\n",
"constant 2.3103 0.068 34.001 0.000 2.177 2.443\n",
"alpha 0.4458 0.020 22.495 0.000 0.407 0.485\n",
"==============================================================================\n"
]
}
],
"source": [
"print(res.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Testing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can check the results by using the statsmodels implementation of the Negative Binomial model, which uses the analytic score function and Hessian."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" NegativeBinomial Regression Results \n",
"==============================================================================\n",
"Dep. Variable: los No. Observations: 1495\n",
"Model: NegativeBinomial Df Residuals: 1490\n",
"Method: MLE Df Model: 4\n",
"Date: Sat, 10 Apr 2021 Pseudo R-squ.: 0.01215\n",
"Time: 01:00:14 Log-Likelihood: -4797.5\n",
"converged: True LL-Null: -4856.5\n",
" LLR p-value: 1.404e-24\n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"type2 0.2212 0.051 4.373 0.000 0.122 0.320\n",
"type3 0.7062 0.076 9.276 0.000 0.557 0.855\n",
"hmo -0.0680 0.053 -1.277 0.202 -0.172 0.036\n",
"white -0.1291 0.069 -1.883 0.060 -0.263 0.005\n",
"constant 2.3103 0.068 34.001 0.000 2.177 2.443\n",
"alpha 0.4458 0.020 22.495 0.000 0.407 0.485\n",
"==============================================================================\n"
]
}
],
"source": [
"res_nbin = sm.NegativeBinomial(y, X).fit(disp=0)\n",
"print(res_nbin.summary())"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"type2 0.221231\n",
"type3 0.706175\n",
"hmo -0.067990\n",
"white -0.129065\n",
"constant 2.310288\n",
"alpha 0.445758\n",
"dtype: float64\n"
]
}
],
"source": [
"print(res_nbin.params)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"type2 0.050592\n",
"type3 0.076132\n",
"hmo 0.053261\n",
"white 0.068542\n",
"constant 0.067947\n",
"alpha 0.019816\n",
"dtype: float64\n"
]
}
],
"source": [
"print(res_nbin.bse)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Or we could compare them to results obtained using the MASS implementation for R:\n",
"\n",
" url = 'http://vincentarelbundock.github.com/Rdatasets/csv/COUNT/medpar.csv'\n",
" medpar = read.csv(url)\n",
" f = los~factor(type)+hmo+white\n",
" \n",
" library(MASS)\n",
" mod = glm.nb(f, medpar)\n",
" coef(summary(mod))\n",
" Estimate Std. Error z value Pr(>|z|)\n",
" (Intercept) 2.31027893 0.06744676 34.253370 3.885556e-257\n",
" factor(type)2 0.22124898 0.05045746 4.384861 1.160597e-05\n",
" factor(type)3 0.70615882 0.07599849 9.291748 1.517751e-20\n",
" hmo -0.06795522 0.05321375 -1.277024 2.015939e-01\n",
" white -0.12906544 0.06836272 -1.887951 5.903257e-02\n",
"\n",
"### Numerical precision \n",
"\n",
"The ``statsmodels`` generic MLE and ``R`` parameter estimates agree up to the fourth decimal. The standard errors, however, agree only up to the second decimal. This discrepancy is the result of imprecision in our Hessian numerical estimates. In the current context, the difference between ``MASS`` and ``statsmodels`` standard error estimates is substantively irrelevant, but it highlights the fact that users who need very precise estimates may not always want to rely on default settings when using numerical derivatives. In such cases, it is better to use analytical derivatives with the ``LikelihoodModel`` class."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
}

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/srv/reproducible-results/rbuild-debian/tmp.FTzzuDJkiI/dbd-tmp-gqMLHgR/diffoscope_1mz05yxs/tmppipp7agn/31ced119-26cb-4874-b001-f44971d00d8a vs.

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Differences: {
"replace": {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Generalized Linear Models"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
" from pandas.core import datetools\n"
]
}
],
"source": [
"%matplotlib inline\n",
"\n",
"from __future__ import print_function\n",
"import numpy as np\n",
"import statsmodels.api as sm\n",
"from scipy import stats\n",
"from matplotlib import pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## GLM: Binomial response data\n",
"\n",
"### Load data\n",
"\n",
" In this example, we use the Star98 dataset which was taken with permission\n",
" from Jeff Gill (2000) Generalized linear models: A unified approach. Codebook\n",
" information can be obtained by typing: "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"::\n",
"\n",
" Number of Observations - 303 (counties in California).\n",
"\n",
" Number of Variables - 13 and 8 interaction terms.\n",
"\n",
" Definition of variables names::\n",
"\n",
" NABOVE - Total number of students above the national median for the\n",
" math section.\n",
" NBELOW - Total number of students below the national median for the\n",
" math section.\n",
" LOWINC - Percentage of low income students\n",
" PERASIAN - Percentage of Asian student\n",
" PERBLACK - Percentage of black students\n",
" PERHISP - Percentage of Hispanic students\n",
" PERMINTE - Percentage of minority teachers\n",
" AVYRSEXP - Sum of teachers' years in educational service divided by the\n",
" number of teachers.\n",
" AVSALK - Total salary budget including benefits divided by the number\n",
" of full-time teachers (in thousands)\n",
" PERSPENK - Per-pupil spending (in thousands)\n",
" PTRATIO - Pupil-teacher ratio.\n",
" PCTAF - Percentage of students taking UC/CSU prep courses\n",
" PCTCHRT - Percentage of charter schools\n",
" PCTYRRND - Percentage of year-round schools\n",
"\n",
" The below variables are interaction terms of the variables defined\n",
" above.\n",
"\n",
" PERMINTE_AVYRSEXP\n",
" PEMINTE_AVSAL\n",
" AVYRSEXP_AVSAL\n",
" PERSPEN_PTRATIO\n",
" PERSPEN_PCTAF\n",
" PTRATIO_PCTAF\n",
" PERMINTE_AVTRSEXP_AVSAL\n",
" PERSPEN_PTRATIO_PCTAF\n",
"\n"
]
}
],
"source": [
"print(sm.datasets.star98.NOTE)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load the data and add a constant to the exogenous (independent) variables:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/datasets/utils.py:89: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n",
" endog = np.column_stack(data[field] for field in endog_name)\n",
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/datasets/utils.py:100: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n",
" exog = np.column_stack(data[field] for field in exog_name)\n"
]
}
],
"source": [
"data = sm.datasets.star98.load()\n",
"data.exog = sm.add_constant(data.exog, prepend=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" The dependent variable is N by 2 (Success: NABOVE, Failure: NBELOW): "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[452. 355.]\n",
" [144. 40.]\n",
" [337. 234.]\n",
" [395. 178.]\n",
" [ 8. 57.]]\n"
]
}
],
"source": [
"print(data.endog[:5,:])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" The independent variables include all the other variables described above, as\n",
" well as the interaction terms:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[3.43973000e+01 2.32993000e+01 1.42352800e+01 1.14111200e+01\n",
" 1.59183700e+01 1.47064600e+01 5.91573200e+01 4.44520700e+00\n",
" 2.17102500e+01 5.70327600e+01 0.00000000e+00 2.22222200e+01\n",
" 2.34102872e+02 9.41688110e+02 8.69994800e+02 9.65065600e+01\n",
" 2.53522420e+02 1.23819550e+03 1.38488985e+04 5.50403520e+03\n",
" 1.00000000e+00]\n",
" [1.73650700e+01 2.93283800e+01 8.23489700e+00 9.31488400e+00\n",
" 1.36363600e+01 1.60832400e+01 5.95039700e+01 5.26759800e+00\n",
" 2.04427800e+01 6.46226400e+01 0.00000000e+00 0.00000000e+00\n",
" 2.19316851e+02 8.11417560e+02 9.57016600e+02 1.07684350e+02\n",
" 3.40406090e+02 1.32106640e+03 1.30502233e+04 6.95884680e+03\n",
" 1.00000000e+00]]\n"
]
}
],
"source": [
"print(data.exog[:2,:])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Fit and summary"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Generalized Linear Model Regression Results \n",
"==============================================================================\n",
"Dep. Variable: ['y1', 'y2'] No. Observations: 303\n",
"Model: GLM Df Residuals: 282\n",
"Model Family: Binomial Df Model: 20\n",
"Link Function: logit Scale: 1.0\n",
"Method: IRLS Log-Likelihood: -2998.6\n",
"Date: Sat, 10 Apr 2021 Deviance: 4078.8\n",
"Time: 01:00:06 Pearson chi2: 9.60\n",
"No. Iterations: 5 \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"x1 -0.0168 0.000 -38.749 0.000 -0.018 -0.016\n",
"x2 0.0099 0.001 16.505 0.000 0.009 0.011\n",
"x3 -0.0187 0.001 -25.182 0.000 -0.020 -0.017\n",
"x4 -0.0142 0.000 -32.818 0.000 -0.015 -0.013\n",
"x5 0.2545 0.030 8.498 0.000 0.196 0.313\n",
"x6 0.2407 0.057 4.212 0.000 0.129 0.353\n",
"x7 0.0804 0.014 5.775 0.000 0.053 0.108\n",
"x8 -1.9522 0.317 -6.162 0.000 -2.573 -1.331\n",
"x9 -0.3341 0.061 -5.453 0.000 -0.454 -0.214\n",
"x10 -0.1690 0.033 -5.169 0.000 -0.233 -0.105\n",
"x11 0.0049 0.001 3.921 0.000 0.002 0.007\n",
"x12 -0.0036 0.000 -15.878 0.000 -0.004 -0.003\n",
"x13 -0.0141 0.002 -7.391 0.000 -0.018 -0.010\n",
"x14 -0.0040 0.000 -8.450 0.000 -0.005 -0.003\n",
"x15 -0.0039 0.001 -4.059 0.000 -0.006 -0.002\n",
"x16 0.0917 0.015 6.321 0.000 0.063 0.120\n",
"x17 0.0490 0.007 6.574 0.000 0.034 0.064\n",
"x18 0.0080 0.001 5.362 0.000 0.005 0.011\n",
"x19 0.0002 2.99e-05 7.428 0.000 0.000 0.000\n",
"x20 -0.0022 0.000 -6.445 0.000 -0.003 -0.002\n",
"const 2.9589 1.547 1.913 0.056 -0.073 5.990\n",
"==============================================================================\n"
]
}
],
"source": [
"glm_binom = sm.GLM(data.endog, data.exog, family=sm.families.Binomial())\n",
"res = glm_binom.fit()\n",
"print(res.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Quantities of interest"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total number of trials: 807.0\n",
"Parameters: [-1.68150366e-02 9.92547661e-03 -1.87242148e-02 -1.42385609e-02\n",
" 2.54487173e-01 2.40693664e-01 8.04086739e-02 -1.95216050e+00\n",
" -3.34086475e-01 -1.69022168e-01 4.91670212e-03 -3.57996435e-03\n",
" -1.40765648e-02 -4.00499176e-03 -3.90639579e-03 9.17143006e-02\n",
" 4.89898381e-02 8.04073890e-03 2.22009503e-04 -2.24924861e-03\n",
" 2.95887793e+00]\n",
"T-values: [-38.74908321 16.50473627 -25.1821894 -32.81791308 8.49827113\n",
" 4.21247925 5.7749976 -6.16191078 -5.45321673 -5.16865445\n",
" 3.92119964 -15.87825999 -7.39093058 -8.44963886 -4.05916246\n",
" 6.3210987 6.57434662 5.36229044 7.42806363 -6.44513698\n",
" 1.91301155]\n"
]
}
],
"source": [
"print('Total number of trials:', data.endog[0].sum())\n",
"print('Parameters: ', res.params)\n",
"print('T-values: ', res.tvalues)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First differences: We hold all explanatory variables constant at their means and manipulate the percentage of low income households to assess its impact on the response variables: "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib/python3/dist-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
" return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
]
}
],
"source": [
"means = data.exog.mean(axis=0)\n",
"means25 = means.copy()\n",
"means25[0] = stats.scoreatpercentile(data.exog[:,0], 25)\n",
"means75 = means.copy()\n",
"means75[0] = lowinc_75per = stats.scoreatpercentile(data.exog[:,0], 75)\n",
"resp_25 = res.predict(means25)\n",
"resp_75 = res.predict(means75)\n",
"diff = resp_75 - resp_25"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The interquartile first difference for the percentage of low income households in a school district is:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-11.8753%\n"
]
}
],
"source": [
"print(\"%2.4f%%\" % (diff*100))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plots\n",
"\n",
" We extract information that will be used to draw some interesting plots: "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"nobs = res.nobs\n",
"y = data.endog[:,0]/data.endog.sum(1)\n",
"yhat = res.mu"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot yhat vs y:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from statsmodels.graphics.api import abline_plot"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots()\n",
"ax.scatter(yhat, y)\n",
"line_fit = sm.OLS(y, sm.add_constant(yhat, prepend=True)).fit()\n",
"abline_plot(model_results=line_fit, ax=ax)\n",
"\n",
"\n",
"ax.set_title('Model Fit Plot')\n",
"ax.set_ylabel('Observed values')\n",
"ax.set_xlabel('Fitted values');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot yhat vs. Pearson residuals:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 0, 'Fitted values')"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots()\n",
"\n",
"ax.scatter(yhat, res.resid_pearson)\n",
"ax.hlines(0, 0, 1)\n",
"ax.set_xlim(0, 1)\n",
"ax.set_title('Residual Dependence Plot')\n",
"ax.set_ylabel('Pearson Residuals')\n",
"ax.set_xlabel('Fitted values')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Histogram of standardized deviance residuals:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from scipy import stats\n",
"\n",
"fig, ax = plt.subplots()\n",
"\n",
"resid = res.resid_deviance.copy()\n",
"resid_std = stats.zscore(resid)\n",
"ax.hist(resid_std, bins=25)\n",
"ax.set_title('Histogram of standardized deviance residuals');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"QQ Plot of Deviance Residuals:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from statsmodels import graphics\n",
"graphics.gofplots.qqplot(resid, line='r')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## GLM: Gamma for proportional count response\n",
"\n",
"### Load data\n",
"\n",
" In the example above, we printed the ``NOTE`` attribute to learn about the\n",
" Star98 dataset. Statsmodels datasets ships with other useful information. For\n",
" example: "
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"This data is based on the example in Gill and describes the proportion of\n",
"voters who voted Yes to grant the Scottish Parliament taxation powers.\n",
"The data are divided into 32 council districts. This example's explanatory\n",
"variables include the amount of council tax collected in pounds sterling as\n",
"of April 1997 per two adults before adjustments, the female percentage of\n",
"total claims for unemployment benefits as of January, 1998, the standardized\n",
"mortality rate (UK is 100), the percentage of labor force participation,\n",
"regional GDP, the percentage of children aged 5 to 15, and an interaction term\n",
"between female unemployment and the council tax.\n",
"\n",
"The original source files and variable information are included in\n",
"/scotland/src/\n",
"\n"
]
}
],
"source": [
"print(sm.datasets.scotland.DESCRLONG)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Load the data and add a constant to the exogenous variables:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[7.12000e+02 2.10000e+01 1.05000e+02 8.24000e+01 1.35660e+04 1.23000e+01\n",
" 1.49520e+04 1.00000e+00]\n",
" [6.43000e+02 2.65000e+01 9.70000e+01 8.02000e+01 1.35660e+04 1.53000e+01\n",
" 1.70395e+04 1.00000e+00]\n",
" [6.79000e+02 2.83000e+01 1.13000e+02 8.63000e+01 9.61100e+03 1.39000e+01\n",
" 1.92157e+04 1.00000e+00]\n",
" [8.01000e+02 2.71000e+01 1.09000e+02 8.04000e+01 9.48300e+03 1.36000e+01\n",
" 2.17071e+04 1.00000e+00]\n",
" [7.53000e+02 2.20000e+01 1.15000e+02 6.47000e+01 9.26500e+03 1.46000e+01\n",
" 1.65660e+04 1.00000e+00]]\n",
"[60.3 52.3 53.4 57. 68.7]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/datasets/utils.py:100: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n",
" exog = np.column_stack(data[field] for field in exog_name)\n"
]
}
],
"source": [
"data2 = sm.datasets.scotland.load()\n",
"data2.exog = sm.add_constant(data2.exog, prepend=False)\n",
"print(data2.exog[:5,:])\n",
"print(data2.endog[:5])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Fit and summary"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Generalized Linear Model Regression Results \n",
"=================================================================================\n",
"Dep. Variable: y No. Observations: 32\n",
"Model: GLM Df Residuals: 24\n",
"Model Family: Gamma Df Model: 7\n",
"Link Function: inverse_power Scale: 0.0035842831734937248\n",
"Method: IRLS Log-Likelihood: -83.017\n",
"Date: Sat, 10 Apr 2021 Deviance: 0.087389\n",
"Time: 01:00:07 Pearson chi2: 0.0860\n",
"No. Iterations: 4 \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"x1 4.962e-05 1.62e-05 3.060 0.002 1.78e-05 8.14e-05\n",
"x2 0.0020 0.001 3.824 0.000 0.001 0.003\n",
"x3 -7.181e-05 2.71e-05 -2.648 0.008 -0.000 -1.87e-05\n",
"x4 0.0001 4.06e-05 2.757 0.006 3.23e-05 0.000\n",
"x5 -1.468e-07 1.24e-07 -1.187 0.235 -3.89e-07 9.56e-08\n",
"x6 -0.0005 0.000 -2.159 0.031 -0.001 -4.78e-05\n",
"x7 -2.427e-06 7.46e-07 -3.253 0.001 -3.89e-06 -9.65e-07\n",
"const -0.0178 0.011 -1.548 0.122 -0.040 0.005\n",
"==============================================================================\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/genmod/generalized_linear_model.py:244: DomainWarning: The inverse_power link function does not respect the domain of the Gamma family.\n",
" DomainWarning)\n"
]
}
],
"source": [
"glm_gamma = sm.GLM(data2.endog, data2.exog, family=sm.families.Gamma())\n",
"glm_results = glm_gamma.fit()\n",
"print(glm_results.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## GLM: Gaussian distribution with a noncanonical link\n",
"\n",
"### Artificial data"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"nobs2 = 100\n",
"x = np.arange(nobs2)\n",
"np.random.seed(54321)\n",
"X = np.column_stack((x,x**2))\n",
"X = sm.add_constant(X, prepend=False)\n",
"lny = np.exp(-(.03*x + .0001*x**2 - 1.0)) + .001 * np.random.rand(nobs2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Fit and summary"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Generalized Linear Model Regression Results \n",
"==================================================================================\n",
"Dep. Variable: y No. Observations: 100\n",
"Model: GLM Df Residuals: 97\n",
"Model Family: Gaussian Df Model: 2\n",
"Link Function: log Scale: 1.0531142558807228e-07\n",
"Method: IRLS Log-Likelihood: 662.92\n",
"Date: Sat, 10 Apr 2021 Deviance: 1.0215e-05\n",
"Time: 01:00:07 Pearson chi2: 1.02e-05\n",
"No. Iterations: 5 \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"x1 -0.0300 5.6e-06 -5361.333 0.000 -0.030 -0.030\n",
"x2 -9.939e-05 1.05e-07 -951.097 0.000 -9.96e-05 -9.92e-05\n",
"const 1.0003 5.39e-05 1.86e+04 0.000 1.000 1.000\n",
"==============================================================================\n"
]
}
],
"source": [
"gauss_log = sm.GLM(lny, X, family=sm.families.Gaussian(sm.families.links.log))\n",
"gauss_log_results = gauss_log.fit()\n",
"print(gauss_log_results.summary())"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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"version": "3.7.3"
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},
"nbformat": 4,
"nbformat_minor": 0
}
}

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/srv/reproducible-results/rbuild-debian/tmp.FTzzuDJkiI/dbd-tmp-gqMLHgR/diffoscope_1mz05yxs/tmpv9p4qxwu/35abc658-a6fa-4574-9e22-61b10afc71fe vs.

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Differences: {
"replace": {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Generalized Linear Models (Formula)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook illustrates how you can use R-style formulas to fit Generalized Linear Models.\n",
"\n",
"To begin, we load the ``Star98`` dataset and we construct a formula and pre-process the data:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
" from pandas.core import datetools\n"
]
}
],
"source": [
"from __future__ import print_function\n",
"import statsmodels.api as sm\n",
"import statsmodels.formula.api as smf\n",
"star98 = sm.datasets.star98.load_pandas().data\n",
"formula = 'SUCCESS ~ LOWINC + PERASIAN + PERBLACK + PERHISP + PCTCHRT + \\\n",
" PCTYRRND + PERMINTE*AVYRSEXP*AVSALK + PERSPENK*PTRATIO*PCTAF'\n",
"dta = star98[['NABOVE', 'NBELOW', 'LOWINC', 'PERASIAN', 'PERBLACK', 'PERHISP',\n",
" 'PCTCHRT', 'PCTYRRND', 'PERMINTE', 'AVYRSEXP', 'AVSALK',\n",
" 'PERSPENK', 'PTRATIO', 'PCTAF']].copy()\n",
"endog = dta['NABOVE'] / (dta['NABOVE'] + dta.pop('NBELOW'))\n",
"del dta['NABOVE']\n",
"dta['SUCCESS'] = endog"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then, we fit the GLM model:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>Generalized Linear Model Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>SUCCESS</td> <th> No. Observations: </th> <td> 303</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>GLM</td> <th> Df Residuals: </th> <td> 282</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model Family:</th> <td>Binomial</td> <th> Df Model: </th> <td> 20</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Link Function:</th> <td>logit</td> <th> Scale: </th> <td>1.0</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>IRLS</td> <th> Log-Likelihood: </th> <td> -189.70</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Sat, 10 Apr 2021</td> <th> Deviance: </th> <td> 380.66</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>01:00:10</td> <th> Pearson chi2: </th> <td> 8.48</td> \n",
"</tr>\n",
"<tr>\n",
" <th>No. Iterations:</th> <td>5</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>Intercept</th> <td> 0.4037</td> <td> 25.036</td> <td> 0.016</td> <td> 0.987</td> <td> -48.665</td> <td> 49.472</td>\n",
"</tr>\n",
"<tr>\n",
" <th>LOWINC</th> <td> -0.0204</td> <td> 0.010</td> <td> -1.982</td> <td> 0.048</td> <td> -0.041</td> <td> -0.000</td>\n",
"</tr>\n",
"<tr>\n",
" <th>PERASIAN</th> <td> 0.0159</td> <td> 0.017</td> <td> 0.910</td> <td> 0.363</td> <td> -0.018</td> <td> 0.050</td>\n",
"</tr>\n",
"<tr>\n",
" <th>PERBLACK</th> <td> -0.0198</td> <td> 0.020</td> <td> -1.004</td> <td> 0.316</td> <td> -0.058</td> <td> 0.019</td>\n",
"</tr>\n",
"<tr>\n",
" <th>PERHISP</th> <td> -0.0096</td> <td> 0.010</td> <td> -0.951</td> <td> 0.341</td> <td> -0.029</td> <td> 0.010</td>\n",
"</tr>\n",
"<tr>\n",
" <th>PCTCHRT</th> <td> -0.0022</td> <td> 0.022</td> <td> -0.103</td> <td> 0.918</td> <td> -0.045</td> <td> 0.040</td>\n",
"</tr>\n",
"<tr>\n",
" <th>PCTYRRND</th> <td> -0.0022</td> <td> 0.006</td> <td> -0.348</td> <td> 0.728</td> <td> -0.014</td> <td> 0.010</td>\n",
"</tr>\n",
"<tr>\n",
" <th>PERMINTE</th> <td> 0.1068</td> <td> 0.787</td> <td> 0.136</td> <td> 0.892</td> <td> -1.436</td> <td> 1.650</td>\n",
"</tr>\n",
"<tr>\n",
" <th>AVYRSEXP</th> <td> -0.0411</td> <td> 1.176</td> <td> -0.035</td> <td> 0.972</td> <td> -2.346</td> <td> 2.264</td>\n",
"</tr>\n",
"<tr>\n",
" <th>PERMINTE:AVYRSEXP</th> <td> -0.0031</td> <td> 0.054</td> <td> -0.057</td> <td> 0.954</td> <td> -0.108</td> <td> 0.102</td>\n",
"</tr>\n",
"<tr>\n",
" <th>AVSALK</th> <td> 0.0131</td> <td> 0.295</td> <td> 0.044</td> <td> 0.965</td> <td> -0.566</td> <td> 0.592</td>\n",
"</tr>\n",
"<tr>\n",
" <th>PERMINTE:AVSALK</th> <td> -0.0019</td> <td> 0.013</td> <td> -0.145</td> <td> 0.885</td> <td> -0.028</td> <td> 0.024</td>\n",
"</tr>\n",
"<tr>\n",
" <th>AVYRSEXP:AVSALK</th> <td> 0.0008</td> <td> 0.020</td> <td> 0.038</td> <td> 0.970</td> <td> -0.039</td> <td> 0.041</td>\n",
"</tr>\n",
"<tr>\n",
" <th>PERMINTE:AVYRSEXP:AVSALK</th> <td> 5.978e-05</td> <td> 0.001</td> <td> 0.068</td> <td> 0.946</td> <td> -0.002</td> <td> 0.002</td>\n",
"</tr>\n",
"<tr>\n",
" <th>PERSPENK</th> <td> -0.3097</td> <td> 4.233</td> <td> -0.073</td> <td> 0.942</td> <td> -8.606</td> <td> 7.987</td>\n",
"</tr>\n",
"<tr>\n",
" <th>PTRATIO</th> <td> 0.0096</td> <td> 0.919</td> <td> 0.010</td> <td> 0.992</td> <td> -1.792</td> <td> 1.811</td>\n",
"</tr>\n",
"<tr>\n",
" <th>PERSPENK:PTRATIO</th> <td> 0.0066</td> <td> 0.206</td> <td> 0.032</td> <td> 0.974</td> <td> -0.397</td> <td> 0.410</td>\n",
"</tr>\n",
"<tr>\n",
" <th>PCTAF</th> <td> -0.0143</td> <td> 0.474</td> <td> -0.030</td> <td> 0.976</td> <td> -0.944</td> <td> 0.916</td>\n",
"</tr>\n",
"<tr>\n",
" <th>PERSPENK:PCTAF</th> <td> 0.0105</td> <td> 0.098</td> <td> 0.107</td> <td> 0.915</td> <td> -0.182</td> <td> 0.203</td>\n",
"</tr>\n",
"<tr>\n",
" <th>PTRATIO:PCTAF</th> <td> -0.0001</td> <td> 0.022</td> <td> -0.005</td> <td> 0.996</td> <td> -0.044</td> <td> 0.044</td>\n",
"</tr>\n",
"<tr>\n",
" <th>PERSPENK:PTRATIO:PCTAF</th> <td> -0.0002</td> <td> 0.005</td> <td> -0.051</td> <td> 0.959</td> <td> -0.010</td> <td> 0.009</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" Generalized Linear Model Regression Results \n",
"==============================================================================\n",
"Dep. Variable: SUCCESS No. Observations: 303\n",
"Model: GLM Df Residuals: 282\n",
"Model Family: Binomial Df Model: 20\n",
"Link Function: logit Scale: 1.0\n",
"Method: IRLS Log-Likelihood: -189.70\n",
"Date: Sat, 10 Apr 2021 Deviance: 380.66\n",
"Time: 01:00:10 Pearson chi2: 8.48\n",
"No. Iterations: 5 \n",
"============================================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"--------------------------------------------------------------------------------------------\n",
"Intercept 0.4037 25.036 0.016 0.987 -48.665 49.472\n",
"LOWINC -0.0204 0.010 -1.982 0.048 -0.041 -0.000\n",
"PERASIAN 0.0159 0.017 0.910 0.363 -0.018 0.050\n",
"PERBLACK -0.0198 0.020 -1.004 0.316 -0.058 0.019\n",
"PERHISP -0.0096 0.010 -0.951 0.341 -0.029 0.010\n",
"PCTCHRT -0.0022 0.022 -0.103 0.918 -0.045 0.040\n",
"PCTYRRND -0.0022 0.006 -0.348 0.728 -0.014 0.010\n",
"PERMINTE 0.1068 0.787 0.136 0.892 -1.436 1.650\n",
"AVYRSEXP -0.0411 1.176 -0.035 0.972 -2.346 2.264\n",
"PERMINTE:AVYRSEXP -0.0031 0.054 -0.057 0.954 -0.108 0.102\n",
"AVSALK 0.0131 0.295 0.044 0.965 -0.566 0.592\n",
"PERMINTE:AVSALK -0.0019 0.013 -0.145 0.885 -0.028 0.024\n",
"AVYRSEXP:AVSALK 0.0008 0.020 0.038 0.970 -0.039 0.041\n",
"PERMINTE:AVYRSEXP:AVSALK 5.978e-05 0.001 0.068 0.946 -0.002 0.002\n",
"PERSPENK -0.3097 4.233 -0.073 0.942 -8.606 7.987\n",
"PTRATIO 0.0096 0.919 0.010 0.992 -1.792 1.811\n",
"PERSPENK:PTRATIO 0.0066 0.206 0.032 0.974 -0.397 0.410\n",
"PCTAF -0.0143 0.474 -0.030 0.976 -0.944 0.916\n",
"PERSPENK:PCTAF 0.0105 0.098 0.107 0.915 -0.182 0.203\n",
"PTRATIO:PCTAF -0.0001 0.022 -0.005 0.996 -0.044 0.044\n",
"PERSPENK:PTRATIO:PCTAF -0.0002 0.005 -0.051 0.959 -0.010 0.009\n",
"============================================================================================\n",
"\"\"\""
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mod1 = smf.glm(formula=formula, data=dta, family=sm.families.Binomial()).fit()\n",
"mod1.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we define a function to operate customized data transformation using the formula framework:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>Generalized Linear Model Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>SUCCESS</td> <th> No. Observations: </th> <td> 303</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>GLM</td> <th> Df Residuals: </th> <td> 282</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model Family:</th> <td>Binomial</td> <th> Df Model: </th> <td> 20</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Link Function:</th> <td>logit</td> <th> Scale: </th> <td>1.0</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>IRLS</td> <th> Log-Likelihood: </th> <td> -189.70</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Sat, 10 Apr 2021</td> <th> Deviance: </th> <td> 380.66</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>01:00:10</td> <th> Pearson chi2: </th> <td> 8.48</td> \n",
"</tr>\n",
"<tr>\n",
" <th>No. Iterations:</th> <td>5</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>Intercept</th> <td> 0.4037</td> <td> 25.036</td> <td> 0.016</td> <td> 0.987</td> <td> -48.665</td> <td> 49.472</td>\n",
"</tr>\n",
"<tr>\n",
" <th>double_it(LOWINC)</th> <td> -0.0102</td> <td> 0.005</td> <td> -1.982</td> <td> 0.048</td> <td> -0.020</td> <td> -0.000</td>\n",
"</tr>\n",
"<tr>\n",
" <th>PERASIAN</th> <td> 0.0159</td> <td> 0.017</td> <td> 0.910</td> <td> 0.363</td> <td> -0.018</td> <td> 0.050</td>\n",
"</tr>\n",
"<tr>\n",
" <th>PERBLACK</th> <td> -0.0198</td> <td> 0.020</td> <td> -1.004</td> <td> 0.316</td> <td> -0.058</td> <td> 0.019</td>\n",
"</tr>\n",
"<tr>\n",
" <th>PERHISP</th> <td> -0.0096</td> <td> 0.010</td> <td> -0.951</td> <td> 0.341</td> <td> -0.029</td> <td> 0.010</td>\n",
"</tr>\n",
"<tr>\n",
" <th>PCTCHRT</th> <td> -0.0022</td> <td> 0.022</td> <td> -0.103</td> <td> 0.918</td> <td> -0.045</td> <td> 0.040</td>\n",
"</tr>\n",
"<tr>\n",
" <th>PCTYRRND</th> <td> -0.0022</td> <td> 0.006</td> <td> -0.348</td> <td> 0.728</td> <td> -0.014</td> <td> 0.010</td>\n",
"</tr>\n",
"<tr>\n",
" <th>PERMINTE</th> <td> 0.1068</td> <td> 0.787</td> <td> 0.136</td> <td> 0.892</td> <td> -1.436</td> <td> 1.650</td>\n",
"</tr>\n",
"<tr>\n",
" <th>AVYRSEXP</th> <td> -0.0411</td> <td> 1.176</td> <td> -0.035</td> <td> 0.972</td> <td> -2.346</td> <td> 2.264</td>\n",
"</tr>\n",
"<tr>\n",
" <th>PERMINTE:AVYRSEXP</th> <td> -0.0031</td> <td> 0.054</td> <td> -0.057</td> <td> 0.954</td> <td> -0.108</td> <td> 0.102</td>\n",
"</tr>\n",
"<tr>\n",
" <th>AVSALK</th> <td> 0.0131</td> <td> 0.295</td> <td> 0.044</td> <td> 0.965</td> <td> -0.566</td> <td> 0.592</td>\n",
"</tr>\n",
"<tr>\n",
" <th>PERMINTE:AVSALK</th> <td> -0.0019</td> <td> 0.013</td> <td> -0.145</td> <td> 0.885</td> <td> -0.028</td> <td> 0.024</td>\n",
"</tr>\n",
"<tr>\n",
" <th>AVYRSEXP:AVSALK</th> <td> 0.0008</td> <td> 0.020</td> <td> 0.038</td> <td> 0.970</td> <td> -0.039</td> <td> 0.041</td>\n",
"</tr>\n",
"<tr>\n",
" <th>PERMINTE:AVYRSEXP:AVSALK</th> <td> 5.978e-05</td> <td> 0.001</td> <td> 0.068</td> <td> 0.946</td> <td> -0.002</td> <td> 0.002</td>\n",
"</tr>\n",
"<tr>\n",
" <th>PERSPENK</th> <td> -0.3097</td> <td> 4.233</td> <td> -0.073</td> <td> 0.942</td> <td> -8.606</td> <td> 7.987</td>\n",
"</tr>\n",
"<tr>\n",
" <th>PTRATIO</th> <td> 0.0096</td> <td> 0.919</td> <td> 0.010</td> <td> 0.992</td> <td> -1.792</td> <td> 1.811</td>\n",
"</tr>\n",
"<tr>\n",
" <th>PERSPENK:PTRATIO</th> <td> 0.0066</td> <td> 0.206</td> <td> 0.032</td> <td> 0.974</td> <td> -0.397</td> <td> 0.410</td>\n",
"</tr>\n",
"<tr>\n",
" <th>PCTAF</th> <td> -0.0143</td> <td> 0.474</td> <td> -0.030</td> <td> 0.976</td> <td> -0.944</td> <td> 0.916</td>\n",
"</tr>\n",
"<tr>\n",
" <th>PERSPENK:PCTAF</th> <td> 0.0105</td> <td> 0.098</td> <td> 0.107</td> <td> 0.915</td> <td> -0.182</td> <td> 0.203</td>\n",
"</tr>\n",
"<tr>\n",
" <th>PTRATIO:PCTAF</th> <td> -0.0001</td> <td> 0.022</td> <td> -0.005</td> <td> 0.996</td> <td> -0.044</td> <td> 0.044</td>\n",
"</tr>\n",
"<tr>\n",
" <th>PERSPENK:PTRATIO:PCTAF</th> <td> -0.0002</td> <td> 0.005</td> <td> -0.051</td> <td> 0.959</td> <td> -0.010</td> <td> 0.009</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" Generalized Linear Model Regression Results \n",
"==============================================================================\n",
"Dep. Variable: SUCCESS No. Observations: 303\n",
"Model: GLM Df Residuals: 282\n",
"Model Family: Binomial Df Model: 20\n",
"Link Function: logit Scale: 1.0\n",
"Method: IRLS Log-Likelihood: -189.70\n",
"Date: Sat, 10 Apr 2021 Deviance: 380.66\n",
"Time: 01:00:10 Pearson chi2: 8.48\n",
"No. Iterations: 5 \n",
"============================================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"--------------------------------------------------------------------------------------------\n",
"Intercept 0.4037 25.036 0.016 0.987 -48.665 49.472\n",
"double_it(LOWINC) -0.0102 0.005 -1.982 0.048 -0.020 -0.000\n",
"PERASIAN 0.0159 0.017 0.910 0.363 -0.018 0.050\n",
"PERBLACK -0.0198 0.020 -1.004 0.316 -0.058 0.019\n",
"PERHISP -0.0096 0.010 -0.951 0.341 -0.029 0.010\n",
"PCTCHRT -0.0022 0.022 -0.103 0.918 -0.045 0.040\n",
"PCTYRRND -0.0022 0.006 -0.348 0.728 -0.014 0.010\n",
"PERMINTE 0.1068 0.787 0.136 0.892 -1.436 1.650\n",
"AVYRSEXP -0.0411 1.176 -0.035 0.972 -2.346 2.264\n",
"PERMINTE:AVYRSEXP -0.0031 0.054 -0.057 0.954 -0.108 0.102\n",
"AVSALK 0.0131 0.295 0.044 0.965 -0.566 0.592\n",
"PERMINTE:AVSALK -0.0019 0.013 -0.145 0.885 -0.028 0.024\n",
"AVYRSEXP:AVSALK 0.0008 0.020 0.038 0.970 -0.039 0.041\n",
"PERMINTE:AVYRSEXP:AVSALK 5.978e-05 0.001 0.068 0.946 -0.002 0.002\n",
"PERSPENK -0.3097 4.233 -0.073 0.942 -8.606 7.987\n",
"PTRATIO 0.0096 0.919 0.010 0.992 -1.792 1.811\n",
"PERSPENK:PTRATIO 0.0066 0.206 0.032 0.974 -0.397 0.410\n",
"PCTAF -0.0143 0.474 -0.030 0.976 -0.944 0.916\n",
"PERSPENK:PCTAF 0.0105 0.098 0.107 0.915 -0.182 0.203\n",
"PTRATIO:PCTAF -0.0001 0.022 -0.005 0.996 -0.044 0.044\n",
"PERSPENK:PTRATIO:PCTAF -0.0002 0.005 -0.051 0.959 -0.010 0.009\n",
"============================================================================================\n",
"\"\"\""
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def double_it(x):\n",
" return 2 * x\n",
"formula = 'SUCCESS ~ double_it(LOWINC) + PERASIAN + PERBLACK + PERHISP + PCTCHRT + \\\n",
" PCTYRRND + PERMINTE*AVYRSEXP*AVSALK + PERSPENK*PTRATIO*PCTAF'\n",
"mod2 = smf.glm(formula=formula, data=dta, family=sm.families.Binomial()).fit()\n",
"mod2.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As expected, the coefficient for ``double_it(LOWINC)`` in the second model is half the size of the ``LOWINC`` coefficient from the first model:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-0.02039598715480017\n",
"-0.020395987154799313\n"
]
}
],
"source": [
"print(mod1.params[1])\n",
"print(mod2.params[1] * 2)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
}

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/srv/reproducible-results/rbuild-debian/tmp.FTzzuDJkiI/dbd-tmp-gqMLHgR/diffoscope_1mz05yxs/tmpqg2lt48q/d5b2bb01-c943-4060-83d5-d5cfcd9ebf7f vs.

Similarity: 0.0%
Differences: {
"replace": {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Generalized Least Squares"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
" from pandas.core import datetools\n"
]
}
],
"source": [
"from __future__ import print_function\n",
"import statsmodels.api as sm\n",
"import numpy as np\n",
"from statsmodels.iolib.table import (SimpleTable, default_txt_fmt)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Longley dataset is a time series dataset: "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[1.00000e+00 8.30000e+01 2.34289e+05 2.35600e+03 1.59000e+03 1.07608e+05\n",
" 1.94700e+03]\n",
" [1.00000e+00 8.85000e+01 2.59426e+05 2.32500e+03 1.45600e+03 1.08632e+05\n",
" 1.94800e+03]\n",
" [1.00000e+00 8.82000e+01 2.58054e+05 3.68200e+03 1.61600e+03 1.09773e+05\n",
" 1.94900e+03]\n",
" [1.00000e+00 8.95000e+01 2.84599e+05 3.35100e+03 1.65000e+03 1.10929e+05\n",
" 1.95000e+03]\n",
" [1.00000e+00 9.62000e+01 3.28975e+05 2.09900e+03 3.09900e+03 1.12075e+05\n",
" 1.95100e+03]]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/datasets/utils.py:100: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n",
" exog = np.column_stack(data[field] for field in exog_name)\n"
]
}
],
"source": [
"data = sm.datasets.longley.load()\n",
"data.exog = sm.add_constant(data.exog)\n",
"print(data.exog[:5])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
" Let's assume that the data is heteroskedastic and that we know\n",
" the nature of the heteroskedasticity. We can then define\n",
" `sigma` and use it to give us a GLS model\n",
"\n",
" First we will obtain the residuals from an OLS fit"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ols_resid = sm.OLS(data.endog, data.exog).fit().resid"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Assume that the error terms follow an AR(1) process with a trend:\n",
"\n",
"$\\epsilon_i = \\beta_0 + \\rho\\epsilon_{i-1} + \\eta_i$\n",
"\n",
"where $\\eta \\sim N(0,\\Sigma^2)$\n",
" \n",
"and that $\\rho$ is simply the correlation of the residual a consistent estimator for rho is to regress the residuals on the lagged residuals"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-1.4390229839734257\n",
"0.17378444788819086\n"
]
}
],
"source": [
"resid_fit = sm.OLS(ols_resid[1:], sm.add_constant(ols_resid[:-1])).fit()\n",
"print(resid_fit.tvalues[1])\n",
"print(resid_fit.pvalues[1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" While we don't have strong evidence that the errors follow an AR(1)\n",
" process we continue"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"rho = resid_fit.params[1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we know, an AR(1) process means that near-neighbors have a stronger\n",
" relation so we can give this structure by using a toeplitz matrix"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[0, 1, 2, 3, 4],\n",
" [1, 0, 1, 2, 3],\n",
" [2, 1, 0, 1, 2],\n",
" [3, 2, 1, 0, 1],\n",
" [4, 3, 2, 1, 0]])"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from scipy.linalg import toeplitz\n",
"\n",
"toeplitz(range(5))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"order = toeplitz(range(len(ols_resid)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"so that our error covariance structure is actually rho**order\n",
" which defines an autocorrelation structure"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sigma = rho**order\n",
"gls_model = sm.GLS(data.endog, data.exog, sigma=sigma)\n",
"gls_results = gls_model.fit()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Of course, the exact rho in this instance is not known so it it might make more sense to use feasible gls, which currently only has experimental support. \n",
"\n",
"We can use the GLSAR model with one lag, to get to a similar result:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" GLSAR Regression Results \n",
"==============================================================================\n",
"Dep. Variable: y R-squared: 0.996\n",
"Model: GLSAR Adj. R-squared: 0.992\n",
"Method: Least Squares F-statistic: 295.2\n",
"Date: Sat, 10 Apr 2021 Prob (F-statistic): 6.09e-09\n",
"Time: 01:00:05 Log-Likelihood: -102.04\n",
"No. Observations: 15 AIC: 218.1\n",
"Df Residuals: 8 BIC: 223.0\n",
"Df Model: 6 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const -3.468e+06 8.72e+05 -3.979 0.004 -5.48e+06 -1.46e+06\n",
"x1 34.5568 84.734 0.408 0.694 -160.840 229.953\n",
"x2 -0.0343 0.033 -1.047 0.326 -0.110 0.041\n",
"x3 -1.9621 0.481 -4.083 0.004 -3.070 -0.854\n",
"x4 -1.0020 0.211 -4.740 0.001 -1.489 -0.515\n",
"x5 -0.0978 0.225 -0.435 0.675 -0.616 0.421\n",
"x6 1823.1829 445.829 4.089 0.003 795.100 2851.266\n",
"==============================================================================\n",
"Omnibus: 1.960 Durbin-Watson: 2.554\n",
"Prob(Omnibus): 0.375 Jarque-Bera (JB): 1.423\n",
"Skew: 0.713 Prob(JB): 0.491\n",
"Kurtosis: 2.508 Cond. No. 4.80e+09\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"[2] The condition number is large, 4.8e+09. This might indicate that there are\n",
"strong multicollinearity or other numerical problems.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib/python3/dist-packages/scipy/stats/stats.py:1394: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=15\n",
" \"anyway, n=%i\" % int(n))\n"
]
}
],
"source": [
"glsar_model = sm.GLSAR(data.endog, data.exog, 1)\n",
"glsar_results = glsar_model.iterative_fit(1)\n",
"print(glsar_results.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Comparing gls and glsar results, we see that there are some small\n",
" differences in the parameter estimates and the resulting standard\n",
" errors of the parameter estimate. This might be do to the numerical\n",
" differences in the algorithm, e.g. the treatment of initial conditions,\n",
" because of the small number of observations in the longley dataset."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[-3.79785490e+06 -1.27656454e+01 -3.80013250e-02 -2.18694871e+00\n",
" -1.15177649e+00 -6.80535580e-02 1.99395293e+03]\n",
"[-3.46796063e+06 3.45567846e+01 -3.43410090e-02 -1.96214395e+00\n",
" -1.00197296e+00 -9.78045986e-02 1.82318289e+03]\n",
"[6.70688699e+05 6.94308073e+01 2.62476822e-02 3.82393151e-01\n",
" 1.65252692e-01 1.76428334e-01 3.42634628e+02]\n",
"[8.71584052e+05 8.47337145e+01 3.28032450e-02 4.80544865e-01\n",
" 2.11383871e-01 2.24774369e-01 4.45828748e+02]\n"
]
}
],
"source": [
"print(gls_results.params)\n",
"print(glsar_results.params)\n",
"print(gls_results.bse)\n",
"print(glsar_results.bse)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
}

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/srv/reproducible-results/rbuild-debian/tmp.FTzzuDJkiI/dbd-tmp-gqMLHgR/diffoscope_1mz05yxs/tmprsq5xf2r/c9679c2b-2769-41f3-9eae-02a1a18f09de vs.

Similarity: 0.0%
Differences: {
"replace": {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Interactions and ANOVA"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note: This script is based heavily on Jonathan Taylor's class notes http://www.stanford.edu/class/stats191/interactions.html\n",
"\n",
"Download and format data:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
" from pandas.core import datetools\n"
]
},
{
"ename": "URLError",
"evalue": "<urlopen error [Errno 111] Connection refused>",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-1-699e2abfd95f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0msalary_table\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'salary.table'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 17\u001b[0m \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# recent pandas can read URL without urlopen\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, doublequote, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m 677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 678\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 679\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 439\u001b[0m \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 440\u001b[0;31m \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 441\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 786\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 787\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 788\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m 1013\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'c'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1014\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1015\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m 1707\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1708\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1709\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mFileNotFoundError\u001b[0m: File b'salary.table' does not exist",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mConnectionRefusedError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/usr/lib/python3.7/urllib/request.py\u001b[0m in \u001b[0;36mdo_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m 1316\u001b[0m h.request(req.get_method(), req.selector, req.data, headers,\n\u001b[0;32m-> 1317\u001b[0;31m encode_chunked=req.has_header('Transfer-encoding'))\n\u001b[0m\u001b[1;32m 1318\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# timeout error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1243\u001b[0m \u001b[0;34m\"\"\"Send a complete request to the server.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1244\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1245\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36m_send_request\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1289\u001b[0m \u001b[0mbody\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_encode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'body'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1290\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendheaders\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1291\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36mendheaders\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m 1238\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mCannotSendHeader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1239\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage_body\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1240\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36m_send_output\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m 1025\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_buffer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1026\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1027\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 965\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_open\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 966\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 967\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 937\u001b[0m self.sock = self._create_connection(\n\u001b[0;32m--> 938\u001b[0;31m (self.host,self.port), self.timeout, self.source_address)\n\u001b[0m\u001b[1;32m 939\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetsockopt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIPPROTO_TCP\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTCP_NODELAY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/socket.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address)\u001b[0m\n\u001b[1;32m 726\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0merr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 727\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 728\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/socket.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address)\u001b[0m\n\u001b[1;32m 715\u001b[0m \u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource_address\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 716\u001b[0;31m \u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msa\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 717\u001b[0m \u001b[0;31m# Break explicitly a reference cycle\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mConnectionRefusedError\u001b[0m: [Errno 111] Connection refused",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mURLError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-1-699e2abfd95f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# recent pandas can read URL without urlopen\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0murl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'http://stats191.stanford.edu/data/salary.table'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0mfh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0murlopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 20\u001b[0m \u001b[0msalary_table\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfh\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0msalary_table\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'salary.table'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/urllib/request.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[1;32m 220\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 221\u001b[0m \u001b[0mopener\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_opener\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 222\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mopener\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 223\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 224\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0minstall_opener\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopener\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/urllib/request.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[1;32m 523\u001b[0m \u001b[0mreq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmeth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 524\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 525\u001b[0;31m \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 526\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 527\u001b[0m \u001b[0;31m# post-process response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/urllib/request.py\u001b[0m in \u001b[0;36m_open\u001b[0;34m(self, req, data)\u001b[0m\n\u001b[1;32m 541\u001b[0m \u001b[0mprotocol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 542\u001b[0m result = self._call_chain(self.handle_open, protocol, protocol +\n\u001b[0;32m--> 543\u001b[0;31m '_open', req)\n\u001b[0m\u001b[1;32m 544\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 545\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/urllib/request.py\u001b[0m in \u001b[0;36m_call_chain\u001b[0;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[1;32m 501\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mhandler\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mhandlers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 502\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandler\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 503\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 504\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 505\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/urllib/request.py\u001b[0m in \u001b[0;36mhttp_open\u001b[0;34m(self, req)\u001b[0m\n\u001b[1;32m 1343\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1344\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mhttp_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1345\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdo_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhttp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mHTTPConnection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1346\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1347\u001b[0m \u001b[0mhttp_request\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAbstractHTTPHandler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdo_request_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/urllib/request.py\u001b[0m in \u001b[0;36mdo_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m 1317\u001b[0m encode_chunked=req.has_header('Transfer-encoding'))\n\u001b[1;32m 1318\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# timeout error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1319\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mURLError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1320\u001b[0m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1321\u001b[0m \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mURLError\u001b[0m: <urlopen error [Errno 111] Connection refused>"
]
}
],
"source": [
"%matplotlib inline\n",
"\n",
"from __future__ import print_function\n",
"from statsmodels.compat import urlopen\n",
"import numpy as np\n",
"np.set_printoptions(precision=4, suppress=True)\n",
"import statsmodels.api as sm\n",
"import pandas as pd\n",
"pd.set_option(\"display.width\", 100)\n",
"import matplotlib.pyplot as plt\n",
"from statsmodels.formula.api import ols\n",
"from statsmodels.graphics.api import interaction_plot, abline_plot\n",
"from statsmodels.stats.anova import anova_lm\n",
"\n",
"try:\n",
" salary_table = pd.read_csv('salary.table')\n",
"except: # recent pandas can read URL without urlopen\n",
" url = 'http://stats191.stanford.edu/data/salary.table'\n",
" fh = urlopen(url)\n",
" salary_table = pd.read_table(fh)\n",
" salary_table.to_csv('salary.table')\n",
"\n",
"E = salary_table.E\n",
"M = salary_table.M\n",
"X = salary_table.X\n",
"S = salary_table.S"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Take a look at the data:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'salary_table' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-2-06b5e3c35d99>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0msymbols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'D'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'^'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mcolors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'r'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'g'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'blue'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mfactor_groups\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msalary_table\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'E'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'M'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfactor_groups\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'salary_table' is not defined"
]
},
{
"data": {
"text/plain": [
"<Figure size 432x432 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(6,6))\n",
"symbols = ['D', '^']\n",
"colors = ['r', 'g', 'blue']\n",
"factor_groups = salary_table.groupby(['E','M'])\n",
"for values, group in factor_groups:\n",
" i,j = values\n",
" plt.scatter(group['X'], group['S'], marker=symbols[j], color=colors[i-1],\n",
" s=144)\n",
"plt.xlabel('Experience');\n",
"plt.ylabel('Salary');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Fit a linear model:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'salary_table' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-3-bd1483e9558c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mformula\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'S ~ C(E) + C(M) + X'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mlm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mols\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mformula\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msalary_table\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'salary_table' is not defined"
]
}
],
"source": [
"formula = 'S ~ C(E) + C(M) + X'\n",
"lm = ols(formula, salary_table).fit()\n",
"print(lm.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Have a look at the created design matrix: "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'lm' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-4-15c89faef8a8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexog\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'lm' is not defined"
]
}
],
"source": [
"lm.model.exog[:5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Or since we initially passed in a DataFrame, we have a DataFrame available in"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'lm' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-5-136b4afd409d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0morig_exog\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'lm' is not defined"
]
}
],
"source": [
"lm.model.data.orig_exog[:5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We keep a reference to the original untouched data in"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'lm' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-6-45902db7bdbe>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframe\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'lm' is not defined"
]
}
],
"source": [
"lm.model.data.frame[:5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Influence statistics"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'lm' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-7-cf91966f823d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0minfl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_influence\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minfl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'lm' is not defined"
]
}
],
"source": [
"infl = lm.get_influence()\n",
"print(infl.summary_table())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"or get a dataframe"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'infl' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-8-4a89b3b617d2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf_infl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minfl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'infl' is not defined"
]
}
],
"source": [
"df_infl = infl.summary_frame()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'df_infl' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-9-c7ae236e6fec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf_infl\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'df_infl' is not defined"
]
}
],
"source": [
"df_infl[:5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now plot the reiduals within the groups separately:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'lm' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-10-b1fbd2073437>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mresid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfactor_groups\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mgroup_num\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m2\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mj\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;31m# for plotting purposes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'lm' is not defined"
]
}
],
"source": [
"resid = lm.resid\n",
"plt.figure(figsize=(6,6));\n",
"for values, group in factor_groups:\n",
" i,j = values\n",
" group_num = i*2 + j - 1 # for plotting purposes\n",
" x = [group_num] * len(group)\n",
" plt.scatter(x, resid[group.index], marker=symbols[j], color=colors[i-1],\n",
" s=144, edgecolors='black')\n",
"plt.xlabel('Group');\n",
"plt.ylabel('Residuals');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we will test some interactions using anova or f_test"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'salary_table' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-11-63ad3de263bb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0minterX_lm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mols\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"S ~ C(E) * X + C(M)\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msalary_table\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minterX_lm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'salary_table' is not defined"
]
}
],
"source": [
"interX_lm = ols(\"S ~ C(E) * X + C(M)\", salary_table).fit()\n",
"print(interX_lm.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Do an ANOVA check"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'lm' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-12-99371545e58d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mstatsmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapi\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0manova_lm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtable1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0manova_lm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterX_lm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtable1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'lm' is not defined"
]
}
],
"source": [
"from statsmodels.stats.api import anova_lm\n",
"\n",
"table1 = anova_lm(lm, interX_lm)\n",
"print(table1)\n",
"\n",
"interM_lm = ols(\"S ~ X + C(E)*C(M)\", data=salary_table).fit()\n",
"print(interM_lm.summary())\n",
"\n",
"table2 = anova_lm(lm, interM_lm)\n",
"print(table2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The design matrix as a DataFrame"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'interM_lm' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-13-1dd5303dacd2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0minterM_lm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0morig_exog\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'interM_lm' is not defined"
]
}
],
"source": [
"interM_lm.model.data.orig_exog[:5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The design matrix as an ndarray"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'interM_lm' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-14-127a7082e299>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0minterM_lm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexog\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0minterM_lm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexog_names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'interM_lm' is not defined"
]
}
],
"source": [
"interM_lm.model.exog\n",
"interM_lm.model.exog_names"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'interM_lm' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-15-4a4b0ea23d42>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0minfl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minterM_lm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_influence\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mresid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minfl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresid_studentized_internal\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfactor_groups\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'interM_lm' is not defined"
]
}
],
"source": [
"infl = interM_lm.get_influence()\n",
"resid = infl.resid_studentized_internal\n",
"plt.figure(figsize=(6,6))\n",
"for values, group in factor_groups:\n",
" i,j = values\n",
" idx = group.index\n",
" plt.scatter(X[idx], resid[idx], marker=symbols[j], color=colors[i-1],\n",
" s=144, edgecolors='black')\n",
"plt.xlabel('X');\n",
"plt.ylabel('standardized resids');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Looks like one observation is an outlier."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'resid' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-16-b012081228e7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdrop_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdrop_idx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# zero-based index\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0midx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msalary_table\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdrop_idx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mlm32\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mols\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'S ~ C(E) + X + C(M)'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msalary_table\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'resid' is not defined"
]
}
],
"source": [
"drop_idx = abs(resid).argmax()\n",
"print(drop_idx) # zero-based index\n",
"idx = salary_table.index.drop(drop_idx)\n",
"\n",
"lm32 = ols('S ~ C(E) + X + C(M)', data=salary_table, subset=idx).fit()\n",
"\n",
"print(lm32.summary())\n",
"print('\\n')\n",
"\n",
"interX_lm32 = ols('S ~ C(E) * X + C(M)', data=salary_table, subset=idx).fit()\n",
"\n",
"print(interX_lm32.summary())\n",
"print('\\n')\n",
"\n",
"\n",
"table3 = anova_lm(lm32, interX_lm32)\n",
"print(table3)\n",
"print('\\n')\n",
"\n",
"\n",
"interM_lm32 = ols('S ~ X + C(E) * C(M)', data=salary_table, subset=idx).fit()\n",
"\n",
"table4 = anova_lm(lm32, interM_lm32)\n",
"print(table4)\n",
"print('\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Replot the residuals"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'interM_lm32' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-17-3f5881af6ee4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mresid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minterM_lm32\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_influence\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'standard_resid'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'interM_lm32' is not defined",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-17-3f5881af6ee4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mresid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minterM_lm32\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_influence\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'standard_resid'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mresid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minterM_lm32\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_influence\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'standard_resid'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'interM_lm32' is not defined"
]
}
],
"source": [
"try:\n",
" resid = interM_lm32.get_influence().summary_frame()['standard_resid']\n",
"except:\n",
" resid = interM_lm32.get_influence().summary_frame()['standard_resid']\n",
"\n",
"plt.figure(figsize=(6,6))\n",
"for values, group in factor_groups:\n",
" i,j = values\n",
" idx = group.index\n",
" plt.scatter(X[idx], resid[idx], marker=symbols[j], color=colors[i-1],\n",
" s=144, edgecolors='black')\n",
"plt.xlabel('X[~[32]]');\n",
"plt.ylabel('standardized resids');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Plot the fitted values"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'salary_table' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-18-799bf5928998>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlm_final\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mols\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'S ~ X + C(E)*C(M)'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msalary_table\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdrop_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mmf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlm_final\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0morig_exog\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mlstyle\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'-'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'--'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'salary_table' is not defined"
]
}
],
"source": [
"lm_final = ols('S ~ X + C(E)*C(M)', data = salary_table.drop([drop_idx])).fit()\n",
"mf = lm_final.model.data.orig_exog\n",
"lstyle = ['-','--']\n",
"\n",
"plt.figure(figsize=(6,6))\n",
"for values, group in factor_groups:\n",
" i,j = values\n",
" idx = group.index\n",
" plt.scatter(X[idx], S[idx], marker=symbols[j], color=colors[i-1],\n",
" s=144, edgecolors='black')\n",
" # drop NA because there is no idx 32 in the final model\n",
" plt.plot(mf.X[idx].dropna(), lm_final.fittedvalues[idx].dropna(),\n",
" ls=lstyle[j], color=colors[i-1])\n",
"plt.xlabel('Experience');\n",
"plt.ylabel('Salary');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From our first look at the data, the difference between Master's and PhD in the management group is different than in the non-management group. This is an interaction between the two qualitative variables management,M and education,E. We can visualize this by first removing the effect of experience, then plotting the means within each of the 6 groups using interaction.plot."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'S' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-19-078f1a51de12>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mU\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mS\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mX\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0minterX_lm32\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'X'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m interaction_plot(E, M, U, colors=['red','blue'], markers=['^','D'],\n\u001b[1;32m 5\u001b[0m markersize=10, ax=plt.gca())\n",
"\u001b[0;31mNameError\u001b[0m: name 'S' is not defined"
]
}
],
"source": [
"U = S - X * interX_lm32.params['X']\n",
"\n",
"plt.figure(figsize=(6,6))\n",
"interaction_plot(E, M, U, colors=['red','blue'], markers=['^','D'],\n",
" markersize=10, ax=plt.gca())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Minority Employment Data"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "URLError",
"evalue": "<urlopen error [Errno 111] Connection refused>",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-20-6329e9f7fab4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mjobtest_table\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'jobtest.table'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# don't have data already\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, doublequote, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m 677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 678\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 679\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 439\u001b[0m \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 440\u001b[0;31m \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 441\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 786\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 787\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 788\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m 1013\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'c'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1014\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1015\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m 1707\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1708\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1709\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mFileNotFoundError\u001b[0m: File b'jobtest.table' does not exist",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mConnectionRefusedError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/usr/lib/python3.7/urllib/request.py\u001b[0m in \u001b[0;36mdo_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m 1316\u001b[0m h.request(req.get_method(), req.selector, req.data, headers,\n\u001b[0;32m-> 1317\u001b[0;31m encode_chunked=req.has_header('Transfer-encoding'))\n\u001b[0m\u001b[1;32m 1318\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# timeout error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1243\u001b[0m \u001b[0;34m\"\"\"Send a complete request to the server.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1244\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1245\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36m_send_request\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1289\u001b[0m \u001b[0mbody\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_encode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'body'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1290\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendheaders\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1291\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36mendheaders\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m 1238\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mCannotSendHeader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1239\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage_body\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1240\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36m_send_output\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m 1025\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_buffer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1026\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1027\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 965\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_open\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 966\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 967\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 937\u001b[0m self.sock = self._create_connection(\n\u001b[0;32m--> 938\u001b[0;31m (self.host,self.port), self.timeout, self.source_address)\n\u001b[0m\u001b[1;32m 939\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetsockopt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIPPROTO_TCP\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTCP_NODELAY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/socket.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address)\u001b[0m\n\u001b[1;32m 726\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0merr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 727\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 728\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/socket.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address)\u001b[0m\n\u001b[1;32m 715\u001b[0m \u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource_address\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 716\u001b[0;31m \u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msa\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 717\u001b[0m \u001b[0;31m# Break explicitly a reference cycle\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mConnectionRefusedError\u001b[0m: [Errno 111] Connection refused",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mURLError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-20-6329e9f7fab4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# don't have data already\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0murl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'http://stats191.stanford.edu/data/jobtest.table'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mjobtest_table\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mfactor_group\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjobtest_table\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ETHN'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, doublequote, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m 676\u001b[0m skip_blank_lines=skip_blank_lines)\n\u001b[1;32m 677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 678\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 679\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 680\u001b[0m \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 422\u001b[0m \u001b[0mcompression\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_infer_compression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompression\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 423\u001b[0m filepath_or_buffer, _, compression, should_close = get_filepath_or_buffer(\n\u001b[0;32m--> 424\u001b[0;31m filepath_or_buffer, encoding, compression)\n\u001b[0m\u001b[1;32m 425\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'compression'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompression\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 426\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_filepath_or_buffer\u001b[0;34m(filepath_or_buffer, encoding, compression, mode)\u001b[0m\n\u001b[1;32m 193\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 194\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m_is_url\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 195\u001b[0;31m \u001b[0mreq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_urlopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 196\u001b[0m \u001b[0mcontent_encoding\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Content-Encoding'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 197\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcontent_encoding\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'gzip'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/urllib/request.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[1;32m 220\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 221\u001b[0m \u001b[0mopener\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_opener\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 222\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mopener\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 223\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 224\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0minstall_opener\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopener\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/urllib/request.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[1;32m 523\u001b[0m \u001b[0mreq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmeth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 524\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 525\u001b[0;31m \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 526\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 527\u001b[0m \u001b[0;31m# post-process response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/urllib/request.py\u001b[0m in \u001b[0;36m_open\u001b[0;34m(self, req, data)\u001b[0m\n\u001b[1;32m 541\u001b[0m \u001b[0mprotocol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 542\u001b[0m result = self._call_chain(self.handle_open, protocol, protocol +\n\u001b[0;32m--> 543\u001b[0;31m '_open', req)\n\u001b[0m\u001b[1;32m 544\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 545\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/urllib/request.py\u001b[0m in \u001b[0;36m_call_chain\u001b[0;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[1;32m 501\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mhandler\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mhandlers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 502\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandler\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 503\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 504\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 505\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/urllib/request.py\u001b[0m in \u001b[0;36mhttp_open\u001b[0;34m(self, req)\u001b[0m\n\u001b[1;32m 1343\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1344\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mhttp_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1345\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdo_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhttp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mHTTPConnection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1346\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1347\u001b[0m \u001b[0mhttp_request\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAbstractHTTPHandler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdo_request_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/urllib/request.py\u001b[0m in \u001b[0;36mdo_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m 1317\u001b[0m encode_chunked=req.has_header('Transfer-encoding'))\n\u001b[1;32m 1318\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# timeout error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1319\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mURLError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1320\u001b[0m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1321\u001b[0m \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mURLError\u001b[0m: <urlopen error [Errno 111] Connection refused>"
]
}
],
"source": [
"try:\n",
" jobtest_table = pd.read_table('jobtest.table')\n",
"except: # don't have data already\n",
" url = 'http://stats191.stanford.edu/data/jobtest.table'\n",
" jobtest_table = pd.read_table(url)\n",
"\n",
"factor_group = jobtest_table.groupby(['ETHN'])\n",
"\n",
"fig, ax = plt.subplots(figsize=(6,6))\n",
"colors = ['purple', 'green']\n",
"markers = ['o', 'v']\n",
"for factor, group in factor_group:\n",
" ax.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n",
" marker=markers[factor], s=12**2)\n",
"ax.set_xlabel('TEST');\n",
"ax.set_ylabel('JPERF');"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'jobtest_table' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-21-2dda11467a64>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmin_lm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mols\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'JPERF ~ TEST'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mjobtest_table\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmin_lm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'jobtest_table' is not defined"
]
}
],
"source": [
"min_lm = ols('JPERF ~ TEST', data=jobtest_table).fit()\n",
"print(min_lm.summary())"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'factor_group' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-22-3bca97a45134>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mfactor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfactor_group\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m ax.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n\u001b[1;32m 4\u001b[0m marker=markers[factor], s=12**2)\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'factor_group' is not defined"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(6,6));\n",
"for factor, group in factor_group:\n",
" ax.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n",
" marker=markers[factor], s=12**2)\n",
"\n",
"ax.set_xlabel('TEST')\n",
"ax.set_ylabel('JPERF')\n",
"fig = abline_plot(model_results = min_lm, ax=ax)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'jobtest_table' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-23-ad7cfc5a299c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m min_lm2 = ols('JPERF ~ TEST + TEST:ETHN',\n\u001b[0;32m----> 2\u001b[0;31m data=jobtest_table).fit()\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmin_lm2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'jobtest_table' is not defined"
]
}
],
"source": [
"min_lm2 = ols('JPERF ~ TEST + TEST:ETHN',\n",
" data=jobtest_table).fit()\n",
"\n",
"print(min_lm2.summary())"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'factor_group' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-24-f7126666bc31>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mfactor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfactor_group\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m ax.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n\u001b[1;32m 4\u001b[0m marker=markers[factor], s=12**2)\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'factor_group' is not defined"
]
},
{
"data": {
"image/png": 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uNsvsxa1J7k6S7v58kh/P+ufsTLNUTzbb7eD7WIazttyLqnplkg9lPfYX6/O0yRZ70d1PdPdl3X11d1+d9dczjnT3tj9D5AK2zP+Rv8/62X2q6rKsP8XzyF4OuUeW2YuvJ3ldklTVy7Me/DN7OuWF4XiSNy3+WudVSZ7o7m9s9UW7+pRO+1iGH1hyL96X5PlJPrl43frr3X1k34beJUvuxQhL7sU9SX6jqh5K8j9J3tHdF91vwUvuxduTfLiq/ijrL+C++WI8QayqT2T9h/xli9cr3pXkuUnS3R/M+usXNyY5leTJJL+71P1ehHsFwDl4py3AEIIPMITgAwwh+ABDCD7AEIIPMITgAwwh+ABD/B99HGQLpuj7VwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(6,6));\n",
"for factor, group in factor_group:\n",
" ax.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n",
" marker=markers[factor], s=12**2)\n",
"\n",
"fig = abline_plot(intercept = min_lm2.params['Intercept'],\n",
" slope = min_lm2.params['TEST'], ax=ax, color='purple');\n",
"fig = abline_plot(intercept = min_lm2.params['Intercept'],\n",
" slope = min_lm2.params['TEST'] + min_lm2.params['TEST:ETHN'],\n",
" ax=ax, color='green');"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'jobtest_table' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-25-26270b8e0d6d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmin_lm3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mols\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'JPERF ~ TEST + ETHN'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjobtest_table\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmin_lm3\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'jobtest_table' is not defined"
]
}
],
"source": [
"min_lm3 = ols('JPERF ~ TEST + ETHN', data = jobtest_table).fit()\n",
"print(min_lm3.summary())"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'factor_group' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-26-5bc7e2ba2d68>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mfactor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfactor_group\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m ax.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n\u001b[1;32m 4\u001b[0m marker=markers[factor], s=12**2)\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'factor_group' is not defined"
]
},
{
"data": {
"image/png": 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uNsvsxa1J7k6S7v58kh/P+ufsTLNUTzbb7eD7WIazttyLqnplkg9lPfYX6/O0yRZ70d1PdPdl3X11d1+d9dczjnT3tj9D5AK2zP+Rv8/62X2q6rKsP8XzyF4OuUeW2YuvJ3ldklTVy7Me/DN7OuWF4XiSNy3+WudVSZ7o7m9s9UW7+pRO+1iGH1hyL96X5PlJPrl43frr3X1k34beJUvuxQhL7sU9SX6jqh5K8j9J3tHdF91vwUvuxduTfLiq/ijrL+C++WI8QayqT2T9h/xli9cr3pXkuUnS3R/M+usXNyY5leTJJL+71P1ehHsFwDl4py3AEIIPMITgAwwh+ABDCD7AEIIPMITgAwwh+ABD/B99HGQLpuj7VwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(6,6));\n",
"for factor, group in factor_group:\n",
" ax.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n",
" marker=markers[factor], s=12**2)\n",
"\n",
"fig = abline_plot(intercept = min_lm3.params['Intercept'],\n",
" slope = min_lm3.params['TEST'], ax=ax, color='purple');\n",
"fig = abline_plot(intercept = min_lm3.params['Intercept'] + min_lm3.params['ETHN'],\n",
" slope = min_lm3.params['TEST'], ax=ax, color='green');"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'jobtest_table' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-27-0cc0b240e06d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmin_lm4\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mols\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'JPERF ~ TEST * ETHN'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjobtest_table\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmin_lm4\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'jobtest_table' is not defined"
]
}
],
"source": [
"min_lm4 = ols('JPERF ~ TEST * ETHN', data = jobtest_table).fit()\n",
"print(min_lm4.summary())"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'factor_group' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-28-10026649b950>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mfactor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfactor_group\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m ax.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n\u001b[1;32m 4\u001b[0m marker=markers[factor], s=12**2)\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'factor_group' is not defined"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(8,6));\n",
"for factor, group in factor_group:\n",
" ax.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n",
" marker=markers[factor], s=12**2)\n",
"\n",
"fig = abline_plot(intercept = min_lm4.params['Intercept'],\n",
" slope = min_lm4.params['TEST'], ax=ax, color='purple');\n",
"fig = abline_plot(intercept = min_lm4.params['Intercept'] + min_lm4.params['ETHN'],\n",
" slope = min_lm4.params['TEST'] + min_lm4.params['TEST:ETHN'],\n",
" ax=ax, color='green');"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'min_lm' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-29-647f7d87fe70>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# is there any effect of ETHN on slope or intercept?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtable5\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0manova_lm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmin_lm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_lm4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtable5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'min_lm' is not defined"
]
}
],
"source": [
"# is there any effect of ETHN on slope or intercept?\n",
"table5 = anova_lm(min_lm, min_lm4)\n",
"print(table5)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'min_lm' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-30-39645dd00862>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# is there any effect of ETHN on intercept\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtable6\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0manova_lm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmin_lm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_lm3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtable6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'min_lm' is not defined"
]
}
],
"source": [
"# is there any effect of ETHN on intercept\n",
"table6 = anova_lm(min_lm, min_lm3)\n",
"print(table6)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'min_lm' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-31-e0085d24d296>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# is there any effect of ETHN on slope\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtable7\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0manova_lm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmin_lm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_lm2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtable7\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'min_lm' is not defined"
]
}
],
"source": [
"# is there any effect of ETHN on slope\n",
"table7 = anova_lm(min_lm, min_lm2)\n",
"print(table7)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'min_lm2' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-32-a9a6172af9d0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# is it just the slope or both?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtable8\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0manova_lm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmin_lm2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_lm4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtable8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'min_lm2' is not defined"
]
}
],
"source": [
"# is it just the slope or both?\n",
"table8 = anova_lm(min_lm2, min_lm4)\n",
"print(table8)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## One-way ANOVA"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "URLError",
"evalue": "<urlopen error [Errno 111] Connection refused>",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-33-d3ea2b9d3e10>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mrehab_table\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'rehab.table'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, doublequote, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m 677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 678\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 679\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 439\u001b[0m \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 440\u001b[0;31m \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 441\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 786\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 787\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 788\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m 1013\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'c'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1014\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1015\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m 1707\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1708\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1709\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mFileNotFoundError\u001b[0m: File b'rehab.table' does not exist",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mConnectionRefusedError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/usr/lib/python3.7/urllib/request.py\u001b[0m in \u001b[0;36mdo_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m 1316\u001b[0m h.request(req.get_method(), req.selector, req.data, headers,\n\u001b[0;32m-> 1317\u001b[0;31m encode_chunked=req.has_header('Transfer-encoding'))\n\u001b[0m\u001b[1;32m 1318\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# timeout error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1243\u001b[0m \u001b[0;34m\"\"\"Send a complete request to the server.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1244\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1245\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36m_send_request\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1289\u001b[0m \u001b[0mbody\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_encode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'body'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1290\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendheaders\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1291\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36mendheaders\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m 1238\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mCannotSendHeader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1239\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage_body\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1240\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36m_send_output\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m 1025\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_buffer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1026\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1027\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 965\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_open\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 966\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 967\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 937\u001b[0m self.sock = self._create_connection(\n\u001b[0;32m--> 938\u001b[0;31m (self.host,self.port), self.timeout, self.source_address)\n\u001b[0m\u001b[1;32m 939\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetsockopt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIPPROTO_TCP\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTCP_NODELAY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/socket.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address)\u001b[0m\n\u001b[1;32m 726\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0merr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 727\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 728\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/socket.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address)\u001b[0m\n\u001b[1;32m 715\u001b[0m \u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource_address\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 716\u001b[0;31m \u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msa\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 717\u001b[0m \u001b[0;31m# Break explicitly a reference cycle\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mConnectionRefusedError\u001b[0m: [Errno 111] Connection refused",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mURLError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-33-d3ea2b9d3e10>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0murl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'http://stats191.stanford.edu/data/rehab.csv'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mrehab_table\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdelimiter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\",\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mrehab_table\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'rehab.table'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, doublequote, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m 676\u001b[0m skip_blank_lines=skip_blank_lines)\n\u001b[1;32m 677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 678\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 679\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 680\u001b[0m \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 422\u001b[0m \u001b[0mcompression\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_infer_compression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompression\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 423\u001b[0m filepath_or_buffer, _, compression, should_close = get_filepath_or_buffer(\n\u001b[0;32m--> 424\u001b[0;31m filepath_or_buffer, encoding, compression)\n\u001b[0m\u001b[1;32m 425\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'compression'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompression\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 426\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_filepath_or_buffer\u001b[0;34m(filepath_or_buffer, encoding, compression, mode)\u001b[0m\n\u001b[1;32m 193\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 194\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m_is_url\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 195\u001b[0;31m \u001b[0mreq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_urlopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 196\u001b[0m \u001b[0mcontent_encoding\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Content-Encoding'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 197\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcontent_encoding\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'gzip'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/urllib/request.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[1;32m 220\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 221\u001b[0m \u001b[0mopener\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_opener\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 222\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mopener\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 223\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 224\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0minstall_opener\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopener\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/urllib/request.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[1;32m 523\u001b[0m \u001b[0mreq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmeth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 524\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 525\u001b[0;31m \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 526\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 527\u001b[0m \u001b[0;31m# post-process response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/urllib/request.py\u001b[0m in \u001b[0;36m_open\u001b[0;34m(self, req, data)\u001b[0m\n\u001b[1;32m 541\u001b[0m \u001b[0mprotocol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 542\u001b[0m result = self._call_chain(self.handle_open, protocol, protocol +\n\u001b[0;32m--> 543\u001b[0;31m '_open', req)\n\u001b[0m\u001b[1;32m 544\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 545\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/urllib/request.py\u001b[0m in \u001b[0;36m_call_chain\u001b[0;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[1;32m 501\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mhandler\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mhandlers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 502\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandler\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 503\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 504\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 505\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/urllib/request.py\u001b[0m in \u001b[0;36mhttp_open\u001b[0;34m(self, req)\u001b[0m\n\u001b[1;32m 1343\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1344\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mhttp_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1345\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdo_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhttp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mHTTPConnection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1346\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1347\u001b[0m \u001b[0mhttp_request\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAbstractHTTPHandler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdo_request_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/urllib/request.py\u001b[0m in \u001b[0;36mdo_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m 1317\u001b[0m encode_chunked=req.has_header('Transfer-encoding'))\n\u001b[1;32m 1318\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# timeout error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1319\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mURLError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1320\u001b[0m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1321\u001b[0m \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mURLError\u001b[0m: <urlopen error [Errno 111] Connection refused>"
]
}
],
"source": [
"try:\n",
" rehab_table = pd.read_csv('rehab.table')\n",
"except:\n",
" url = 'http://stats191.stanford.edu/data/rehab.csv'\n",
" rehab_table = pd.read_table(url, delimiter=\",\")\n",
" rehab_table.to_csv('rehab.table')\n",
"\n",
"fig, ax = plt.subplots(figsize=(8,6))\n",
"fig = rehab_table.boxplot('Time', 'Fitness', ax=ax, grid=False)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'rehab_table' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-34-d3bb1b06817c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrehab_lm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mols\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Time ~ C(Fitness)'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrehab_table\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mtable9\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0manova_lm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrehab_lm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtable9\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrehab_lm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0morig_exog\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'rehab_table' is not defined"
]
}
],
"source": [
"rehab_lm = ols('Time ~ C(Fitness)', data=rehab_table).fit()\n",
"table9 = anova_lm(rehab_lm)\n",
"print(table9)\n",
"\n",
"print(rehab_lm.model.data.orig_exog)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'rehab_lm' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-35-99d31a5bc5c4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrehab_lm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'rehab_lm' is not defined"
]
}
],
"source": [
"print(rehab_lm.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Two-way ANOVA"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "URLError",
"evalue": "<urlopen error [Errno 111] Connection refused>",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-36-cbc31ddb699c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mkidney_table\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'./kidney.table'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, doublequote, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m 677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 678\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 679\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 439\u001b[0m \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 440\u001b[0;31m \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 441\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 786\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 787\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 788\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m 1013\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'c'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1014\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1015\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m 1707\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1708\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1709\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mFileNotFoundError\u001b[0m: File b'./kidney.table' does not exist",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mConnectionRefusedError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/usr/lib/python3.7/urllib/request.py\u001b[0m in \u001b[0;36mdo_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m 1316\u001b[0m h.request(req.get_method(), req.selector, req.data, headers,\n\u001b[0;32m-> 1317\u001b[0;31m encode_chunked=req.has_header('Transfer-encoding'))\n\u001b[0m\u001b[1;32m 1318\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# timeout error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1243\u001b[0m \u001b[0;34m\"\"\"Send a complete request to the server.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1244\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1245\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36m_send_request\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1289\u001b[0m \u001b[0mbody\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_encode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'body'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1290\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendheaders\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1291\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36mendheaders\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m 1238\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mCannotSendHeader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1239\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage_body\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1240\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36m_send_output\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m 1025\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_buffer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1026\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1027\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 965\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_open\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 966\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 967\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 937\u001b[0m self.sock = self._create_connection(\n\u001b[0;32m--> 938\u001b[0;31m (self.host,self.port), self.timeout, self.source_address)\n\u001b[0m\u001b[1;32m 939\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetsockopt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIPPROTO_TCP\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTCP_NODELAY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/socket.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address)\u001b[0m\n\u001b[1;32m 726\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0merr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 727\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 728\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/socket.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address)\u001b[0m\n\u001b[1;32m 715\u001b[0m \u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource_address\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 716\u001b[0;31m \u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msa\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 717\u001b[0m \u001b[0;31m# Break explicitly a reference cycle\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mConnectionRefusedError\u001b[0m: [Errno 111] Connection refused",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mURLError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-36-cbc31ddb699c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0murl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'http://stats191.stanford.edu/data/kidney.table'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mkidney_table\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdelim_whitespace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/usr/lib/python3/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, doublequote, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m 676\u001b[0m skip_blank_lines=skip_blank_lines)\n\u001b[1;32m 677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 678\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 679\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 680\u001b[0m \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 422\u001b[0m \u001b[0mcompression\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_infer_compression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompression\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 423\u001b[0m filepath_or_buffer, _, compression, should_close = get_filepath_or_buffer(\n\u001b[0;32m--> 424\u001b[0;31m filepath_or_buffer, encoding, compression)\n\u001b[0m\u001b[1;32m 425\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'compression'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompression\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 426\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_filepath_or_buffer\u001b[0;34m(filepath_or_buffer, encoding, compression, mode)\u001b[0m\n\u001b[1;32m 193\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 194\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m_is_url\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 195\u001b[0;31m \u001b[0mreq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_urlopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 196\u001b[0m \u001b[0mcontent_encoding\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Content-Encoding'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 197\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcontent_encoding\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'gzip'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/urllib/request.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[1;32m 220\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 221\u001b[0m \u001b[0mopener\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_opener\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 222\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mopener\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 223\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 224\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0minstall_opener\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopener\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/urllib/request.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[1;32m 523\u001b[0m \u001b[0mreq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmeth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 524\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 525\u001b[0;31m \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 526\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 527\u001b[0m \u001b[0;31m# post-process response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/urllib/request.py\u001b[0m in \u001b[0;36m_open\u001b[0;34m(self, req, data)\u001b[0m\n\u001b[1;32m 541\u001b[0m \u001b[0mprotocol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 542\u001b[0m result = self._call_chain(self.handle_open, protocol, protocol +\n\u001b[0;32m--> 543\u001b[0;31m '_open', req)\n\u001b[0m\u001b[1;32m 544\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 545\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/urllib/request.py\u001b[0m in \u001b[0;36m_call_chain\u001b[0;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[1;32m 501\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mhandler\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mhandlers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 502\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandler\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 503\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 504\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 505\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/urllib/request.py\u001b[0m in \u001b[0;36mhttp_open\u001b[0;34m(self, req)\u001b[0m\n\u001b[1;32m 1343\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1344\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mhttp_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1345\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdo_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhttp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mHTTPConnection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1346\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1347\u001b[0m \u001b[0mhttp_request\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAbstractHTTPHandler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdo_request_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/urllib/request.py\u001b[0m in \u001b[0;36mdo_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m 1317\u001b[0m encode_chunked=req.has_header('Transfer-encoding'))\n\u001b[1;32m 1318\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# timeout error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1319\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mURLError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1320\u001b[0m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1321\u001b[0m \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mURLError\u001b[0m: <urlopen error [Errno 111] Connection refused>"
]
}
],
"source": [
"try:\n",
" kidney_table = pd.read_table('./kidney.table')\n",
"except:\n",
" url = 'http://stats191.stanford.edu/data/kidney.table'\n",
" kidney_table = pd.read_csv(url, delim_whitespace=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Explore the dataset"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'kidney_table' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-37-fff8acd40403>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mkidney_table\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'kidney_table' is not defined"
]
}
],
"source": [
"kidney_table.head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Balanced panel"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'kidney_table' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-38-9312bae60782>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mkt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkidney_table\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m fig = interaction_plot(kt['Weight'], kt['Duration'], np.log(kt['Days']+1),\n\u001b[1;32m 4\u001b[0m colors=['red', 'blue'], markers=['D','^'], ms=10, ax=plt.gca())\n",
"\u001b[0;31mNameError\u001b[0m: name 'kidney_table' is not defined"
]
}
],
"source": [
"kt = kidney_table\n",
"plt.figure(figsize=(8,6))\n",
"fig = interaction_plot(kt['Weight'], kt['Duration'], np.log(kt['Days']+1),\n",
" colors=['red', 'blue'], markers=['D','^'], ms=10, ax=plt.gca())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You have things available in the calling namespace available in the formula evaluation namespace"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'kt' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-39-c7e1132390fe>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mkidney_lm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mols\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'np.log(Days+1) ~ C(Duration) * C(Weight)'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mtable10\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0manova_lm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkidney_lm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m print(anova_lm(ols('np.log(Days+1) ~ C(Duration) + C(Weight)',\n",
"\u001b[0;31mNameError\u001b[0m: name 'kt' is not defined"
]
}
],
"source": [
"kidney_lm = ols('np.log(Days+1) ~ C(Duration) * C(Weight)', data=kt).fit()\n",
"\n",
"table10 = anova_lm(kidney_lm)\n",
"\n",
"print(anova_lm(ols('np.log(Days+1) ~ C(Duration) + C(Weight)',\n",
" data=kt).fit(), kidney_lm))\n",
"print(anova_lm(ols('np.log(Days+1) ~ C(Duration)', data=kt).fit(),\n",
" ols('np.log(Days+1) ~ C(Duration) + C(Weight, Sum)',\n",
" data=kt).fit()))\n",
"print(anova_lm(ols('np.log(Days+1) ~ C(Weight)', data=kt).fit(),\n",
" ols('np.log(Days+1) ~ C(Duration) + C(Weight, Sum)',\n",
" data=kt).fit()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sum of squares\n",
"\n",
" Illustrates the use of different types of sums of squares (I,II,II)\n",
" and how the Sum contrast can be used to produce the same output between\n",
" the 3.\n",
"\n",
" Types I and II are equivalent under a balanced design.\n",
"\n",
" Don't use Type III with non-orthogonal contrast - ie., Treatment"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'kt' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-40-e0c1ed608c29>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m sum_lm = ols('np.log(Days+1) ~ C(Duration, Sum) * C(Weight, Sum)',\n\u001b[0;32m----> 2\u001b[0;31m data=kt).fit()\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0manova_lm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msum_lm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0manova_lm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msum_lm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtyp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'kt' is not defined"
]
}
],
"source": [
"sum_lm = ols('np.log(Days+1) ~ C(Duration, Sum) * C(Weight, Sum)',\n",
" data=kt).fit()\n",
"\n",
"print(anova_lm(sum_lm))\n",
"print(anova_lm(sum_lm, typ=2))\n",
"print(anova_lm(sum_lm, typ=3))"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'kt' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-41-95381847ac17>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m nosum_lm = ols('np.log(Days+1) ~ C(Duration, Treatment) * C(Weight, Treatment)',\n\u001b[0;32m----> 2\u001b[0;31m data=kt).fit()\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0manova_lm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnosum_lm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0manova_lm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnosum_lm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtyp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0manova_lm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnosum_lm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtyp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'kt' is not defined"
]
}
],
"source": [
"nosum_lm = ols('np.log(Days+1) ~ C(Duration, Treatment) * C(Weight, Treatment)',\n",
" data=kt).fit()\n",
"print(anova_lm(nosum_lm))\n",
"print(anova_lm(nosum_lm, typ=2))\n",
"print(anova_lm(nosum_lm, typ=3))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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"pygments_lexer": "ipython3",
"version": "3.7.3"
}
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"nbformat_minor": 0
}
}

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/srv/reproducible-results/rbuild-debian/tmp.FTzzuDJkiI/dbd-tmp-gqMLHgR/diffoscope_1mz05yxs/tmp5hje2139/6c19a1d8-df4c-49dc-9a47-f08bb09e159d vs.

Similarity: 0.0%
Differences: {
"replace": {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Markov switching autoregression models"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook provides an example of the use of Markov switching models in Statsmodels to replicate a number of results presented in Kim and Nelson (1999). It applies the Hamilton (1989) filter the Kim (1994) smoother.\n",
"\n",
"This is tested against the Markov-switching models from E-views 8, which can be found at http://www.eviews.com/EViews8/ev8ecswitch_n.html#MarkovAR or the Markov-switching models of Stata 14 which can be found at http://www.stata.com/manuals14/tsmswitch.pdf."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
" from pandas.core import datetools\n"
]
},
{
"ename": "ModuleNotFoundError",
"evalue": "No module named 'pandas_datareader'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-1-67b3d8188f6e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;31m# NBER recessions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas_datareader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDataReader\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 12\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mdatetime\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0musrec\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'USREC'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'fred'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1947\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2013\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pandas_datareader'"
]
}
],
"source": [
"%matplotlib inline\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import statsmodels.api as sm\n",
"import matplotlib.pyplot as plt\n",
"import requests\n",
"from io import BytesIO\n",
"\n",
"# NBER recessions\n",
"from pandas_datareader.data import DataReader\n",
"from datetime import datetime\n",
"usrec = DataReader('USREC', 'fred', start=datetime(1947, 1, 1), end=datetime(2013, 4, 1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Hamilton (1989) switching model of GNP\n",
"\n",
"This replicates Hamilton's (1989) seminal paper introducing Markov-switching models. The model is an autoregressive model of order 4 in which the mean of the process switches between two regimes. It can be written:\n",
"\n",
"$$\n",
"y_t = \\mu_{S_t} + \\phi_1 (y_{t-1} - \\mu_{S_{t-1}}) + \\phi_2 (y_{t-2} - \\mu_{S_{t-2}}) + \\phi_3 (y_{t-3} - \\mu_{S_{t-3}}) + \\phi_4 (y_{t-4} - \\mu_{S_{t-4}}) + \\varepsilon_t\n",
"$$\n",
"\n",
"Each period, the regime transitions according to the following matrix of transition probabilities:\n",
"\n",
"$$ P(S_t = s_t | S_{t-1} = s_{t-1}) =\n",
"\\begin{bmatrix}\n",
"p_{00} & p_{10} \\\\\n",
"p_{01} & p_{11}\n",
"\\end{bmatrix}\n",
"$$\n",
"\n",
"where $p_{ij}$ is the probability of transitioning *from* regime $i$, *to* regime $j$.\n",
"\n",
"The model class is `MarkovAutoregression` in the time-series part of `Statsmodels`. In order to create the model, we must specify the number of regimes with `k_regimes=2`, and the order of the autoregression with `order=4`. The default model also includes switching autoregressive coefficients, so here we also need to specify `switching_ar=False` to avoid that.\n",
"\n",
"After creation, the model is `fit` via maximum likelihood estimation. Under the hood, good starting parameters are found using a number of steps of the expectation maximization (EM) algorithm, and a quasi-Newton (BFGS) algorithm is applied to quickly find the maximum."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Get the RGNP data to replicate Hamilton\n",
"from statsmodels.tsa.regime_switching.tests.test_markov_autoregression import rgnp\n",
"dta_hamilton = pd.Series(rgnp, index=pd.date_range('1951-04-01', '1984-10-01', freq='QS'))\n",
"\n",
"# Plot the data\n",
"dta_hamilton.plot(title='Growth rate of Real GNP', figsize=(12,3))\n",
"\n",
"# Fit the model\n",
"mod_hamilton = sm.tsa.MarkovAutoregression(dta_hamilton, k_regimes=2, order=4, switching_ar=False)\n",
"res_hamilton = mod_hamilton.fit()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>Markov Switching Model Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>y</td> <th> No. Observations: </th> <td>131</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>MarkovAutoregression</td> <th> Log Likelihood </th> <td>-181.263</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Sat, 10 Apr 2021</td> <th> AIC </th> <td>380.527</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>01:00:08</td> <th> BIC </th> <td>406.404</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Sample:</th> <td>04-01-1952</td> <th> HQIC </th> <td>391.042</td>\n",
"</tr>\n",
"<tr>\n",
" <th></th> <td>- 10-01-1984</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>approx</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<caption>Regime 0 parameters</caption>\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>const</th> <td> -0.3588</td> <td> 0.265</td> <td> -1.356</td> <td> 0.175</td> <td> -0.877</td> <td> 0.160</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<caption>Regime 1 parameters</caption>\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>const</th> <td> 1.1635</td> <td> 0.075</td> <td> 15.614</td> <td> 0.000</td> <td> 1.017</td> <td> 1.310</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<caption>Non-switching parameters</caption>\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>sigma2</th> <td> 0.5914</td> <td> 0.103</td> <td> 5.761</td> <td> 0.000</td> <td> 0.390</td> <td> 0.793</td>\n",
"</tr>\n",
"<tr>\n",
" <th>ar.L1</th> <td> 0.0135</td> <td> 0.120</td> <td> 0.112</td> <td> 0.911</td> <td> -0.222</td> <td> 0.249</td>\n",
"</tr>\n",
"<tr>\n",
" <th>ar.L2</th> <td> -0.0575</td> <td> 0.138</td> <td> -0.418</td> <td> 0.676</td> <td> -0.327</td> <td> 0.212</td>\n",
"</tr>\n",
"<tr>\n",
" <th>ar.L3</th> <td> -0.2470</td> <td> 0.107</td> <td> -2.310</td> <td> 0.021</td> <td> -0.457</td> <td> -0.037</td>\n",
"</tr>\n",
"<tr>\n",
" <th>ar.L4</th> <td> -0.2129</td> <td> 0.111</td> <td> -1.926</td> <td> 0.054</td> <td> -0.430</td> <td> 0.004</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<caption>Regime transition parameters</caption>\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>p[0->0]</th> <td> 0.7547</td> <td> 0.097</td> <td> 7.819</td> <td> 0.000</td> <td> 0.565</td> <td> 0.944</td>\n",
"</tr>\n",
"<tr>\n",
" <th>p[1->0]</th> <td> 0.0959</td> <td> 0.038</td> <td> 2.542</td> <td> 0.011</td> <td> 0.022</td> <td> 0.170</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" Markov Switching Model Results \n",
"================================================================================\n",
"Dep. Variable: y No. Observations: 131\n",
"Model: MarkovAutoregression Log Likelihood -181.263\n",
"Date: Sat, 10 Apr 2021 AIC 380.527\n",
"Time: 01:00:08 BIC 406.404\n",
"Sample: 04-01-1952 HQIC 391.042\n",
" - 10-01-1984 \n",
"Covariance Type: approx \n",
" Regime 0 parameters \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const -0.3588 0.265 -1.356 0.175 -0.877 0.160\n",
" Regime 1 parameters \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const 1.1635 0.075 15.614 0.000 1.017 1.310\n",
" Non-switching parameters \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"sigma2 0.5914 0.103 5.761 0.000 0.390 0.793\n",
"ar.L1 0.0135 0.120 0.112 0.911 -0.222 0.249\n",
"ar.L2 -0.0575 0.138 -0.418 0.676 -0.327 0.212\n",
"ar.L3 -0.2470 0.107 -2.310 0.021 -0.457 -0.037\n",
"ar.L4 -0.2129 0.111 -1.926 0.054 -0.430 0.004\n",
" Regime transition parameters \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"p[0->0] 0.7547 0.097 7.819 0.000 0.565 0.944\n",
"p[1->0] 0.0959 0.038 2.542 0.011 0.022 0.170\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Covariance matrix calculated using numerical differentiation.\n",
"\"\"\""
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"res_hamilton.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We plot the filtered and smoothed probabilities of a recession. Filtered refers to an estimate of the probability at time $t$ based on data up to and including time $t$ (but excluding time $t+1, ..., T$). Smoothed refers to an estimate of the probability at time $t$ using all the data in the sample.\n",
"\n",
"For reference, the shaded periods represent the NBER recessions."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'usrec' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-4-9b61339d54f4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres_hamilton\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfiltered_marginal_probabilities\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfill_between\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0musrec\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwhere\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0musrec\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'USREC'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'k'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xlim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdta_hamilton\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdta_hamilton\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Filtered probability of recession'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'usrec' is not defined"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 504x504 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots(2, figsize=(7,7))\n",
"ax = axes[0]\n",
"ax.plot(res_hamilton.filtered_marginal_probabilities[0])\n",
"ax.fill_between(usrec.index, 0, 1, where=usrec['USREC'].values, color='k', alpha=0.1)\n",
"ax.set_xlim(dta_hamilton.index[4], dta_hamilton.index[-1])\n",
"ax.set(title='Filtered probability of recession')\n",
"\n",
"ax = axes[1]\n",
"ax.plot(res_hamilton.smoothed_marginal_probabilities[0])\n",
"ax.fill_between(usrec.index, 0, 1, where=usrec['USREC'].values, color='k', alpha=0.1)\n",
"ax.set_xlim(dta_hamilton.index[4], dta_hamilton.index[-1])\n",
"ax.set(title='Smoothed probability of recession')\n",
"\n",
"fig.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From the estimated transition matrix we can calculate the expected duration of a recession versus an expansion."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 4.07604793 10.42589261]\n"
]
}
],
"source": [
"print(res_hamilton.expected_durations)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this case, it is expected that a recession will last about one year (4 quarters) and an expansion about two and a half years."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Kim, Nelson, and Startz (1998) Three-state Variance Switching\n",
"\n",
"This model demonstrates estimation with regime heteroskedasticity (switching of variances) and no mean effect. The dataset can be reached at http://econ.korea.ac.kr/~cjkim/MARKOV/data/ew_excs.prn.\n",
"\n",
"The model in question is:\n",
"\n",
"$$\n",
"\\begin{align}\n",
"y_t & = \\varepsilon_t \\\\\n",
"\\varepsilon_t & \\sim N(0, \\sigma_{S_t}^2)\n",
"\\end{align}\n",
"$$\n",
"\n",
"Since there is no autoregressive component, this model can be fit using the `MarkovRegression` class. Since there is no mean effect, we specify `trend='nc'`. There are hypotheized to be three regimes for the switching variances, so we specify `k_regimes=3` and `switching_variance=True` (by default, the variance is assumed to be the same across regimes)."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "ProxyError",
"evalue": "HTTPConnectionPool(host='127.0.0.1', port=9): Max retries exceeded with url: http://econ.korea.ac.kr/~cjkim/MARKOV/data/ew_excs.prn (Caused by ProxyError('Cannot connect to proxy.', NewConnectionError('<urllib3.connection.HTTPConnection object at 0xebd394cc>: Failed to establish a new connection: [Errno 111] Connection refused')))",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mConnectionRefusedError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 158\u001b[0m conn = connection.create_connection(\n\u001b[0;32m--> 159\u001b[0;31m (self._dns_host, self.port), self.timeout, **extra_kw)\n\u001b[0m\u001b[1;32m 160\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/util/connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m 79\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0merr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 80\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 81\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/util/connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource_address\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m \u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msa\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 71\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msock\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mConnectionRefusedError\u001b[0m: [Errno 111] Connection refused",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mNewConnectionError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m 599\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 600\u001b[0;31m chunked=chunked)\n\u001b[0m\u001b[1;32m 601\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m 353\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 354\u001b[0;31m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mhttplib_request_kw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 355\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1243\u001b[0m \u001b[0;34m\"\"\"Send a complete request to the server.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1244\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1245\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36m_send_request\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1289\u001b[0m \u001b[0mbody\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_encode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'body'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1290\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendheaders\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1291\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36mendheaders\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m 1238\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mCannotSendHeader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1239\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage_body\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1240\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36m_send_output\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m 1025\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_buffer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1026\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1027\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 965\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_open\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 966\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 967\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connection.py\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 180\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 181\u001b[0;31m \u001b[0mconn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_new_conn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 182\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prepare_conn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 167\u001b[0m raise NewConnectionError(\n\u001b[0;32m--> 168\u001b[0;31m self, \"Failed to establish a new connection: %s\" % e)\n\u001b[0m\u001b[1;32m 169\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNewConnectionError\u001b[0m: <urllib3.connection.HTTPConnection object at 0xebd394cc>: Failed to establish a new connection: [Errno 111] Connection refused",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mMaxRetryError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/usr/lib/python3/dist-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m 448\u001b[0m \u001b[0mretries\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_retries\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 449\u001b[0;31m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 450\u001b[0m )\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m 637\u001b[0m retries = retries.increment(method, url, error=e, _pool=self,\n\u001b[0;32m--> 638\u001b[0;31m _stacktrace=sys.exc_info()[2])\n\u001b[0m\u001b[1;32m 639\u001b[0m \u001b[0mretries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/util/retry.py\u001b[0m in \u001b[0;36mincrement\u001b[0;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[1;32m 397\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnew_retry\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_exhausted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 398\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mMaxRetryError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_pool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merror\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mResponseError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcause\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 399\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mMaxRetryError\u001b[0m: HTTPConnectionPool(host='127.0.0.1', port=9): Max retries exceeded with url: http://econ.korea.ac.kr/~cjkim/MARKOV/data/ew_excs.prn (Caused by ProxyError('Cannot connect to proxy.', NewConnectionError('<urllib3.connection.HTTPConnection object at 0xebd394cc>: Failed to establish a new connection: [Errno 111] Connection refused')))",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mProxyError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-6-9e237cd253ae>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Get the dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mew_excs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'http://econ.korea.ac.kr/~cjkim/MARKOV/data/ew_excs.prn'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mraw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mew_excs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheader\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskipfooter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'python'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mraw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdate_range\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'1926-01-01'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'1995-12-01'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'MS'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/requests/api.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(url, params, **kwargs)\u001b[0m\n\u001b[1;32m 73\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'allow_redirects'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 75\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'get'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 76\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 77\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/requests/api.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(method, url, **kwargs)\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[0;31m# cases, and look like a memory leak in others.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 59\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0msessions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 60\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 61\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/requests/sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m 531\u001b[0m }\n\u001b[1;32m 532\u001b[0m \u001b[0msend_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 533\u001b[0;31m \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 534\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 535\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/requests/sessions.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m 644\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 645\u001b[0m \u001b[0;31m# Send the request\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 646\u001b[0;31m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 647\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 648\u001b[0m \u001b[0;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m 508\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 509\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreason\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_ProxyError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 510\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mProxyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 511\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 512\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreason\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_SSLError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mProxyError\u001b[0m: HTTPConnectionPool(host='127.0.0.1', port=9): Max retries exceeded with url: http://econ.korea.ac.kr/~cjkim/MARKOV/data/ew_excs.prn (Caused by ProxyError('Cannot connect to proxy.', NewConnectionError('<urllib3.connection.HTTPConnection object at 0xebd394cc>: Failed to establish a new connection: [Errno 111] Connection refused')))"
]
}
],
"source": [
"# Get the dataset\n",
"ew_excs = requests.get('http://econ.korea.ac.kr/~cjkim/MARKOV/data/ew_excs.prn').content\n",
"raw = pd.read_table(BytesIO(ew_excs), header=None, skipfooter=1, engine='python')\n",
"raw.index = pd.date_range('1926-01-01', '1995-12-01', freq='MS')\n",
"\n",
"dta_kns = raw.ix[:'1986'] - raw.ix[:'1986'].mean()\n",
"\n",
"# Plot the dataset\n",
"dta_kns[0].plot(title='Excess returns', figsize=(12, 3))\n",
"\n",
"# Fit the model\n",
"mod_kns = sm.tsa.MarkovRegression(dta_kns, k_regimes=3, trend='nc', switching_variance=True)\n",
"res_kns = mod_kns.fit()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'res_kns' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-7-e40786696a94>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mres_kns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'res_kns' is not defined"
]
}
],
"source": [
"res_kns.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Below we plot the probabilities of being in each of the regimes; only in a few periods is a high-variance regime probable."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'res_kns' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-8-0cf0c0fb70ab>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres_kns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msmoothed_marginal_probabilities\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Smoothed probability of a low-variance regime for stock returns'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'res_kns' is not defined"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x504 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots(3, figsize=(10,7))\n",
"\n",
"ax = axes[0]\n",
"ax.plot(res_kns.smoothed_marginal_probabilities[0])\n",
"ax.set(title='Smoothed probability of a low-variance regime for stock returns')\n",
"\n",
"ax = axes[1]\n",
"ax.plot(res_kns.smoothed_marginal_probabilities[1])\n",
"ax.set(title='Smoothed probability of a medium-variance regime for stock returns')\n",
"\n",
"ax = axes[2]\n",
"ax.plot(res_kns.smoothed_marginal_probabilities[2])\n",
"ax.set(title='Smoothed probability of a high-variance regime for stock returns')\n",
"\n",
"fig.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Filardo (1994) Time-Varying Transition Probabilities\n",
"\n",
"This model demonstrates estimation with time-varying transition probabilities. The dataset can be reached at http://econ.korea.ac.kr/~cjkim/MARKOV/data/filardo.prn.\n",
"\n",
"In the above models we have assumed that the transition probabilities are constant across time. Here we allow the probabilities to change with the state of the economy. Otherwise, the model is the same Markov autoregression of Hamilton (1989).\n",
"\n",
"Each period, the regime now transitions according to the following matrix of time-varying transition probabilities:\n",
"\n",
"$$ P(S_t = s_t | S_{t-1} = s_{t-1}) =\n",
"\\begin{bmatrix}\n",
"p_{00,t} & p_{10,t} \\\\\n",
"p_{01,t} & p_{11,t}\n",
"\\end{bmatrix}\n",
"$$\n",
"\n",
"where $p_{ij,t}$ is the probability of transitioning *from* regime $i$, *to* regime $j$ in period $t$, and is defined to be:\n",
"\n",
"$$\n",
"p_{ij,t} = \\frac{\\exp\\{ x_{t-1}' \\beta_{ij} \\}}{1 + \\exp\\{ x_{t-1}' \\beta_{ij} \\}}\n",
"$$\n",
"\n",
"Instead of estimating the transition probabilities as part of maximum likelihood, the regression coefficients $\\beta_{ij}$ are estimated. These coefficients relate the transition probabilities to a vector of pre-determined or exogenous regressors $x_{t-1}$."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "ProxyError",
"evalue": "HTTPConnectionPool(host='127.0.0.1', port=9): Max retries exceeded with url: http://econ.korea.ac.kr/~cjkim/MARKOV/data/filardo.prn (Caused by ProxyError('Cannot connect to proxy.', NewConnectionError('<urllib3.connection.HTTPConnection object at 0xebd3916c>: Failed to establish a new connection: [Errno 111] Connection refused')))",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mConnectionRefusedError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 158\u001b[0m conn = connection.create_connection(\n\u001b[0;32m--> 159\u001b[0;31m (self._dns_host, self.port), self.timeout, **extra_kw)\n\u001b[0m\u001b[1;32m 160\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/util/connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m 79\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0merr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 80\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 81\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/util/connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource_address\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m \u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msa\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 71\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msock\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mConnectionRefusedError\u001b[0m: [Errno 111] Connection refused",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mNewConnectionError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m 599\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 600\u001b[0;31m chunked=chunked)\n\u001b[0m\u001b[1;32m 601\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m 353\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 354\u001b[0;31m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mhttplib_request_kw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 355\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1243\u001b[0m \u001b[0;34m\"\"\"Send a complete request to the server.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1244\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1245\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36m_send_request\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1289\u001b[0m \u001b[0mbody\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_encode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'body'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1290\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendheaders\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1291\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36mendheaders\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m 1238\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mCannotSendHeader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1239\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage_body\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1240\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36m_send_output\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m 1025\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_buffer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1026\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1027\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 965\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_open\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 966\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 967\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connection.py\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 180\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 181\u001b[0;31m \u001b[0mconn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_new_conn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 182\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prepare_conn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 167\u001b[0m raise NewConnectionError(\n\u001b[0;32m--> 168\u001b[0;31m self, \"Failed to establish a new connection: %s\" % e)\n\u001b[0m\u001b[1;32m 169\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNewConnectionError\u001b[0m: <urllib3.connection.HTTPConnection object at 0xebd3916c>: Failed to establish a new connection: [Errno 111] Connection refused",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mMaxRetryError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/usr/lib/python3/dist-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m 448\u001b[0m \u001b[0mretries\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_retries\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 449\u001b[0;31m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 450\u001b[0m )\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m 637\u001b[0m retries = retries.increment(method, url, error=e, _pool=self,\n\u001b[0;32m--> 638\u001b[0;31m _stacktrace=sys.exc_info()[2])\n\u001b[0m\u001b[1;32m 639\u001b[0m \u001b[0mretries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/util/retry.py\u001b[0m in \u001b[0;36mincrement\u001b[0;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[1;32m 397\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnew_retry\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_exhausted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 398\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mMaxRetryError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_pool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merror\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mResponseError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcause\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 399\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mMaxRetryError\u001b[0m: HTTPConnectionPool(host='127.0.0.1', port=9): Max retries exceeded with url: http://econ.korea.ac.kr/~cjkim/MARKOV/data/filardo.prn (Caused by ProxyError('Cannot connect to proxy.', NewConnectionError('<urllib3.connection.HTTPConnection object at 0xebd3916c>: Failed to establish a new connection: [Errno 111] Connection refused')))",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mProxyError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-9-e3772af85a7a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Get the dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfilardo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'http://econ.korea.ac.kr/~cjkim/MARKOV/data/filardo.prn'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mdta_filardo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilardo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m' +'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheader\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskipfooter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'python'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mdta_filardo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'month'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'ip'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'leading'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mdta_filardo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdate_range\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'1948-01-01'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'1991-04-01'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'MS'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/requests/api.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(url, params, **kwargs)\u001b[0m\n\u001b[1;32m 73\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'allow_redirects'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 75\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'get'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 76\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 77\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/requests/api.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(method, url, **kwargs)\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[0;31m# cases, and look like a memory leak in others.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 59\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0msessions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 60\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 61\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/requests/sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m 531\u001b[0m }\n\u001b[1;32m 532\u001b[0m \u001b[0msend_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 533\u001b[0;31m \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 534\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 535\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/requests/sessions.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m 644\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 645\u001b[0m \u001b[0;31m# Send the request\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 646\u001b[0;31m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 647\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 648\u001b[0m \u001b[0;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m 508\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 509\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreason\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_ProxyError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 510\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mProxyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 511\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 512\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreason\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_SSLError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mProxyError\u001b[0m: HTTPConnectionPool(host='127.0.0.1', port=9): Max retries exceeded with url: http://econ.korea.ac.kr/~cjkim/MARKOV/data/filardo.prn (Caused by ProxyError('Cannot connect to proxy.', NewConnectionError('<urllib3.connection.HTTPConnection object at 0xebd3916c>: Failed to establish a new connection: [Errno 111] Connection refused')))"
]
}
],
"source": [
"# Get the dataset\n",
"filardo = requests.get('http://econ.korea.ac.kr/~cjkim/MARKOV/data/filardo.prn').content\n",
"dta_filardo = pd.read_table(BytesIO(filardo), sep=' +', header=None, skipfooter=1, engine='python')\n",
"dta_filardo.columns = ['month', 'ip', 'leading']\n",
"dta_filardo.index = pd.date_range('1948-01-01', '1991-04-01', freq='MS')\n",
"\n",
"dta_filardo['dlip'] = np.log(dta_filardo['ip']).diff()*100\n",
"# Deflated pre-1960 observations by ratio of std. devs.\n",
"# See hmt_tvp.opt or Filardo (1994) p. 302\n",
"std_ratio = dta_filardo['dlip']['1960-01-01':].std() / dta_filardo['dlip'][:'1959-12-01'].std()\n",
"dta_filardo['dlip'][:'1959-12-01'] = dta_filardo['dlip'][:'1959-12-01'] * std_ratio\n",
"\n",
"dta_filardo['dlleading'] = np.log(dta_filardo['leading']).diff()*100\n",
"dta_filardo['dmdlleading'] = dta_filardo['dlleading'] - dta_filardo['dlleading'].mean()\n",
"\n",
"# Plot the data\n",
"dta_filardo['dlip'].plot(title='Standardized growth rate of industrial production', figsize=(13,3))\n",
"plt.figure()\n",
"dta_filardo['dmdlleading'].plot(title='Leading indicator', figsize=(13,3));"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The time-varying transition probabilities are specified by the `exog_tvtp` parameter.\n",
"\n",
"Here we demonstrate another feature of model fitting - the use of a random search for MLE starting parameters. Because Markov switching models are often characterized by many local maxima of the likelihood function, performing an initial optimization step can be helpful to find the best parameters.\n",
"\n",
"Below, we specify that 20 random perturbations from the starting parameter vector are examined and the best one used as the actual starting parameters. Because of the random nature of the search, we seed the random number generator beforehand to allow replication of the result."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'dta_filardo' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-10-7d8be23b1f6f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m mod_filardo = sm.tsa.MarkovAutoregression(\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdta_filardo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'dlip'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk_regimes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mswitching_ar\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m exog_tvtp=sm.add_constant(dta_filardo.ix[1:-1, 'dmdlleading']))\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m12345\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'dta_filardo' is not defined"
]
}
],
"source": [
"mod_filardo = sm.tsa.MarkovAutoregression(\n",
" dta_filardo.ix[2:, 'dlip'], k_regimes=2, order=4, switching_ar=False,\n",
" exog_tvtp=sm.add_constant(dta_filardo.ix[1:-1, 'dmdlleading']))\n",
"\n",
"np.random.seed(12345)\n",
"res_filardo = mod_filardo.fit(search_reps=20)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'res_filardo' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-11-254b3810b2f9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mres_filardo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'res_filardo' is not defined"
]
}
],
"source": [
"res_filardo.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Below we plot the smoothed probability of the economy operating in a low-production state, and again include the NBER recessions for comparison."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'res_filardo' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-12-1a1095e831fe>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres_filardo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msmoothed_marginal_probabilities\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfill_between\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0musrec\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwhere\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0musrec\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'USREC'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'gray'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xlim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdta_filardo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdta_filardo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'res_filardo' is not defined"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAsoAAADGCAYAAADLy6YSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAADaNJREFUeJzt3V+oZWd5B+Dfa8ZU0KjQmYJkJibQSXWqQuwhpHhhQFsmuZi5sJUMBKsE56YRW0WIKFHilUotCOOfKZVUQdPRCzngSC5sJCCO5IS0wZkQOYzWTBQyasxN0Jj27cXeluPkmzl7JvvsPZk8DxzYa61vr/VevOzzO99Za3/V3QEAAP7QS5ZdAAAAXIwEZQAAGBCUAQBgQFAGAIABQRkAAAYEZQAAGNg0KFfVl6rqiar64VmOV1V9tqrWq+rhqnrz/MsEAIDFmmVG+e4ke89x/KYku6c/B5N8/vmXBQAAy7VpUO7u+5P86hxD9if5ck8cS/LqqnrNvAoEAIBlmMc9ylcmeWzD9qnpPgAAeMHatsiLVdXBTG7PyMtf/vK/eN3rXrfIywMA8CL04IMP/qK7d5zv++YRlB9PsmvD9s7pvufo7sNJDifJyspKr62tzeHyAABwdlX13xfyvnncerGa5F3Tb7+4IclT3f3zOZwXAACWZtMZ5ar6WpIbk2yvqlNJPpbkpUnS3V9IcjTJzUnWkzyd5D1bVSwAACzKpkG5uw9scryT/P3cKgIAgIuAlfkAAGBAUAYAgAFBGQAABgRlAAAYEJQBAGBAUAYAgAFBGQAABgRlAAAYEJQBAGBAUAYAgAFBGQAABgRlAAAYEJQBAGBAUAYAgAFBGQAABgRlAAAYEJQBAGBAUAYAgAFBGQAABgRlAAAYEJQBAGBAUAYAgAFBGQAABgRlAAAYmCkoV9Xeqnq0qtar6o7B8auq6r6qeqiqHq6qm+dfKgAALM6mQbmqLktyKMlNSfYkOVBVe84Y9tEkR7r7uiS3JPncvAsFAIBFmmVG+fok6919srufSXJPkv1njOkkr5y+flWSn82vRAAAWLxZgvKVSR7bsH1qum+jjye5tapOJTma5H2jE1XVwapaq6q106dPX0C5AACwGPN6mO9Akru7e2eSm5N8paqec+7uPtzdK929smPHjjldGgAA5m+WoPx4kl0btndO9210W5IjSdLd30/ysiTb51EgAAAswyxB+YEku6vqmqq6PJOH9VbPGPPTJG9Lkqp6fSZB2b0VAAC8YG0alLv72SS3J7k3ySOZfLvF8aq6q6r2TYd9MMl7q+q/knwtybu7u7eqaAAA2GrbZhnU3UczeUhv4747N7w+keQt8y0NAACWx8p8AAAwICgDAMCAoAwAAAOCMgAADAjKAAAwICgDAMCAoAwAAAOCMgAADAjKAAAwICgDAMCAoAwAAAOCMgAADAjKAAAwICgDAMCAoAwAAAOCMgAADAjKAAAwICgDAMCAoAwAAAOCMgAADAjKAAAwICgDAMCAoAwAAAMzBeWq2ltVj1bVelXdcZYx76yqE1V1vKq+Ot8yAQBgsbZtNqCqLktyKMlfJTmV5IGqWu3uExvG7E7y4SRv6e4nq+pPtqpgAABYhFlmlK9Pst7dJ7v7mST3JNl/xpj3JjnU3U8mSXc/Md8yAQBgsWYJylcmeWzD9qnpvo2uTXJtVX2vqo5V1d55FQgAAMuw6a0X53Ge3UluTLIzyf1V9cbu/vXGQVV1MMnBJLnqqqvmdGkAAJi/WWaUH0+ya8P2zum+jU4lWe3u33X3j5P8KJPg/Ae6+3B3r3T3yo4dOy60ZgAA2HKzBOUHkuyuqmuq6vIktyRZPWPMNzOZTU5Vbc/kVoyTc6wTAAAWatOg3N3PJrk9yb1JHklypLuPV9VdVbVvOuzeJL+sqhNJ7kvyoe7+5VYVDQAAW626eykXXllZ6bW1taVcGwCAF4+qerC7V873fVbmAwCAAUEZAAAGBGUAABgQlAEAYEBQBgCAAUEZAAAGBGUAABgQlAEAYEBQBgCAAUEZAAAGBGUAABgQlAEAYEBQBgCAAUEZAAAGBGUAABgQlAEAYEBQBgCAAUEZAAAGBGUAABgQlAEAYEBQBgCAAUEZAAAGBGUAABgQlAEAYGCmoFxVe6vq0apar6o7zjHuHVXVVbUyvxIBAGDxNg3KVXVZkkNJbkqyJ8mBqtozGHdFkvcn+cG8iwQAgEWbZUb5+iTr3X2yu59Jck+S/YNxn0jyySS/mWN9AACwFLME5SuTPLZh+9R03/+rqjcn2dXd35pjbQAAsDTP+2G+qnpJks8k+eAMYw9W1VpVrZ0+ffr5XhoAALbMLEH58SS7NmzvnO77vSuSvCHJd6vqJ0luSLI6eqCvuw9390p3r+zYsePCqwYAgC02S1B+IMnuqrqmqi5PckuS1d8f7O6nunt7d1/d3VcnOZZkX3evbUnFAACwAJsG5e5+NsntSe5N8kiSI919vKruqqp9W10gAAAsw7ZZBnX30SRHz9h351nG3vj8ywIAgOWyMh8AAAwIygAAMCAoAwDAgKAMAAADgjIAAAwIygAAMCAoAwDAgKAMAAADgjIAAAwIygAAMCAoAwDAgKAMAAADgjIAAAwIygAAMCAoAwDAgKAMAAADgjIAAAwIygAAMCAoAwDAgKAMAAADgjIAAAwIygAAMCAoAwDAgKAMAAADMwXlqtpbVY9W1XpV3TE4/oGqOlFVD1fVd6rqtfMvFQAAFmfToFxVlyU5lOSmJHuSHKiqPWcMeyjJSne/Kck3knxq3oUCAMAizTKjfH2S9e4+2d3PJLknyf6NA7r7vu5+erp5LMnO+ZYJAACLNUtQvjLJYxu2T033nc1tSb49OlBVB6tqrarWTp8+PXuVAACwYHN9mK+qbk2ykuTTo+Pdfbi7V7p7ZceOHfO8NAAAzNW2GcY8nmTXhu2d031/oKrenuQjSd7a3b+dT3kAALAcs8woP5Bkd1VdU1WXJ7klyerGAVV1XZIvJtnX3U/Mv0wAAFisTYNydz+b5PYk9yZ5JMmR7j5eVXdV1b7psE8neUWSr1fVf1bV6llOBwAALwiz3HqR7j6a5OgZ++7c8Prtc64LAACWysp8AAAwICgDAMCAoAwAAAOCMgAADAjKAAAwICgDAMCAoAwAAAOCMgAADAjKAAAwICgDAMCAoAwAAAOCMgAADAjKAAAwICgDAMCAoAwAAAOCMgAADAjKAAAwICgDAMCAoAwAAAOCMgAADAjKAAAwICgDAMCAoAwAAAMzBeWq2ltVj1bVelXdMTj+R1X179PjP6iqq+ddKAAALNKmQbmqLktyKMlNSfYkOVBVe84YdluSJ7v7T5P8c5JPzrtQAABYpFlmlK9Pst7dJ7v7mST3JNl/xpj9Sf5t+vobSd5WVTW/MgEAYLFmCcpXJnlsw/ap6b7hmO5+NslTSf54HgUCAMAybFvkxarqYJKD083fVtUPF3l9XhC2J/nFsovgoqMvGNEXjOgLRv7sQt40S1B+PMmuDds7p/tGY05V1bYkr0ryyzNP1N2HkxxOkqpa6+6VCymaS5e+YERfMKIvGNEXjFTV2oW8b5ZbLx5Isruqrqmqy5PckmT1jDGrSf5u+vpvkvxHd/eFFAQAABeDTWeUu/vZqro9yb1JLkvype4+XlV3JVnr7tUk/5rkK1W1nuRXmYRpAAB4wZrpHuXuPprk6Bn77tzw+jdJ/vY8r334PMfz4qAvGNEXjOgLRvQFIxfUF+UOCQAAeC5LWAMAwMCWB2XLXzMyQ198oKpOVNXDVfWdqnrtMupksTbriw3j3lFVXVWebH8RmKUvquqd08+M41X11UXXyOLN8Hvkqqq6r6oemv4uuXkZdbI4VfWlqnribF8/XBOfnfbMw1X15s3OuaVB2fLXjMzYFw8lWenuN2Wy2uOnFlslizZjX6Sqrkjy/iQ/WGyFLMMsfVFVu5N8OMlbuvvPk/zDwgtloWb8vPhokiPdfV0mXzLwucVWyRLcnWTvOY7flGT39Odgks9vdsKtnlG2/DUjm/ZFd9/X3U9PN49l8v3dXNpm+bxIkk9k8gf1bxZZHEszS1+8N8mh7n4ySbr7iQXXyOLN0hed5JXT169K8rMF1scSdPf9mXz72tnsT/LlnjiW5NVV9ZpznXOrg7LlrxmZpS82ui3Jt7e0Ii4Gm/bF9N9ku7r7W4ssjKWa5fPi2iTXVtX3qupYVZ1rRolLwyx98fEkt1bVqUy+uet9iymNi9j55o/FLmEN56uqbk2ykuSty66F5aqqlyT5TJJ3L7kULj7bMvlX6o2Z/Pfp/qp6Y3f/eqlVsWwHktzd3f9UVX+ZyXoPb+ju/112YbxwbPWM8vksf51zLX/NJWWWvkhVvT3JR5Ls6+7fLqg2lmezvrgiyRuSfLeqfpLkhiSrHui75M3yeXEqyWp3/667f5zkR5kEZy5ds/TFbUmOJEl3fz/Jy5JsX0h1XKxmyh8bbXVQtvw1I5v2RVVdl+SLmYRk9xu+OJyzL7r7qe7e3t1Xd/fVmdy7vq+715ZTLgsyy++Rb2Yym5yq2p7JrRgnF1kkCzdLX/w0yduSpKpen0lQPr3QKrnYrCZ51/TbL25I8lR3//xcb9jSWy8sf83IjH3x6SSvSPL16bOdP+3ufUsrmi03Y1/wIjNjX9yb5K+r6kSS/0nyoe72n8lL2Ix98cEk/1JV/5jJg33vNhF3aauqr2XyR/P26b3pH0vy0iTp7i9kcq/6zUnWkzyd5D2bnlPPAADAc1mZDwAABgRlAAAYEJQBAGBAUAYAgAFBGQAABgRlAAAYEJQBAGBAUAYAgIH/A3jYr/PRanOQAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 864x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(12,3))\n",
"\n",
"ax.plot(res_filardo.smoothed_marginal_probabilities[0])\n",
"ax.fill_between(usrec.index, 0, 1, where=usrec['USREC'].values, color='gray', alpha=0.2)\n",
"ax.set_xlim(dta_filardo.index[6], dta_filardo.index[-1])\n",
"ax.set(title='Smoothed probability of a low-production state');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using the time-varying transition probabilities, we can see how the expected duration of a low-production state changes over time:\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'res_filardo' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-13-a30b5c1ed40c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m res_filardo.expected_durations[0].plot(\n\u001b[0m\u001b[1;32m 2\u001b[0m title='Expected duration of a low-production state', figsize=(12,3));\n",
"\u001b[0;31mNameError\u001b[0m: name 'res_filardo' is not defined"
]
}
],
"source": [
"res_filardo.expected_durations[0].plot(\n",
" title='Expected duration of a low-production state', figsize=(12,3));"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"During recessions, the expected duration of a low-production state is much higher than in an expansion."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
}

⊟

394 KB

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394 KB

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/srv/reproducible-results/rbuild-debian/tmp.FTzzuDJkiI/dbd-tmp-gqMLHgR/diffoscope_1mz05yxs/tmpkoy11dl8/c3e49089-1161-422d-95e2-39bc48bd1a87 vs.

Similarity: 0.0%
Differences: {
"replace": {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Markov switching dynamic regression models"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook provides an example of the use of Markov switching models in Statsmodels to estimate dynamic regression models with changes in regime. It follows the examples in the Stata Markov switching documentation, which can be found at http://www.stata.com/manuals14/tsmswitch.pdf."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
" from pandas.core import datetools\n"
]
}
],
"source": [
"%matplotlib inline\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import statsmodels.api as sm\n",
"import matplotlib.pyplot as plt\n",
"from datetime import datetime\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Federal funds rate with switching intercept\n",
"\n",
"The first example models the federal funds rate as noise around a constant intercept, but where the intercept changes during different regimes. The model is simply:\n",
"\n",
"$$r_t = \\mu_{S_t} + \\varepsilon_t \\qquad \\varepsilon_t \\sim N(0, \\sigma^2)$$\n",
"\n",
"where $S_t \\in \\{0, 1\\}$, and the regime transitions according to\n",
"\n",
"$$ P(S_t = s_t | S_{t-1} = s_{t-1}) =\n",
"\\begin{bmatrix}\n",
"p_{00} & p_{10} \\\\\n",
"1 - p_{00} & 1 - p_{10}\n",
"\\end{bmatrix}\n",
"$$\n",
"\n",
"We will estimate the parameters of this model by maximum likelihood: $p_{00}, p_{10}, \\mu_0, \\mu_1, \\sigma^2$.\n",
"\n",
"The data used in this example can be found at http://www.stata-press.com/data/r14/usmacro."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Get the federal funds rate data\n",
"from statsmodels.tsa.regime_switching.tests.test_markov_regression import fedfunds\n",
"dta_fedfunds = pd.Series(fedfunds, index=pd.date_range('1954-07-01', '2010-10-01', freq='QS'))\n",
"\n",
"# Plot the data\n",
"dta_fedfunds.plot(title='Federal funds rate', figsize=(12,3))\n",
"\n",
"# Fit the model\n",
"# (a switching mean is the default of the MarkovRegession model)\n",
"mod_fedfunds = sm.tsa.MarkovRegression(dta_fedfunds, k_regimes=2)\n",
"res_fedfunds = mod_fedfunds.fit()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>Markov Switching Model Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>y</td> <th> No. Observations: </th> <td>226</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>MarkovRegression</td> <th> Log Likelihood </th> <td>-508.636</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Sat, 10 Apr 2021</td> <th> AIC </th> <td>1027.272</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>01:00:10</td> <th> BIC </th> <td>1044.375</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Sample:</th> <td>07-01-1954</td> <th> HQIC </th> <td>1034.174</td>\n",
"</tr>\n",
"<tr>\n",
" <th></th> <td>- 10-01-2010</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>approx</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<caption>Regime 0 parameters</caption>\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>const</th> <td> 3.7088</td> <td> 0.177</td> <td> 20.988</td> <td> 0.000</td> <td> 3.362</td> <td> 4.055</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<caption>Regime 1 parameters</caption>\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>const</th> <td> 9.5568</td> <td> 0.300</td> <td> 31.857</td> <td> 0.000</td> <td> 8.969</td> <td> 10.145</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<caption>Non-switching parameters</caption>\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>sigma2</th> <td> 4.4418</td> <td> 0.425</td> <td> 10.447</td> <td> 0.000</td> <td> 3.608</td> <td> 5.275</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<caption>Regime transition parameters</caption>\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>p[0->0]</th> <td> 0.9821</td> <td> 0.010</td> <td> 94.443</td> <td> 0.000</td> <td> 0.962</td> <td> 1.002</td>\n",
"</tr>\n",
"<tr>\n",
" <th>p[1->0]</th> <td> 0.0504</td> <td> 0.027</td> <td> 1.876</td> <td> 0.061</td> <td> -0.002</td> <td> 0.103</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" Markov Switching Model Results \n",
"==============================================================================\n",
"Dep. Variable: y No. Observations: 226\n",
"Model: MarkovRegression Log Likelihood -508.636\n",
"Date: Sat, 10 Apr 2021 AIC 1027.272\n",
"Time: 01:00:10 BIC 1044.375\n",
"Sample: 07-01-1954 HQIC 1034.174\n",
" - 10-01-2010 \n",
"Covariance Type: approx \n",
" Regime 0 parameters \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const 3.7088 0.177 20.988 0.000 3.362 4.055\n",
" Regime 1 parameters \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const 9.5568 0.300 31.857 0.000 8.969 10.145\n",
" Non-switching parameters \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"sigma2 4.4418 0.425 10.447 0.000 3.608 5.275\n",
" Regime transition parameters \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"p[0->0] 0.9821 0.010 94.443 0.000 0.962 1.002\n",
"p[1->0] 0.0504 0.027 1.876 0.061 -0.002 0.103\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Covariance matrix calculated using numerical differentiation.\n",
"\"\"\""
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"res_fedfunds.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From the summary output, the mean federal funds rate in the first regime (the \"low regime\") is estimated to be $3.7$ whereas in the \"high regime\" it is $9.6$. Below we plot the smoothed probabilities of being in the high regime. The model suggests that the 1980's was a time-period in which a high federal funds rate existed."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"res_fedfunds.smoothed_marginal_probabilities[1].plot(\n",
" title='Probability of being in the high regime', figsize=(12,3));"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From the estimated transition matrix we can calculate the expected duration of a low regime versus a high regime."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[55.85400626 19.85506546]\n"
]
}
],
"source": [
"print(res_fedfunds.expected_durations)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A low regime is expected to persist for about fourteen years, whereas the high regime is expected to persist for only about five years."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Federal funds rate with switching intercept and lagged dependent variable\n",
"\n",
"The second example augments the previous model to include the lagged value of the federal funds rate.\n",
"\n",
"$$r_t = \\mu_{S_t} + r_{t-1} \\beta_{S_t} + \\varepsilon_t \\qquad \\varepsilon_t \\sim N(0, \\sigma^2)$$\n",
"\n",
"where $S_t \\in \\{0, 1\\}$, and the regime transitions according to\n",
"\n",
"$$ P(S_t = s_t | S_{t-1} = s_{t-1}) =\n",
"\\begin{bmatrix}\n",
"p_{00} & p_{10} \\\\\n",
"1 - p_{00} & 1 - p_{10}\n",
"\\end{bmatrix}\n",
"$$\n",
"\n",
"We will estimate the parameters of this model by maximum likelihood: $p_{00}, p_{10}, \\mu_0, \\mu_1, \\beta_0, \\beta_1, \\sigma^2$."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Fit the model\n",
"mod_fedfunds2 = sm.tsa.MarkovRegression(\n",
" dta_fedfunds.iloc[1:], k_regimes=2, exog=dta_fedfunds.iloc[:-1])\n",
"res_fedfunds2 = mod_fedfunds2.fit()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>Markov Switching Model Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>y</td> <th> No. Observations: </th> <td>225</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>MarkovRegression</td> <th> Log Likelihood </th> <td>-264.711</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Sat, 10 Apr 2021</td> <th> AIC </th> <td>543.421</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>01:00:11</td> <th> BIC </th> <td>567.334</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Sample:</th> <td>10-01-1954</td> <th> HQIC </th> <td>553.073</td>\n",
"</tr>\n",
"<tr>\n",
" <th></th> <td>- 10-01-2010</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>approx</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<caption>Regime 0 parameters</caption>\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>const</th> <td> 0.7245</td> <td> 0.289</td> <td> 2.510</td> <td> 0.012</td> <td> 0.159</td> <td> 1.290</td>\n",
"</tr>\n",
"<tr>\n",
" <th>x1</th> <td> 0.7631</td> <td> 0.034</td> <td> 22.629</td> <td> 0.000</td> <td> 0.697</td> <td> 0.829</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<caption>Regime 1 parameters</caption>\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>const</th> <td> -0.0989</td> <td> 0.118</td> <td> -0.835</td> <td> 0.404</td> <td> -0.331</td> <td> 0.133</td>\n",
"</tr>\n",
"<tr>\n",
" <th>x1</th> <td> 1.0612</td> <td> 0.019</td> <td> 57.351</td> <td> 0.000</td> <td> 1.025</td> <td> 1.097</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<caption>Non-switching parameters</caption>\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>sigma2</th> <td> 0.4783</td> <td> 0.050</td> <td> 9.642</td> <td> 0.000</td> <td> 0.381</td> <td> 0.576</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<caption>Regime transition parameters</caption>\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>p[0->0]</th> <td> 0.6378</td> <td> 0.120</td> <td> 5.304</td> <td> 0.000</td> <td> 0.402</td> <td> 0.874</td>\n",
"</tr>\n",
"<tr>\n",
" <th>p[1->0]</th> <td> 0.1306</td> <td> 0.050</td> <td> 2.634</td> <td> 0.008</td> <td> 0.033</td> <td> 0.228</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" Markov Switching Model Results \n",
"==============================================================================\n",
"Dep. Variable: y No. Observations: 225\n",
"Model: MarkovRegression Log Likelihood -264.711\n",
"Date: Sat, 10 Apr 2021 AIC 543.421\n",
"Time: 01:00:11 BIC 567.334\n",
"Sample: 10-01-1954 HQIC 553.073\n",
" - 10-01-2010 \n",
"Covariance Type: approx \n",
" Regime 0 parameters \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const 0.7245 0.289 2.510 0.012 0.159 1.290\n",
"x1 0.7631 0.034 22.629 0.000 0.697 0.829\n",
" Regime 1 parameters \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const -0.0989 0.118 -0.835 0.404 -0.331 0.133\n",
"x1 1.0612 0.019 57.351 0.000 1.025 1.097\n",
" Non-switching parameters \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"sigma2 0.4783 0.050 9.642 0.000 0.381 0.576\n",
" Regime transition parameters \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"p[0->0] 0.6378 0.120 5.304 0.000 0.402 0.874\n",
"p[1->0] 0.1306 0.050 2.634 0.008 0.033 0.228\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Covariance matrix calculated using numerical differentiation.\n",
"\"\"\""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"res_fedfunds2.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are several things to notice from the summary output:\n",
"\n",
"1. The information criteria have decreased substantially, indicating that this model has a better fit than the previous model.\n",
"2. The interpretation of the regimes, in terms of the intercept, have switched. Now the first regime has the higher intercept and the second regime has a lower intercept.\n",
"\n",
"Examining the smoothed probabilities of the high regime state, we now see quite a bit more variability."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"res_fedfunds2.smoothed_marginal_probabilities[0].plot(\n",
" title='Probability of being in the high regime', figsize=(12,3));"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, the expected durations of each regime have decreased quite a bit."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2.76105188 7.65529154]\n"
]
}
],
"source": [
"print(res_fedfunds2.expected_durations)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Taylor rule with 2 or 3 regimes\n",
"\n",
"We now include two additional exogenous variables - a measure of the output gap and a measure of inflation - to estimate a switching Taylor-type rule with both 2 and 3 regimes to see which fits the data better.\n",
"\n",
"Because the models can be often difficult to estimate, for the 3-regime model we employ a search over starting parameters to improve results, specifying 20 random search repetitions."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Get the additional data\n",
"from statsmodels.tsa.regime_switching.tests.test_markov_regression import ogap, inf\n",
"dta_ogap = pd.Series(ogap, index=pd.date_range('1954-07-01', '2010-10-01', freq='QS'))\n",
"dta_inf = pd.Series(inf, index=pd.date_range('1954-07-01', '2010-10-01', freq='QS'))\n",
"\n",
"exog = pd.concat((dta_fedfunds.shift(), dta_ogap, dta_inf), axis=1).iloc[4:]\n",
"\n",
"# Fit the 2-regime model\n",
"mod_fedfunds3 = sm.tsa.MarkovRegression(\n",
" dta_fedfunds.iloc[4:], k_regimes=2, exog=exog)\n",
"res_fedfunds3 = mod_fedfunds3.fit()\n",
"\n",
"# Fit the 3-regime model\n",
"np.random.seed(12345)\n",
"mod_fedfunds4 = sm.tsa.MarkovRegression(\n",
" dta_fedfunds.iloc[4:], k_regimes=3, exog=exog)\n",
"res_fedfunds4 = mod_fedfunds4.fit(search_reps=20)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>Markov Switching Model Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>y</td> <th> No. Observations: </th> <td>222</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>MarkovRegression</td> <th> Log Likelihood </th> <td>-229.256</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Sat, 10 Apr 2021</td> <th> AIC </th> <td>480.512</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>01:00:14</td> <th> BIC </th> <td>517.942</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Sample:</th> <td>07-01-1955</td> <th> HQIC </th> <td>495.624</td>\n",
"</tr>\n",
"<tr>\n",
" <th></th> <td>- 10-01-2010</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>approx</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<caption>Regime 0 parameters</caption>\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>const</th> <td> 0.6555</td> <td> 0.137</td> <td> 4.771</td> <td> 0.000</td> <td> 0.386</td> <td> 0.925</td>\n",
"</tr>\n",
"<tr>\n",
" <th>x1</th> <td> 0.8314</td> <td> 0.033</td> <td> 24.951</td> <td> 0.000</td> <td> 0.766</td> <td> 0.897</td>\n",
"</tr>\n",
"<tr>\n",
" <th>x2</th> <td> 0.1355</td> <td> 0.029</td> <td> 4.609</td> <td> 0.000</td> <td> 0.078</td> <td> 0.193</td>\n",
"</tr>\n",
"<tr>\n",
" <th>x3</th> <td> -0.0274</td> <td> 0.041</td> <td> -0.671</td> <td> 0.502</td> <td> -0.107</td> <td> 0.053</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<caption>Regime 1 parameters</caption>\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>const</th> <td> -0.0945</td> <td> 0.128</td> <td> -0.739</td> <td> 0.460</td> <td> -0.345</td> <td> 0.156</td>\n",
"</tr>\n",
"<tr>\n",
" <th>x1</th> <td> 0.9293</td> <td> 0.027</td> <td> 34.309</td> <td> 0.000</td> <td> 0.876</td> <td> 0.982</td>\n",
"</tr>\n",
"<tr>\n",
" <th>x2</th> <td> 0.0343</td> <td> 0.024</td> <td> 1.429</td> <td> 0.153</td> <td> -0.013</td> <td> 0.081</td>\n",
"</tr>\n",
"<tr>\n",
" <th>x3</th> <td> 0.2125</td> <td> 0.030</td> <td> 7.147</td> <td> 0.000</td> <td> 0.154</td> <td> 0.271</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<caption>Non-switching parameters</caption>\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>sigma2</th> <td> 0.3323</td> <td> 0.035</td> <td> 9.526</td> <td> 0.000</td> <td> 0.264</td> <td> 0.401</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<caption>Regime transition parameters</caption>\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>p[0->0]</th> <td> 0.7279</td> <td> 0.093</td> <td> 7.828</td> <td> 0.000</td> <td> 0.546</td> <td> 0.910</td>\n",
"</tr>\n",
"<tr>\n",
" <th>p[1->0]</th> <td> 0.2115</td> <td> 0.064</td> <td> 3.298</td> <td> 0.001</td> <td> 0.086</td> <td> 0.337</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" Markov Switching Model Results \n",
"==============================================================================\n",
"Dep. Variable: y No. Observations: 222\n",
"Model: MarkovRegression Log Likelihood -229.256\n",
"Date: Sat, 10 Apr 2021 AIC 480.512\n",
"Time: 01:00:14 BIC 517.942\n",
"Sample: 07-01-1955 HQIC 495.624\n",
" - 10-01-2010 \n",
"Covariance Type: approx \n",
" Regime 0 parameters \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const 0.6555 0.137 4.771 0.000 0.386 0.925\n",
"x1 0.8314 0.033 24.951 0.000 0.766 0.897\n",
"x2 0.1355 0.029 4.609 0.000 0.078 0.193\n",
"x3 -0.0274 0.041 -0.671 0.502 -0.107 0.053\n",
" Regime 1 parameters \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const -0.0945 0.128 -0.739 0.460 -0.345 0.156\n",
"x1 0.9293 0.027 34.309 0.000 0.876 0.982\n",
"x2 0.0343 0.024 1.429 0.153 -0.013 0.081\n",
"x3 0.2125 0.030 7.147 0.000 0.154 0.271\n",
" Non-switching parameters \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"sigma2 0.3323 0.035 9.526 0.000 0.264 0.401\n",
" Regime transition parameters \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"p[0->0] 0.7279 0.093 7.828 0.000 0.546 0.910\n",
"p[1->0] 0.2115 0.064 3.298 0.001 0.086 0.337\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Covariance matrix calculated using numerical differentiation.\n",
"\"\"\""
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"res_fedfunds3.summary()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>Markov Switching Model Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>y</td> <th> No. Observations: </th> <td>222</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>MarkovRegression</td> <th> Log Likelihood </th> <td>-180.806</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Sat, 10 Apr 2021</td> <th> AIC </th> <td>399.611</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>01:00:14</td> <th> BIC </th> <td>464.262</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Sample:</th> <td>07-01-1955</td> <th> HQIC </th> <td>425.713</td>\n",
"</tr>\n",
"<tr>\n",
" <th></th> <td>- 10-01-2010</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>approx</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<caption>Regime 0 parameters</caption>\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>const</th> <td> -1.0250</td> <td> 0.292</td> <td> -3.514</td> <td> 0.000</td> <td> -1.597</td> <td> -0.453</td>\n",
"</tr>\n",
"<tr>\n",
" <th>x1</th> <td> 0.3277</td> <td> 0.086</td> <td> 3.809</td> <td> 0.000</td> <td> 0.159</td> <td> 0.496</td>\n",
"</tr>\n",
"<tr>\n",
" <th>x2</th> <td> 0.2036</td> <td> 0.050</td> <td> 4.086</td> <td> 0.000</td> <td> 0.106</td> <td> 0.301</td>\n",
"</tr>\n",
"<tr>\n",
" <th>x3</th> <td> 1.1381</td> <td> 0.081</td> <td> 13.972</td> <td> 0.000</td> <td> 0.978</td> <td> 1.298</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<caption>Regime 1 parameters</caption>\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>const</th> <td> -0.0259</td> <td> 0.087</td> <td> -0.298</td> <td> 0.766</td> <td> -0.196</td> <td> 0.145</td>\n",
"</tr>\n",
"<tr>\n",
" <th>x1</th> <td> 0.9737</td> <td> 0.019</td> <td> 50.206</td> <td> 0.000</td> <td> 0.936</td> <td> 1.012</td>\n",
"</tr>\n",
"<tr>\n",
" <th>x2</th> <td> 0.0341</td> <td> 0.017</td> <td> 1.973</td> <td> 0.049</td> <td> 0.000</td> <td> 0.068</td>\n",
"</tr>\n",
"<tr>\n",
" <th>x3</th> <td> 0.1215</td> <td> 0.022</td> <td> 5.605</td> <td> 0.000</td> <td> 0.079</td> <td> 0.164</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<caption>Regime 2 parameters</caption>\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>const</th> <td> 0.7346</td> <td> 0.136</td> <td> 5.419</td> <td> 0.000</td> <td> 0.469</td> <td> 1.000</td>\n",
"</tr>\n",
"<tr>\n",
" <th>x1</th> <td> 0.8436</td> <td> 0.024</td> <td> 34.798</td> <td> 0.000</td> <td> 0.796</td> <td> 0.891</td>\n",
"</tr>\n",
"<tr>\n",
" <th>x2</th> <td> 0.1633</td> <td> 0.032</td> <td> 5.067</td> <td> 0.000</td> <td> 0.100</td> <td> 0.226</td>\n",
"</tr>\n",
"<tr>\n",
" <th>x3</th> <td> -0.0499</td> <td> 0.027</td> <td> -1.829</td> <td> 0.067</td> <td> -0.103</td> <td> 0.004</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<caption>Non-switching parameters</caption>\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>sigma2</th> <td> 0.1660</td> <td> 0.018</td> <td> 9.138</td> <td> 0.000</td> <td> 0.130</td> <td> 0.202</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<caption>Regime transition parameters</caption>\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>p[0->0]</th> <td> 0.7214</td> <td> 0.117</td> <td> 6.147</td> <td> 0.000</td> <td> 0.491</td> <td> 0.951</td>\n",
"</tr>\n",
"<tr>\n",
" <th>p[1->0]</th> <td> 4.001e-08</td> <td> 0.035</td> <td> 1.13e-06</td> <td> 1.000</td> <td> -0.069</td> <td> 0.069</td>\n",
"</tr>\n",
"<tr>\n",
" <th>p[2->0]</th> <td> 0.0783</td> <td> 0.057</td> <td> 1.372</td> <td> 0.170</td> <td> -0.034</td> <td> 0.190</td>\n",
"</tr>\n",
"<tr>\n",
" <th>p[0->1]</th> <td> 0.1044</td> <td> 0.095</td> <td> 1.103</td> <td> 0.270</td> <td> -0.081</td> <td> 0.290</td>\n",
"</tr>\n",
"<tr>\n",
" <th>p[1->1]</th> <td> 0.8259</td> <td> 0.054</td> <td> 15.201</td> <td> 0.000</td> <td> 0.719</td> <td> 0.932</td>\n",
"</tr>\n",
"<tr>\n",
" <th>p[2->1]</th> <td> 0.2288</td> <td> 0.073</td> <td> 3.126</td> <td> 0.002</td> <td> 0.085</td> <td> 0.372</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" Markov Switching Model Results \n",
"==============================================================================\n",
"Dep. Variable: y No. Observations: 222\n",
"Model: MarkovRegression Log Likelihood -180.806\n",
"Date: Sat, 10 Apr 2021 AIC 399.611\n",
"Time: 01:00:14 BIC 464.262\n",
"Sample: 07-01-1955 HQIC 425.713\n",
" - 10-01-2010 \n",
"Covariance Type: approx \n",
" Regime 0 parameters \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const -1.0250 0.292 -3.514 0.000 -1.597 -0.453\n",
"x1 0.3277 0.086 3.809 0.000 0.159 0.496\n",
"x2 0.2036 0.050 4.086 0.000 0.106 0.301\n",
"x3 1.1381 0.081 13.972 0.000 0.978 1.298\n",
" Regime 1 parameters \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const -0.0259 0.087 -0.298 0.766 -0.196 0.145\n",
"x1 0.9737 0.019 50.206 0.000 0.936 1.012\n",
"x2 0.0341 0.017 1.973 0.049 0.000 0.068\n",
"x3 0.1215 0.022 5.605 0.000 0.079 0.164\n",
" Regime 2 parameters \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const 0.7346 0.136 5.419 0.000 0.469 1.000\n",
"x1 0.8436 0.024 34.798 0.000 0.796 0.891\n",
"x2 0.1633 0.032 5.067 0.000 0.100 0.226\n",
"x3 -0.0499 0.027 -1.829 0.067 -0.103 0.004\n",
" Non-switching parameters \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"sigma2 0.1660 0.018 9.138 0.000 0.130 0.202\n",
" Regime transition parameters \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"p[0->0] 0.7214 0.117 6.147 0.000 0.491 0.951\n",
"p[1->0] 4.001e-08 0.035 1.13e-06 1.000 -0.069 0.069\n",
"p[2->0] 0.0783 0.057 1.372 0.170 -0.034 0.190\n",
"p[0->1] 0.1044 0.095 1.103 0.270 -0.081 0.290\n",
"p[1->1] 0.8259 0.054 15.201 0.000 0.719 0.932\n",
"p[2->1] 0.2288 0.073 3.126 0.002 0.085 0.372\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Covariance matrix calculated using numerical differentiation.\n",
"\"\"\""
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"res_fedfunds4.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Due to lower information criteria, we might prefer the 3-state model, with an interpretation of low-, medium-, and high-interest rate regimes. The smoothed probabilities of each regime are plotted below."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x504 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots(3, figsize=(10,7))\n",
"\n",
"ax = axes[0]\n",
"ax.plot(res_fedfunds4.smoothed_marginal_probabilities[0])\n",
"ax.set(title='Smoothed probability of a low-interest rate regime')\n",
"\n",
"ax = axes[1]\n",
"ax.plot(res_fedfunds4.smoothed_marginal_probabilities[1])\n",
"ax.set(title='Smoothed probability of a medium-interest rate regime')\n",
"\n",
"ax = axes[2]\n",
"ax.plot(res_fedfunds4.smoothed_marginal_probabilities[2])\n",
"ax.set(title='Smoothed probability of a high-interest rate regime')\n",
"\n",
"fig.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Switching variances\n",
"\n",
"We can also accomodate switching variances. In particular, we consider the model\n",
"\n",
"$$\n",
"y_t = \\mu_{S_t} + y_{t-1} \\beta_{S_t} + \\varepsilon_t \\quad \\varepsilon_t \\sim N(0, \\sigma_{S_t}^2)\n",
"$$\n",
"\n",
"We use maximum likelihood to estimate the parameters of this model: $p_{00}, p_{10}, \\mu_0, \\mu_1, \\beta_0, \\beta_1, \\sigma_0^2, \\sigma_1^2$.\n",
"\n",
"The application is to absolute returns on stocks, where the data can be found at http://www.stata-press.com/data/r14/snp500."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Get the federal funds rate data\n",
"from statsmodels.tsa.regime_switching.tests.test_markov_regression import areturns\n",
"dta_areturns = pd.Series(areturns, index=pd.date_range('2004-05-04', '2014-5-03', freq='W'))\n",
"\n",
"# Plot the data\n",
"dta_areturns.plot(title='Absolute returns, S&P500', figsize=(12,3))\n",
"\n",
"# Fit the model\n",
"mod_areturns = sm.tsa.MarkovRegression(\n",
" dta_areturns.iloc[1:], k_regimes=2, exog=dta_areturns.iloc[:-1], switching_variance=True)\n",
"res_areturns = mod_areturns.fit()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>Markov Switching Model Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>y</td> <th> No. Observations: </th> <td>520</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>MarkovRegression</td> <th> Log Likelihood </th> <td>-745.798</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Sat, 10 Apr 2021</td> <th> AIC </th> <td>1507.595</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>01:00:15</td> <th> BIC </th> <td>1541.626</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Sample:</th> <td>05-16-2004</td> <th> HQIC </th> <td>1520.926</td>\n",
"</tr>\n",
"<tr>\n",
" <th></th> <td>- 04-27-2014</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>approx</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<caption>Regime 0 parameters</caption>\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>const</th> <td> 0.7641</td> <td> 0.078</td> <td> 9.761</td> <td> 0.000</td> <td> 0.611</td> <td> 0.918</td>\n",
"</tr>\n",
"<tr>\n",
" <th>x1</th> <td> 0.0791</td> <td> 0.030</td> <td> 2.620</td> <td> 0.009</td> <td> 0.020</td> <td> 0.138</td>\n",
"</tr>\n",
"<tr>\n",
" <th>sigma2</th> <td> 0.3476</td> <td> 0.061</td> <td> 5.694</td> <td> 0.000</td> <td> 0.228</td> <td> 0.467</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<caption>Regime 1 parameters</caption>\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>const</th> <td> 1.9728</td> <td> 0.278</td> <td> 7.086</td> <td> 0.000</td> <td> 1.427</td> <td> 2.518</td>\n",
"</tr>\n",
"<tr>\n",
" <th>x1</th> <td> 0.5280</td> <td> 0.086</td> <td> 6.155</td> <td> 0.000</td> <td> 0.360</td> <td> 0.696</td>\n",
"</tr>\n",
"<tr>\n",
" <th>sigma2</th> <td> 2.5771</td> <td> 0.405</td> <td> 6.357</td> <td> 0.000</td> <td> 1.783</td> <td> 3.372</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<caption>Regime transition parameters</caption>\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>p[0->0]</th> <td> 0.7531</td> <td> 0.063</td> <td> 11.871</td> <td> 0.000</td> <td> 0.629</td> <td> 0.877</td>\n",
"</tr>\n",
"<tr>\n",
" <th>p[1->0]</th> <td> 0.6825</td> <td> 0.066</td> <td> 10.301</td> <td> 0.000</td> <td> 0.553</td> <td> 0.812</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" Markov Switching Model Results \n",
"==============================================================================\n",
"Dep. Variable: y No. Observations: 520\n",
"Model: MarkovRegression Log Likelihood -745.798\n",
"Date: Sat, 10 Apr 2021 AIC 1507.595\n",
"Time: 01:00:15 BIC 1541.626\n",
"Sample: 05-16-2004 HQIC 1520.926\n",
" - 04-27-2014 \n",
"Covariance Type: approx \n",
" Regime 0 parameters \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const 0.7641 0.078 9.761 0.000 0.611 0.918\n",
"x1 0.0791 0.030 2.620 0.009 0.020 0.138\n",
"sigma2 0.3476 0.061 5.694 0.000 0.228 0.467\n",
" Regime 1 parameters \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const 1.9728 0.278 7.086 0.000 1.427 2.518\n",
"x1 0.5280 0.086 6.155 0.000 0.360 0.696\n",
"sigma2 2.5771 0.405 6.357 0.000 1.783 3.372\n",
" Regime transition parameters \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"p[0->0] 0.7531 0.063 11.871 0.000 0.629 0.877\n",
"p[1->0] 0.6825 0.066 10.301 0.000 0.553 0.812\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Covariance matrix calculated using numerical differentiation.\n",
"\"\"\""
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"res_areturns.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first regime is a low-variance regime and the second regime is a high-variance regime. Below we plot the probabilities of being in the low-variance regime. Between 2008 and 2012 there does not appear to be a clear indication of one regime guiding the economy."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"res_areturns.smoothed_marginal_probabilities[0].plot(\n",
" title='Probability of being in a low-variance regime', figsize=(12,3));"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
}

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/srv/reproducible-results/rbuild-debian/tmp.FTzzuDJkiI/dbd-tmp-gqMLHgR/diffoscope_1mz05yxs/tmpqrib2i4u/04237c5f-f9e8-4d95-9274-d671b24e2922 vs.

Similarity: 0.0%
Differences: {
"replace": {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Ordinary Least Squares"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
" from pandas.core import datetools\n"
]
}
],
"source": [
"%matplotlib inline\n",
"\n",
"from __future__ import print_function\n",
"import numpy as np\n",
"import statsmodels.api as sm\n",
"import matplotlib.pyplot as plt\n",
"from statsmodels.sandbox.regression.predstd import wls_prediction_std\n",
"\n",
"np.random.seed(9876789)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## OLS estimation\n",
"\n",
"Artificial data:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"nsample = 100\n",
"x = np.linspace(0, 10, 100)\n",
"X = np.column_stack((x, x**2))\n",
"beta = np.array([1, 0.1, 10])\n",
"e = np.random.normal(size=nsample)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Our model needs an intercept so we add a column of 1s:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"X = sm.add_constant(X)\n",
"y = np.dot(X, beta) + e"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Fit and summary:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: y R-squared: 1.000\n",
"Model: OLS Adj. R-squared: 1.000\n",
"Method: Least Squares F-statistic: 4.020e+06\n",
"Date: Sat, 10 Apr 2021 Prob (F-statistic): 2.83e-239\n",
"Time: 01:00:09 Log-Likelihood: -146.51\n",
"No. Observations: 100 AIC: 299.0\n",
"Df Residuals: 97 BIC: 306.8\n",
"Df Model: 2 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const 1.3423 0.313 4.292 0.000 0.722 1.963\n",
"x1 -0.0402 0.145 -0.278 0.781 -0.327 0.247\n",
"x2 10.0103 0.014 715.745 0.000 9.982 10.038\n",
"==============================================================================\n",
"Omnibus: 2.042 Durbin-Watson: 2.274\n",
"Prob(Omnibus): 0.360 Jarque-Bera (JB): 1.875\n",
"Skew: 0.234 Prob(JB): 0.392\n",
"Kurtosis: 2.519 Cond. No. 144.\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
]
}
],
"source": [
"model = sm.OLS(y, X)\n",
"results = model.fit()\n",
"print(results.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Quantities of interest can be extracted directly from the fitted model. Type ``dir(results)`` for a full list. Here are some examples: "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Parameters: [ 1.34233516 -0.04024948 10.01025357]\n",
"R2: 0.9999879365025871\n"
]
}
],
"source": [
"print('Parameters: ', results.params)\n",
"print('R2: ', results.rsquared)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## OLS non-linear curve but linear in parameters\n",
"\n",
"We simulate artificial data with a non-linear relationship between x and y:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"nsample = 50\n",
"sig = 0.5\n",
"x = np.linspace(0, 20, nsample)\n",
"X = np.column_stack((x, np.sin(x), (x-5)**2, np.ones(nsample)))\n",
"beta = [0.5, 0.5, -0.02, 5.]\n",
"\n",
"y_true = np.dot(X, beta)\n",
"y = y_true + sig * np.random.normal(size=nsample)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Fit and summary:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: y R-squared: 0.933\n",
"Model: OLS Adj. R-squared: 0.928\n",
"Method: Least Squares F-statistic: 211.8\n",
"Date: Sat, 10 Apr 2021 Prob (F-statistic): 6.30e-27\n",
"Time: 01:00:10 Log-Likelihood: -34.438\n",
"No. Observations: 50 AIC: 76.88\n",
"Df Residuals: 46 BIC: 84.52\n",
"Df Model: 3 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"x1 0.4687 0.026 17.751 0.000 0.416 0.522\n",
"x2 0.4836 0.104 4.659 0.000 0.275 0.693\n",
"x3 -0.0174 0.002 -7.507 0.000 -0.022 -0.013\n",
"const 5.2058 0.171 30.405 0.000 4.861 5.550\n",
"==============================================================================\n",
"Omnibus: 0.655 Durbin-Watson: 2.896\n",
"Prob(Omnibus): 0.721 Jarque-Bera (JB): 0.360\n",
"Skew: 0.207 Prob(JB): 0.835\n",
"Kurtosis: 3.026 Cond. No. 221.\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
]
}
],
"source": [
"res = sm.OLS(y, X).fit()\n",
"print(res.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Extract other quantities of interest:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Parameters: [ 0.46872448 0.48360119 -0.01740479 5.20584496]\n",
"Standard errors: [0.02640602 0.10380518 0.00231847 0.17121765]\n",
"Predicted values: [ 4.77072516 5.22213464 5.63620761 5.98658823 6.25643234 6.44117491\n",
" 6.54928009 6.60085051 6.62432454 6.6518039 6.71377946 6.83412169\n",
" 7.02615877 7.29048685 7.61487206 7.97626054 8.34456611 8.68761335\n",
" 8.97642389 9.18997755 9.31866582 9.36587056 9.34740836 9.28893189\n",
" 9.22171529 9.17751587 9.1833565 9.25708583 9.40444579 9.61812821\n",
" 9.87897556 10.15912843 10.42660281 10.65054491 10.8063004 10.87946503\n",
" 10.86825119 10.78378163 10.64826203 10.49133265 10.34519853 10.23933827\n",
" 10.19566084 10.22490593 10.32487947 10.48081414 10.66779556 10.85485568\n",
" 11.01006072 11.10575781]\n"
]
}
],
"source": [
"print('Parameters: ', res.params)\n",
"print('Standard errors: ', res.bse)\n",
"print('Predicted values: ', res.predict())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Draw a plot to compare the true relationship to OLS predictions. Confidence intervals around the predictions are built using the ``wls_prediction_std`` command."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"prstd, iv_l, iv_u = wls_prediction_std(res)\n",
"\n",
"fig, ax = plt.subplots(figsize=(8,6))\n",
"\n",
"ax.plot(x, y, 'o', label=\"data\")\n",
"ax.plot(x, y_true, 'b-', label=\"True\")\n",
"ax.plot(x, res.fittedvalues, 'r--.', label=\"OLS\")\n",
"ax.plot(x, iv_u, 'r--')\n",
"ax.plot(x, iv_l, 'r--')\n",
"ax.legend(loc='best');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## OLS with dummy variables\n",
"\n",
"We generate some artificial data. There are 3 groups which will be modelled using dummy variables. Group 0 is the omitted/benchmark category."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"nsample = 50\n",
"groups = np.zeros(nsample, int)\n",
"groups[20:40] = 1\n",
"groups[40:] = 2\n",
"#dummy = (groups[:,None] == np.unique(groups)).astype(float)\n",
"\n",
"dummy = sm.categorical(groups, drop=True)\n",
"x = np.linspace(0, 20, nsample)\n",
"# drop reference category\n",
"X = np.column_stack((x, dummy[:,1:]))\n",
"X = sm.add_constant(X, prepend=False)\n",
"\n",
"beta = [1., 3, -3, 10]\n",
"y_true = np.dot(X, beta)\n",
"e = np.random.normal(size=nsample)\n",
"y = y_true + e"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Inspect the data:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[0. 0. 0. 1. ]\n",
" [0.40816327 0. 0. 1. ]\n",
" [0.81632653 0. 0. 1. ]\n",
" [1.2244898 0. 0. 1. ]\n",
" [1.63265306 0. 0. 1. ]]\n",
"[ 9.28223335 10.50481865 11.84389206 10.38508408 12.37941998]\n",
"[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
" 1 1 1 2 2 2 2 2 2 2 2 2 2]\n",
"[[1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]]\n"
]
}
],
"source": [
"print(X[:5,:])\n",
"print(y[:5])\n",
"print(groups)\n",
"print(dummy[:5,:])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Fit and summary:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: y R-squared: 0.978\n",
"Model: OLS Adj. R-squared: 0.976\n",
"Method: Least Squares F-statistic: 671.7\n",
"Date: Sat, 10 Apr 2021 Prob (F-statistic): 5.69e-38\n",
"Time: 01:00:10 Log-Likelihood: -64.643\n",
"No. Observations: 50 AIC: 137.3\n",
"Df Residuals: 46 BIC: 144.9\n",
"Df Model: 3 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"x1 0.9999 0.060 16.689 0.000 0.879 1.121\n",
"x2 2.8909 0.569 5.081 0.000 1.746 4.036\n",
"x3 -3.2232 0.927 -3.477 0.001 -5.089 -1.357\n",
"const 10.1031 0.310 32.573 0.000 9.479 10.727\n",
"==============================================================================\n",
"Omnibus: 2.831 Durbin-Watson: 1.998\n",
"Prob(Omnibus): 0.243 Jarque-Bera (JB): 1.927\n",
"Skew: -0.279 Prob(JB): 0.382\n",
"Kurtosis: 2.217 Cond. No. 96.3\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
]
}
],
"source": [
"res2 = sm.OLS(y, X).fit()\n",
"print(res2.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Draw a plot to compare the true relationship to OLS predictions:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"prstd, iv_l, iv_u = wls_prediction_std(res2)\n",
"\n",
"fig, ax = plt.subplots(figsize=(8,6))\n",
"\n",
"ax.plot(x, y, 'o', label=\"Data\")\n",
"ax.plot(x, y_true, 'b-', label=\"True\")\n",
"ax.plot(x, res2.fittedvalues, 'r--.', label=\"Predicted\")\n",
"ax.plot(x, iv_u, 'r--')\n",
"ax.plot(x, iv_l, 'r--')\n",
"legend = ax.legend(loc=\"best\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Joint hypothesis test\n",
"\n",
"### F test\n",
"\n",
"We want to test the hypothesis that both coefficients on the dummy variables are equal to zero, that is, $R \\times \\beta = 0$. An F test leads us to strongly reject the null hypothesis of identical constant in the 3 groups:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[0 1 0 0]\n",
" [0 0 1 0]]\n",
"<F test: F=array([[145.49268198]]), p=1.2834419617283574e-20, df_denom=46, df_num=2>\n"
]
}
],
"source": [
"R = [[0, 1, 0, 0], [0, 0, 1, 0]]\n",
"print(np.array(R))\n",
"print(res2.f_test(R))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also use formula-like syntax to test hypotheses"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<F test: F=array([[145.49268198]]), p=1.2834419617283514e-20, df_denom=46, df_num=2>\n"
]
}
],
"source": [
"print(res2.f_test(\"x2 = x3 = 0\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Small group effects\n",
"\n",
"If we generate artificial data with smaller group effects, the T test can no longer reject the Null hypothesis: "
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"beta = [1., 0.3, -0.0, 10]\n",
"y_true = np.dot(X, beta)\n",
"y = y_true + np.random.normal(size=nsample)\n",
"\n",
"res3 = sm.OLS(y, X).fit()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<F test: F=array([[1.22491119]]), p=0.3031864410631362, df_denom=46, df_num=2>\n"
]
}
],
"source": [
"print(res3.f_test(R))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<F test: F=array([[1.22491119]]), p=0.3031864410631362, df_denom=46, df_num=2>\n"
]
}
],
"source": [
"print(res3.f_test(\"x2 = x3 = 0\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Multicollinearity\n",
"\n",
"The Longley dataset is well known to have high multicollinearity. That is, the exogenous predictors are highly correlated. This is problematic because it can affect the stability of our coefficient estimates as we make minor changes to model specification. "
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from statsmodels.datasets.longley import load_pandas\n",
"y = load_pandas().endog\n",
"X = load_pandas().exog\n",
"X = sm.add_constant(X)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Fit and summary:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: TOTEMP R-squared: 0.995\n",
"Model: OLS Adj. R-squared: 0.992\n",
"Method: Least Squares F-statistic: 330.3\n",
"Date: Sat, 10 Apr 2021 Prob (F-statistic): 4.98e-10\n",
"Time: 01:00:11 Log-Likelihood: -109.62\n",
"No. Observations: 16 AIC: 233.2\n",
"Df Residuals: 9 BIC: 238.6\n",
"Df Model: 6 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const -3.482e+06 8.9e+05 -3.911 0.004 -5.5e+06 -1.47e+06\n",
"GNPDEFL 15.0619 84.915 0.177 0.863 -177.029 207.153\n",
"GNP -0.0358 0.033 -1.070 0.313 -0.112 0.040\n",
"UNEMP -2.0202 0.488 -4.136 0.003 -3.125 -0.915\n",
"ARMED -1.0332 0.214 -4.822 0.001 -1.518 -0.549\n",
"POP -0.0511 0.226 -0.226 0.826 -0.563 0.460\n",
"YEAR 1829.1515 455.478 4.016 0.003 798.788 2859.515\n",
"==============================================================================\n",
"Omnibus: 0.749 Durbin-Watson: 2.559\n",
"Prob(Omnibus): 0.688 Jarque-Bera (JB): 0.684\n",
"Skew: 0.420 Prob(JB): 0.710\n",
"Kurtosis: 2.434 Cond. No. 4.86e+09\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"[2] The condition number is large, 4.86e+09. This might indicate that there are\n",
"strong multicollinearity or other numerical problems.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib/python3/dist-packages/scipy/stats/stats.py:1394: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=16\n",
" \"anyway, n=%i\" % int(n))\n"
]
}
],
"source": [
"ols_model = sm.OLS(y, X)\n",
"ols_results = ols_model.fit()\n",
"print(ols_results.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Condition number\n",
"\n",
"One way to assess multicollinearity is to compute the condition number. Values over 20 are worrisome (see Greene 4.9). The first step is to normalize the independent variables to have unit length: "
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"norm_x = X.values\n",
"for i, name in enumerate(X):\n",
" if name == \"const\":\n",
" continue\n",
" norm_x[:,i] = X[name]/np.linalg.norm(X[name])\n",
"norm_xtx = np.dot(norm_x.T,norm_x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then, we take the square root of the ratio of the biggest to the smallest eigen values. "
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"56240.87062245847\n"
]
}
],
"source": [
"eigs = np.linalg.eigvals(norm_xtx)\n",
"condition_number = np.sqrt(eigs.max() / eigs.min())\n",
"print(condition_number)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Dropping an observation\n",
"\n",
"Greene also points out that dropping a single observation can have a dramatic effect on the coefficient estimates: "
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Percentage change -13.35%\n",
"Percentage change -55778.29%\n",
"Percentage change 121935204.58%\n",
"Percentage change 1282863.00%\n",
"Percentage change 998687.27%\n",
"Percentage change -47263797.15%\n",
"Percentage change 677415.26%\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib/python3/dist-packages/ipykernel_launcher.py:1: DeprecationWarning: \n",
".ix is deprecated. Please use\n",
".loc for label based indexing or\n",
".iloc for positional indexing\n",
"\n",
"See the documentation here:\n",
"http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
" \"\"\"Entry point for launching an IPython kernel.\n"
]
}
],
"source": [
"ols_results2 = sm.OLS(y.ix[:14], X.ix[:14]).fit()\n",
"print(\"Percentage change %4.2f%%\\n\"*7 % tuple([i for i in (ols_results2.params - ols_results.params)/ols_results.params*100]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also look at formal statistics for this such as the DFBETAS -- a standardized measure of how much each coefficient changes when that observation is left out."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"infl = ols_results.get_influence()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In general we may consider DBETAS in absolute value greater than $2/\\sqrt{N}$ to be influential observations"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.5"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"2./len(X)**.5"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" dfb_const dfb_GNPDEFL dfb_GNP dfb_UNEMP dfb_ARMED \\\n",
"0 -0.016406 -169.822675 1.673981e+06 54490.318088 51447.824036 \n",
"1 -0.020608 -187.251727 1.829990e+06 54495.312977 52659.808664 \n",
"2 -0.008382 -65.417834 1.587601e+06 52002.330476 49078.352378 \n",
"3 0.018093 288.503914 1.155359e+06 56211.331922 60350.723082 \n",
"4 1.871260 -171.109595 4.498197e+06 82532.785818 71034.429294 \n",
"5 -0.321373 -104.123822 1.398891e+06 52559.760056 47486.527649 \n",
"6 0.315945 -169.413317 2.364827e+06 59754.651394 50371.817827 \n",
"7 0.015816 -69.343793 1.641243e+06 51849.056936 48628.749338 \n",
"8 -0.004019 -86.903523 1.649443e+06 52023.265116 49114.178265 \n",
"9 -1.018242 -201.315802 1.371257e+06 56432.027292 53997.742487 \n",
"10 0.030947 -78.359439 1.658753e+06 52254.848135 49341.055289 \n",
"11 0.005987 -100.926843 1.662425e+06 51744.606934 48968.560299 \n",
"12 -0.135883 -32.093127 1.245487e+06 50203.467593 51148.376274 \n",
"13 0.032736 -78.513866 1.648417e+06 52509.194459 50212.844641 \n",
"14 0.305868 -16.833121 1.829996e+06 60975.868083 58263.878679 \n",
"15 -0.538323 102.027105 1.344844e+06 54721.897640 49660.474568 \n",
"\n",
" dfb_POP dfb_YEAR \n",
"0 207954.113589 -31969.158503 \n",
"1 25343.938289 -29760.155888 \n",
"2 107465.770565 -29593.195253 \n",
"3 456190.215133 -36213.129569 \n",
"4 -389122.401699 -49905.782854 \n",
"5 144354.586054 -28985.057609 \n",
"6 -107413.074918 -32984.462465 \n",
"7 92843.959345 -29724.975873 \n",
"8 83931.635336 -29563.619222 \n",
"9 18392.575057 -29203.217108 \n",
"10 93617.648517 -29846.022426 \n",
"11 95414.217290 -29690.904188 \n",
"12 258559.048569 -29296.334617 \n",
"13 104434.061226 -30025.564763 \n",
"14 275103.677859 -36060.612522 \n",
"15 -110176.960671 -28053.834556 \n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/stats/outliers_influence.py:309: RuntimeWarning: invalid value encountered in sqrt\n",
" return self.results.resid / sigma / np.sqrt(1 - hii)\n",
"/usr/lib/python3/dist-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater\n",
" return (self.a < x) & (x < self.b)\n",
"/usr/lib/python3/dist-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less\n",
" return (self.a < x) & (x < self.b)\n",
"/usr/lib/python3/dist-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal\n",
" cond2 = cond0 & (x <= self.a)\n",
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/stats/outliers_influence.py:323: RuntimeWarning: invalid value encountered in sqrt\n",
" dffits_ = self.resid_studentized_internal * np.sqrt(hii / (1 - hii))\n",
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/stats/outliers_influence.py:352: RuntimeWarning: invalid value encountered in sqrt\n",
" dffits_ = self.resid_studentized_external * np.sqrt(hii / (1 - hii))\n"
]
}
],
"source": [
"print(infl.summary_frame().filter(regex=\"dfb\"))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
}

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1.9 MB

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/srv/reproducible-results/rbuild-debian/tmp.FTzzuDJkiI/dbd-tmp-gqMLHgR/diffoscope_1mz05yxs/tmpz4cepo3j/f2a8ee8a-4bc6-4164-9c45-d95445b2adb8 vs.

Similarity: 0.0%
Differences: {
"replace": {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Box Plots"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following illustrates some options for the boxplot in statsmodels. These include `violin_plot` and `bean_plot`."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
" from pandas.core import datetools\n"
]
}
],
"source": [
"%matplotlib inline\n",
"\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import statsmodels.api as sm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Bean Plots"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following example is taken from the docstring of `beanplot`.\n",
"\n",
"We use the American National Election Survey 1996 dataset, which has Party\n",
"Identification of respondents as independent variable and (among other\n",
"data) age as dependent variable."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data = sm.datasets.anes96.load_pandas()\n",
"party_ID = np.arange(7)\n",
"labels = [\"Strong Democrat\", \"Weak Democrat\", \"Independent-Democrat\",\n",
" \"Independent-Independent\", \"Independent-Republican\",\n",
" \"Weak Republican\", \"Strong Republican\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Group age by party ID, and create a violin plot with it:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Age')"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.rcParams['figure.subplot.bottom'] = 0.23 # keep labels visible\n",
"plt.rcParams['figure.figsize'] = (10.0, 8.0) # make plot larger in notebook\n",
"age = [data.exog['age'][data.endog == id] for id in party_ID]\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111)\n",
"plot_opts={'cutoff_val':5, 'cutoff_type':'abs',\n",
" 'label_fontsize':'small',\n",
" 'label_rotation':30}\n",
"sm.graphics.beanplot(age, ax=ax, labels=labels,\n",
" plot_opts=plot_opts)\n",
"ax.set_xlabel(\"Party identification of respondent.\")\n",
"ax.set_ylabel(\"Age\")\n",
"#plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def beanplot(data, plot_opts={}, jitter=False):\n",
" \"\"\"helper function to try out different plot options\n",
" \"\"\"\n",
" fig = plt.figure()\n",
" ax = fig.add_subplot(111)\n",
" plot_opts_ = {'cutoff_val':5, 'cutoff_type':'abs',\n",
" 'label_fontsize':'small',\n",
" 'label_rotation':30}\n",
" plot_opts_.update(plot_opts)\n",
" sm.graphics.beanplot(data, ax=ax, labels=labels,\n",
" jitter=jitter, plot_opts=plot_opts_)\n",
" ax.set_xlabel(\"Party identification of respondent.\")\n",
" ax.set_ylabel(\"Age\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = beanplot(age, jitter=True)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = beanplot(age, plot_opts={'violin_width': 0.5, 'violin_fc':'#66c2a5'})"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = beanplot(age, plot_opts={'violin_fc':'#66c2a5'})"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = beanplot(age, plot_opts={'bean_size': 0.2, 'violin_width': 0.75, 'violin_fc':'#66c2a5'})"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = beanplot(age, jitter=True, plot_opts={'violin_fc':'#66c2a5'})"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = beanplot(age, jitter=True, plot_opts={'violin_width': 0.5, 'violin_fc':'#66c2a5'})"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Advanced Box Plots"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Based of example script `example_enhanced_boxplots.py` (by Ralf Gommers)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from __future__ import print_function\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import statsmodels.api as sm\n",
"\n",
"\n",
"# Necessary to make horizontal axis labels fit\n",
"plt.rcParams['figure.subplot.bottom'] = 0.23\n",
"\n",
"data = sm.datasets.anes96.load_pandas()\n",
"party_ID = np.arange(7)\n",
"labels = [\"Strong Democrat\", \"Weak Democrat\", \"Independent-Democrat\",\n",
" \"Independent-Independent\", \"Independent-Republican\",\n",
" \"Weak Republican\", \"Strong Republican\"]\n",
"\n",
"# Group age by party ID.\n",
"age = [data.exog['age'][data.endog == id] for id in party_ID]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, \"US national election '96 - Age & Party Identification\")"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Create a violin plot.\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111)\n",
"\n",
"sm.graphics.violinplot(age, ax=ax, labels=labels,\n",
" plot_opts={'cutoff_val':5, 'cutoff_type':'abs',\n",
" 'label_fontsize':'small',\n",
" 'label_rotation':30})\n",
"\n",
"ax.set_xlabel(\"Party identification of respondent.\")\n",
"ax.set_ylabel(\"Age\")\n",
"ax.set_title(\"US national election '96 - Age & Party Identification\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, \"US national election '96 - Age & Party Identification\")"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Create a bean plot.\n",
"fig2 = plt.figure()\n",
"ax = fig2.add_subplot(111)\n",
"\n",
"sm.graphics.beanplot(age, ax=ax, labels=labels,\n",
" plot_opts={'cutoff_val':5, 'cutoff_type':'abs',\n",
" 'label_fontsize':'small',\n",
" 'label_rotation':30})\n",
"\n",
"ax.set_xlabel(\"Party identification of respondent.\")\n",
"ax.set_ylabel(\"Age\")\n",
"ax.set_title(\"US national election '96 - Age & Party Identification\")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, \"US national election '96 - Age & Party Identification\")"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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CIAiCMBGknBEEQRAEQZgIUs4IgiAIgiBMBClnBEEQBEEQJoKUM4IgCIIgCBNByhlBEARBEISJIOWMIAiCIAjCRBjWvmmcKZfLOH36NBqNBpxOJ2677TbYbKQHE4NhaWkJy8vLAIDnPe95CIVCQ5aIIAiC0AJpDAawubmJU6dOIR6P46GHHkIulxu2SMQY8eijj+LcuXN47LHHcOnSpWGLQxAEQWiElDMDKBaLmJ6exote9CIEAgEUi8Vhi0SMEZVKBc9+9rOxsLCAarU6bHEIgiAIjZByZgClUglutxsA4PF4UCqVhiwRMU5UKhW4XC44HA5UKpVhi0MQBEFohJQzAygWi3C5XAAAl8uFQqEwZImIcaJSqcDpdMLpdKJcLg9bHIIgCEIjpJwZQD6fh8fjASBYzsitSQwSyXLmdDrJckYQBGFBSDkzgHw+j4mJCQCA2+1GPp8fskTEOFEul+F0OuFyuUg5IwiCsCCknBlALpdrWs4mJiZIOSMGSqVSgcPhgNPpJKstQRCEBaE6ZwaQy+Xg9XoBCMpZJpMZskTEuMA5R7lcJrcmMRSSyeSeF4JoNNpMjiIIQj2knBlAoVDYo5xtbm4OWSJiXKjVarDb7bDb7XC5XJQQQAyU9773vZiYmABjDPl8HrfffjvuvPPOYYtFEJaDlDMDyGazzZgzr9dLRWiJgSFlagIgyxkxUOr1OorFIt7whjeAMYZvf/vb9OwjiB6hmDOdqdVqqFarzZgzr9eLbDY7ZKmIcaFVOSPLGTFIpPqOjDEAlKlOEP1AljOdkTI1pQeUz+dDLpcD57y5jSCMQsrUBEg5IwZLqVRq1ncEBOWMajwSg4Jzvqcjis1mg8NhXRXHupKblNZkAABwOBxwOBwolUpNVydBGIWUDACAitASA6VYLDY9BgApZ8Rg+cIXvoBHH30UNpvgELTb7XjrW9/afFm1GqSc6UxrAVqJiYkJ5HI5Us4Iw5HKaACC5axaraLRaDQfWARhFMVicU9mJilnxCCJx+O45557cOzYMQDAgw8+iEwmg2g0OmTJeoOe2DqTzWbh8/n2bKOkAGJQtFrOGGOUFEAMDFLOiGGSTqfh9/ub//f5fJaO9yblTGcymcw+yxklBRCDolU5AyjujBgccspZpVJBo9EYolTEuJDJZPYpZ1auMUrKmc60TxBAUM6sPEkI69CaEABQ3BkxOPL5/B7ljDEGl8uFUqk0RKmIcaBaraJcLu+J9/Z6vUin00OUqj9IOdOZdtMqQMoZMThKpdIe5YwsZ8SgKBQK+7wGbrebymkQhpPJZOD1evdURPD7/aScEVfJZDL7Ys6sPkkI69BezoCUM2JQyClnFHdGDAI5o0gwGEQymRySRP1DypnOpNNpBAIBAIBr+RQmP/FaHMg/Q8oZMRCCW4/hRd/7P3AtnwJAbk1icJDljBgWcspZIBBAKpUakkT9Q8qZjkjtSyS/t//MA3CkVnDw/EdIOSMGwjXLn4K3uA3/mQcAkOWMGBxyZYRIOSMGQSqV2uexCoVCpJwRAtlsFl6vt1lTKnfiPtTCh5F/4f3IZrPgnA9ZQmLU+e7kj6AcOIDcifsAgAKyiYFRKBT21XIktyYxCJLJJEKh0J5tUrawVV9OqQitjqTT6T3ae2XxDiQW7wAAuFwXkcvlmi5PgjCCy46jmL77NVhYWAAguDVJOSMGQSe3JilnhNHs7u5ifn5+zzbGWNO1OTs7OyTJeocsZzoilwwgYfWaK4Q1KJfLexZIcisRg6DRaKBSqcgmBFABbsJokskkgsHgvu1WTgogy5mOyPm9JQKBANLpNA4cODBgqaxHJpPB17/+dTQaDdx44404fvz4sEWyDO2FQN1uN70UEIYjzbvWUgaA0Lpue3t7SFJZi1qthoceeqjZvNvpdOKee+6xdPPuQVCv15HJZGSVMysnBZDlTEdSqZTsBAGonIYW1tfXce7cOezu7uLs2bPDFscycM5lq7ST5YwwGjmXJkAxZ1pIpVJ44okn4Ha74Xa78cQTT1hWsRgkmUwGExMTsNvt+z4LBALY3d0dglT9Q8qZjiSTyY4xZX6/H4lEYsASWZNyuYypqSkcPXqUFAsN1Go1MMb2vGmTW5MYBO0vBRITExOknKmkUCggFArh5ptvxs0334xQKIR8Pj9ssUxPJ5cmAITDYcuuu6Sc6Ugikeg4SUKhkGV934NGKqTqdrspmF0D7QVoAVLOiMGgZDkjBUMd+Xx+T7YrKbbqkMvUlAiFQojH4wOWSB9IOdMJzrmiBm/lwMRBUywW4XQ6SbHQiJz1gtyaxCDopJxFYk/iZ+J/B5z/yhCkshbtvUlJsVVHIpHo6LGSap1ZsYwVKWc6USwWwRiTfUABgnJmVfPqoCkWi/B4PPB4PGQ500CpVNqnnJGCSwyCYrG4z2oLAOEn/xaRehKNL//pEKSyFtlsdp/lLJvNDlEiaxCLxTpazlwuF+x2uyUzhkk50wmleDNAeAtqNBq0UKpAUs5IsdCGnOXM5XKhVquhXq8PSSpiHGh3yUnkbn0z0o4oCrf9pyFIZS2y2eyebH+v10vKmQoSiURH5QwQrGdWNIyQcqYTSpmagFAQj1yb6pCUDKmApRVN0sNATjljjJGSSxhOJ+WssngH/nnuV5Cde8EQpLIWUocZCZ/PR8pZFzjniMfjCIfDHfcJBAKWXHdJOdOJxjNfwivW3tNsON2K1AD9erZmyUkyaKT4FYfDAZvN1qz7QyjjvHIK91x55745SHFnhNHkcrl9ypn03DveWKbAdhVkMpk9zbupcHl3pOdap7nnWj5l2XIapJzpxMzZDyJQ3W02nG5FaoD+/PRXLWleHTStwcWkWKhn9gcfRKAS3zcHqdYUYTRylrPW5x7Nv+6Q5Uw7nZLwpLnnP/MAwuEwKWfjzOPBl6EWPNhsON2K1AB99drXWDatd5C0BraTS0495xZeiZJ3ft8cJAWXMBo55Ux67j1z4JWknHWBc45cLrfPcpbNZimsQ4FO5aukuZc7cZ9llTPqC6ET3y/P4JpXvV82MFFqgF5fXkbs+98fgnTWolAoNB/0pJyp54rrGKp3vAPXLV63Zzul5BNGI+fWlJ57qUceAbNgttwgKRaLsNlscDqdzW1OpxMOhwPFYnGPRY24SqcyGtLcA4BQLkfK2bhSrVaRz+ebGrxr+RT8Zx5A7sR9qIgTBAAikYglJ8kgqVar4Jw3q9yTcqaeTkHZbreblDPCMOr1umzTcwmv12vJUgaDpD1TU0LK2CTlTJ72TM3WtRcAgt/4I0xxjgP556JcLst2sTAr5NbUgd3dXQSDwWbT31Z/dyuBQACFQgHlcnkYYloCqV6SNJbkklNPJ+XM4/HQ4kgYRj6fh8fj2df0XIKUs+50Us4oKUCZeDy+RzlrXXv9Zx6AY/c8HLsX8LLGY5brU0rKmQ60p/K2+rtbYYxZ1v89KNorjbtcLrL6qCSfz8u+YXu9XhpDwjA6zTsJUs66k8lkZJUzv99PypkC7Zaz1rU3d+I+1KLXoTZ1HX4w+3LLJeORW1MHYrHYHuWs1d/dTjgcRjwex8LCwqDEsxRSAVoJ6i+nDqnAsZxryev1Ym1tbQhSEeNAJ4utBCln3UmlUh1frNLp9BAkMj/1eh3ZbHZPzFn72ht/vfB7+mtfs1wZK7Kc6cDOzo5iEbxWQqEQYrGYwRJZl/ZCquTWVEehUIDb7YbNtv+WpkrjhJFIbs1O0PzrTjqdlg1sDwQClnPHDYp0Og2v1wu73d51XysWgCflTAc2NzcxNTWlat9oNIrNzU2DJbIu7W7NiYkJeutWQS6X6+ha8vl8NIaEYWQyGUXLmdvtRr1ep2LSCnRq/2fV6vaDoFvLxFZCoZDlwolIOeuTRqOB3d1dTE5Oqto/Go1iZ2fHYKmsi2QBkqACqupQUs6kmLNGozFgqYhxoFs2IWOMmnh3IZVKyZZhCgaDZDnrQCqVUq2cBYNBy8WckXLWJ4lEAj6fb099GiUikQiSySS9RXag3UUyMTFBwewqkKszJWGz2Zp9SglCb9rbDslB1tvOcM6RSqVkx1Bya1Ih2v10GjM5rDiOpJz1ydbWlmqrmWv5FKY/9Xo8171DcWcdyOfzOJB/ptkXjSxn6ujmWqK4H8Io/Bun8fzHfke2r7AEWc46k8/n4XA44HK5mj0hfWdOCv9u/BucTicptjJ0at0kh8fjAeccpVLJYKn0g5SzPtGinEk1WF5UegTb29sGS2ZNCoUCrrnyqWatGikhwEpvPMOgm/WC+vQRRvGc7S9gorgl21dYgup1dabVPdes03X6ZPMZSEkB8mhRzhhj8Pv9lsp8JeWsTzY2NjA9Pa1qX6kGy/Kxn6OkgA7k83nsPPf1zVo1drsdDoeDCvd2IZ1Ok3JGDBzOOb5hP4GGTF3HVqy2MA6SVvdcs07Xrfc3n4GknMmjxa0JWO8Fgeqc9cn6+jpuueUWVftKNVhsy8tYox6bshSLRdSO/igSz//p5jbJtamUrj/upNNpXHfddR0/n5iYoMWR0J1isYhl93Ekf/5PFPfz+/0UytGB1v6QrXW68ifuBwAEVk5ZLpjdaDjn+2qcdcNqyhlZzvogl8uhUqnIZtkAaMYPtMZiuJZP4eZHfwvu5YfJVSdDoVBAJPbknnGj/prd6VQnSSIQCJByRuiOWusFZR12pr3KfTuhUIiUszYKhQLsdrvqRDxAUM6s9Awky1kfbGxsYGpqqmNPudY+X9LbkP/MA3Bk1/Hi6hZ2d3dV10cbBzjnKBaLiH73H+BIrzbHjboEKFOv15HP5xUXyUAgQK50BXZ2dvDFL36x4wvT7bffjmuuuWbAUpmfbu50CarX1ZlYLIYbb7yx4+ehUAirq6sDlMj89NIM3mqtsMhy1gfr6+uKypVcj01p2zPzr8DGxsYgxLQMpVJJyEy69c17xo0sZ8pks1lMTEzIdgeQoMVRmStXriCTyWBxcXHfT71ex9NPPz1sEU2JWuXM7/cjn8+jXq8PQCpr0c1yRv2Y99OpUbwSZDkbI1ZWVnDkyJGOn8v12JS21R57DKurq7jpppsMltI6SK2b2seNlDNl1CyQkluTc97R0jvOJBIJLCws4Pjx4/s+45xjZWVlCFKZn93dXVVxPzabrbk4qs1uHweq1SpyuZxi1mEgEEA+n0e1WtXkxhtllOo6dsLv91sqKYosZz3COcfq6irm5+d7On5+fh7Ly8s6S2Vt2vtqSrhcLnJrKiCnnLXHO7rdbjDGSMntQCKR6LhAWrEv36DY3d1V3VfYilXajSb35KfxhsrH4Vn9Vsd9bDYb/H4/zcEWJG+BXFx3JyghYEyQbhQtqbytzMzMYGtrC7VaTU+xLE0n5YwK0SqTTCb3zcPWeEcJSsnvTDKZ7OhakpQKSuDZz+7urqJLrhVyre/H+fC7MMlTijXiAHJttiO1q5N7znVCil22yn1MylmPrKysYG5urufjXS4XQqEQxZ21UCqVZJUzcmsqI9cAWC7ekZSzzijF/UjuE5qDe2k0GopKbTuhUAjxeNxgqazF0pGfQXFiTrFGHCC8INDYXUVSzuSec52w2+1wuVyWuY8p5qxHrly50pdyBgBzc3NYWVnB4cOHdZLK2ihZzra2toYgkTVIJBK4/vrr92yTi3ck14g85XIZ1WpVMYZFUmy1ZoiNMplMBm63Gy6XS9X+kUgEV65cMVYoi3HRtoj12/4ENy0qxx5HIhGqE9dCLpfDwYMHUVl89r7nnBIej6ep2Jkdspz1yNLSEhYWFvo6x/z8PJaWlnSSyPoUCgVZ5YxKaSjTLdtLgtxK8kiWR6VECRq7/WhxaQKCgkHWn71sb28jEol03W9ycpJa/rWQz+c1JwQAwlpiFcsZKWc9UCgUkEqlMDMz09d5Dh48iCtXrljGB240nSxn5NbsTKPRQCqVUtVjLhQKUdyKDGp69JFytp9YLKZJOQuFQkgmk1ROQ4Rzjlgshmg0CkC+aLnE5OQkYrEYrRUAcP4r+InVd2My/u3mJt+Zk5h9943wnTmpeKjb7bbMiz4pZz2wvLyMubk5xbpSavD7/XC5XNjZ2dFJMmujFHNWKpWGIJH5yWQymJiYgMPRPUIhHA5TtpwMiUSia2JPMBi09ywrAAAgAElEQVQkxbYNtVYfCYfDAb/fT3NQJJ/Po9FoNC1ASsHtkhsun88PVEZT8tU/Q7CWwPTZB5ub/KdPwlbOwX9aWTnzeDyWedEn5awHLl++3HMJjXbm5ubItSmSz+fJcqYRuWSATkiWC3r73osayxm10NnP9vZ20+qjlkgkQi+jIrFYDJFIpOlO7xbcHgqFKO4MQP2lv4EEC6Fw6/3Nbblb70fD7UeuZZscLpfLMi/6pJz1wKVLl3Dw4EFdznXgwAFcvHhRl3NZnVKpJNvc3Ol0otFoUNkRGdQoFhJOpxNOp9NShRgHgZrYKbI67oVzjp2dHc3KWTgcJuVMRFLOJCqLdyDxcx9GpUOAu+TaHHdKh2/HR0P3onLk6jjlT9yP7becbTaL7wS5NUeYfD6PRCLRd6amxOHDh3H58mU0Gg1dzmdlOrk1GWNwu90ol8tDkMrcJBIJ1ZYzgAqqyqGmkKrUuJvuU4FcLod6va65hc7U1BSVDxLZ3NzU5BaORCLUHxf71wm1hWhdy6fw4h/8IXzr/2a0iLpAyplGlpaWdIk3k/D7/fB4PHTToXNCAGAtc/Qgicfjqiu0A1SlvR3OOVKpVFfLmcPhgMfjIaujiOTS1NoKbHp6msriiGxtbSn2Zm5namqKxg5XezBLqC1E6z/zAHylHRxZ+qTRIurCUJQzxth/YYz9gDF2ljH2EcaYhzF2lDF2mjF2kTH2McaYuuI5A+bixYs4cOCAruc8cOAALl++rOs5rUgnyxkguORIOduPlvY5gJB1SMrZVbLZbNPd2w0au6tsbW311CMzEokglUqhUqkYIJV14Jxje3u7J+Vs3GNGy+Xyntp6agvR5k7ch7JvAWenf8xoEXVh4MoZY+wAgF8DcAvn/EYAdgCvAfAnAP6Sc34cQBLAGwctWzc45zh//rxis/NeOHToEC5cuKDrOa0G51zRckYZm/vhnGtWzsLhMNWaakFLzB65hK+ysbGhSbGQsNlsiEQiY1+zK5PJwGazaSqG6vV6YbfbLdUf0ggqlcqel6lusXqt+52/81244jpmtIi6MCy3pgPABGPMAcALYBPAnQAke+ODAH5qSLJ1JJFIoFKp9PTGqMShQ4ewvLyMarWq63mtRLVahc1mg91ub25rjSUgt+Z+isUiGo2GbBJFJ6iFzl60ZLtSh4WrrK2tYXZ2tqdjKe5MexkSiUgkMvauzXK5rGjpVopBczqdloldHrhyxjlfB/AOACsQlLI0gCcApDjnUjreGgBZ3yFj7E2MsccZY48POnPl4sWLOHjwoOY4i2643W5Eo1EsLy/rel4rUSqV9rWBaY0lIMvZftRUtm8nEomQa64FNTXOJKiIr0C5XEYymdScqSkxPT099srZ1tZWT+MXjUbHXjlrt5y1oxSD5nK5LONSH4ZbMwLgVQCOAlgA4AOg2gnMOX8f5/wWzvkt09PTBkkpz/nz53Ho0CFDzn3w4MGxdm3KldFojSWgmLP9qG3b1MrExARqtRrVjRNJJBKq3cLhcJiUM1yNN+s1KWpmZgarq6s6S2UtNjY20Mv6NT09jfX1dQMksg6VSkWx6LZSDJrT6STlTIG7ASxxzmOc8yqATwG4HUBYdHMCwEEAppqB9Xodly5d0j3eTGJxcRHPPPOMIee2Au1BnsDeWAKynO1HaxkNQChLQrFTV9HSH5IK0Qqsra311bpuenoasVjMMoukEayvr/c0hjMzM2Of2d9NOVOKQSO3pjIrAF7IGPMywR9zF4CnAHwNwM+K+9wL4NNDkK0jOw9/BL9Y+ihCO4/v2a62xko35ubmkMlkkE6n+zqPVWlNj94Tayb+vpB72jLFAweFFqtPK9Qn8irJZBLz2adU3cM+nw+lUmmslQoAWF1d7Us5czgcmJycHFv3XLlcRiqVQiQS0bx+RCIRpNPpsX5RlV7ke1l7nU6nZWK7hxFzdhpC4P+TAL4vyvA+AL8F4DcYYxcBRAH8/aBlU8J56p2Issw+P7baGivdYIyNtWuzWCw23ZqtYyr9fnz1n8kV14bWxtMS1CdSoFarIZ/PY+bsg6ruYcYYAoEAUqnUgCQ0JysrK323r5uenh5b16YUb2az2TSvHzabbezjziTLWS9rr91uR6PRsEQx6aFka3LOf49zfgPn/EbO+S9wzsuc88uc8xOc8+Oc85/jnJvK9njK+UI0wov7/Nhqa6yo4fDhwzh//nzf57EirQkBrWMq/b55w2tJOWujl5gzgNxzEul0Gj6fD7lb36z6Hvb7/WOtnOVyORSLxZ4yDVuZnZ0dW+WstQxJL+vHuGe7VqtVOJ3OnsaOMWYZ61lnxy3RJJvN4nulabzgjZ/YFwRbWbwDiS71VdRy5MgRPPLII6jX63tKSowDxWKx6dZsH9PE4h0ora+j+MQTwxLPdNRqNeRyOc0xZ4BgOTt37pwBUlkLKdtVyz087paztbU1TE9P952xvrCwgCeffFInqayFNIZAb+vHuCcFSJazXtdeh8OBSqXSsaamWaD2TSq4dOkSFhYWVGcnKfnC5T6TtkXiT8Lv92NtbU032a1CoVBQrNfl8Xgo5qwFyerTPifVxGFQSQiBVCqlqNzKjeW4t7/qN95MIhwOo1QqjWVB1dXV1Z5rxAHjbXUEupfS6PYMdDgclrCckXKmgnPnzuHw4cOq91fyhct91rrt4MGDuHjxoi5yW4lCoYCJiYmOn3s8HnJrttCpsr2aOIxgMIh0Om2JuAsjSSaTijXO5MZy3JWzpaUlLCws9H0exhhmZ2fH7kW03xpxgFDrLJlMWibrUG/6qXMGkHI2MjQaDVy4cEFTCQ0lX7jcZ63bjhw5MpYlNdRazsa9r5xEOp2WVSzUxGFQE2+BbqVI5MZynDNd6/U61tfX+04GkJibmxu7wtubm5uIRqN9ha3YbDZMTU2NbUmNarXac50zwDrKGcWcdWFzcxMul0tTbI+SL1zus9ZtC/U6dnZ2UCgUNPVdszr5fF7Rcma32+FwOFAulzW1KxpVUqmUrHKmNg7D7/cjnU73lFAwKiSTSRw9erTj53JjGQqFxlY529raQiAQ0C1WZ2FhAY8//nj3HUeI9fX1norPtjM1NYW1tTXD6m6amW6Ws27PQKsoZ2Q564LUsgnQVtOs1/pndrsdc3NzuHz5ck/yWhUlt6Y0ltezNYo7E+mlAG0rgUBgbGvqSWhpei7h9XpRLpfHstbZ8vJyX7FS7czOzmJra8sSC6Ve9BtvJjHOcWeSciatC74zJzWttVbpEkDKWRcuXLjQjDfTUleln/pnhw4dGru4MyXLmTSWLyp9i5QzkW7B7N3wer1jnXVYr9eRz+dV99WUYIw1rY7jxpUrV3RzaQLCIjk5OTlWmYcrKyuYm5vr+zxzc3NYWVnRQSLrIZXSaK6xp09qWmvJcjYCVCoVrK6uNi1nWuqq9FP/bHFxEefPnx+b+Kp6vS7bW1NCGsvvT/8Y8vn8gKUzJ5lMpi/lzO/3j2WmnEQmk4HX6+2pP+Q4jh3nHFeuXMGBAwd0Pe/s7OzYKBn5fB6FQqHvGnGA0CmgWCwil8vpIJm1kCxnzTX21vs1rbVWsZxRzJkCKysriEajzeKoWuqq9FP/bHJyEpVKBclkEpOTkz2dw0pIVrNOtZOksUx++ctj+TBqh3OOTCaj2erTit/vH+tClp0SKtQwjpazZDKJRqOh2Q3cjYWFBSwtLeElL3mJruc1I1K8Wb814gDBgisVo73uuut0kM4aNBqNpuWsdY3Nn7hf9Tms0l+TLGcKLC0t6f6mqAbGGBYWFnDlypWBX3sY5HI5xWQACbfbPfYZhgCaD5b2RvFaGEcFo5V+lDOfzzd2LuHV1VXMzc3poli0srCwgOXl5bHwErQWn9WDcYw7k6xm/cxDKbHM7JBypkDxu5/DXRfeAd+Zk5j64Msx9cGX72nG3W+zcyUOHDiAS5cuGXZ+M6FWOfP5fGPnTpIjl8vB6/V2nYdyDeSlfX0+31hbIdPpdM/Z0OPYJWBpaQnPcW2reu4pFdpuP9bn88HtdmNnZ8cQuc2EFG+mdf3otP84KmflcrljpqbacXW73ZZoHE/KWQfK5TKetfl5+Mo7QsDh7nk44uf3NOPut9m5ElIx2nF4o1TroqMMQwFJme02D+UayEv7er1e5HK5sZhfcvSTUDGOtc6WlpbwnK3Pq3rudSu03c7s7OzI1zvjnDetj1rXj077z8/PY3V1dazu4XK53NFjoHZcXS4XKWdWZm1tDd+fuQd1KeAweh1qU9ftacatR7PzToTDYdTr9bF4Q5eCs7sxjoHYckjKWbd5KNdAXtpXesBZwbxvBP0qZ+P0klAsFpFKpVC+/ddUPfe6FdpuZ35+fuRDOJLJJGw2G/x+v+b1o9P+Uvu2cepYUSqVOipnasfV7XZbotsMJQR0YGVlBdUjL0Hijv8JYH/AoV7NzjshtTdZXV3VJbvHzKiN/xm3RbETUk24bkkncg3kW5mYmOjamWFU0SMhgHOuewyWGVldXcX09DSqR1+CxNHugfvdCm23s7CwgO9+97u6yGpW1tfXmz1JtSaLKe0/MzODjY2NvtpBWQkl5UztuJJb0+Jo6SHXSwyammPm5uawtLSk+pxWJZFIqMoC8/l8KBaLqNVqA5DKvBQKheYDqp/4R6u8QRpBOp3u2XImVcgfF6vjysqKrsVn25mcnESxWBxpq7ik4Hai1zXkJzf/GoXvfFYPES1BsVjs+2VSagVodkg5k4FzjpWVFdXKWS8xaGqOWVhYGIsaQGqVM8YYfD7f2FvP8vl88wHVT/yjVR5SelOr1VAqlfpqj+b1ekdamWjF6Kz1cWiC3q34bK9rSLC6i/mnP6yHiJagWCz23T7MKs89Us5k2N3dhdvtVpVBCPRWcFbNMdPT04jFYpaoZtwrjUYDmUxGdf2kcQzGbqf17bGf+MdxtZxls1l4vd6+XJJ+v38syrrU63Wsra3pUtVeiVFOCmg0Gtjc3FS0Pva6hvDJI/gq+2HU63U9RDU9rV6DXrGKckYxZzJobU7bS8FZNcc4HA6EQiFsbW3h0KFDms5vFTKZDDweDxwOdVORlDMh7qLX+JVWrFIpW28k5awfxsVytr29Db/fb3hc4sLCAp588klDrzEsYrEYvF6vosWn1zWksngHdj70IcRiMcMVaDMglRHqB4/Hg0qlgnq9DrvdrpNk+kOWMxn0KhaoJo6gvRbV1Adfjum/vaNZU21mZmake8+pdWlKhEIhxONxAyUyP0pBsXJI80qaUxJWqZStN6ScqWd1dbX5ImAks7Oz2NzcHMl40vaXfaX6g70wPT09Nt0+9FDOGGOWsJ6RcibDxsaGLsqZmjiCfbWods/DkbrSrKkWjUaxtbXVtyxmJZFIIBQKqd4/HA4jFosZKJH5USrEKEdzXolzSsIq9X70JpvNwufz9XWOcSmIrFej7m64XK6ml2DUaH/ZV6o/2AvT09NjU4xWD+UMEDLVzV6Em5SzNjjn2N7extTUVN/nUhNHsK8WVfQ61MJHmjXVRv2taGdnR5NyFolExt5yJrUwUUtzXolzSsLhcIylW1NtXT0lxqXm3urqKubn5wdyrenp6ZFMClhbW9sTb6ZUf7AXZmdnR9q70koul+v7xQoQLN/5fF4HiYyDYs7ayOVyqNfrukwANXEE7fvEX793/2iphO3t7ZGtqbSzs4Njx46p3j8cDiOZTJo+XsBIqtWq6hg9QJhj7fMKGG/lrN8G3uNgOSsWi0in05icnBzI9ebm5rC6uooXvvCFA7neIKjX69ja2trjGu5Wf1Ar09PT2NraGotnYjqd1k05M3tCD1nO2ojH44hEIqZRhDweD5xO58guBDs7O10f/q1xGQ6HA36/f6yqYrdzsHAOi//yK333dnU6nSMZ49MNPdyaM6nv454r7wTOf0UnqcyHVNzUZpNfJqT7MvjQb2L23TfCd+akqvOOU6/IeDwOn88Hl8ulW0/m9pi1uU+/Ac92bo28R6FcLoNzvi/etpdxtULMKClnbezu7iIcDg9bjD2EQiHs7u4OWwzdKZfLyGazXd2a7XEZ4XB4LBold+JE/ptwZtf77u1qt9vHUjnLZDL9K2dnH0SwGgf/6p/pJJX56Ja1Lt2Xvu9/HLZyDv7T6pSzTnFW0WhUaBM1QkkqW1tbzRAZvXoyy8Wsvbj8CDY3N/UQ2bRIL1XthpNextXn85m+NSIpZ23E43FNMVCDYFSVs1gshnA43PHNXKI9LiMSiWB7e3sQIpqSb7luQy10qO/erjabDY1GQyeprIMecSv5F96PpC2C6ot/XSepzEe3TE3pvsw/99VouP3I3Xp/x33ljmufvzabDZOTkyMVY9vaWkmvnsxyMWvLx149UuMmRyeXZi/jaoVWgBRz1kY8HsfBgweHLcYeRrV8xPb2tqqecO0xGtPT02MTACvHJfsRxP6/39eUFCCHzWYbm+KVEvV6vdmbtB8qi3fgU9O/jDfOvQD9pw6Zk/X1ddx8880dP2+9LzP3qLcgKsXiTk9PY3NzE0ePHtUmrEnZ2NjADTfcAKC/moStyMasLS1h85ln+j63mclkMrL9cHsZ10AgQJYzq5FKpVT33NPq627fv73+VKfz+f1+00+kXtjc3Oypqbv0AB9XGo0GPKvf6jt+hTE2dpYzqdF7N2utGqyQjt8rxWIRuVyu6/3pO3NSU7xZN0btxWtra6uja1ivGDTg6jORc973ucxKKpXSpYwGAASDQdOvqaSctZFOp1Vncmn1dbfv315/qtP5rDCRemF9fb2nhsrhcBjZbHYsa3RJBB9/ny7xK2ZJfBkUuVyub6uZxCgrZ1tbW4hGo13nh//0SU3xZt0YpXIauVwOtVqtowtdrxg0QIihqtfrpi8P0Q+JREK3eHCPx9PssWtWSDlroVaroVgsqtbOtfq62/dvrz/V6XxWMMFqhXPec7FfKTZlnOPOsrf8ii7xK+NGPp/X7e3bCrWSemVzc1NVrcfcrfdrijfrRjQaRSKRGIlElZ2dHcXMf71i0ADhJWvUn4m7u7t9l8CRYIyZfl2lmLMW8vk8JiYmVFsTtPq62/eXqz8ldz5pERilWmeJRAIul6tnK8bU1BQ2NjawuLios2TWoLz4YiSueemwxbAcuVxOtz6RHo/H9LWSekVtPGj+xP3In9BHMQOE2nvBYBCxWGxgxW+NIhaLKbqF9YpBk5AKdGupG2klksmkbsoZcNXoYdaepGQ5a0FSzvpB775pgPDAstvtI5VirsZqpjR+o+T+0IrNZpONLdE630ZJ2VeLHve4hNfrHVm35sbGhi5dUnphVCxA29vbPcXU9ko4HB7J9leA4NXKZrOqlDO1z8FgMGjqepmknLWQz+fhdrv7OofefdMkJiYmRsqFoqa5vNL4jWLBSrV0CuTXOt8ajYYugfFWQq/efIA1qoz3QqPRwM7OjirLmRGMSj9hNQW29SQajY5s/cdUKgW/36/qeaX2ORgMBk1dBWG8nsxdKJVKfbs89O6bJuF2u1EsFvs+j1lYXV3tmgygNH7RaBTJZHKkrIlq6aScaZ1vjUZj5Nu9tJPNZnW1nI3SC5NEKpWCy+Xq+0W1VyYnJ0dCOZO6zQyKUe47nEgkVLs01T4Hw+GwqeuHUsxZC+VyeV9rCK3o3TdNwuVyjYwi0mg0sL6+jrvuuktxP6WYDLvdjqmpKWxubuLIkSMGSGleHA6HrHKmNYZlXC1nesWYjGq2ZiwWG6jFp51oNIpHH310aNfXg2q1inw+r2uMVDcCgQAKhQIqlUrf65jZ2N3dVV0cXu1zMBwOIxaL9SuaYYzXk7kL5XJZdWHP9hplwP6aP3rWAHI6nSOjnMXjcXi9XkULhpq4gXGNO7Pb7aqLxyqNY6PR0NRAfRSgmLPu7OzsDLWFXTgcRi6Xs/TzLpFIIBAIdIzpVLove4lbdi2fQvSTr8ONrm1Tx1H1Si+de6Sx8505KTuGoVAI2WzWtJnBpJy1UCqVVCtn/jMPwBE/D8fu+at1y9pq/uhZA8jpdKJSqfR9HjOwtrbWNdhYTdzA7OwslpeX9RbP9GhRzpTGsV6vj51bU8+YM7fbjVqtZtqHe68MOlaqHcaY6V1O3eiWWah0X/YStyztd1vpW6YuD9Er3TJf5WiO3emTsmNot9vh9/uRTCb1FFU3SDlroVqtqrYk5E7ch9rUdahFr7tat6yt5o+eNYBGqUn1yspKX/FmEnNzc2OZFOBwOFQrZ0rjWKvVxspyxjnX1XLGGIPH40GhUNDlfGZh2MoZYP2Wdd06zSjdl73ELUv7nT/4kyOrnGm15jbH7tb7O46hmftWj8+TWQW1Wk21JUGuRll7zR89awCNmnJ2++23K+6jJm4gHA6jXC4jk8kMNLZj2DidTtVzQWkc6/W6boqKFahWq+Cc6xqP4/F4Bh5bZCSc856sFHoTCoVMHQ/UjUQiIdsHUkLpvuwlblk6pvD446iMmFuzUqkgl8tpdmu2jmOndViaZ1L/UzNBlrMWtChng2ZUmlRXKhXEYjHMzMz0fS7GGGZmZsYu7szhcOiiqI+b5UxPq5nExMTESFnOCoUCGo3G0JV2q2ceplKpoSjsZq963wtSpqYRyUtmnmeknLXAOe84AeSaluvVtFbN+Ww220g0qd7Y2EA0GlVUgrWM7czMzNi5NrW4NZWo1+uqYyxHAanpOaDf/StZzkaFRCKBUCjUd3FitQHvnQiHw6ZdNNWQTqebljO95pqa8/j9fqTT6b6uYzZ6SQZoRWnczFzwmJSzFhqNRseHkmzTcp0KzKo5H2NMtiq81VhdXe1qNdMytvPz82OXFKDFranEOCtnet2/oxZzpqWelBJqA947EYlEEIvFLPvMa1XO9Jpras4TCARGTjnr182uNG6Tk5OmnWeknLWg9AXJNi3XsfF0t/ONinK2vLzctc6UlrGdm5vD+vr6SFgV1aKncjZubk1JOdPr/vV4PCNVTkOv5tJqA947IfU4tmLhbc45crkcfD4fAP3mmprz+Hw+ZLPZkVgrJKQG8r2iNG4TExNoNBqmfMEanydzn8g1Ldezaa2a81n9huOcY2VlBc9//vMV99Myth6PBz6fD9vb25ZvlKwWvZSzcYs5KxQKzVgqve7fUat1pqXYpxJaAt47EQgEkEgkdCt9MihKpRIcDkfz3tJrrqk5j91uh9PpRKlUGnrcoF7s7Ozg2muv7fl4pXGTyrbE4/GmMm0WyHLWgl5NoLvFBvQSgzAKTaozmQxqtZrim7nc2HQbr5mZGayvr+sur1lxuVz7lLNe5lStVhsrt2Yul+vank3rOI5az9vWlkNGx9l2IxgMmrYGlRJGJJ4o0V4QfZTmJOe8OSflivN2KjCrRPs8Nmt8IylnLdhsNl2sU91iA3qNQbB6q521tTXMzs4qKplyY9NtvGZnZ7GysqK7vGZFLluzlzk1jm7NblYYreM4apYzKSEAMD7OthuS5cxqtLrPB0F7QfRRUs7S6XSzz6tscd4OBWaVaJ/H4XDYlEkB1l7tdUavuK5usQG9xCAoJStYhdXVVUxPTyvuIzc23cZr3JIC5LI1e5lT46acqWl6rnUcR2khrFQqKJVKHWOl9I6z7UYoFLKkclYqlQbaNL69ILrL5UKpVBrY9Y2ktfisbHFehQKznWifx5OTk9jZ2TFE/n4YnyezChhjugSWd4sN6CUGQanMh1VYWVnBc5/7XMV95Mam23hNTU0hmUyiVCoN9I11WDidzn3Wml7m1Li5NdW4m7SOo9frHRnlTKpqL70EGh1n241gMGjJGobFYnGgyll7QXT3Mw9ZMpFCjlblrFNxXq2F3tvPMzk5iUceeUQHafXF2qu9zkgWCS3NZo2IwWhvmO5aPoWXPv1HiMSe1PU6g6TRaGB9fb1j26ZOsWbtzeXl8Kx+C/+x+gkkTn9Cd7nNiNPpRDTxnb7nXqPRMG3RZSPI5XK6xwJ5PB6USqWRKBCdSqUUq9oPGitbzrp1oeh17eh03J54rBGynDXOfRk/evkvNDWBl0Pp2FAohFwuh2q1qofIukHKWQtSoVetzWb1jsHY10D9zAPwlWNYeObDul5nkOzs7MDr9Xa0bHWMNWtrLt/p2Emehudb79FdbjNit9txw/pn+5574+bWVBNzphXGGNxu90hYKrr1gxw0wWAQmUzGcmVyyuVy1/uq17Wj03Gt251OJ8rlsma5zcjixY/BX4lragIvh9KxNpsNwWDQdD02STlrweFwoNFoaG42q3cMxr4G6ifuQ841hZ0b79X1OoNkfX1dsfhsx1iztubynY6tBg7iyfCduspsVhwOB34w9/K+5169Xh8by1mtVkOtVjPE3TQxMTESSQGJRMJUypndbsfExASy2eywRdFEpVLpajnrde3odFzrdpfLNTLK2TfsJ9DQ2ARejm7HhsNh0/VyHZ/XZhVIWXBqYyuMisFob5heWbwDX7zmN/D8Q8r1wcxMt2SATrFm7c3lOx2b/Kkb8fhnP4u7+5bU/Njtdmz4noXEq97S13nGya0pxZsZkVQzKv01E4lE1wLRgyYQCCCZTOpSe21QlMvlrrGcva4dnY5r3e7YfXIk3JqFQgEX2GGkXvMHzfu21/W223hLDdDNBFnOWpAsZ2bEzE3Z1bC2trbvwa9nzF44HEa5XLbcW3Yv6JUY0mg0LJ9kohYja0+NiuUsmUwiGAyqimsaFFbsFWlEcWctcdAv/M7bMLn7HV2vPwzi8Tie695B9JOvM3zOmTFjczyezCrRq6G0EVjZylGr1bCzs7PPcqZnzB5jDFNTU9jc3Oz7XGZHio3sl3FSzoxIBpAYlebnqVQKoVBIVVzToPD5fJYrRFutVnV/VmuJg54obuP48qd0vf4wiMfjuK348EDmXDQaJeXMzNjtdtNazqwcvL29vY1QKLTP1K93zN709PRYdArQSzkbhfIsajFSOfN6vX5/yS8AACAASURBVJa32Eo1zrxer6q4pkERCoUsp5wZYTnTEgdd8s3jB7Mv1/X6w2BnZwdLR392IHMuEolgd3fXVOu/NVd7gzC7cmZVy9n6+jqi0ei+7XrH7M3Ozo5VMVpCPUYqZz6fD5lMxpBzD4p0Og2/3w/GmKq4pkERDAaxsbEx0Gv2ixEWaS1x0Bdun8Pm6qqu1x8GsVgME8dehsS1v2z4tVwuF1wuF9LpdF9N1vVkPF6bVSLXFkeOYcReWNly1ilTU20dM7XMzMxYsmhlvwxjPlqNTCZjWAPtUbCcyZXRMMO8CgaDlqt1ZmQ3FzXfiV7F1IdNLBbD5OQkAGPnorQO/VLmfch/+zO6n79XSDlrQa3lbBixF1aOOZN6arajto6ZWoLBIKrVquUXSq0MYz5ajUwmY1iBVb/fPxKWM6ltk4QZ5lUgEEAqldKlrd6gMFI5U/Od6BX2MEzq9fqeLF0j56K0DkWrW/Cd+Wvdz98rpJy1oFY5G0bshVXdmrVaDbFYTLaMhto6ZmqRkgKs5gbRSvtC1et81KuXrBXIZDL7lA+98Pl8ln8hSCaT+yxnw3jOteN0OuF2uy03vkYpZ2b4TgZBIpGA3+9veouM/LuldSjnX8RTJorVs6afzCDsdruqbM1hxF5Y1XK2s7ODYDAo65JVW8dMC9PT09jc3MT111+v63nNRPtc6HU+jor7Qw1STJUReL1elEolQwLBB0UikdgXFzqM55wcgUAA6XQawWBw2KIMHbN8J0YTj8ebPTUBY/9uaR1aXl7GU2fP4nZDrqIdspy10M1yZlRvLzVY1XK2ubmpWHxWCa1j5lo+hbsvvgO1px7q6XpWoV6v6/JmbuYEGD3hnCObzRqmnDHGLB93ZuZCr36/H6lUathiaEKySOsdVyth1HnNQiwWM1wZb19fJicnTVWIlpSzFrr56o3q7aUGq5Y9WFtbw9TUVE/Hah0zqQfpNSNQ40cJvbLBGGOmreunJ/l8Hi6Xy1CrltVdm3JuTbPg9/stVU6jNVzAf+YBOHbPwxHXJ65WQum8nHPD3KqDYmdnRzbDX0/a1xe/349SqWSa7grWW+0NpJslwcjeXt2wasHQbj01ldA6ZrkT94FHFnHKcetIFAXthF5FLh0OB6rVqg4SmRsjkwEkrFjJXqJaraJQKBg+Rr1itYzNVsUod+I+1KLXCTFNOsZLKZ3Xqi/yrezs7Bhe0qJ9fWGMIRQKIR6PG3pdtVgzQMIgulnO+vF79+szt+INV6/XsbW11bNbU+uYSftn/umfsLW1hWPHjvV0XbNTqVR0sQKNi3JmZLyZhM/ns5zrTULK1DTr8yUUCuH8+fPDFkM1reuIEXG13c5rdcsZ53xPGQ2jkFtfwuEw4vE4Dh48aOi11WDOu3FI6Dmh9a7LYsUbLh6Pw+fzCQX+BlgzadQzNqvVKuZzT/c9nuOinDXOfRmvWHuPprHSOl8DgYClrDutyNU4a6eX+1evez4UCllqbOV6NA/y+WfV+GQJqU+tmqLRWnqOqtkvHA6bJu6MlLMW9FR+9K7LYkXL2ebmZjPebJA1k6anp0e6GG2xWMT1a5/uezxdLhfK5bKOkpmT6e9/AMHarqax0jpfg8GgpeKiWlETb9bL/avXPR8MBpFKpSyTvCKX9T/I55+VC5YDwku9Wpemlp6javaLRqPY3t5WLauRWGu1Nxg9SwvoXZfFivWo1tfXm8rZIOvzzM7OjrTlrFQqYfXa1/Q9ni6XyzTBr0byncjdqPgPahorrfM1HA5byrrTihrlrJf7V6973uFwWKrWmZzlbJDPv0ajYXnlrLWMhhJaeo6q2W9yctI0DdCt+w0ahF7WMyPqsljNrbm2toabb74ZwGDr80QiEaTTaZRKJXg8noFcc5AUi0UUjt+GxEte29d5xkU5+0F1DrM/8T5NsY9a56tkObNi4k48Hsfc3JziPr3cv3re89L4mrXcRys2m21fG8BBPv/q9brl5mAr29vbqpUzLT1H1ewXDoeRTCZN4Rq27jdoAGa3TpldvlYajQY2NjZ6TgboB5vNhsnJSWxubg782oOgUCjoonS6XC4UCgUdJDIvjUYDiUTC8EXd6XTC4/FYso2TFZQeK5XTcDqdqno0G0WtVoPT6Rza9ftlEGU0OuFwOOD3+01hBSflrI1DpfOKgYNaAhDligR2O9535iRm330jfGdO7jnmdfkPw3bxaz38RcNhd3cXHo9HMahTz2DO9n2mp6dH1rWZy+V0aeJt9cKpashkMnC73XC5XIZfKxgMYnd31/Dr6E0ikVBtqdCbTvd2+/ZAIGAp5axer+/7G3pNCtB6XL1et7Rytr293TVTU2591SvpIhKJmCIpgJSzFjjneEHm64qBg5oCEGWaenc73n/6JGzlHPynT+45JtJIwf6Nv+jhrxoOajoD6BnM2b7P9PQ01tfXtQtucjjnyOfzuilnUmbUqKIlfqVfwuGw5ZSzYrGIWq02NPd/p3u7fXsoFLLM2DocDtRqtf1FTntMCtB6nJXbiJVKJRSLxa7dAeTWV72SLsLhsCmSAkg5a6HRaODJ8F2KgYOaAhBlmnp3Oz536/1ouP3I3Xr/nmNS9ggqd/x6D3/VcFDTGUDPYM72febm5kYyY7NaraLRaOjyZjwOylksFhuocmaGh7oWkskkgsHg0OJZO93b7dul+lNWQLKctf8NvSYFaD2uVqsNxFJsBNL92m0+yq2veiVdmCUpwJrqtUE0Gg2s+25A4mf+Z8d9tAQgyhUJ7HZ8/sT9yJ+4f8+2yuId+GjoXvz60Zd2va5ZWF1dbSYDdELPYM72fSYnJ5FKpUYuKSCTycDn8+mymFq5qr1aBlFpXCIajeKpp54ayLX0Ythtmzrd2+3brZQNKylnlcWX7vkbek0K0Hqcld2aOzs7qorPyq2veiVdRKNRnD17tu/z9AtZzlrQq6F0K3r5wW02m2X6IErJAL20bZIbr17G0GazIRqNjlxSgJ6tiAKBADKZjKUSTbSytbWlqbdrP/drNBrF1taWpcYzkUgY2mBar+efz+dDqVSyRF2+TgkB/RTy9Z05qfpYK9c5297eHtjLVKfvY3JyEru7u0Nfb0k5a8GI9Fm9/OByhQ3NSiwWg9fr7cliJTdevY7hKBajlSxneuBwOOB0Oke2DynnfE8hZDX0c7/6/X7U63VLjefu7q6hmZp6Pf8YY5ZJCpAsZ+30Vcj39EnVx1o5W3Nra2tgmZqdvg+n0wmfzzd0S+1QlDPGWJgx9knG2DOMsacZY7cxxiYZY19ijF0Q/x2M+txCrVbTXTnTyw9ut9uHmp6thX5KaMiNV69jODs7i9XV1Z7kMCvJZFI35QwQrGdW7QnZjXQ6DYfDoaoNjEQ/9ytjDJOTk9ja2tJ87LCIx+OGKmd6Fl8NhUKWUc7kntV9FfK99X7Vx1pdOdPyMtUPSt+HGeLOhmX7fBeAf+Wc/yxjzAXAC+CtAL7COf9jxthvA/htAL81SKGMUM708oNbSTlbWVnpyaUJyI9Xr2M4NzeHxx9/vCc5zIre2YdScU8zNPrVG61WM6D/+3Vqagqbm5s4fvx4z+cYJLu7u4YmTOhdiNYKGZudlLN+C/m2xyJ3wqrKWT6fR6VSGVgMpNL3EYlEsL29jec85zkDkUWOgVvOGGMhAC8B8PcAwDmvcM5TAF4F4EFxtwcB/NSgZatWq3376o1qcGulJtUrKytdK45LGNkQOBwOo1QqjVQtL70XU6sseL3Q2j5sUFjJlV6r1ZDNZg2NOZPQ4z4PBoOWyNikIrS9ISXv6BX33c+cm5qaGnqdzGG4NY8CiAF4P2Ps24yxv2OM+QDMcs6l6O0tALNyBzPG3sQYe5wx9rjeheIqlUrfyplRDW4dDgcqlYqu5zSCSqWCnZ0d1ZYzIxsCM8YwMzNjmcVSDXorZ1YqUaCV1dXVni24vTI3N2cZV7qUqTmIVj963OdWydgk5aw39I4362fOTU9PDz2ZbBjKmQPADwP4a8758wDkIbgwm3Ah3Uk25Ylz/j7O+S2c81v0bg1UrVb7ntRGNbh1Op2WUM42NjYQjUZVK7lGNwSemZnBysqKIeceNIVCAZVKRdeYs0gkMvTYCiPgnGN1dRULCwsDvW4kEkGhULBE/bhBltHQ4z63youEVIR2WJByJtDPnAuHw8hkMkPNDh5GzNkagDXO+Wnx/5+EoJxtM8bmOeebjLF5AANfMUqlUt+T2qgGt1ZRzrRaK4xuCDw/P2+KmjV6IMWb6VnuRQp85ZwPrRCpEcTjcTidTl06KWih1Vp7ww03DPTaWhlEz1EJPe7zYDCITCZjiqbUSgzbcmbVUhobGxs4ceKEbufrZ87ZbLbmi+uhQ4d0k0mTDIO+IOd8C8AqY+x6cdNdAJ4C8BkA94rb7gXw6UHLVigU4Ha7FfcxMkZK8bouF0ql0kCv2QvLy8uq480kjBzTubk5rK+vW6YMiRKxWEz3GkATExOw2+2WbNitxDBcmhKzs7OWsNbG4/GBxJvpxcTaI/iPlY8j9+TAlwZNOByOjs8bI5910rkPFc9bznLWaDSws7NjSIxoL7XiXMun8PPJ96Pwnc/qLo9ahlXn7C0APswY+x6AHwLwdgB/DOBHGGMXANwt/n+glMvlrm0vjIyRUsLpdJpeOeOcY3l5WbMrycgx9Xg88Pv9lipv0InNzU1DCjSaIW1cb5aWlgbu0pQ4cOAALl++PJRra2GQfUf1wH/mAUR4Go5T7xy2KIo4nc6OyVtGPuukc7+w8LDllLPd3V1MTEx0NY70Qi+14vxnHkConkT423+nuzxqGYpyxjn/jhg3dhPn/Kc450nO+S7n/C7O+bWc87s55wOP/CwWi12VM6NjpDrhdrtRLBYHek2tSMG6Wt/GjR7Tubk5S1gyutFr14VuTE5ODj34VW8uXbo0tPIg8/Pz2NzcNH0YQiKRsJRyljtxH4oTs1g59uphi6KIZDmT6xRh5LNOOvc3nScs59Y0svhsL7XicifuQy10CP/mfbEhMqnBWt+gwRQKha4FK42OkeqEx+MxfbHQ1dVVzM7KJtkqYvSYzs3NYXl5Gbfddpth1zAaqdr9nXfeqfu5zZCZpCdST1U1PfqMwOl0IhKJYHV1FceOHRuKDN1oNBpIJpMDiznTg8riHXjs+b8Ph8OBZw1bGAVsNhsYY2g0Gvti44x81lUW70Ds4Itw+YEHBpKBqyebm5uGKWe91IqrLN6B7KtP4MkPfACvbDSGMp7W+gYNplgsaqomPoj4M9fyKUx98OW49VtvQXDb3AVVl5aWMD8/D0Db2PQzjmqOldxMVup52E4qlYLD4ZANcG8dg/bxUDM+MzMzWF9fN0z2QXP58mUcOHBgqAkOBw8exKVLl4Z2/W5kMhl4PJ6uFhaluTUMIpGIJerydcvYNGosa7Wa5axmgFCTsFv1Bd+Zk5h9943wnTkJwPj11+12Y2JiYmjlW0g5ayGfz2tSzgYRf+Y/8wAcu+fhyy7jWRvDC05Uw9LSUtOVpGVs+upnqOLYUCgEzrklWr90QqmgausYtI+HmvGZnJxsWptGgQsXLgy948Hhw4dx4cKFocqghNqG50pzaxiEQiFLKGdOpxONRqPj50aNZaPRsFy8Gecc6+vrXUM2/KdPwlbOwX9aUM4GMR+j0ejQvAqknInU63WUSiVNzboHEX+WO3EfatHrUIkcwyMTw/N/dyOXyyGTyTQVCC1j0884qjmWMYbZ2VksLy9rPr9ZWFtb6/jwah2D9vFQMz42mw3RaHToFbH1oNFo4MKFC1hcXByqHPPz84jFYqZtgp5MJlUpZ0pzaxhI/TXNbgW32+2KGeJGjaXZy4zIkc1m0Wg04Pf7FffL3Xo/Gm4/crcKrslBzMdhPhetZ/80iEKhAI/Ho8kVMoj4s8riHYi//g5UKhU8/f73G3qtfpBKaEjjp2Vs+hlHtccuLCxgaWkJz3ve83q6zrBZWVnBjTfeKPtZ+xi0/q52fKTaXNdcc03/wg6RjY0NuN3uoZeIsNvtWFhYwKVLl3DTTTcNVRY5EomEqgK0SnNrGLhcLjidzoG1neqVbr2QjVo7rKicSfFm3dbe/In798SMDWL9nZmZwfnz5w29RifIcibSyaUpxXxNffDlmuqkAPt95O3n1XIuyUxu1gwwKd5sWDEq3a518OBBS5Q3kKNer2N9fb0Zz9cvcmM1Pz+PK1eu6HL+YXL+/PmhuzQlDh8+jGeeeWbYYsiyu7uLUCik6z3ay7l6OSYQCJg+OcrhcCi6NY2iXq9bMhlAbX0ztfNFr3k9MzODjY2NoVhqrfUtGkgul5NVzqSYL0f8vKY6KcB+H/m+82o4F2MMXq/XtG1hpCDsYcWodLtWNBpFPp+3ZLHVra0tBAIB3WoAyY3VwsIClpeXTe8u6sZTTz1lGuvf0aNHce7cOVMWQJaUMz3v0V7O1csxgUDA9PGjjLGh3Eucc8spZ2tra12TASTUzhe95rXf7wfnfCjrhrW+RQPJZrOyPQulmK/a1HWa6qQA+33k+86r0V/u8/mQzWZV7z8oCoUCdnd3MTs7O7QYlW7XYoxZ1jrUS9cFJeTGyu/3w263W6J3YSfS6TQSiQQOHDgwbFEACEqEz+czZY09qYyGnvdoL+fq5RirKGfDsJw1hlT2oR/UJANIqJ0ves7rqampoSQFdI05Y4zNQqjgv8A5/3HG2LMB3MY5/3vDpRsg6XRatkyBFPMlobZOirRvp/178Zd7vV5TWn6Wl5cxOzsLu90+tBgVNeO5sLCAy5cvmzIGSImlpSVdlbNOYzU/P4/l5WXVb7Fm45lnnsGhQ4dMtTgdOXIETz31FI4ePTpsUZpUKhWUy2V4vV5d43Z6OVcvx4RCoaGVN1CL3W4fmnJmpZizQqGAQqGguvOJ2vmi57yWMjYH3StXzVPsAwAeAiD1QjkP4D8bJdCwSKfTspYzM+H1epFOp4ctxj4uXbo0tFY5Wjh06JCpa0/JwTnH5cuXB9J81ypthzpx9uxZ0xV9PX78OM6ePWsqd3EqlUIgELBso/tgMGj6chpWHdtBs7W1hcnJSVOPl5QsNWjUKGdTnPOPA2gAAOe8BsB8QRR9kkqlcKh4XvcAdj0Dbv1+vymVs8rZf8G/O/+nmhMm1CA3fr0GhU5NTSGXy5lyDDuxs7MDl8ulKrOuX6TCqWZSJNSSz+ex+v+z9+bRkRz3nec3C3UAVagL930f3Y1GH2QTILt5Nk9TEjWSLFk+ZO3YY5umvLuz0mq8Xr9dz/xh+Xk0lOwdT4tvZM2spZVHpiRrrJGswxZJkRKlhrob3TgbNwpHAagbdaCqUEfuH+gEC4WsqjwiMhMAP+/xsVGVGREVGZUZFfH9fX+rq6pbaOTDuZ5ryaYkFAqhX7emiIE2jTpsNpvmtzUB7H+PaAdG5ZbPsqymJzr5CA0GUCoQgC9Jem1trSrfXyGTsxjDMNUAWABgGOZBAEfn6SaQYDCIjsVvEBewkxTcavGmFI1Gcc73A5jjW6IDJoTA139SRaEMw6C5uflI6c4WFxeJbmkWw263I5vNan5Vgo/p6Wk0NzdrzoCTYRh0dXVhfHxc7abss729jUvhN5Qx0KZQh9VqRTgcVmXbUCi5AQG0A6O0YA4sFbfbLUhGoVQgAF+SdIfDsb/9qiRCJmefBPBtAN0Mw/wUwJcB/M9UW6UwLMsiFApREbCTLFOLIeTLy8u42/Q+ZEQmlhUKX//JEYU2Nzcfqa3Nubk5tLW1KVIXwzCaTztUiDt37qCvr0/tZvDS39+PO3fuaGZFMhQKYbH9g8oYaFOoQ6/Xw2QyadbgFzgozKcdGJVbvlpRolIRGqmpVCAAX5J0hmFQXV2Nzc1NSWVKpWRAAMuytxiGeQxAPwAGwAzLsinqLVOQeDwOAGB7ryLQ9yTRskkKEx0Oh+aEsPPz89D1PIHApU8DEBcwIQS+/pMjCm1tbcV3vvOdI7H8n8lksLi4iCtXrihWZ0tLC+bm5jA8PKxYnXKJRCJYXV3FU089pXZTeKmpqQHDMFhbW1NEO1iKYDAIZ9sVBAZ+l2o9NE1COYmHEtv9Ushms5IMuaWQW77O69X0imIuqVQKgUBAUMJzpQIBCiVJ54IClLTpKblyxjDMBwG8gL3JWR+A9zEM8yTDMMJiX48AXCqT/Id1of3rYvvaNPUF5eXlyGQy+5NJLbCwsCBZ55Nr8Cu3v4SUZXS9hZ5//gO07MxobpLLx/r6OqxWq+B8r6XGnhD9XltbGxYWFo7MDR4AJicn0dbWdmhLUwuJujl6enpw584dtZsB4J2AgKOMxWLRZOQ6R8vODLr+6RO83zUx2tz8MVxqTKtl4SEFr9cLu90uKFG7lHsbCbhy+7CquJ2GkG3N3wbw1wB+/d5/X8TeVudPGYb5GMW2KUYwGOS9WRXavy62r01z/59hGE3pzra3txGLxQS7O+dzwOBXrlngyCvQ+2ah9xcuq3LkFei3V/E4e+NIRCWKTeBdauwJ0e9ZLBZYLBZVopOkMjo6iv7+/kOva0mLw21tasGQNhwOl8xjqHW0Pjm7f/s1mKJu/u+aCG1u/hguNaZ1Ot2RmZxtbm4KWjUDpN3bSMCV27f+bcWDAoRMzrIATrMs+yGWZT8E4AyABIBhAH9Is3FK4ff7efO0Fdq/LravTVtfYLPZNLPqs7i4iKamJsnbgwcMfmX2V3ToRaRr+pCuLlwWd202T/8G5ufnZdWnBNPT06L8sUqNPaH6vba2NtXyyYnF7/fD6/Xyrt5qIVE3h9PphNlsVv1HAed2fhwmZ1rT3+byc8ujSFlb+L9rIrS5+WO41JgulXBdS2xsbKCqqkrQsVLubSTgyk089Afwer2K9q2QxOcdLMtu5fztAdDPsmyAYZhjoT3zer28JniF9q+L7WvT1hfY7XbNuLjPz8/LymOYb/ArByFlcdfGGolg4etf17TuLBqNwufzicqnWWrsCdXvdXR0YGRkRLMarlzGxsbQ3d3NazyrRGJkMfT19WF0dBS9vb2qtSGRSIBhGBiNRtXaQAKtT84WyzrgfuF/PzAJLqRnKkb+GC41pkslXNcSbrcbAwMDgo6Vcm8jQW65lZWr8Pl8qK+vJ14PH0JWzt5iGOY7DMN8nGGYjwP4BwBvMgxjAaDdb4cIvF4v2pJzmtGnAIX30B0OB7xer0qtegeWZTE3N6cJgbNYrFYr9Hq94tE3YpidnUVzc7Mqbt9NTU3w+/2aTBWWC8uyuHXrFk6fPq12UwTR39+P6elpJJNJ1doQjUapmW0rqfGzWCza9Suc/RE+EvgvqNy4zvs2zX7S6/XY3d0lXi5pWJbF1tbWviSGpP44H1L9XV1dDY/HQ6hVpREyOfsEgP8K4MK9/24AYFmWjbEs+wTNxikBy7Lw+Xxonfs7zehTgMJ76E6nUxOTM271zuFwqNwSabS0tKi+xVSMu3fvqmaoqtPp0NzcjJmZGVXqF8ra2hoymYxiv2TlYjabUV9fj6mpKdXaEI1GBQeYiEVJjZ9W8wwDAPvaZ2HPBOEc5c9wSLOfDAYDUqmU5u00YrEYstns/g8FIZphqZDqb6fTqegP+pKTM3bvKi8CSAP4AIAnAExTbpdixGIxsCyL+IOf0Iw+BSi8h15VVQWPx6P6l29hYQHNzc2a3RYsRWtrK+bm5tRuBi/pdBqzs7OKhm3n09nZielpbX/Nb968id7e3iM1Bk+dOoWbN2+qVn80GuXNIUykbAU1fhaLBdFolHo9Usg+9kkEdQ7Ehn+f932a/VRWVgaGYTSvO/N4PHA4HPvfXSGaYamQ6m+lE6AX1JwxDNMH4Ffv/ecD8HcAmOOwWpaL1+uFw+HAbscjCHRoR59SaA+9oqICer0e4XAYdrtdhZbtITaSUGu0tLTg9ddfRyaT0Vyi4OXlZTgcDmoPUSF0dHTgrbfeQiqV0pzrPrA3gR0bG8Ov/MqvqN0UUXR1deHNN99EKBRSZdU5FouhvLycStlKavzKy8uRSCQOmL1qhUTbFXzN/nH8K5V0yUajEclkUpBFhVp4PJ4DwQAk9cf5kOrv6upqjIyMEGiRMIqN6rsArgJ4L8uyD7Ms+x9xDHNqJsf/ER/y/bUqWjOp3i1qb21ms1ksLi6in1nb9+2hpReQS6E+rKiogM1m06RlxOTkpOAoTRr6FaPrLTR/51/hfIVPs9kCZmZmUFVVVdCvS2mPM6H16fV6dHV1qeZ5JmZbk3QfkixPp9PBZDIpnlJHCIlEgkjAhdT+MhqNSCQSsuunyebmJnqyLmJ5k5XA4XAgHA4rpukrNjn7IIANAK8zDPNFhmGexF6GgGOF8/aXYE8HVNGaSfVucTqd2Nra4j1HCTY2NmA2m1Ez/v++49tDSS8gl2J93NTUpDndGcuymJqaQnd3t6DjaehXuDKv7P5MVX1UMW7cuMHrbcahtMeZmPq4rU01pAliJmek+5B0eRUVFZpM4URqcia1v4xGo6aMyvnweDw45f4fxPImK4FOp4Pdblcs93DByRnLsv+dZdmPAjgF4HUA/xpAHcMwX2AY5hlFWqcAI5ZHkba1qqI1k+rdwqWSUIuFhQU0NTUd9O2hpBeQS7E+bm1t1Zyf1/r6OvR6PTH/HynkevtMTU1pztQyGo1iaWmpqCWF0h5nYuprbGxEKpVSZdU2FosJnpyR7kPS5ZlMJk1OQnZ2dohsHUvtr/Lyck32Sy5erxeRod8nmjdZCRwOh2JWVkJya8YA/C2Av2UYxgngw9gzn/0h5bZRh2VZ3Iw4cOZj/x+1CKZiSPVuqampUXVSMT8/j56eHuy294j27VGaYn3c3NyMH/7wh9jd3dWM79PExAQ6OjoEH09Dv8KVqQdgHvsaXC6XKDNc2oyNjaG9vb3oNVPa40xMfQzDoK+vD7du3VLcikbMxIF0H5IuT6vbmqR0fVL7S6srihw7OzvY3d2Frv8pdCz9SgAAIABJREFUBE49feA9pXJoSsVutysmKRKlpGRZNsiy7H9mWZZsdnCViNz87/hY7G9h99w49J6W8vLlU1NTo7hbMUc6nYbL5TrSwQAcRqMR1dXVWFlZUbspAPZ+LExMTKhqUppPV1cXJiYm1G7GAW7evFnS20zL318AOH36NMbGxhQ3DM2dOMjR95DqXzltqKio0OQKkdDVSVpjtLy8XNOTM7/ffyBSk0PKOFP6e865JSiBtsJcFEb3xsuoZsKK58iUi8FgQGVlpSpBAevr67Db7dQivpSmqakJS0tLajcDwJ5INpVKoba2Vu2m7NPT04Px8XHVrVs4tra2EA6HS644afn7C+ylYXM4HIqvgCcSif3vrhx9D6n+ldMGg8GgSeF7OBwWFGlNa4yazWbtGvRib3LG5zQgZZwp/T1XMhjvRE/OZlvej4S5QZUcmXJRS3fG5dM8LmjJ74yL0tSSb1dVVRUMBoNmVhdv376Nnp6ekn2k9e8vsJcxYHR0VNE64/E4TCYTAHn6HlL9K6cNWtWchUKhglHEudAao1arVdOprXw+H+/kTMo4U/p7zmnOlPixql0jFAUYT9Yh8sRforf98DaS1vLy5VNTUwO3242LFy8qWu/CwgJOnTqlaJ00aWxsxObmJpLJ5P5DSw1YlsXY2Bgef/xx1dpQiJ6eHkxMTKiWsYCDZVmMjo7i+eefL3ms1r+/ANDb24u3334b8XhcEc1rNptFKpXa1+rJ0feQ6l85bdCq5mx7exs9PT0lj6M1Rq1Wq6aze3g8HjQ0NBx6Xco4U/p7Xl5ejrKyMkQiEdhsNqp1ndiVM5Zlsba2VjT1S+5+ttb8VxoaGrC6ukq1jnzS6TRWV1fR3NwsuQxS+hWp5eQfo9frUVtbq/rK0NbWFhKJhKBURKT6S+ixvb29GBsbU31r0+VyoaysbD8f31HHZDKhqakJk5OTitSXTCZhNBo1tTIrB85sVWsEg0HUb0+o5hFntVoRDAaJ1EsDTnMmBVq+jmLKtNvtivTviZ2chcNhZDKZosvPufvZWvNfqaurw8bGhqI2B5zeTM4KEyn9itRy+I5pbGzE8vKyuA9CmImJCXR1dQl6cJLqL6HHVlVVQa/Xqz6BvX37tqaCJUjQ39+P27dvK1LX7u6uJrM9SMVkMmlOc5ZKpbCzs4O6ib9RzSPOarUiHo9rMgE6l8taanYbmr6OQsu02WwIBALE6i/EiZ2cud1u1NbWFn0Y5u5na81/xWQywWw2KxoUsLy8jMbGRlllkNKvSC2H75iWlhZVnfBZlsWdO3fQ19cn6HhS/SXm2O7uboyPjwuujzTZbBYTExNFjWePIp2dnVhbW1MkT2QymTxWkzMtOuEHg0FUVlYiOnzYw0sOYr6/DMNodvVsZ2cHOp1O8jY+TV9HoWVarVZFjGhPrOZsfX29ZFRc/n621vxXamtr4Xa7BW2FkWBpaUl2Mm5S+hWp5fAd09DQALfbrVoeyc3NTezu7gq+jqT6S8yxfX19+Pa3v43nn39elVyGS0tLsFgsquaTpYFer0dbWxumpqYwNDREta7jtnJmMBg0tzrERSKq7RFnt9sRCAQUezYIJRAICAqWKARNX0ehOByOdydnNHG5XId+hTd//xsqtUYY68/9Moyut1A58gqiQy+irq4OLpdLkaCAbDaL5eVlPPzww1TKz/1cuyW+KIWOzX9dSJlG11uoGnkF58pPwe12qyJ6HxsbQ3d3d8FVXDF9Uwi5ZVRVVcFkMqlmSDs+Pi77h4FW6enpwdjY2LGcnIkdd6LuAxrUnHk8HqoJ7YX2j8PhwNbWVkk/QKUJBoNFf2AZXW/B9uM/AwCEH/sjAJA8fgxb46i8fg3R4Zf2TdKlPCPysdvtiljgnMhtTZZlsbq6yhsxonVy98eVDArweDyoqKgQ5N8jBTmaqEKvi9GgXUm+DZfLJe9DSICL0iy2pUlCZ0GijO7uboyNjUk+XyrZbBZTU1OCIuCOIu3t7VhdXaUeeajG5EzsuBNzvF6vRyqVkttEomxubqK6uppa+UL7p6qqStX8y4UIBoOldd45eZrljJ/K69egS0ZRef0a7/t8fwvB4XAosmV8IlfOfD7fvmYrl/XnflmlFgknOvTigZUzj8ejSPqhlZUVqpPZ3M8l9dj814WUyR2z1fReLC8v49FHH5X3QUSyvr6ObDZbdItdTN/QLKOvrw/f+ta38L73vU/Rrc319XUYjUY4nU7F6lQSg8GA5uZmzMzMUF0FT6VS0OuVveWLHXdijjcajZrb1tzY2KAatCK0f2pqahSLAhZDIBAoakERHXoRtnhw/98AJI+f3JUzvvf5/haC2WxGIpGgbr90Iidna2trqKurU7sZksjdH9fjHTNa2ttxq6urVPULcjVRfK+L0aBVRCJY+frXwbKsolYD4+PjRbc0c9soBxJlOBwOmM1mLC0tobu7W1ZZYpienlbdY402nZ2dmJqaoj45U3rlTOy4E3O81lbOUqkU/H4/VasXof1TXV0Nv9+vmo62ED6fD2fPni34/m77I/D95sHPJ3X87LY/cijns5RnRD5cwEUoFKL6TDyR25oul0tzQkmp1NbWKrK16XK5ZEdqahluqV3JCCcuSvMoRSCqsbV59+5dUcngjyKdnZ2Yn5+nmi93d3cXZWVl1MpXGr1ej3Q6raidUDG2trbgcDgUX53kQ6/Xw+FwYHNzU+2mHCAQCByLoB6bzUb9WaH+KFKBlZUVXLly5dDrpcSCxcSDco6VQ0NDA1wuFzWhPrCXjy8UClHVUqhFfoCF2+1GVVWVInWvrq6irKzsSPVrX18fvv71r+OFF15Q5EEfDocRDAaJ/jDIFx3L/Q6SwGw2w2q1YnV1ldpENJVKFbxmRyEYCjh87ywrK0MqlVI1uwfH+vp60VUzoYFMpKitrcX6+nrJPLRKkclkEIlEJEVrcn2UbL8Ck+unB56zpb7LcoNScv8G9rZZe4wPUk+RdeJWzpLJJLxeL++2ZimxYDHxoJxj5dDY2AiXy0XVvX1jYwPV1dWqWCjQJvda1NbWYm1tTbG6uS3No4TNZoPNZsPi4qIi9S0uLqK5uZno2KsceQV6/yz0vlnFEiYLobW1FfPz89TKPw5WGvn3ToPBgHQ6rXKr9nC5XEV1uUIDmUjR0NCgunF0Ltvb2zCbzZJ+1O330fVrh5+zJb7LcoNSDgQZ3Pv3Oe8PqNtpnLiVM+7XDd8AKSUWLCYelHOsHOx2OzKZDLa3t6mFcAvxhDuq5F6LerYeExMTitSbzWYxNjaG973vfYrURxJua1MJt35uckYSPtGxFmhpacGdO3fw1FNPUSl/d3e34JbbUQiGAg7fO7WkO1teXi6a91VoIBMpmpqaFMs+IQShCeH54Poo2f6r+ytn3Oulvstyg1Ly/64ceQWbzS+8OzkjzerqasFggFJiwWLiQTnHyoFhGNTV1WFtbe3dyZkEcq9FfTyOf/qnf1IkKGB1dRUGg0GxLVSS9PX14Wtf+xre//73U9fXzM3N4T3veQ/RMvlEx1qgqakJ3//+96lFXx+HlbP8e2dZWZkmIjYjkQji8XjRiGKhgUykcDqdiMfjCIfD1JN0CyEYDPK2Q/CWuvUDQODe/6e3gOl759X91t7/c1/jO7fY+0WOX3/ulw+Z0Wc9Hvhff11YWRI5fvtUJeATtiuVrFwIRtdbqPny86j58vOwjFwT1K76+nqqy9dutxudqUXJibO517jPlZtIvtBnJHlN+BLY89VbUVGBsrIybG9vy66zFLRNVWmO6crKStjtdupbm+FwGIlE4lhYaORfD77rw03W19fXqbQhlUqhLjRO/fumJEajURPbmt63v4bf2n0VppWfqN2UfRiG0UTeYA652QG0hN1uRygUoionOlErZyzLYmVlBQ8++OCB13P3lJVKvVQIbg8dLFB5/RrYcmfJdjU1NeHGjRtU2pNKpRAIBNA8+w/Qb68K6iO+/jzwue7t5etDKzBs3uH9jCSvSb6GoFi91dXV2NzcpOryzbIsxsfHqW5p0h7TXV1dmJiYEJwPVApra2uor69X1NqEFvnXo9D14VbBaWRh2N3dRefyN6FP+al+35REKytnlp9/ARZ2G3qN9V9zczMWFhZw7tw5tZsCv9/PuwNzVLbUczGZTCgrK0MsFkNlZSWVOk7UylkgEADDMIc6k1aycuvLL4s+Jzr0ItLVfUjX9CE6/JKgdtXX12Nzc5PKL0ifzwebzYaYiES+BZOSc58rN5F8gc9I8prkllWqXqfTSd1Ze3V1FXq9nuqWJq0xzdHX14fJyUmq1g9ra2vHZjs9/3oUuj719fXUMlWkUim4+3+N+vcNkHbvk4JWNGdv6C6BdbZrSsMIAG1tbZibm6O6wiMULu/ocYG2ncaJWjnjfonnQ2vP3/q5zyHyqU+JOidfD5NvoseHwWCA3W7HxsYG8bBpn88Hp9Mp2yS2mLkg32ckeU0KJbDnq7eqqgoej4dIvYWYnJyknieS1pjmsFqtqKysxPLyMrWI0/X1dcEpm46CFYQQTWpdXR21VfBkMomdUw8h8NBHDr1HerxIufdJQQvJz/1+P2bRiod/7U80t8pbVVWF3d1dBAIBVS17WJaF3++nuiOhNFarFYFAgJpVyYlaOVtZWTkUDCBGayFFl0HjHL736+vrqZjRejwe9LIrR0aPkqspy9W4FTou/z3aOelYlkX4xrfw9OLnSvanUuNNarnc1iYtNjc3qbqtK8XVyAyvBpPvOjkcDkQiESoJvaUEBKitRStVvxYmZ7Ozs2hraxM0MRPSnySO4d43rfwEra2tiiTqLkY8Hkc2m0VFRYWq7SCJzWajGrF5olbOVlZWMDQ0dOA1MVoLIcdaX34Z1s99bv/vmssfBQDon12C57+8Laidperhe5/W5GxrawvPer4HfTpwJPQoXN8YNu+ASSf2NW757S7Ux1VVVfB6vdQiNjc3N/FA5A1UZDMwluhPKTogWtohvnK7u7vx7W9/Gy+88ALxvorH40gkEoKjzLSsW6n6+q/zazB5rpNOp4PD4YDX60VLSwvRdkiJAhUznvLvfU33LFAin/yk5FW0UvVrYXJ29+5d/BYbAQSs3n5v8Qvo3A1g6bv/Fr/U9fvUjsl9v+PpV3D37l089NBD4j4YQbicmlpbWZQD9z2lxYlZOUulUtja2jq0ciZGayHk2MinPgX3+jrc9yKufG9/DYm/eByhP/lzwW0tVQ/f+5wZLWkCgQD853+Lqn6JJAc0ZTkat4LH5b1XXl4OnU6HWCxGpX13797FcteHkRHQn1J0QLS0ZnzlVlVVQa/XU4ku5NK8qHUzJ6mZKqjBLHCdHA4HAoEAsfo5EomE6MmZmPGUf+/j/i1ne7NU/QaDAYlEQnL5ckkmk6Luu5+tvYolYxU+W3uV6jG577e1tcHlclFZjRXKcUnblIvT6YTP56NW/olZOeMi8PKX9Ukk3Fb6HL73nU4ndnZ2EIvFYLFYRNVXCJZl4fP5UPZLH0PgvvcTKZM2uX1TTK9XrI/tdjsCgQCVKJzJyUk88MALCLR+ouSxSo03OeW2t7fj7t27xFd5gsEgtSgoIZDUTBXSYBa6TpWVlVSExslkUvTkjLZ2UW79RqNR1UnH/Pw86urqsP7cvxBx1h/jK4oc88779fX1mJ+fx8DAgJAGEsfr9R7LyRnNXZYTs3K2traGQZOHqn4iXwcQ+eQnqdTDVx/DMKipqSG6ihGPxwFgXyegtv5EKWhF4YTDYfj9fuKO90pQyLvuueW/QPjmt4jXFwwGiXoiFRu7WhvXNMZfJpNBOp2WbEJbqI8KvV7q3ie1z/PPM5lM2NnZEVUGSSYnJ6nYnpCms7MTk5OTqtXv9XrhdDo1810T2w6+4ysqKsAwDKLRKJU2nqjJ2cXgP1PJYcaR76dFO1opvz7OI4kU29vbB1YvaOWA0xoWi4VKUtuZmRm0tLQcyRylfNe+cuQVmBMenPP9kHh/RaNRmM1mYuUJyXXr+Hd/iKbm5n2tFPdvpWwhOCwWC8LhMNEyE4kETCaT5F/4YvNClrr3Sb2X5J9nNBr3f0QqTTqdxt27d49Eftzu7m5MT0+rZtjr8XjgdDo18wyRm2+Tw+l0UgsKOHpPCYmsrq5i+/7fpaqdou0tVaq++vp6qpMzpT+fWlitViorZzMzM2hvbyderhIU0k1lHG2Ya3k/8YTd0WiUaGRXsbHLvRf6kz8nrpmSQkVFBXHNYzweh8lkknx+of6Tek8gdV55eblqk7OFhQU4HA5Vt9+FYrFY4HQ6sbCwoHjd2WwWfr8fVVVVijxDhPyYEtuOQsfb7XZqurMToTlLJpMIBoMwDX4EgfPvpVaP0vqM/Prq6urw05/+lFj54XD4gH5Nbf2JUlRWVmJpaYlomZlMBgsLC4eyUxwViummDFNTuHv3Li5dukSsvp2dHTQ0NBArT6lctySgMTnb2dmRNTkjnReS1HkVFRWqbWtOTEwciVUzju7uboyPj6O/v1/RekOhEIxGI4xGoyLfNSF6UbHtKHQ8TdPyE7FytrW1herq6kPbSXw5F+X6z4iFpM+azWZDKpVCJBIh0rZIJMK7tSS23/iQ25e0c0eS6kOO1dVVWK1WIlt1Sus2StXX0dGB+fl5otkCksmkqkm6aetFi2EwGIi73sfjcZSXlxMtUwpCcoyKgcZEVgjpdBqTk5Po7e0VdLwWtFa9vb2YnJxUPKMCpzeTihTfz1LlFfLAFDs+afpinojJmdvt5nVHzt1HFrIHTWO/XEyZpY5lGAbV1dXY2Ngg0rZCkzOx/caH3L6kqV0wm83EJ2eLi4vEAgGU1m2Uqs9sNqOyshJut5tYnel0Gnq9egv7Sm9l5kIjJVEsFtPE5Cx/LMkdy9zkTOn0RHNzc3saKoFbmlrQWlVWVqKqqgpzc3OK1svpzaRSqu8qR16B8Vu3UXP5o4L0opUjr0Dvm4XeP1tSQ1mq7pqamncnZ3LY2NjgdRrnzblYZA+axn45aZ81LnE3CfK3NfnaobTmhNT5xbBYLIhGo0Rv+HNzc8TSfKitbeSjubkZi4uLxOrMZDIoKysjVt5RoqysjHjO0kI/tJRGaI5RoXDWIEob0d6+fVvwqhmgHb1ub28v7ty5o2idm5ubslJHCfH93P3ABfje/pogvWh06EWka/qQrj7sgSl2fFqtViQSCSq6xxOhOXO73YcyAwCFcy4WgsZ+OWmftZqaGmIrZzs7O7yibLH9xofcvqSpXSgrK4Ner0cymSSy2rC7uwu3243nnnuOQOvU1zby0dLSgtnZWTz22GNE6mQYRhPJmtUgm80S902KRCKaSJ2TP5ZIjGWz2YxoNCpLUyeGZDKJmZkZfOxjHxN8jlZ0jb29vfjKV76CZDKpWH9tbm7KyiUsxfezVHn5eZ4LlVWqbIZh9vMxkw72OvaTs2w2C4v75xj4yavYefAl7Iq4iEbXW6gceWVvZp53XrH3SCGljrq6OmL5Dnd2dohMTkj3lRLlmUwmYjqdtbU1VFVVFdVQKZW4+2pkBp/2vobP1l7Fa1bhwuDc9Eh8/dWTdaFh+s+QmW5F2elnZLdTp9Mhm83KLkdJpI7L/PNomFqGw2GiARal4D5Tsv0KTK6fUr1PcjIEpRJ7T01NoaGhoehk1zJyDZXXryE6/NK+GbYSz4xSVFRUoLGxEVNTU7h48SL1+rLZLLxeL7FrI6QPldaLckEBpCdnx35bMxQK4ZHUdRjDa0R1UUpoCKTU4XQ6EQgEiGyLyA2/5yDdV0qUx03OSOByuRR9MBbj097X0LkbwKe9r0kug6+/nLe/hGqEkfrBZ0g0E3q9nvjWHm1I6S8zmQzxYIhQKETU1LcU+5/p+jX698nKSuK+cMUYHR0tGfFYef0adMkoKq9fe+c1DejOAKCvrw+jo6OK1OX3+2E2m0VnpiiEkD5UWi9KUuedy7FfOfN6vZip+yU0G8Yk6aK4WbqY90ghpQ69Xo/KykoEAgHU1tbKql/s0neh1Z+rOINPpzbxWZzBayJWiP7kH76Hf/f+XyJWXqHk2Hz9bDQaieXsW1paQk9Pj6S2kcboqkflyCuoHnoR6xJ/vfP1V3ToRez6/xQb7R8EiQQx5eXlqqblkYLUe0L+ebu7u8TF+9vb24pOzrjPlGz/1f2VM1qYzWYqptF8RCIRrKys4Mknnyx6XHT4pf2Vs/3XFHhmCKGrqwtvvvkmwuEwbDYb1bq2trZQVVVFrDyt9GEutbW1uHXrFvFyj/3kzOPxINb8IAKP/aHoc9X2RpJaB5fzS87kLJPJIJPJEImYe83aL2oLjePffvv7vJMzqeUVgq+fSdkZsCyLlZUVYlosuZAYt4U8z6Ye/Rw8Hg+RyVlFRcWRm5yR8u5KJBJEJ2eZTAaxWExRs1ShOW5JQMs0mo/x8XG0t7eXXNmMDb106HNrRXdmMBjQ3t6O8fFxXLlyhWpdm5ubRCdnWunDXLggPNJyhGM/OfP5fLxhvGL2/3OPBaC6bqAQXDt7y6/Idi1OpVIwGAyCBhtXrzevT4T2Md9xlpG97YBQlQep+sFD75PQbxTVFBqNRCLA/H4/TCbTIX2KZeQarG//P8hYarH9zN5WoO3HfwYACD/2R7I/pxr6loaGBmK/IC0Wi6o5E9VkZ2eHN0paKtvb2zCbzUcybZgQbDabYs73t27dwv3337//txZ0ZLkIbU9/fz9u3rxJfXLmdrsFa7Hk9qWSOsdcKioqYDAYEAqFZFmG5HM8v605eL1eOByOQ69L9RfTim6AD65tZ73fl53va3d3V/Cqmdi8e8XOt778Mpqam2H/wJ8CAOwf+FPUXP4ojN+6faAcEtehWBl6vZ7I5MztdvPauFRevwZmNwp9aPmdccXjvaOWj5wUnE4nYrEYkUmVzWY7sZOzWCwGu91OrLxgMEh9+0pNHA4HAoEA9Xq8Xi9CodABSxytPQ+Etqe1tRXBYBBer5dqe9xut+AdHGLelwroHPMhaWHFcewnZ36/n3dyJtVfTCt+NXxwbfOe/bjsL106nRbsMyU3717ucZFPfQru9XVsf+uPAQDb3/pj+N7+GnY/cOGQxknudShWRllZGZEkwWtra7w3p+jwS2CNlUg7Ot4ZVzzeO2r5yEmBYRhiVi52u10V53ctEIlE3p2cicBmsyEYDFKP7h0fH0d3d/eBFUitPQ+Etken06GnpwdjY2PU2hKPxxGPxwWPZWLel8MvKX5NqqqqiE/OjvW2ZiqVws7ODq8QVo6/mNb2vDm4durCYfinvyWrLDEO7XLz7vEdFxt6CXb86b5uo1BeRzkUK0On0xGZnG1ubqKvr+/Q63yaFD7vHaVzF8qFu0nJzTnodDoVjcDTEpFIhGj+Q5/Pd6wnZwaDAeXl5QiHw7w/xEnAsixGR0fxxBNPHHid1vfM+vLLkqIOxbSnv78fr732Gq5evUrcugV4JxhAaNkkvS9p6xzzqa2txfo9A1xSHOuVs3A4DLPZTGXgaZnKykrEYjFZVgSZTEZ1jYqa+Q11Oh0RK4fNzU3ebc3jSnV1NTweD5Fytre3T6QR7fb2NlHPLrm5DY8CdrtdtpSjGG63G6lUCvX19dTqyMX6uc9Rr6O+vh7pdJr4pIJDbmaAo0RtbS3xNE7HenK2vb0tKkJJSnJay8g11P/Hs/sCdlLHymmXTqdDRUWFrPyQpVzKlUgCr7RfTW79JCZnOzs7SCaTiloY5CLkGpFOKkzK84cT2bIz/6x6wmgxyP1esCz77uRMArQnZ2NjY+jp6Sn5Q7/Q9Rf7uhhynytiymMYBj09PRgfH5dcdzHETs60kBy+UFtKtc3hcCAUChFNI3asJ2fhcFjU5EyKIJHPbJDEsbLbVVmJ7e1tUfXkks1mi66c0RDCqi2uza2fhEM9p3dUa+VWSH8KSSos5ppUVVVha2uLyIpXfX09LCNf0JTguhRyx3A4HEZFRQWxVEuZTAbBYJDadp9WsNvtRFZs+WBZFmNjY7zyhHzEBkfxBUMJSd59oIyc54rY8dfX14exsTEqK9SFgqEKofb9v1hbSrVNp9PtZwogxbGenInNJydFkBgdfglZU+UBs0ESx8ptV0VFhSxBdanJmdpJ4GmQWz+JyVkgEFBN62N9+WVB/SkkqbCYa1JRUQGdTkck0rK5uRlT9c9rSnBdCrlj2OPxoKmpiVh7AoEALBYLEb9CLUNqO50PbttPyCqQ2OAovmAoIcm7D5SR81wRO/5qamrAMAzW1tYEHS8UlmWxtbUlauVM7ft/sbYIaVtVVRXR6Ndj/Y2NRqOiJmdSBIl8wm4Sx8ptl9lsphrtpnYSeBrk1s9sjcguz+/3qzc5+9znEPnUesn+JJ1UGNiLnuMmBXJoamrCzY1mdH74q7LKURK5Y9jj8aD53soJCU7CliawN3EirfnhmJycRFdXl6AVcLHBUSTuefnPFbHldXV1YXJy8oBFiFy2t7dhNBpFmSmrff/PRWwCdGAviIlkxOaxXjmLRqMwm80lj5Oy151/ju0Hn0bjf2iH7QefltxeqXXzUV5ejmg0KquelviMqH132nD1W0auydZSKYHf7ydqiXBU4KwN5NLS0kLtgaskYsbi1tYW0Yek1+s9EWPQarUiHo8TS7nGwbIswje/hWeWPi/rXpI7BoSMByWDoc6bfeh/41+DnflnYmV6PB6cNW4V/JxKaJbFnCPkmVIK0l5nx3pyFovFBK2cSdJ05Z1jGX8VyKb3/k8ZIe0tLy+XvXJ2f+g1UfvutBFjMii3rSQ0GEr7S0nVrJDGYrHI0jtyVFdXI5PJyP6RoTZCx2I2m4XX60VLSwuxuk9KxBzDMPtp60ji8/lwX/BHMMe3iBleay15d+vs12BL+ZH6wWeIlen1evFA5I2Cn1MrmmWSxrWktzVVm5wxDFPGMMwowzDfufd3J8Mw1xmGmWcY5u8YhpGdxj6RSAjJzp6OAAAgAElEQVRK3C1Ja5Z3TmzwI4BOv/d/yghpr9zE3QzD4Kbjquh9d5qIMRkk0Va5Qn6xASlykapZIY3cYBQOhmHQ3t5OLdRfKYSORZ/PB7vdLmi1XygnycrF6XQS153NzMxgseNDyBA0vFb7PppPdPj3kbQ0Yqb5BWJler1erPZ+tODn1IpmmaRxrc1mQzQaJZKTGVBXc/a/ApgGwC0t/DmAz7Ms+zWGYV4B8NsAviCnAqGTMyl73fnnhJ/9LMLPflZ0G6UgpL3l5eWyblQMw2DF1IvAh/9YVL00EWMyKLetpaxESsGyrOKTM61QWVlJTGDc09ODxcVFoqasSiN0LK6srMg2780lk8nA5/MRTTytZUhrfgBgdnYWPYPPI9Dzv8gqR8tG5rvtj2D+qb/C3NwcBgmV6fV6UX/2CQTa/6eCdWpBs0zSuFan08FqtcLv96OhoUFWWYBKK2cMw7QAeA+Av773NwPgKoBv3DvkbwD8C7n1JJNJGAwGucXIRmn9k9H1Fs69/W9Q5RuVXAbDMJK39vj0Ffl7+iT3+oW0Qywsy8oy4U0mk2AYBkajUVZb+M6jpVmRO0658xsik8S2Iru6urC+vk7VJ0oqpOve2NhAT08PkbKAvW11i8Wi6D1QzetBOihgd3cXuoXXcenm/yVLj1zoNS3R2tqKlZUVYj5dhdIm5mJ0vYWaLz+Pmi8/L1iLx50n9jiS999i2O12InpbQL1tzb8A8G8AcF4F1QBCLMty+XLWAPCGLDEM87sMw9xgGOZGqf3dTCYjOD8kTZTWalWOvAJTbAOD3u9LLkOn00menPHqK/L29JVIUiun30tZiZRiZ2fnQKQSyQTmtDQrpBIPN8/8N2JJy+vr65FKpVD+9l+J8o9SApJ1ZzIZbGxsoKOjQ37D7uHxeBSP1FTzepCenK2srOCJ7A0Yw2uy9MiFXtMSJpMJNTU1cLlcssvidKKlzLcrR16B3j8LvW9WsBZv/zyRx5G8/xY9vrISoVBIVB2FUHxyxjDMewF4WJa9KeV8lmX/M8uyl1iWvVQq271WJmdKawyiQy9it7IJt5xPSi6jrKxMskM+r74ib09fiSS1cvo9m83KGjs7OzsHttRJJjCnNZ5IJR6OXPo9YjYuDMOgr68PUw38fmdq6ndI1u12u1FbWyvbfiQXj8ejuPmsmtfDZrPtJ9smwfLyMpa7Pixbj1zoNa3R2NhIZHLGpU0s9eM2OvQi0tV9SNf0idLiSTmO5P23GFarFYFAQFQdhVBDc3YFwAsMwzwPoBx7mrO/BOBgGEZ/b/WsBYBsFXA6nVY9PySgvFZrt/0RrD7Th5Wf/ERyGXJWzgrpK3L39JVIUiun3+Vua8bj8QOTM5IJzGmNJ1KJh1mWRfyH/wksyxLJjtDf34/r10No4fE7U1MHSbLupaUlnDp1ikhZHFziaSVR83pwEZs+n4+IHcni4iIGBp5FoFPcA13J7yxJmpqaMDExIbscoWkTd9sfge83D/aJkD4S2pckdH5ir5vNZiOmt1V85sKy7B+xLNvCsmwHgI8CeI1l2V8H8DqAX7532McB/APpuvP3uPnel6sLUENfxqdv6P6nT6BlZ0ZyuVJWzrSuqxCD3JWz3d3dfb1ZLmL7SAt9Kjb/JsMw0Ov1xPQrPT09cLvdSKfTpQ/WIEKu4erqqqD0QGLweDxHPhhA7Ph3OBzw+Xyy681kMlhbWxOUrUFLzw055TQ2NmJ9fV12TmE+Cysh95Biz2YxZUmlWLlC82KbzWZielv1l5Xe4Q8BfJJhmHnsadC+JLfA/BQ8+Xvc+ZDQBaihL+PTNxij67hv+zXJ5er1etFfUq3rKsSQyWRkpbwpFIwiWsOggT6Vkn/TYDAQm5xZLBY0NDRgdXWVSHlKU6r/gsEgUqkUUX8zlmWPRaSm2PHvcDiIeE35fD5YLBZB0f5aem7IKcdkMsFiscjuP77MPILuIb5Z6P38z2YxZUmlWLlC82Ifm8kZy7JvsCz73nv/XmRZdohl2R6WZT/MsmxSbvllZWUHtuby97jzIaELUENfxqdvSFqacLvqKcnlSpmcHQVdhVDS6bSsydnu7i7v5ExsH2mhT6Xk3zQajUgmZX+F9xkYGMDS0hKx8pSkVP8tLi7izJkzRLaAOcLhMPR6vaDJhZYRO/5JeZ1tbGwINu/V0nNDbjkkXO6j0eihtE2C7iE1fUhX8z+bxZQllWLlCs2LTXJydqxza+ZvzfHtcedCQheghr6MT9+w+EQnNkelW2lImZwdBV2FULLZrKzJWaFgFLF9pIU+lZJ/U05ACR+nT5/GG2+8QUzHpiSl+m9paQnPPvss0TqDweCxSNskdvw7HA7cvn1bdr1ut1uwea+Wnhtyy6mpqYHb7caFCxckl5FMJg9JOoTcQ4o9m8WUJZVi5QrNi200GrG7u0vkPqWlbU3imEwmYm69haCpIco/Vsy5Fatv471rfwXM/khQu/LR6/VIp9MlgwKU1EQJqYvUMXJXzrQSjKIWOp2OqEaspqYGNpsNbrebWJlaIBaLIRgMEjWfBfYmZ6WsDEigxPdfTB2cz5Tc9Gter/fAlrBWckHmnstptCwj10RpqYt5Bj4y+RmULf5Y2ge6RyG9rRiK9Y2YfhOqFSPVPp1Oh7KyMiL3vmP99CC9tcIHTQ1R/rFizm26+7ewpvzAa9KyFuh0Ouh0upKrH0pqooTUReqYdDoty7wznU5rwsZFLUivnAHA4OAg5ufniZapNvPz8zh16pSsHwJ8BAIBRSZnSnz/xdRhMplQVlYm28ol30RVK7kgD5zLabSuXyuq1xL6HKkceQWW5Bb61+TF4iWTSdnjuaj+S8wzVKBWjGT7SOltj/XkzGQyERMlF4Kmhij/WDHnLnR8APGKBuDqpwW1iw+DwVBy5VFJTZSQukgdk8lkZE3O5FpxHHUYhjkQjEMCTndGIim9VlhYWMDgIKmkOe8QCoUUmZwp8f0XW4fFYkEkEpFcXzabPbQtrJVckAfO5TRawy8V1WsJfY5Eh15E1tGB15lLsr67JPxFi+q/xDxDBWrFSLaP1L3vWGvOKisriRkSFoKmhij/WDHnrlX0I3zl36O+T7oRrdFoLLk8q6QmSkhdpI5JpVKyl+bfhSx1dXUoLy/H5uYmGhsb1W6ObHZ2duDz+dDb20u87HA4jPr6euLl5qPE919sHWazGeFwWPIYicfjMBgMB36caSUXZO65uRqtYnoooc+R3fZHsNv+CNa+9CXs7OxIzgssJ/VfqTaWei8foVoxsRRrAyld7LH+aW+1WiWlkZGiBZCrHyCtZ4jH47KTbhfS7CmR55B0njWxpFIp6jkJaeaIU+paKKk5ZBgG58+fx+zsLPW6lIDb0qQxzvKNQElpMeWcR+oeWaocs9ksa+UsFosdiDaUer/j3heSP1gLfoYc5eXlsraF5RiYqwXpsf/u5KwElZWVkgaZFC2AbP0AYT1DIpGQnQqGizzJp5hmgdTnIJ1nTSwkVs5K3aBo5ohT6loo7cM2ODiIxcXFI3fz52Nubg7nz5+nUnY8HofZbN7/m5QWU855pO6RpcopLy+Xlds130RV6v1u/30B+YO14GfIIXdyRkNvShuSY19uXmaOYz05czgckr6kUrQAsvUDpPUM0ShsNpusMoxGI+/KWTHNAqnPQTrPmlhSqZQsj6h8A2Q+aOaIU+paFHpPboaFQtTV1cFisRz5qM1YLIZAIICenh4q5ScSCdG5XWnnHyR1jyxVjslkQiKREF0HR74VhNT73f77AvIHa8HPkMNkMskKpCsvL6eu9SYNqbHPsix2d3eJ+Asea82Zw+GQtLwtRQsgVz9AWs8QiURkJz2uqKjg/ZIW0yyQ+hxS86eRIJPJyPY5E+ITRzNHnFLXotB7tCZnAHDhwgXMzs6iubmZSvlKMDs7i4GBASpbmul0+lCGC1JaTDnnkbxHFivHZDLJWjnLj7SWer8Tkz9YC36GHEKi9ItRUVGBUChEsEX0ITX2uYn9uytnJXA4HMTceo8S6XQaiURCdrRWeXk5dSsSLcL59MjRDZSVlRGPVjxKkFra54Pb2jzK/Ts/P49z585RKZubXBw1s15S6PV6Wf6WmUzmREday/XpMpvNJ/K5AeytWOenrpLKsR6BNpsN8Xhc8BdVbiCAUqLOQsnOude2t7dRVVUl+wZDw4pEiWACKW3INXXUL/5Y9hdMr9cfqckD6f6X6xNXjOrqajidTqytrRU8RmsBOrmEw2GEw2HixrMcNCfGYiERTCL2egiRFBSjVLSdGKG/GNRI6M33ntxoS6vVKttnrlDbaCA1WCb3mcG9Ho1GiWXm0MY3mBI6nQ7V1dUIBoOCjpcbCKCUqLNQsnPuNb/fj9raWtn10PgFpEQwgZQ2VI68Ar1/FnrfLJyjfy17cmYwGIg65NOGdP+n02mqViSloja1FqCTy8zMDM6ePUtt2zebzWpm1YxEMInY6yF3clZq1VuM0F8MqiT05nkvf0tcLFLlRELaRgOpwTIHjIDvvc4tjJDgWE/OAKC+vh5+v1/QsXIDAZQSdRZKds695vf70dDQILueQpozOSgRTCClDdGhF5Gu7kO6pg9rfb96KHGvWAoFU2gV0v1P24pkcHAQS0tLBbUxWgvQyWVhYYHalqbWIGEmKvZ6yI3kLaUXFSP0F4MqCb153pM7ObPb7UTkRGo+T4Ucc8AI+N7rkUgETqeTSLuOdUAAADQ0NMDr9Qo6lkQggBKizkLJzrnXQlPfIxIFVlFRQXxbU4lgAiltyDV19MzOwhxbl1XHUZuckex/lmWpr5w5HA7U1dVhZWUFnZ2dh97XWoAORzAYRDwe520zKbQ09kiYiYq9HnLHXqn+EyP0F4MaCb353pMrSaisrEQ2m0U8Hpe1A6HUM0FqsAxfsvZQKETsu33sV86amprg8/lEnVNoDzo3iapSWrNS9fC95vV6iTiom81m3pB0mmaVNA2AhWpF4vG4bI+4UuHkWjKdlEv+Z9nd3YVer6eue7pw4QLm5uao1kGaubk5DA4OUu0bg8GATCZTcAWJ9NjT2liWa2VQ6L4HHE44TuJz07gexRKhl0Lu/Y9hGDQ2NiI5/o+y9IZi+oWkIbCYOvJfI7VrBZyAyVljYyN8Pp+ope6CuqicJKpKac1K1ZP/WiKRQDweR01Njey6C21r0jSrpGkALFQrQsLAt1Skq5ZMJ+WS/1mSySSxiKViDAwMwOVyHSnDSyW2NBmGKZp8mfTY09pYljs5s1gsBdP+HdAZEdKcUbke9/SzUsok8eO0qakJNWP/VZbeUEy/kDQEFlNH7mvpdBrhcJiI3hs4AZMzq9UKvV6PcDgs+JyCuqicJKpKac1K1ZP/msfjQWNjI5Ff5mazmfcmRdOskqYBsFCtCKmVs2KTMy2ZTsol/7Mkk0nZmj0h2Gw21NfXw+VyUa+LBMFgEMlkEm1tbdTrstvtBSPmSI89rY3leDwuy4DbYrEgkUjwBgUc0BkR0pxRuR739LNiy8xms0gkEgeyS0ihubkZN+1XZekNxfQLSUNgMXXkvub1elFTUyNLr5fLsdecMQyDtrY2bGxsCA5xLbQHnZ9EVQmtWSlNW/77brcb7e3tROoulDieplklTQNgoVqRRCIhOy8pF+laKCxfS6aTcsn/LDs7O7Int0I5f/48ZmZm0NXVpUh9cpidnaW+pclhtVoRiUR4I8dIjz2tjeVYLCZrclZWVgar1YpwOHzIyFtMwnGh0Lge+VoooUQikf0FDTm0tbXhe1Enzv/Lr/K+T/pZQNIQWEwdua+t37hB9D507FfOAKCzsxMbGxuydGKWkWuo/4t+1H7xEcW0FVK0CJubm8QEieXl5Uin00fKEkKu3gIgs3Km1+uh1+s1YcaotF9Q+cpPZU9uhXLmzBmsrKwcia3NpaUlnD17VpG6xNoZkPAjEwsN3a7R9RaeXvwcagJ3ZJVTVVUl2uVeK9o7Oe0IhUJErCCqq6vRsbsA29/+ChH9sFCfMSUoNG43NjaIBvqciMlZe3s7Njc3ZenEKq9fg243Bn1oWTFthdg2ZrNZeDwetLa2EqmfYRhYLBZZqVCURq7eAiC38lNZWamJvlPaL6h59r/Jzk4hFLvdjtraWqyuripSn1RCoRDi8TixVe1S1NbWCvZ3BNRJbk9Dt1t5/Quw7npRdftLssqpra0VPTnTivZOTjuCwSARzRTDMLiKm2CC4p6XYnww+XzGlIB33F7/Ara2tohKFk7E5KyxsRGRSASB878lWScWHX4JWaMFaUeHYtoKsW30eDyw2+1Et5RsNtuRSoElR2/BsbOzQ2RyYbVaNTE5U9ovaLL+lxRbOQP2PM8WFhYUq08K8/PzGBgYUMy5v76+XtTkjIQfmVho6Ha3Bn4TYX01yp7+P2SV09DQIDrKXyvaOzntIBltGHngRQQYOxH9sFCfMSXgG7fL3R+Gw+GQtZ2ez7HXnAF7GoLOzk7MohV9H35nD1zMPn++3kwJxGoRVlZW0NfXR7QNxYTFWkSO3gLY8/hJpVKyBbHA3sSWhFO2XJT2C1r+wQ9wgVAKEyGcOXMGr7/+esm0O2qytLSE559/XrH66urqEAgEBB9Pwo9MLDQ8Il2mHiye+T/R1fekrHKamprws5/9TNQ5WtHeyWmH3+/Ho48+SqQdjY/9Bl6+sYXfbrkMobkwxPhgyr3XS4Vv3I795Cc4c4ZMZgCOE7FyBgC9vb1YWVmhXo8cbxa5moW1tTUi5rO58K2c5fq9aQm5/Wd0vYWqV38NZ00eIg95h8MhaNWRpnZCDR3Mzs4O0V+QpaiqqoLNZoPb7VasTjHEYjGi5pRCcDqdSKVSvCu3Svucia1PigcW9zdmf4SWlhbxHyKP+vp6BAIB2VpGOX3N9xlJ3Sf42pXNZhEIBIitnFmtVtTW1mJ9XZ6htxAK6cCUuv+5XC709/cTLfPETM56enqwtrYmO7VHKeR4s8jRCqRSKXg8HnR0dIg+txhOp/PQ6k+u35uWkKv5qBx5BWXbq7iS+CmR9ghddaSpnVBDBxOLxRTTnHGcPXsWi4uLitYplMXFRfT39xMLsReCTqdDR0cH74NRaZ8z0fpeCR5Y3N/tC68S0fWZTCbU1NQIzi4jtJ1yziV5n+Brl8fjQXV1tSyPuHwGBwcxPz9PrLxCFNIvKnH/CwQCSKfTaG5uJlruiZmc1dbWoqysTNRSvxTkeLPI0Qq4XC60t7cT/WIBe6s/+ROMXL83LSFX8xEdehGx8josdnyISHuErpzR1E4orYNhWRaRSOSQBQFtTp06pVm/s6WlJZw5c0bxeru6ungnZ0r7nInW90rwwIoOvYiUrRWvM5eIBUR1dHRgbW1NVhly+pr3+UDoPsHXrvX1deI/7gcHB7G4uEg9mrqQflGJ+9/MzAzOnz9PXE96IjRnwF70yJkzZ7C4uIjq6mpq9cjxZpGjFVheXsbp06clnVsMu91+aOVMDf2dEORqPnbbH8HPTukF++GVwul0CjI/pqmdUFoHE4vFUF5eTjWvJh9NTU3IZDIIBAJErABIkUqlsLGxgd7eXsXr7ujowM9//vNDryvtcya2PikeWLvtj2CxrBOxX/yCmAFyZ2cnb/+JgWSOV5L3Cb52bW5uYnh4mEj5HE6nEzU1NQVz4JKimH6R5v2PZVnMzc3hN37jN4iXfWJWzoC9X9fLy8vU6yGlJeM0BrVffKSozoBlWSp73gBQ7b+N967/p6L5xOQgpKxCGjcaeoJoNAqn00mkLG5LWMhWulxtilY0a9vb26pMjhiGwenTp7G0tKR43cVYXV1FS0uLIums8mlubkYymcT29rbsspTWLkqpb2FhgegPVM4f07D0piK5k/n+VopsNgu320185QwA7rvvPkxPTwOQnuNSKx5y+WxsbMBgMBDf0gRO2OSss7MToVCIur0BKS3ZvmdXaLmob9fGxgYsFguVFUHz9WtwZAKw/PydiRHJfXxB+pICGjcaeoJwOExsclFeXg6DwSBovMnWpmhEsxYMBonkdZVCf3+/IkE/YqC1oi0EnU63v1sgF6W1i1Lqc7lcOHXqFLE2WK1WOJ1OGH/yl4rkTub7Wyk2NzeJW0FwnD9/Huvr64jH45JzXGrFQy6fyclJDA0NUYkSP1GTM71ej1OnTlEXKJLSku17djk6ivp2zc/P4/z587LbzQdz9dOIGmuxcfqdZVuS+/iC9CUFNG409ASRSITYyhmwp3UUonOUrU3RiGYtGAyirq6OaBuE0tXVBY/HUzDht9KwLEvF3kYMpFYTldYuiq0vEAiAZVk0NjYSbUdfXx/uVD+jSO5kvr+VgtbOCwBUVFTg9OnTmJ6elpzjUisecrkkk0ksLS3hwoULVMo/MZozjrNnz+LHP/4xzp07R60OUloyIRoDlmWxtLSExx57THT5guh7Em8N/DHaKtrAqWZIalaElFVI40ZaO5NKpWQnTc6HC8kvJVKWq03RimYtFAphcHCQSltKYTKZ0NbWhtXVVXR3d6vShlyCwSB0Oh0Rx3Wp9PT04NVXX0U0GpVlDKy0dlFsfXfv3sWFCxeIr2CcOnUKfz81hZ6P8ueIlAtJ3bEcXC4XPvCBD1Ar/4EHHsA3vvENXPy1X8OuhByXWvGQy2VmZga9vb3UItNP1MoZsOd35vF4eBN6C0Fpj6BSeDweGAwGYt40fPTr1nDqjf9NVo40krnzaGkPuLxyJKNuhK6cCUWr2guOUCik2rYmIH9rk2T/Li8v49SpU6oa4xoMBgwODuLu3buqtUEupTSVnCibxgpGW1sbdnZ2BAX25ELi/idHqyym7nA4jFgsRjW1WEdHBwwGw36aNTl+oELfE4sYPRzLshgfH8eDDz4ou95CnLjJmdFoxKlTpzA3NyfpfKU9gkrBhfHSfAD0r/8PlO9syMqRRix3HkXtQSAQIL4l19DQQHRyplXtBQDs7u4iFotRjYYuRU9Pj6zJGcn+XV1dVSVKM58LFy5Ivt9pgVL5ct1uNyoqKqj8QOV0e2KlMCTuf7K0yiLqXlhYwJkzZ6imFmMYBpcvX8bY2JigNuWiVN5XMXq4tbU16PV6dHV1ya63ECducgbsCRSl3qyU9ggqBsuyWFhYoLpFCwCJh/4AQcYhK0caqX6jqT2gNTnz+/3EzI+1qL3g8Pl8qKurQ1mZ0GQt5Kmvr0c2m5UcoUiqfzOZDDY3NxXNClCIjo4OZLNZbG5uqt0USZTKlzs5OYlLly5R+4E6MDAgOqiCxP1PllZZRN2Li4sYGBgQVYcULly4gM3NTWxvb8vyAxX6nljE6OFu376NK1euUF0UOXGaM2Bva1OqDkNpj6BiuN1uWCwW6gJs26UP4No/z+B32x6G0KFIS0tBU3sQDAaJr3RYLBYYjUaEw2Ei/mla1F5w+Hw+NDU1qdoGhmH2U7VJ0b6R6t+NjQ3U1dURydEqF51Oh+HhYYyPj1OVP9CimKYyHo/D5XLhIx/5CLX6u7u7EQqFEIlEBOuLSNz/5GiVhdYdiUQQDAaJp/3jw2g0Ynh4GKOjo3j88ccl+4EKfU8suWUV08MFg0F4vV5qgQAcJ3LlTK/X49y5c/veK3xoRdvD1w7utdDIN3HfffdR17RUVFTAZDIhM/1D2Tn0in0ekn0tpcxgMEhFvN3S0oKtrS3i5YqFtj/d1tYWFb8fsfT29u5rW5SG65fE2HdVjdLM5/7778fS0pJkra1WmZiYwMDAANVJsF6vx+DgIGZmZmSVo6SnmdD77OzsLM6ePatYarGHHnoIc3NzBcchrT4p1fe5erNcfSNfe0ZHRzE8PEzdaPtETs4A4OLFi5idnS34vla0PXztqBx5BWWhFXQuf5P6liZHQ0MDrL+Qn0Ov0Och3ddiy8xkMgiHw1TE7O3t7ZrYUqLtT+f1eomlzpEDZx5KO48uH1y/tC28SlWPIhaLxYKBgQFMTEyo3RRiZLNZTE1NURVlc5w7d062BZOSnmZC77Nzc3PUbJj4sNlsOHv2LMbHx3nfp9Unpfr+gN4sR9+Yf1w8Hsf8/Dweeughou3j48ROztrb28GyLDweD+/7WtH28LUjOvQiIoZqzDa/oFgOw6amJkzWPy87h16hz0O6r8WW6ff7UVVVBYPBQKwNHC0tLQXHmZLQ9Kfb3d1FOBxGfX297LLl4nA4YDKZqOfR5WMvx2ML3tCRy/FIiocffhjj4+PU8xwqxdzcHKqqqtDS0kK9rs7OTuzu7sLn80kuQ0lPMyH3Wb/fj2Qyqbgu8uGHH8bExATS6fSh92j1Sam+P6A3y9E35h83OjqKCxcuyLKlEQqjxq9LUly6dIm9ceOG5PN/9KMfwePx0PMIo8i3v/1tDA0N4b777lOkvrGxMYyMjOD5559XpD6lmZiYQCgUoqJdSSQS+MxnPoPf+Z3fUVUsTxOXy4U7d+7gxRe1Eajw93//9ygvL6euC+FjeXkZ4+Pj+L3f+z3F6y7Fl770JbS3t6uSiJ0kLMvi1VdfxXPPPadYBoYf/vCHCAQCePTRRxWpjzZvvvkmnE4nnn32WcXr/vKXv4za2lpFV+3kkkwm8ZWvfAWf+MQnZEWkMwxzk2XZS6WOO7ErZ8De1ubc3NyR+yUZi8WwtbWFs2fPKlYnF3V4XKEpZi8vL0ddXZ0mtjZpsba2poioWChdXV1wu92q1L2+vq4JE1w+Hn30UYyOjqqy5UuStbU1ZLNZaq72fFy8eBHz8/PIZrOK1UmLbDaL+fl5xX7c5/P444/j9u3bR6ovx8bG0NfXp5hV0ImenFVVVeF+awiWr3xIthmemOPkih6npqYwODhIXZCYS01NDXZ2dpBIJAQdLyY4QGwSXBp4vV6qYvauri4kx/+x4OfkkrvbfvBp1ftCDNw1LFt8g0rSZKm0t7erpjtzu92asNDgo6enBxUVFVhYWFCtDVIDh3K5ceMGHn/8careXPnU1tj5+mUAACAASURBVNaitraWSK5StVlcXNz/PGrQ1taGmpqakubIWgnMS6fTGBsbU3SX7URPzgDg4dTPoQu6ZJvhiTlOjuiRZVnMzMzg/vvvF32uHHQ6HZqamgRrp0QFB4hMgkuabDYLv99PPC9fLl1dXWid/7vCZor3krtbxl/VRCCKULjglEHPDzSlsXI4HDAYDAgGg4rWm06n4ff7FdFBSYFhGDzxxBO4efOmaqtnUgOHODY2NhCLxVTZsh4aGsLk5KTi9ZJmcnISDzzwgKptuHr1Km7dulV0HGolMG9ychJtbW1UnxH5nPjJmfk9/zd8rBXesx8veaxQsWKp4+SIHt1uN/R6Pdra2kSfK5fW1lbBW3OiggNEJsEljd/vh91uR3l5ObU6Ojo68DruR8rWwm+meC+5e2zwI5oIRBFKdOhFRI01mGl+H0wmk9rN2YdhGFW2Nre2tlBfX6+pvsjnzJkzYBgGLpdLlfqlBg5x/OIXv8Bjjz2min5zYGAAXq8XkUhE8bpJEYlE4PV6FZXF8NHV1QWLxVI0ClYLgXmZTAajo6O4evWqovWeSBPaXAwDz2Hi4f8ANsxiuMSxQg3vSh0nxzhvcnISQ0NDquTra21txdtvvy3oWCGfUajpH202NjaoT3bLy8ux2/EIbg0MoLP98JZXoeTuWme3/RG80Z7R5EpRR0cHpqamFH0Ira2taXZLk4NhGFy9ehWvvfYa2tvbFb+XiL035LK5uYlQKKT4zgGH0WjEfffdh/HxcVy+fFmVNshlfHwcFy9eVFQWwwfDMHjyySfx3e9+Fz09PbzjUAum29PT06ivr1d8Z+DEr5wBwAMPPIC7d+9KWuZXck88mUxieXlZleV8YG9y5vF4jryYOD+R8tbWFtWkvxz9/f0HVitIaRhJIkUHuLq6qqlgAI729nbFzX+3trY0pb0rBDdhXV5eLniMUgmnxXD9+nU88cQTipmm8jE0NITp6WnJgWQ0+jX3vGJlZDIZTE9PY2hoSFLbSdPf3w+DwYClpaWix6k13rLZLEZHR/HEE08oWi/w7uQMwJ6Hl9VqlZQwWck98bt376Kvr08RjxU+7HY79Hq95LyFWqFy5BXofbPQ+/eMBjc3NxXZJuYmZ9zklpSGkSRidYCBQADZbFZRLYZQamtrkUgksLOzo0h9LMtia2tLFcmBWHQ6HZ588kmMjIwU/LGlVMJpobjdbmxvb6u2asZRV1eH+vp6yUEVNPo197xiZSwsLKC+vl4TfoTAOxrIGzduaFJ7Njc3B7vdrspq+LuTM+wNkKGhIUnu2UrtibMsi6mpKQwPl9p8pQfDMOjs7MT6+rpqbSBBdOhFpGv6kK7ug//cv0QikaCenxTYS8qt1+v3jSxJaRhJIlYHuLCwsK9h0ho6nQ7t7e2K6c4CgQAsFotqP57EcvbsWZSVlRWMPlQq4bRQrl+/jieffFLVVTOOhx56qKDLfSlo9GvuecXKGB8fVySjghgGBgaQzWaxtrZW8Bg1xhvLsrhx4wauXr2qyv1N/VGuEc6dO4fvfe97iMVisFgsgs9Tak98a2sL2WxW9ZQwnZ2dmJ6exsDAgKrtkENuIuW52Vl0dLCKhOQzDIOBgQEsLCygtraWmIaRJGJ1gEtLS3jve99Lu1mS6ejogNvtVmTb1e12K7I9TgqGYfD000/ju9/9Lrq6ug49gJRKOC2EtbU1xONx1Xy58jlz5gy+853vwOfziU75RqNf88/jK8Pn8yESiWju3q3T6fD444/jZz/7WUFdlxras8XFRVRUVKC3t1fRejneXTm7R3l5Oc6dOycrTNoycg31f9GP2i8+QnxvfHx8HMPDw6qvUHR0dBAxUxWbAF2opkIs6+vrik54uclZKYRqv9T0AYpEIgiHw5rWWLW1tWFra0tUP0nt083NzSM1OQOAU6dOwWw2F80zrDYsy+LnP/85nnzySc1k2CgrK8ODDz6IO3fuAJCnFcvVv9JkbGwMDz74oGb6MJfz588jFothY2NDdlkk7oncqtnjjz+u2jP33clZDpzQU6rgvfL6Neh2Y9CHlonujScSCSwtLWniV2NdXR3S6TTC4bCscsQmQBeqqRDLxsaGonqC1tbWfS+sYgjVfqnpAzQzM4OzZ89qYpupEM3NzfD7/bBc/4LgfpLap0ppF0nCMAyeeeYZjIyMaNatfXl5GZlMRnOpfi5duoSFhQUkEgl5WrGcRNu0SCQSmJ+fV93brBBlZWV49NFHIScdIweJe+La2hpYllU1zdm7k7McuMAAqf4/0eGXkDVakHZ0EN0bv3v3Lk6dOqUJLQvDMOjp6ZEUPJGL2AToQjUVYojFYtjZ2aGWtokPnU6H8+fPY2ZmpuhxQrVfavoALSws4Ny5c4rXKwaTyYSamhqs9X5UcD9J6dN4PI6dnR1FtIuk6e7uhtPpxNTUlNpNOQTLsrh+/TqefvppRbMBCMFqtWJgYADj4+PytGI5ibZpMTExgYGBAVitVmp1yOX++++Hz+eTnSaQxD1RjQwU+Wj3J68KMAyD4eFhjI6OStqqoeFVxbIsJicn8aEPfYhouXLo7e3F7du3ZflH8WkIxGgxSOgPXC4Xuru7Ff8Cnj9/Hl/5ylfw0EMPFVwyF6r9UssHKBgMYmdnR3UNpBDa2toww+ph+fBXBR0vpU83NzfR2tqquQmEEBiGwbPPPouvfvWrOH36tKa2vRYWFmA0GjWnk+K4fPky/uZv/gb33/+b2JWoFeP0r7TIZrOYnJzExz72Mar1yMVgMODKlSu4efMmnnnmGcnlyL0nbm1tIRKJqP7D8+jdSShz7tw5bG5uCnaA5nIiWkauUWmP2+1GWVmZpowtu7u7sb6+TsXvLF8vQFpTlVve6uqqKmLPpqYmmEymglGEWsknx8HXnunpaVy4cOFITEaU8Dvb2Ng4cnqzXNrb29HY2CgpYp0PUrqf69ev45lnnlFda1uI5uZmVFVVFYx4FdsPJHzO8llcXITdbtekUXQ+w8PDWFlZEfz8pfG8uHXrFh555BHV5Rrav7MqjMlkwsWLFwXfpLiciJXX6UzOtBIIkIvD4YDNZiMSGJBPvl6AtKbqHS3XF7C6uoq+vj4i5YqBYRhcunQJ09PTvO9rJZ8cR357WJbF7Oys6n5TQmlpaRGcE1YqHo/nyOnN8nnmmWdw8+ZNpNNp2WWRGMMzMzOwWq2qRcsJ5cqVK/uBAfmI7QcSPmf53LlzBw8//LCo8tSioqICly5dwq1btwQdT/p5sb29jfX1dVy6dEnS+SR5d3LGAxcYIEQgy+VEjA6TT70Tj8exurqKixcvEi9bLqdPny7p6iyFfL0AaU0VV56r+yOw2+1wOBxEyhXL+fPnsbS0hFQqdeg9LeSTyyW/Paurq7BarWhoaFC5ZcKoqalBKpWiZkbLsiw8Hg+am5uplK8Uzc3N6OjowNjYmOyy5I7hbDaLX/ziF3j66ac19cOUjzNnziAWi/H+ABDbDyR8znLxeDyIRqOa3Rbm4/Lly5idnUUymSx5LOnnxejoKIaHhzWRG/ddzRkP9fX1qK2txeLiYkl/JJo5ETkRp9lsplK+HPr7+/HNb36TeH65fL0AaU0VV97YT3+KU6dsxMoVi81mQ0dHB2ZnZw/dOLWQTy6X/PZMTk5qNuqLD4Zh0NraSs3vLBgMwmw2ayJgRy5PPfUUvvjFL2JwcBAGg0FyOXLH8PT0NKqqqo6EprGsrAyXL1/G6Ogonn322QPvie0HUj5nHLdv38bly5c1pSMshcPhQH9/PyYnJ0s6FJB8XiQSCczNzeE973mPpPNJ8+7KWQGGh4d5tzb5/KdoaIRYlsX09LSqGQGK0dbWhng8XjCVk1Z0U4Xasby8rGqYNLA3xuT46olFrLcc37mY/RHW1tY0Z2tQivb2dirb8MDRM58tRkNDA7q7uwtu0ylBNpvFjRs38NRTT2l+1Yzj0qVLWFlZATvzz1Q1sxxCyt3Z2YHL5TpSP6Q4Hn74YYyPj8Ow9KZi+YcnJiZw+vRp2O12yWWQ5N3JWQEGBgYQCAQQDAYPvM7nP0VDI7SysgKz2axZEadOp8PAwADm5+d539eKboqvHYFAAKlUSvW+7evrQzwe30/nRBux3nJ855a9+XkMDg6ioqKCdnOJ0traSk13dlTyaQrl6tWruHPnDu+WuxJMT0+jpqZG0+bG+ZjNZpw/fx76Nz9PVTPLIaTcsbExnD9/XpM7L6VoaWmBw+FA2Y8/L+r+JLWfuYhW0jtBcnh3clYAg8GA+++//9DqGZ//FA2NEJcDTcu/HM+cOVNQd6YV3RRfO+bn5zEwMKB63+p0OgwPDxPR+AhBrLfcoXPtrfhR9uKR/CXe3NwMr9dLxWh1a2urYNqZo0hDQwO6urpUWT3LZrO4efPmkVo147hy5Qp+mDmPlK2VmmaWo1S56XRac5MNsVy+fBlvGYYVyT+8tLSkuYjWdzVnRXjggQdw7dq1A3v2hfynSGqEuDQWH//4x4mVSYPu7m6EQiFEIpFD5oZa0U3xtWNhYQEf/OAHVWrRQS5duoTPf/7zuHLlCnURqlhvufzjbmSaEbh9W1M3MKGYzWbYbDYEAgHRuRCLkUqlEP7/2zvv8Liq43+/I8m9IDdZsizLvfeKK8YBQiCkQSC0AAktjUAKIV8SAqQQEkiBFMKPAElIAUJCMSWm1wAB9yo3ucqyZUmusmXL8/tjztrXssruaqVt530ePdp27x4d3TJn5jMzu3cnTXJEuMyZM4cHHniAsWPHtmhJgVWrVtG1a9ek8pqF6NGjB4f7ncJbvS9mRKHpSJvrOtjYfletWkV+fn5SFkUOMWLECObO7cmAj11N165dG/xsU+d5yZIlCWfIes9ZA3Tv3p38/HxWr159wnuN9YGMpl9aaJ+lb/2dMWPGJETGSENkZWUxcuTI4/ryxaJXXLg9NiPdR+sNb9Lpb+eTt2d5wmiEOnfufFT8Gk/C6eW5ePFipk2blnQejRB9+vQ5oXdfU7UqpaWl5Obmxr0mUqzJy8ujT58+LXpcqirz58/n1FNPTdpjbPr06SxevPi4GpDNWauxLlSVxYsXJ035jPrIyspiypQpUXtww533iooKKioqmlRUvTnwxlkjnHzyyXVeoBrtAxlFv7SO799HZuVG8lY8wuTJk5s07pZi9OjRx+nOOr5/H1llRWTtjL5XXLg9NiPdR8f374Od6zktc2FCFU+dOnUqS5cubZaivuHSWC/PkP5y1KhRcRph0ykoKDihGG1TtSrJXny2IU499VQWLlzYYj03165dS7t27Zolo7alGDRoECLCli1bjr7WbLUa69lfqLh1oteHC4cJEyawdu3aqGrvhTvvS5cuZcKECQm3wEqcO1SCMnToUHbv3k15eflxrzfaBzKKfml7J1/L3tbdWdL9oy3a77Ep9O/fn6qqqqPzs3fytRzuPpjD3aLvFRduj81I97F30rXsqOlAxkdujGpczUWfPn3o2LFjs9SNC5fGenkuWrSIyZMnN6m8QrypKymgqVqV7du3p5TeLEifPn3o3r17o31gY8X8+fOZPXt20nrNwMq2TJs27ThvT3PVaqxvfwsXLmT69OlJPY8hunTpQkFBwXHRmXAJZ95ramooKipKiKKztUksUzEByczMZOLEiSxdupRZs2Ydfb2xPpDR9EurLpzJ3G67Gq3tkkhkZGQwfvx4li9fzowZM2LSKy6SHpuR7GN9q/683evLfH3q+U0aX6wREWbMmMFbb70Vt7pODfXyPHDgAGvWrOGcc86Jx9BiRs+ePdm9ezfV1dW0bt0aiE0fvmTU4IXLKaecwjPPPMPQoUOb9Wa/ZcsWDh8+HPfyNrFg3LhxzJs3j927d9O5c+dmq9VYF3v27KGkpITPf/7zMfu+eDNlyhReeumliI+NcOZ93bp15ObmxlSHGiu85ywMJk6cSFFRETU1Nc36PXv37qWkpCTpQkfjxo2jqKioxcIf0bJ8+XImTJiQkCvKUaNGsWfPHnbs2BHvoZzAkiVLGDFiBJ07x69obyzIysoiLy8vZn029+7dC9jqPlUZNGgQGRkZbNq0qVm/J9TPMJHkBtHSpk0bJk6c2GJZ2EEWLVrEhAkTEl6vHAlDhgxh9+7dVFZWxnzfK1asSEivGXjjLCy6detGfn5+vTW9GiIcoXWI5cuXJ0UiQG169uxJly5dKC4ujvdQ6qT1hjfJfvRCKHqZsWPHxns4dZKZmcn06dPD7ilXH7EWH9fU1LB06dKEy2SKlsLCwhOSAqKlpKSEgoKChDT2Y4WIMGvWrIiPy/qOw7per6ioYMeOHQnZpi5apkyZwsqVK2PSpzRcDh8+zMqVKxO2cHm0ZGZmMnbs2Hp7EUfL/v37KS0tTVhvrTfOwmTSpEksX7484u0aE1qHCHUESFQrvjFautp9JHR8/z50x1pOz1x4QsmPRGLSpEls2rTpqEcmGmItPl61ahW5ublJo4FsjFgWoy0pKUmp4rP1MXr06KMZbeFS33FY1+uLFi1i0qRJR0PNqUC3bt3o06dPi+n1AIqKiigoKEjIEF1TGT9+PEVFRTFNmlqxYgUjRoxIWGeIN87CZPjw4VRUVNTbrqg+GhNah9i8eTPt27dP2ubJo0aNorS0tEmGRXOxd/K17DjSkSOzvxnvoTRIu3btGD9+PAsWLIh6H7EUH6sqCxcuZObM+NerixW9e/eOmXG2Y8eOlE0GCNKqVSsmTZoUUZiuvuOw9uvV1dWsXr065bw9YEVU62oB2FwkYq2uWJGXl0fbtm1j5vUGK0aeqJEU8MZZ2GRlZTF27NiIvWfVhTMp/+xf2Tf5y5R/9q9U1yNQXLZsGZMnT07aEEmbNm0YM2YMS5YsifdQTmBD64E83u2L9Jp9abyH0ijTp09n1apVHDx4MKrtQ8dbfcdZJGzcuJGsrKyUSMkPkZ2djYiwZ8+eJu3nyJEj7NixI2kXU5EyefJkioqKqK6uDuvz9R2HtV9fsWIFAwcOJDs7O+ZjjjcDBw7k8OHDzdbTNci2bds4fPhwSp2rQUSEsWPHRpW1WReVlZXs378/bglY4eCNswiYMGECB5c8R9fHLoppI9sDBw6wceNGRo8eHbN9xoOpU6eyfPlysta9HlVT+KbqperbfvHixUydOjUpxMZdunRh8ODBUa24Y603W7BgAbNmzUraBUNdiAgFBQVNXoHv3LmT7OzspOsxGi3Z2dn069cvZjdHMM/ssmXLOPnkk2O2z0Qi1J4tuGCN9hxtbLslS5YwZcqUpLjGRcuoUaNYt25d2IlnDc3ZqlWrGD16dELPV+KOLAHJzc1l+sF30J3rY9rItqioiMGDB9OhQ4eY7TMe5OTkkJeXR+brv4iqKXxT9VJ1bb9//342btyYVOVJZs6cyeLFiyPODo6l3mzHjh3s2rUr6RcMddGnT58mezNCyQDpxJQpU6LS3dZHaWkpqprQ3oumMmHCBNavX8+BAweA6M/RhrY7cOAA69evT6prXDR0796dzp07Hy2y2xgNzdnatWsTviqCN84iQETYO+XL7KRzTBvZrly5kgkTJsRsf/Fk+vTpvCoTo2oK31S9VF3bL168mDFjxtC+ffuo9hkP8vPz6dmzZ8ReiljqzebPn8+MGTMSrmp2LCgoKGhyyZLS0tK0SAYIMmjQIKqqqigrK4vJ/pJdyhEOHTp0YNiwYUczDaM9RxvabsWKFQwdOpSOHTvGZMyJzKhRo1i7dm1Yn61vziorKzl48GDCn7/eOIuQwtOu4IHMc9mbFxsBa0VFBfv27UvqliVBhgwZQnHrASyZ9jOqC2dGpIFqql6q9vaHDx9m2bJlSSmSnTVrFgsXLowoOylWerPdu3ezadMmJk2a1KT9JCr5+fns2LGjSXX5duzYkdLFZ+siIyODSZMmxSQr+9ChQ6xbty6lymfUx+TJk1m+fDmqGvU5Wt92qsry5cuTpt1fUxk+fDjr168P67pY35ytWbOGESNGJHRIE7xxFjEdO3akX79+J9Q8i1ZLsHLlSsaOHUtmZmYshxk3MjIymDFjRoMZh3XVfou02Xk49eNWrlxJQUEBOTk5sfnjWpBBgwaRmZnJxo0bo95HtMfkggULmDRpEm3bto36uxOZtm3bkp2dzc6dOyPetvWGN+ny6IV0L19Ebm5uM4wusRkzZgxr1649wbCN9Fhbt24dBQUFSV/YOBz69u1LRkZGvTrHpmhFt23bhojQr1+/pg4zKcjJyaFNmzYnZFxHUlevuLg4YWubBfHGWRSMHz/+hPo10WgJVJWioqKETueNhvHjx1NaWlpvRee6ar9F2uy8sfpxqsqiRYuStgxEqPhnU8pqRHNMHjhwgKKiIqZOnRr19yYDBQUFUenOOr5/H5QXM4f5KbOgioQePXrQpUuXExYNkR5rq1atSguvGdi5HPKe1UVTtKLpEBoOIiIMGzbshD7E4dbVC/WBTgZjtsWNMxEpEJFXRWS5iCwTka+717uKyIsistr9TtieKEOHDqWsrIz9+/cffS0aLcG2bdto06ZNyhT4DNG6dWumTJlSr2FRV+23SJudN1Y/bv369bRr1y6pxcajR49m165dUWt8ojkmlyxZwvDhw1OytEGQgoKCqNo47Z18LbtbdWPL0IuaYVTJQaggaJBIjrWqqiq2bduWFN6LWDF27FjWr1/PoUOHTngvWh3aoUOHWL9+fcot7htjyJAhJywOwq2rt2HDBvr370+rVq1abLzREg+172Hgm6o6X0Q6AR+KyIvA5cDLqvpTEbkJuAn4ThzG1yitWrVi2LBhx3m9omluG9o+FVc906ZN4+6772bKlCkniPHra7IdSbPzhhp1g4XmTjnllKSe26ysLKZNm8b8+fM544wzIt4+0mPyyJEjLF26lCuuuCLi70o2evfuzdtvvx3xdtWFM3mu1x4mjUlNPV44jBgxgnnz5lFTU3PUexjJsbZ27VoGDx6csJXZm4NOnTrRt29fVq9efYJRGm1j9NWrV1NYWJgWoeEghYWFVFZWUlVVdbSUTX1zWPv1DRs2MGLEiBYba1Nocc+Zqpao6nz3eA+wAsgHPgn8yX3sT8CnWnpskTBmzBhWr14d9faqmhTpvNHSsWNHxowZw8KFCxv9bKzrc5WUlHDgwIGkOQkbYtKkSWzYsIF9+/YdfS1SfV64FBUVkZOTk3Ke3LrIzc1l165ddXoyGmP79u1pU3y2Ljp37kxubi4bNmyIavu1a9emZImWxpg4cSJ75z8Vs7qPq1atStp2f00hKyuL6T0OkP2PCyO6zqkqmzdvTppCvXHVnIlIX2Ac8B7QU1VDisltQM96trlaRD4QkQ+amg7fFAYOHMiuXbuirjS+detWOnfuTI8ePWI8ssRh5syZrFixotGq4rHuB/nBBx8wc+bMlNAEtW/fnrFjx7Jo0aKjr0WqzwuHZNfoRUpWVhY9e/aMuJXT/v37qa6uplu3bs00suRg9OjRJyRFhcOBAwcoLS1NmhtkLBk8eDAjd7xARnlxk+s+7t27l507dzJkyJDmGGrCM2nPa2Tt3hTRdW7nzp20a9eOLl0SVjF1HHEzzkSkI/AEcL2q7g6+p5YnW2eurKrer6oTVXViPA2bzMxMRowYEdUFCswlneqrx27dujFw4MBGWzrFsj7Xzp07KSsrS5m6cWC141asWMHhw4eBhucr2rksKSmhpqaGwYMHx2TMyUDv3r0j1p2VlpZSUFCQ1OHyWDB06FA2btwYcSPq4uJi+vfvn1YhzRCtWrVi27BLqMjs0uS6jytWrGDUqFFJoZ1qFuZ8mzLtxN5J4V/niouLk6pkVVyMMxFphRlmf1XVf7mXS0Ukz72fB8SmO3EzMmLECNatWxfxdqrK+vXr00IQe8opp7Bo0aIGq93Hsh/khx9+yPTp01PqotW9e3cKCgqOZgg3NF/RzuXChQuZPn16wtf+iSXRFKPdtm1b2tU3q4suXbpw0kknRdwGa/369QwbNqyZRpX45J/6ef7S/qIm131M9Kbdzc1Jkz7DY12uYHt2+LKgrVu3JpXHNh7ZmgL8EVihqr8IvPU0cJl7fBnwVEuPLVIGDBhAeXk5VVVVEW1XWlpKu3btUjqkGaJXr1706tXraIXsxmhKL87du3ezceNGpkyJTYHgRGL69OksWbIkYk9FOOzZs4etW7emTWmDEHl5eREbZ2VlZd44cwwbNiyixemRI0fYvHlz2obiwMTs1dXVlJeXR72P8vJyDh48SGFhYQxHllyICP3792fz5s1h3TOOHDnCtm3bkmrO4rFMng5cCswRkYXu5yzgp8DpIrIaOM09T2hatWrFoEGDIvaerVu3jhEjRqRNaOTUU09lwYIFYRkWTenFOX/+fCZNmpSSzagHDhyIqobdVy4SlixZwrhx41K26Gx95OTksHfv3kY1kUHKysrIy8trxlElD0OGDGHz5s1hf76kpIQuXbqkXXZhkIyMDEaPHt2kBvJFRUUJ37S7JejXrx9bt24N655RVlZG586dk6rFVTyyNd9SVVHV0ao61v08p6o7VfUjqjpIVU9T1eiXFi3I0KFDKS4ujmib4uJihg4d2jwDSkD69u1L586dw9LnRduLs6qqitWrVzN9+vRYDDnhEBFOPvlkli5dGtP91tTUsGLFipT0NjZGZmYmPXv2DNt7tn//fmpqapJGUNzc9O7dm927d4cdOdi4cWNaaRrrY/To0WH3h6yLVM7yj4S+fftSUlIS1j1j8+bNSVfzMr1N7xgwePBgtmzZ0qCmKsiePXuoqqqioKCgmUeWOIgIs2fPZv78+Y16z6Ltxblw4ULGjBmT0qvycePGsXHjxojD6A2xdu1acnNzk7LFVSzIz88POymgtLSU/Pz8tPF4N0ZmZib9+/cPu8XY5s2bk0qQ3VwUFBRw6NAhKioqIt62oqKC6urqtLp/1EePHj04dOgQlT0mNHrP2LZtifLU0AAAIABJREFUG3379m25wcUAb5w1kY4dO5KTkxN2uKm4uJjBgwennUt6yJAhR+vMxJrq6mqWLVvGjBkzYr7vRKJ9+/aMHDkypt6zpUuXpqXXLETv3r3D7rGZ7vXN6mLQoEFhndOHDh2ivLw8qTQ/zYWIMHz48Kgy/ZOlaXdLICIUFBSEde/dvn170mlF/X84BgwZMiTsgoybNm1KS0FsRkYGs2bN4sMPP4z5vpctW8aAAQPo3r17zPedaEycOJFVq1bFJDGgsrKSysrKtMgaro+8vLyw22Pt3LkzLQr0RkJhYWFYGZslJSXk5eWlVBZ1Uxg5cuQJ/SHDobi4OCWKa8eKUGizIfbt20dNTU3S1Sb0xlkMGDhwYFirxyNHjrBly5aki33HirFjx7Jr166IM+Qa4siRIyxevJhZs2bFbJ+JTJ8+fcjKyopJYsDy5csZN24cWVnx6OKWGOTk5FBZWRmWLGHnzp3k5ua2wKiSh9zcXPbv399oqH3z5s0MGDCghUaV+PTt2/doC6JwqaqqoqKiIimadrcUvXv3bnRxVVJSkpS1Cb1xFgPCFcZu376dzp07p7QuqiFCvSLra4geDWvWrKFr165po8EQESZNmsTy5cubtJ8jR46watWqlCrWGw2tWrWiS5cujZY2qK6uZt++fUm3+m5uMjIy6Nu3L1u2bGnwc6WlpT6kGSArK4uBAwdG5D0rLi5mwIABab2Yqk2oHE5DkYRkDGmCN85iQmZmJoWFhY1eoDZt2pT2gti6ekU2hYULF6ZNy6EQY8eOpbi4OKq+kCE2b958tEdiupOfn99oG6eysjJycnJSoiVYrCksLGTbtm31vq+qSXuDbE6GDh0aUX/SDRs2pFWWfzh07NiRtm3bUllZWe9nysrKklIr6o2zGDFgwIBGjbNt27albUgzRF29IqOlpKSEQ4cOpd0Fq1OnThQUFETVnSLEypUrGT9+fAxHlbzk5uY2mhTg9Wb107t37walCuXl5XTq1In27du34KgSn4EDB7Jly5aw9KPJ1rS7JWlscZWstQm9cRYj+vbt26AOSFWTrkJxczF16lRWrFgRdvmR+li0aBHTpk1Ly8ylcePGRV3I8vDhw2zYsMHXSnLk5uY2GtYsKyujZ8+eLTSi5CJ0c6zPyAhpfjzHk52dTceOHcPS4JaVldGhQweys7NbYGTJRa9everVnVVVVVFTU8NJJ53UwqNqOul3V2smevXqxa5du+oNNZWXl9OhQ4ekqlDcXOTk5NCrV68mVcnet28fmzZtSlvvz/DhwykpKYmq5tn69evp3bt32mofa5OTk9NozamKigofAq6H9u3b0759+3pDS77lVf0MGjQorNDmhg0b0l4SUx95eXn1Lq527NhBXl5e0iUDgDfOYkZWVha5ubn1FrQsKSmhT58+LTyqxGXatGksWbIk6u2XLl3KmDFjUrJVUzi0adOGgQMHRhXaXLNmDaNHj26GUSUn2dnZVFdXc+DAgXo/U1FRkRa9cKOlV69e9XqAfJZr/fTv3z/sUiTeOKubnJycemUJIeMsGfFpHzGksLCQ+fPn12mgrVu3Lq2LfdZmyJAhPPnkk5SVlUVcn0xVWbFiBZdffnnzDC5JGD16NO+8805EdY8OHz7Mpk2buOCCC5pxZMmFiJCTk8N7771Xp2f7yJEj1NTU0KlTpziMLjnIz89n2bJl7Nmz54T3ysrKvHFWD4WFhTz++OOoar3eHS+JaZiuXbtSVVXFoUOHTqijV1FRkbSaZO85iyETJ06kT58+ZGZmnvAzePDgtC72WZuMjAwmTpzIsmXLIt5248aNdO7cOWlXRLFi8ODBbNu2jYMHD4a9zYYNG8jPz/fh9VrMnj2bk046qc5zt1WrVpx11llJGRppKUaPHk2/fv3qnL85c+bQoUOHeA8xIenYsSOdO3duUHdWVlZGp06d/DlbDxkZGXTr1q3O0GZlZWXSery95yyG9OzZk7POOivew0gaJkyYwL333sv06dMjqt2zbNkyJk2alPY3y7Zt21JYWMjGjRvDzuJav369rzBeByNGjPDz0gS6d+/ur31R0q9fP7Zu3Vpvf9utW7cmXV/IliaU1FM7aSeZ5Qjec+aJG127dqVXr14UFxeHvc2BAwfYvHmz10w5hg0bFnYhS1Vlw4YNadk+zONJVAoKChosBVFaWur1yo3Qo0ePE5J6qqqqUNWk9dp6z5knrowbN44FCxaELXZds2YNgwYNSttEgNoMHjyYF198MazEgL1799K+fXtf5d7jSSB69+7d4DlcUlLCRz/60RYeVXLRrVu3E7JeKysr6d69e9JGWLxx5okrI0aMYO7cuRw4cIC2bds2+vmioiJOPfXUFhhZctC1a1fGjx/P2rVrw/r8Rz7ykWYekcfjiYScnBwGDBhQ7zk8YMCAekOeHqNbt27s2rXruNeSOaQJ3jjzxJl27doxYMAA1q1b12jCxP79+ykrK2Pw4MEtNLrk4Oyzz473EDweT5RkZGRw/vnnx3sYSU3Xrl1PyBTetWtXUkcJvObME3dGjBgRVlhu7dq1DBky5IR0aY/H4/GkL+3bt6empobq6uqjr+3du5cuXbrEcVRNw3vOPHFn6NChPPXUU5SXlzeoD1izZg2zZs1qwZF5PB6PJ9EREbKzs9myZcvRFlcVFRVJ3e7KG2eeuNO+fXvGjRvH888/3+DnOnTo4Bv/ejwej+cEBg0axDvvvHP0eatWrZJaqyf1NatNBiZOnKgffPBBvIfh8Xg8Ho/H0ygi8qGqTmzsc15z5vF4PB6Px5NAeOPM4/F4PB6PJ4HwxpnH4/F4PB5PAuGNM4/H4/F4PJ4EwhtnHo/H4/F4PAmEN848Ho/H4/F4EghvnHk8Ho/H4/EkEN4483g8Ho/H40kgvHHm8Xg8Ho/Hk0B448zj8Xg8Ho8ngfDGmcfj8Xg8Hk8C4Y0zj8fj8Xg8ngTCG2cej8fj8Xg8CYQ3zjwej8fj8XgSCG+ceTwej8fj8SQQoqrxHkPUiMgOYEO8x9EA3YGyeA8iifHzFz1+7pqGn7+m4eevafj5i55En7tCVe3R2IeS2jhLdETkA1WdGO9xJCt+/qLHz13T8PPXNPz8NQ0/f9GTKnPnw5oej8fj8Xg8CYQ3zjwej8fj8XgSCG+cNS/3x3sASY6fv+jxc9c0/Pw1DT9/TcPPX/SkxNx5zZnH4/F4PB5PAuE9Zx6Px+PxeDwJhDfOPB6Px+PxeBIIb5ylACIySETaxXscyYaIDBGR8e5xVrzHkw6ISHsRae0eS7zH4/F4PE1FRGJuS/kbUmrwEWCniLQFylT1+XgPKJEREVETW+4C/iAifwYqRORZVa2O8/BSFhHpAUwElojIYaA0zkNKetyioqeqbon3WJIFEclU1Zp4jyPeiEiGqh6J9ziSmdACsznm0XvOkhAxgv+7tcD/Az4OLIrPqBKf0Lw5wwxV3Qa0Bn4AvOwNs+ZBRDIBVHUHMA74E/AIMMh7z5pMb+BsESkQkUHxHkwyEDLMRGSCiLSK93jiRcigEJHPicgn4j2eZEQdIjJMRB4Ukeki0jkW+/bGWRLijocjIjJARIYBHwL3AK+p6tY4Dy9hCcxbvoicJyInAdcDm4H24ENtzUEtL8USrL3KL1S1KGQoe8Kn1uJsNzAZeBu4xB+/J1J7TkRkqoj8C7gT+IGIFMRnZC1LHfMwWURuBz4GfEtEZsVnZMlF0DEiIlkicjlwB7AGmA18Lhbf442zJCHkfQg9FpFvAE8DlwA3A7cBJzsLvo2IdIvTUBOK2loAEbkZ+AsWCv4qZiw8gF2oAdLiQt2cOONBAs9PF5GnReQW4FXseB0kIj3jNsgkJeT5dYuMXFUtB8qB14G/uFW8v647XAhTA8+HAz8GnlXV04DtwBXxGl9LEYwYBLgF6K6ql2HGxWfdgtXTAAGPYzvgJGAssF5VfwK8DPQSkXFN/R5/EicJAVf8HOAzwBZgJPAOcCEwHHgMMzieAcbHZ6SJReBEOk9EBgNLgY9iN7OzgCtV9SGgs4i8Dkz23ofoCen5nJHQVUROB84F/g/oDNwFvADkAeeIyG0iUhjHIScVzihr5wzdF0TkauDXwD+BS0OfiecYEwlVrXHzdZW7Ya4D5gED3Ef+DRSmqtcoqIkSkdYi8gsRuUlEhgI3Av2d4fY8kAmcH8/xJiJ1eBw/LiLPAD8FDgFPALtFZCSwANPSfrKp3+uNswSltq5MRLqJyH3YyfMS8C/gR5hL+lfA/ar6LPBb4Cuq+mIchp0Q1Jq37iLyA+CzQLWqPgV8A9PnPQCMEpGpwDXANar6Tx9qi5zQnDujTETkeuC7wF7gBmAKMAE4DxiNGRR9sASWDfEZdeJTh+d3CPAgINi5PwpbpG0BVET+JCLXtfhAE4Q65msq8F+gH3ATduy9AFSLyCxV3YR5c3NbeqzNSfB8dM97YBGDMmxB/xtgPbAYk3YAPASkfaJEkKDnVURaiWX3n4M5QQQz0FYDJcAc4Ahm/D/S1O/22ZoJijsgVER6YV6wUiAHmKuqFSKSA0wFLgJOBcpFpLeqLofjMhLTDrdK7IGtXp4BxgD/UdVi95EM4EWgI1AFVKnqdizE4YkQd6yFPJS5wBDgO8AwVa10BsUYVT1VRO4AfqKqZ4rID9L1GG0MEclS1cOBeZ2KhS9XYwbvVlUtEZF52PXhaeyGcK17nFa4m2hNYL7GY16NcsyIPQy8CXwB+CWwCjhPRN4B/poqx6GItFPVqsA8TANOV9XbnBSmFSbhaI+do3cAr4nIn1X1f8D/4jX2RMFJiHJUtcR5XlsDPwGWA08CXwc+j91XemJe6+XAyW67tbEYh/ecJRB1rPouwrxknYGFwJ+BESLSwxkT/wP+AfRQ1bNVdXNo21S52IRDaN4Cv8/EQrx9VLUUO3n6i8hJ7sR7Bzu5ugE3qOrC+Iw8NXDesski8lcs5P4hMB84232kC6aHfBxog90Ugl42H0Z2uOm4APPyhOrCPYx5IW/AJAx/BkaLSFfgWSzBYoCqrlHVb6lqcbrMqZj+9iwsCxgRaSsivwHudR8pAvIxw/Ur2FyNx47P3zoDOOQZSdo5c/MwDrgu8Nq3sehKyODai2mT7wQuBy4AOgGfVtWywHZJOw8x4lNADwAR6YBFphR4wmk8ewBDVPUU4HFMTrAI+GUsS9p4z1mCEPQ+uOetsFDQx0JhHxH5ABgEXIwdMDcDrVV1v3s/7er31Jq30EVlIPCUqv7KPf87MB04E/inqr4uIh9X1aoWHm5KUPs4cyvL6zCR9d/caz8BrhGRuar6rlu1twuF20Oe3XRaRDSG0/4cEZF3gTPESmPswrxkl4vIL4GrsOP408AlqnqPiNzpFmvH7Scuf0QLEDp2RORC7Oa4SFW3iAm0c4FsVZ0e+PxQYC5QiN3zSlV1Ve39Juux6LROFwI/UNUFIjJQVddgZYK+raofAqjqLhHJxoz8KuB7qrqx9v6SdR6aioj8ENitqj8XkZ4iMgoz7rti3v5d7qO5wAAReRFYCdwRei+WESvvOUsQAt6HP4nIxZj7uQ12IQ5xGBMcHhErOFujqvtFJMMdFGllmMHReRsqIn8EfujCGXuAShFp4z6Wibmjh+AWJN4wix49lpwyRUS6A9lYIdS/BT72HqaD+qrb5q2AYVZX5ljaEzCoxmDalXOxUhmfE5H3sJDmx4G2mGao0p3326F5C2ImEoFj50IsoWeLiHwZK2GwGzhJRPoENinGrqdTgS+o6istOd7mIuDhOoTdGz4uVlpprnu9P5ZNGPp8fyzsvRT4vqo+0YLDTVjc4hJMy/kZsYzVj2MetAHAW8Dp7rN52P3l/4DbVPVrqloUOPdidl0Tf42MD3V4H8Zgce0/A30xke/NmPD/KuCLmOjwAReqS0tqewWccfAYNm+ZmC6vDXYCZWEh4XHAZ1V1X8uPOPmpY86HY2GipZg34hzMk/uuqt4vlqGZjWXEHnahAE8t3AVdnLcsAzMgfufefg84BQvPTcAyjL+ECZD3AV9OdSOsNiLSUVX3Bp4PwebnAmAalhzxNjAY0//ci4XQHwZeVdXDbruk9iyK6WmPqOrOwGtn4OqVYTUv3wNWYKHc/wEzsOPmS6p6yG2T1PPQVETkk2oJYkc1niJyK6bH+577eQ+bt0sxI3gmcEvQsG2uefTGWQtTx41uvKrOF5EJwK2qeo57/VUsVJSPheRaATcHvBZpJfivY97OxLKw2mDzdDvmxr8AO4neAC4DqoF7vGEWHcHjLBB2uwbIUtXfish3MFHsncDvMY9Pe+BGVV1aex8eI3g8i9Uk3OVuDncDD6nqUhE5D5imqt8Q05/OBl5Q1X8F9pMWcysiU4Afq+ppbkE2SFX/63RVnTCj9QvAQeBvWPb1OKzzx8OB/SS1QSIi+Vh25ZNYSO3rwPNY4sNHgP2Y5+wpLOrSEUsa26yqjwb2kxbHTX04LdmD2LFyANMgP66qT4qVVLoG6IVJi/4N7MAWS28FJQTNOsY0/v+0KLXdnmJZbY9gQsPXsVopFwH/VdXnxCo3/9tpCIIX8rQ7qWoZCPnA1zCD9QNsDr8IPKKq74jIj4HlqvrXZL8Qx5Nax1x7rD5ZKZaAMgC4QlU/695/E0sE2AUMVdXF8Rl1cuGuCV/CiqC+CPwRC2WuVtV/i8gM4FZMuP7vWtumxbFd69x/GmtVNwwLmWdji7HnsWtCB/f8SVV9Jz4jbn5E5CqsDM1gYAO2GN0FvMux0kpXYQlRV9faNi2Om3AQkelY676NWBmRr2HZ/SXAtap6voj8DPO6Ph/YrkXm0BtnLYwT+f4Ey2hb5S7C38Q8Y6+590qxIp2fVetHGNo27QyzECLSGyuamIedLL8TkbuwbNbemIdxCmbsfk9VF8RtsCmEWAmHQVjnhCXA1RwLr72CecpOA64Oeidrh+3THRe21ICh0RMrY1CJzeWN2CKtE5bQ0h071p/DVvR7Q/tJx5ur09jmY2HL05xn8Z/Y4uwgZuBeBMwC3k+V+Qpd8wNe60FYId2fAm1V9Wvutc9iNcvygVy10hk9QxKYdL53wAlGfhsszLsaCwNnqOpXRaQvlmAyBTsXb8OOpd3xGLNPCGhGJNByyT2/DAv9PIet+s5xb83DxJsV2IrnQVWdHTTMIH2yaOqYt49jnpvFWEmRfLFs1scwoeYCLKx2j1pJEW+YRUjIsxv4PVMsyeJWrKzDP1X1aUy/ch6W8VWIhdpurx029obZ8ajqEXeTHSMi49xNcztQ7sIkT2BtYNZiYv8twE9V9SFV3RvwvCetoREtIjIZ+BnmJXoJKwMBpsm9wnk1tgIdVfWVoC4t2ecrdM0P/B1PYIV0/wnsF5GRqroaK++wEiut8le3TanUKkabbtQRscrCrltzgBHYudZbRPqo1cFc4Ob6YuCVkGEW2k9L4o2zZiSgD+vvXqrCepk9hBkTlSIyR1WXYAbGAFVdq8ey2jLr2m+qEjiRQvM2wl1c2mFainlYVX8Bpqrq+5jWopuqHlDVF+Iz8uRGAtmTgYv4PcAOVf0ocB/m2cE9vgDorKq3qOplqroqHhevREfkuE4VGSLyI6yDx0ViPV7/CPQTkX6q+l/suB6pquWqeqeqfiiOdLi5yvH9g7NE5EznMV+LVbM/D/Panisip2KeslUAqnq9qlbGYdgxJ3jcuOdfEpFL3dMfA99U1fewa9/X3HE1GchU1c1qZTSA5DdOo6X2eSNW/+5GYI6qFmGSmCGYJvldYK6I/APIdAbcJuepjHkWZrh44yyGiNXZCT6fISJzgZvFeuC9D8wTkStUtQJz0V/pTsb7VfW54Pbp4n0QS08Orm5GiMgbmMv5+1jGzAZggvM4rATOFJFOwJ2qOi8+I08N3EWojYjcISJfFMsG+yYWrkRV7wFyROQ8N/9XBm8A4ktjHEfAWxG8MZ6E1XmbAazBwnBbMcPjC+4z96jqHwP7SYtacIH5Ci3KumJC9imYh6McS/6Z6Db5AybmrsCuD8ftJ9lRPa4rBJgh8VV3PDwK9HRRmL9hSVDFWKj3hNpt6UjwvBErs3Sxe6scGO7uN69j8oFpWGj8GeAXqvpFDRQmjue55zVnMcDFqm/GhJmHgO+q1R/7KRbCrAB+joU0N2Cx7KuwPmeDVXVlYF9psUqGo7qbmzA902Jgnao+IiKfB4pV9Q0R2YgJNWuwOkWPYlqBgc7j6IkQETkf1wpIVTe4m+HvsZDlh1j5gbPda0tU9V4R+Rg25/fWt1/P8YjIOVgtrqdU9VGxWmVtsWP4fvexaixT7A96rMRB2lwDgojVKLwN05DdhWlJC7Ab52ZMtL1AVX8mIr1UdavbLiV0ZYHnk7EM9Gws2nI9Vp/ssJqW7JuYB3EQ5sEOFUBNa52nHJ/E1BbT4X0CK6u0B9OTnYZV85+HHU8rsEXRzrr2E0+8cdZExDJnrsRCFA8CdwObsPYg44B+WHbRrzE9QGvsRHvXhTLSEhE5F/h/WB2eedgc3Y31w/w2MBQrrPh0yJsgljb/sqrOj8ugkxyxcgQ3YYU6i7BQyO8xr8T1WPmRLwKVagLZIdjiYrT6UiQNEroxujBIGyz81BMzwi4HlmE3hZ9hN4hbMfH2peo6fKQTtQ0JEfkqtvh6EKtXOAbzLLbBhewwQ+V+PVai5bgki2SktiEgVgD1KeANVb1FRK7EkhxCPVNDhsaDqvqa2yZuobdERUQ+imWtfl5V/yci38fqMu7Czr9PYvXvfq2q1W6bhFoUeeOsiYjIncB2Vb3bPe+KVarei5UcGIatBrtioYy7sVIPaT3xIlIAvIBpbELhzBuwFPEfYSuaqdjq8S7gFuDtRFjRJCNiJUh+CSxV1dvda6dgJUkqsISU0zDjbAVwlqo+ISJDVXVl6MKVaBewREJEOgKtVLVCRH6LCdQvc2Hi57Gb7FnYnK9U1T8Etk2Lea2lA2qDhZXexUKWXwVuUtX1InI95i37NxZlQFXvi8+omxencboJO+/ewuppXarHal4+hTkA2mD13V6N11gTGRE5GxPyz8OKkt8LLFbVP4jIJKxw8xViyWR56lpXJYqnrDYpEaOPBwF9wwNYHHucWw2WYzqSAvfeXEy/cy1wr6ouS4eLcEO4edqExfqP3qBU9ZdYNfQ2wDcwI/eHwP+p6puJeAIlC2oNed8ANjrDGCyEuQIrP7IYW5mfDryMNYUnFHJPBA1GIiEiHxORgYHnVwH/wQTan8c8ZB1FZIRa1vX/MGPtn6p6Q8gwEyeCT/V5FZGPiEingGE2FYsuzMFq5y3BJB9nO6OtOzBMVWtU9b6QYZbsujIRuUREzgnpbEWkECsoW4Nd936AaWzLReSbzuDIAvaqif1fddsl9Tw0BRHpLyI/F5EbXPgSsSLulwDfxRb1PwF+gbU+Ox9zjFSI6cIPq+pGOdb2MCHvK2n7D46G4AkR+oeqpTEvxHpxZbu338KqM+9W1T9j7R4uVisom1ZZbfVcREI3+jsww3Z64L13gU6q+ggm9j8rncO/0SK1MgXdw+cxEexIEWmjVnKgK1as8i+Ym78S+JSq3o/nBAJzOQZX0kGswv9krDTORkzr0go7lh8WkUewhIDdgf0cl5mcqoj1C56LeYZuF5GT3VtjsQ4et2LC/xmYFi/Usqo3riRE8JqZqDfSxhArofIqViR2AlYSA0xz+DJ2bl6AHSPbMT3Uxdgx9W2t1Qs4WeehqbhF0D8w6dDJmNMDrG3fHswwGwwsVNW1WC3Gy4BnVfUbqloVWGgeSeRFkTfOIiAgNhzmfocuGg9j/TAL3GtXA6+FhJqqutl9Pq2y2sT6hd7sQr2IyOUikq+WHRhKm78XE/wjltGaj1VoRl0vPE9kiMg4TNAfCrOHLkZrMU/ZaEwPBZYYsM29v15V/6Cq29N5Zd4QgZvik8BosSr+PbB5/CU279/BvEAPYiVy/qeqF6nqgcB+Uv46IFYG41vAe6p6OlaweIx7uwcm0P4r8GlMY/UhVsdsm6perqrrUijc2xWTcFysqrdiZZTysBZBM7DSNXdwzFhdjBkha1V1uaRZWaUGGAA8oZZBfg9m5IM5SMYDZ6vqqcAOsZZnv8Y8kvMhucpT+QtwBIjI+SLyV+AxsaJ1IQ3OHmzlcwuWoluAGR3HkS6rncCNfTXmqRnn4vyTMNeyhDwGaqnhvURkE9a4+CaNU0XmZCdw4VkC/Nh5bK5w7+W6Ved8LCnlDBG5B8sKe6vWfhLW1R8PahuqIvJ1rHjlbiyMsh+7+VZi2qDZWDj+CKaZmpOOxq5blL4BrBVrAdYVqy0FVu9tF5aQ0hd4znnQ5wKjRGSSkz8kvWHmzqdXgX+KyI1i/Wj7YdnPH2IllZ7EPEGvYPUuy7HEgEkikpPqHtbGCJw/fwQGiZUS+RXQV0TOVdUSLAnngIj8GXOQLHH3ktewmnhJ5an2CQH1UFskKCKjsBj2VVjZjI2q+sNa23wFWyV+4J6nyqovbOqYt49jQvMXgM9ooNebiGSpNXseAfRW1f+0/IiTH5ETe686w2yiqg51rz8BFKnqd0XkdODLmFfnx8l0wWpJ6hIKi4m3fwXcqqplbp7/gXnOhmI9RldhXRN2Of3UOZie71AqXw9qHYehdkMDsNDTaZjXNgszRp7HsrE/iRlst6hrKC2WqPKBpkiGsBzL5M0HlmMew8cwKUwxJmDfgxkQD6nqtsC27WqHNNOFus4/9/oXsLl6GMs0/xbW8vBB7Jjqr6pv1d4u2fDGWQOISBcsi20JcAamxblSrD7XU8DXVfW9OgwSweY2Lb0PItIB88i8o6qvizVxPwt4FfPSvA7sV5fC7IkNTjx8Ppap9A52A7hHVV8UkY56rN+gYOL0Pe55WtdHqo2I5ABVgfmZgoUqn8NuCH/GZAv3i8hpmLF2hqpuFZE8t4pPq8WZC6X3VtVnXCi9IiD+/xSmBboZi9YXBFB7AAAVsUlEQVTMwcJNp6vLmHOfy0pmKYOI5GLZpy+o1bkchtVuPBgw0L4PdFDVm8TKZnwHMyzuCcxXQmYPxgsRGaaqKwILz47Y8fNbVZ0vVoz8Wqyq/z8C24UWCEl5HnrjzFGHgXUpVlZgAxa+uA4zKr6sx5ruFqvqt4L//GQ9EKKljnkLXYT/i2X8lWAZmX/GXNJTgJHYxeiVlh9xalDLS9EJE1z3AV7EdD0vYsftjzBB7DDgTeBA8FiF9NA/hYtYBua1WHgt1KViOlaTbzxmXPwB8/xciXl+RgE3qOp6t4+0mdeA0ZGFhcwXYx7ZX2C6xk9gx915WBb7c6paKSKjNFBEOhUMErE2fT8HfqSW/PUX7PwrguM6oLwLfL/2osm9l1b3j4YQy7L8JKaRPVstwzJkoJ2LJVd8P7QYSjXSTgdRG3HUYWD8FLhWVS/DMq0+hvXD/IE7ueYDs0VkTPBkSqcTy12YQwZCjnt5KxYyewzTklwDtMc8ZiNV9XvABd4wi46Q9sKtCNuKpYa3xgTWa9Wygx/FboYfYCGT32B996pqH6vpdLyGg1pbqmJgOJCLlThoq6pzsfT8yViV/69hoZX9qvqpkGHm9pHy8xo4Dmvc9fMwZphNVNW73d//W6Cfqq7ADLYhQAe33XHdPZLVMJPjM/jXYefbJ0Vkmr2kq0LHgxzThP4ay0alljc7re4fQeREXecojkmI3sUWmMGSPk9g8oHqWtulTDUE7zlziMhgTET4OLAOy5rZoNYqZCQmXD0T00z0cJ95CLir9oUmVQmsWn4FPKyqC12I9w9Y0d2tmAenF5YQcT3WASAXm9thqrogPqNPbtxK8Tl1+hMRuQYLYb6GeSkPYllvv1PVNSJyG1Yb6efJHi5qThrQSV2BLSg+wApH/0NVnxeRy4FzVfWcWh7ztAwNRxhK76QpmuwjlsywEvNW/wYz4Jdgx83meI4tWYhWRpSqpKXnrA4r/RRMTFiOFeG8AbsgjxSR/mrtQl7Cwhd7sFXPK1ift3QxzDJwqz1gB2YIgIla/6Wql2AhjC8CeZhBdgpmzL6mqge8YRY5gWN1FpaNhFgR2fGY+BysWO8B7OZwoXvtd8BfwEqS1D7mPWGXHMkG/gRc6Yyxh7HrA26hEvK8p7xhFjyGRKSTiPwYO/ZexjSlp2DlIL7rjtFpItIuZMSq6u5U8GwE/wYRyRORh7DF/B1YhOUuTEbQFvi9iDwuIjMb2k+6Ucc9+FIss/k67N77LDBYREaqainWLeKz7uPBPqQpO4dpdcEOuI5DK+XPifV0OwS8oqo/Ae7DLsg9sF54X3Pb3Kaq77pt1wJnquqv4vBntDhiAsyfY2FdMO3dYTHhf2fgXKfB+zvwN1X9HxZaGw78UFUfjMOwU4LACvFeYKbz6nTHFgk/wNre3IWF2RcBB0UkW1VLVXVb7WPeE1HJkTZYwdAlWB2lQWChz2AYKtVDUbEOpbf4HxAjnK7uqFHuXh4IPKpWx60K+B4WVXkFWKPWgulmVX2z9v6SeS6ixcuIwicr3gNoKZwGoBXwuoj0wrwQ2zGPWR9gr4gUquoGEdmHVfr+I04j4faRoVZVeFPL/wUtjzMEqlV1k4hUApNFZBbWhLgTdvN6E/OW3YhpA+4WkbnpYrg2B7Xd9mJ9BmdzLLnio9iF611sNXkl8CngPlV9L7ivVL54RUrg/D2qkxKRkE7qEveZ32IlR/6fiCzEjN83tFbZnHSY1zBC6X8BPi0iA1X1fRfi/Lqq/jQVQukBGceFwFw9lr37TaC/iLyhqo+KSHcReQ7L5s3FDLTfYZ5EVLUouL+4/DEJQkAGEJQRrcHqvH0C8/7/jGMyorc4JiMagtUOTAtS2nMmIn1F5CaxStUrMRfzX7DVziFM6LsKExYeAm4UkQewcMZhVa1U1S1p7H04BbhGRMZimpK9WGuMblgG23i1mm4/xtLjnwReUhNPeyIk6KEIvHYSJkK/RFU/gXkspmJFTsuBZ7Dj+T5VrXTbpKyrvykEPOZnY22VPoKJjleL1X4DuExVv+sezwXu1kBpjJYeczzwofSjtHa/Twe+DiBWdb4fJlT/joichyU+7cHKrKzGDLRNai3RjpKuhpmXEUVHyiYEiMg3MEv8WeB+LPz2CGaQTRGrSfM37GK8ybnq5wA9nK4k7XHhn7OxC/E3MX3ZXqy8yK+xkMbn3Gdz1BWR9DQNEZmMzfefVPU5Efkb5sF4REQ+A/xKVfu4z/ZRVyvKr8xPRGJUciQd51WsrMjfsfO/M9brMQNrofMdzBCZiC3i7g8uDpJ5vsS6mXwF6KWqN4rIqRzT012HRZyqsdDb19RqcD2OdUD4kbrm5G5fST0XTaH23y4in8MkGfMxWdAtItIduB3Tdc7AauXdUGs/BcAODbQ/SweSfWXTEPnAjar6c1Xd5UKRn8HaO4xWq8L8H+A2AKeNeDZkmEkS9eBqLlS1RlWfxnQkv8PCattU9R1sxVwgIiEdjjfMoiB0nDkpRiux1kDfxXQ7E93zHwFfFZHhmOdynYj0cxe/jQEdR1reBOrC66Qiow7vxvWYljEUSl+MRRT2YqH0OcC3gVWq+rOQYQbJO1/uNGqHhc76YDKO4VjVebCSQIuwUkF7VPU095mpwMWq+pGQYRY6r5N1LpqKkxHNco97iSVNjMMWQx04JiMqA4IyorsC+widw5vSzTCDFDXORKQtJs78tIicJyLznCetE+Y+vUWsLtc8YLeYsP04NA2yr8JFVX8BlGHenFPdyz8GrlHV1XEbWAoQOM66ut85WHX1P2JNoUdiPRvvwAqiqqrOVmtSHsou1HS9CdRGRM4Va3kT8pZdg3nPv4XdHP4CdA7ppIAtOJ0UViLj+XiNPR74UPpxfBszvLoC/3SvTcG8PVMwT9o8TOLRR0RexgzWZeq6nQSMsrS7f3gZUWxJ5bDmMGx1Nx74EKvBda2qnums+F5YVe/lcRxm0uBOuBsxMfrwdLz4xIKQhyvw+1xM97QMWyxUYt6cJ1T1XRH5ElYf7joJiKwlTetq1Yccq1H2a6BGVb/hwiHfw47b64BCzAs0Duipqj8Uq6EkzpOeEpXqo8GH0o+GvS/GvNPfBm7BMvNLsQScN1X1a86gHYeFvpfFa7yJhJcRxZ6UNc5q47xjt6jqd0Ski6pWBN5LywtypLgV9X5VPRTvsSQjtY8z5+G9F/NInO5+fostHCZgup6OQHdNw9ZA0ZCuOqlIkGMtlwTTT30Zywaei8lBdmHhpweBL2A33TOxciPFocUFpN5x6M7Ju4D9mKHRGiuu+w/Mq32RWh280OfTuo9yCBG5Gysp8n7gtW7AvzBd3mIR+Q4wRFW/UMf2frFZi5QMa4YQkSwRaS/Wo+vfQIa7CFe490Mu6LQ+scLFafe8YRYlzrOTLSKXiqWS98Y8ZVcDFwC/UauHtAzYCRSo6h5Ns9ZA4eJ1UtHhQ+n147RNN2HJIldhocx9mDfod0HDzH1e0/3+4WVEzUPKe85EZDZ2kv1OVd+O83A8aUQdnrJPYo18qzEdy51YyG2H+30mpvW5S32CRb3U5el2Xt3fA1er6l4ReQP4P0AwL+THMKP39nTzlvlQeuSIyBQsrDlaVQviPZ5Ex8uIYk86GGfHXVB8CNPTEtRhmBVgoaI7VfUhdwM8hAnSB2OZTeXAT4IhzHQwHqLF66Qax4fSo0csEz1bVf9X28CN99gSHS8jajopb5yF8AeEp7lxGote6golutXkLViV68ewfqQjnUeiDabHuFCt5+CAUMjE3wBOxOukokdEsoFzgPewMhHXAAcxL+3tqvqWiIzEahr+PWTEegx/PoaHWHur1lg9zCuxsiM3huYunTyvsSClNWdBvGHmaU5c9tEZwCVO59gdyxT8PVbk9Amsjs8wEfkKlj1YjWsPFjDMMvyN4ES8Tio86tDhfRL4BdbR4ydAF6yifVes7mOuiPwM2K6qd3rD7ERS/ZiJFS78PRmTbtymqt8Ozp03zCIjbTxnHk9zIyL9gcux7K75wH3AQ5jg/wNVvc3pfW7AjLYnndjYUwuvk4ocH0r3xBsvI4odaeM583hiSShEFvg9FbgV80qchlUT3wXcqqrnAI+JyPWq+gQmlq1Q1X0uFOAJEPQeOsOsLRaivBpYjoUqW2OV/T/j/gePAL902xwWR6obZiLSTURGwdFs4GEi8nfnnT2Aec3GuY8/iIXW31bVX2Mhp6tUdX26hnw9sSV0vkkdxY09keGNM48nQpxn5qjx4F7+CvA8loFZjhXr/SVQJCI/x0TXRe6zDwHnOiPkMJ7jUF9yJCx8KN2TqHijrOl448zjCRMR+RQc9cxkicgVInKKe3sj8KFaxfDlWH2t/cBXgWdVdY6qPue2f15Vv+gvYIbXSUWHqlZhIv+DWIgyA2gHnAScD7zsPBn3ARdiHttLVLWk1n78cejxJBg+pOLxhIGIdAQuFZGDwDbgi0Bb4HIRuRjT8swRke2YWD0b6K/Wi+81t4+00T+FSz06qTs5Xic1BgvJDQb+xDGd1Ha3TVropOrQ4U0FvuTebo/1MwyF0ie4EOf1qvorEbmAQCjde2w9nsTGJwR4PGEiVtD461gj3wdU9dcich2QCfwHmIlVot8G/EBrVRP3GL7kSOTUZVCJyCNYi6HFWIbcTuC/wHeBzVjdsrvUasB9DEuguMp7yjyexMeHNT2e8Pkv8DoWRip0rz0ATAF6q+ofsAr1lwQNiLiMNEHxOqnI8KF0jyc98caZxxMmqnoQeAFYBcwUkTmquh94FAtxoqrr4LhspZQ3ICLB66TCJxBK/5iIjAN+henwbheR3hwLpWdzfCh9p6q+5vaRGZ/RezyepuDDmh5PBDij63NY2YzlwGfSwVCIlkZ0UtuB3wA/wHoYTnAhzo86ndRjwMMuLJeWOikfSvd40hPvOfN4IsAZYq9jobcfesOsfnzJkZjgQ+keTxriPWcejyemiMinVPVJ9zgLuBRYp6qvi8hPMG9YkdNTnY4ZZxXAqFA4znMMERkKfB/LVv2Oqr4iIp8GalT16cDnfDV2jydF8KU0PJ4oSadswXDxJUeahSIsK3MScJ2IvKaq/679IW+YeTypg/eceTyemOJ1UrFHRPKBS4CXVPXDeI/H4/E0L94483g8McXVJvsSZky8oarfEJH2WCHZB1V1noj0D2W2um28F9Lj8XgcPiHA4/HEFF9ypPnwYn+PJz3wnjOPxxNzfMkRj8fjiR6fEODxeGKOqh4RkVDJkZe8YebxeDzh4z1nHo/H4/F4PAmE15x5PJ5mxeukPB6PJzK858zj8Xg8Ho8ngfCeM4/H4/F4PJ4EwhtnHo/H4/F4PAmEN848Ho/H4/F4EghvnHk8KYCI1IjIQhFZKiKPu4r8kWx/fRTbPCci2XW8fquIfCuSfQW27SsiFwWeTxSRe9zjNiLykvs7LxCRB0RkeBTfMVZEzgo8/4SI3BTNeCP4zutEZIWI/LU5vydWiMjDInJelNseN78ejydyvHHm8aQGVao6VlVHAtXAteFuKCKZwPVARMaZqp6lqpWRDbNR+gJHjTNV/UBVr3NPxwGt3N/5qKpeqarLo/iOscBR40FVn1bVnzZl0GHwZeAsVb24vg+ISKrUnTxufj0eT+R448zjST3exJqOIyJPisiHIrJMRK4OfUBE9orI7SLyHnAz0At4VUReFZEviMivAp+9SkR+WftLRKRYRLq7xzeLyCoReQkYEvjMABF5wY3hTREZ6l5/WETuEZF3RGRdwEvzU6zl00IRuUFEZovIXBHJAR4Bxrr3BojIayIy0e3vTBGZLyKLRORl99pkEfmviCxw3zNERFoDtwMXBDxwl4vIb9w2fUXkFRFZLCIvi0ifRsZbe06+4byXS0XkevfafUB/4GkRuaHW5y93ns5ngHnutW+LyP/cGG5zr3UQkWfd37dURC4I/A/uFJH33U/o/x7R3yHGb0RkuYg8C+QExjhBRF53/8P/iEiee/21wHcXicjMuua3rnnyeDyNoKr+x//4nyT/Afa631nAU8CX3POu7nc7YCnQzT1X4PzA9sVAd/e4I7AW81IBvAOMquM7i4HuwARgCeZ56wysAb7lPvMyMMg9ngK84h4/DDyOLRCHA2vc67OBuYHvOPq8jvdeAyYCPYBNQL9af3NnIMs9Pg14wj2+HPhNYD9HnwPPAJe5x18AnmxovLXmIzQPHdwcLgPG1Z7fWttcDmwOjPkM4H5A3HfNBWYB5wL/L7DdSYH93uwefz4wVxH9HcBngBeBTMxQrwTOA1q5/38P97kLsOb1ofm/2z0+C+sEccL8+h//438i/0kVN7rHk+60E5GF7vGbWNskgOtE5NPucQEwCNgJ1ABP1LUjVd0rIq8AHxeRFZiRtqSB754J/FutuTki8rT73RGYBjwux+rQtgls96RaW6flItIz/D/1BE4G3lDV9W785e71k4A/icggzBhtFca+pmKGCsBfgJ9FMN4Z2DzsAxCRf2Fzs6CR73wxMOYz3E9om47Y/+xN4G4RuRMzwN4MbP/3wO+QhzPSv2MW8HdVrQG2uv8/mBd0JPCi+x9mAiWBff3L/f4QC0l7PJ4Y4I0zjyc1qFLVscEXRGQ25jGaqqr7ReQ1oK17+4C7EdfHA8D/ASuBh6IcUwZQWXtcAQ4GhxvldzTED4FXVfXTItIX8/Q0heYa775a+71DVf9Q+0MiMh7zUN0hIvNU9Xb3VrCSeDhVxSP5OwRYpqpTG9lXDf5+4vHEDK8583hSl5OACmeYDcU8TPWxB+gUeqKq72Getos45pmpjzeAT4lIOxHpBJzj9rEbWC8in4WjuqYxjezruHGEybvALBHp576nq3v9JGCLe3x5mN/xDvA59/hizGMVLm9i89BeRDoAn45we4D/AF9wXkdEJF9EckSkF7BfVR8B7gLGB7a5IPD7v1H+HW9gOrFMpyk71b2+CughIlPdeFqJyIhG9hXN/9Dj8QTwxpnHk7q8AGSJyGLMi/RuA5+9H3hBRF4NvPYY8LaqVjT0Jao6H3gUWIiFSoOGwMXAF0VkEabB+mQjY14M1Djh+w2NfDb0/TuAq4F/ue951L31M8zL9DYWjgvxKjC8HsH614Ar3JxdCnw9nDG4cczHNF3vA+8BD6hqYyHN2vuYB/wN+K+ILAH+iRk6o4D3Xej6ZuBHgc3aiCV2fB0IzVmkf8e/gdWYZu73wOtuPNWY9uxON7cLsVB1Qxw3v2LlUB5o/K/3eDwhfG9Nj8dTJyIyF/ilqr4c77F46kZEioGJqloW77F4PJ7Y4T1nHo/nOEQkW0SKMB2bN8w8Ho+nhfGeM4/H4/F4PJ4EwnvOPB6Px+PxeBIIb5x5PB6Px+PxJBDeOPN4PB6Px+NJILxx5vF4PB6Px5NAeOPM4/F4PB6PJ4H4/+8UJEa2PJc/AAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Create a jitter plot.\n",
"fig3 = plt.figure()\n",
"ax = fig3.add_subplot(111)\n",
"\n",
"plot_opts={'cutoff_val':5, 'cutoff_type':'abs', 'label_fontsize':'small',\n",
" 'label_rotation':30, 'violin_fc':(0.8, 0.8, 0.8),\n",
" 'jitter_marker':'.', 'jitter_marker_size':3, 'bean_color':'#FF6F00',\n",
" 'bean_mean_color':'#009D91'}\n",
"sm.graphics.beanplot(age, ax=ax, labels=labels, jitter=True,\n",
" plot_opts=plot_opts)\n",
"\n",
"ax.set_xlabel(\"Party identification of respondent.\")\n",
"ax.set_ylabel(\"Age\")\n",
"ax.set_title(\"US national election '96 - Age & Party Identification\")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, \"US national election '96 - Age & Party Identification\")"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Create an asymmetrical jitter plot.\n",
"ix = data.exog['income'] < 16 # incomes < $30k\n",
"age = data.exog['age'][ix]\n",
"endog = data.endog[ix]\n",
"age_lower_income = [age[endog == id] for id in party_ID]\n",
"\n",
"ix = data.exog['income'] >= 20 # incomes > $50k\n",
"age = data.exog['age'][ix]\n",
"endog = data.endog[ix]\n",
"age_higher_income = [age[endog == id] for id in party_ID]\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111)\n",
"\n",
"plot_opts['violin_fc'] = (0.5, 0.5, 0.5)\n",
"plot_opts['bean_show_mean'] = False\n",
"plot_opts['bean_show_median'] = False\n",
"plot_opts['bean_legend_text'] = 'Income < \\$30k'\n",
"plot_opts['cutoff_val'] = 10\n",
"sm.graphics.beanplot(age_lower_income, ax=ax, labels=labels, side='left',\n",
" jitter=True, plot_opts=plot_opts)\n",
"plot_opts['violin_fc'] = (0.7, 0.7, 0.7)\n",
"plot_opts['bean_color'] = '#009D91'\n",
"plot_opts['bean_legend_text'] = 'Income > \\$50k'\n",
"sm.graphics.beanplot(age_higher_income, ax=ax, labels=labels, side='right',\n",
" jitter=True, plot_opts=plot_opts)\n",
"\n",
"ax.set_xlabel(\"Party identification of respondent.\")\n",
"ax.set_ylabel(\"Age\")\n",
"ax.set_title(\"US national election '96 - Age & Party Identification\")\n",
"\n",
"\n",
"# Show all plots.\n",
"#plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
}

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/srv/reproducible-results/rbuild-debian/tmp.FTzzuDJkiI/dbd-tmp-gqMLHgR/diffoscope_1mz05yxs/tmp0k68izjs/51fe7379-ce25-4fa5-b8f4-ee5cd6f18ba8 vs.

Similarity: 0.0%
Differences: {
"replace": {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Prediction (out of sample)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
" from pandas.core import datetools\n"
]
}
],
"source": [
"%matplotlib inline\n",
"\n",
"from __future__ import print_function\n",
"import numpy as np\n",
"import statsmodels.api as sm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Artificial data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"nsample = 50\n",
"sig = 0.25\n",
"x1 = np.linspace(0, 20, nsample)\n",
"X = np.column_stack((x1, np.sin(x1), (x1-5)**2))\n",
"X = sm.add_constant(X)\n",
"beta = [5., 0.5, 0.5, -0.02]\n",
"y_true = np.dot(X, beta)\n",
"y = y_true + sig * np.random.normal(size=nsample)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Estimation "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: y R-squared: 0.984\n",
"Model: OLS Adj. R-squared: 0.983\n",
"Method: Least Squares F-statistic: 968.4\n",
"Date: Sat, 10 Apr 2021 Prob (F-statistic): 1.48e-41\n",
"Time: 01:00:12 Log-Likelihood: 1.8725\n",
"No. Observations: 50 AIC: 4.255\n",
"Df Residuals: 46 BIC: 11.90\n",
"Df Model: 3 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const 5.0007 0.083 60.377 0.000 4.834 5.167\n",
"x1 0.5039 0.013 39.451 0.000 0.478 0.530\n",
"x2 0.5066 0.050 10.088 0.000 0.405 0.608\n",
"x3 -0.0203 0.001 -18.093 0.000 -0.023 -0.018\n",
"==============================================================================\n",
"Omnibus: 0.966 Durbin-Watson: 2.449\n",
"Prob(Omnibus): 0.617 Jarque-Bera (JB): 0.353\n",
"Skew: -0.136 Prob(JB): 0.838\n",
"Kurtosis: 3.309 Cond. No. 221.\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
]
}
],
"source": [
"olsmod = sm.OLS(y, X)\n",
"olsres = olsmod.fit()\n",
"print(olsres.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## In-sample prediction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 4.49340827 4.9796044 5.42600456 5.80500128 6.0989505 6.30307041\n",
" 6.42622709 6.48947785 6.52261154 6.55925439 6.63134523 6.76388811\n",
" 6.97084416 7.25283783 7.59705429 7.97934488 8.36819465 8.72990019\n",
" 9.03410701 9.25879666 9.39390437 9.44297258 9.422569 9.35956452\n",
" 9.28671845 9.23729623 9.23960401 9.31233806 9.46151299 9.67947356\n",
" 9.94615249 10.23236734 10.50461521 10.73057711 10.88442789 10.95107925\n",
" 10.9286587 10.8288172 10.67481496 10.4976998 10.33120617 10.20621257\n",
" 10.14566839 10.16082335 10.2493792 10.39586731 10.57419016 10.75190904\n",
" 10.89557369 10.97621802]\n"
]
}
],
"source": [
"ypred = olsres.predict(X)\n",
"print(ypred)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create a new sample of explanatory variables Xnew, predict and plot"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[10.96123313 10.81247049 10.54979574 10.21848016 9.87811667 9.58802945\n",
" 9.39274923 9.31111069 9.33164105 9.41536898]\n"
]
}
],
"source": [
"x1n = np.linspace(20.5,25, 10)\n",
"Xnew = np.column_stack((x1n, np.sin(x1n), (x1n-5)**2))\n",
"Xnew = sm.add_constant(Xnew)\n",
"ynewpred = olsres.predict(Xnew) # predict out of sample\n",
"print(ynewpred)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plot comparison"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"fig, ax = plt.subplots()\n",
"ax.plot(x1, y, 'o', label=\"Data\")\n",
"ax.plot(x1, y_true, 'b-', label=\"True\")\n",
"ax.plot(np.hstack((x1, x1n)), np.hstack((ypred, ynewpred)), 'r', label=\"OLS prediction\")\n",
"ax.legend(loc=\"best\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Predicting with Formulas"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using formulas can make both estimation and prediction a lot easier"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from statsmodels.formula.api import ols\n",
"\n",
"data = {\"x1\" : x1, \"y\" : y}\n",
"\n",
"res = ols(\"y ~ x1 + np.sin(x1) + I((x1-5)**2)\", data=data).fit()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We use the `I` to indicate use of the Identity transform. Ie., we don't want any expansion magic from using `**2`"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Intercept 5.000693\n",
"x1 0.503931\n",
"np.sin(x1) 0.506567\n",
"I((x1 - 5) ** 2) -0.020291\n",
"dtype: float64"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"res.params"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we only have to pass the single variable and we get the transformed right-hand side variables automatically"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 10.961233\n",
"1 10.812470\n",
"2 10.549796\n",
"3 10.218480\n",
"4 9.878117\n",
"5 9.588029\n",
"6 9.392749\n",
"7 9.311111\n",
"8 9.331641\n",
"9 9.415369\n",
"dtype: float64"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"res.predict(exog=dict(x1=x1n))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
}

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/srv/reproducible-results/rbuild-debian/tmp.FTzzuDJkiI/dbd-tmp-gqMLHgR/diffoscope_1mz05yxs/tmpmnm3j_96/b718d8a6-4716-496c-93b9-1f4542641b36 vs.

Similarity: 0.0%
Differences: {
"replace": {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Quantile regression"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"This example page shows how to use ``statsmodels``' ``QuantReg`` class to replicate parts of the analysis published in \n",
"\n",
"* Koenker, Roger and Kevin F. Hallock. \"Quantile Regressioin\". Journal of Economic Perspectives, Volume 15, Number 4, Fall 2001, Pages 143\u2013156\n",
"\n",
"We are interested in the relationship between income and expenditures on food for a sample of working class Belgian households in 1857 (the Engel data). \n",
"\n",
"## Setup\n",
"\n",
"We first need to load some modules and to retrieve the data. Conveniently, the Engel dataset is shipped with ``statsmodels``."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
" from pandas.core import datetools\n"
]
},
{
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" <td>485.680014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>639.080229</td>\n",
" <td>402.997356</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>750.875606</td>\n",
" <td>495.560775</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" income foodexp\n",
"0 420.157651 255.839425\n",
"1 541.411707 310.958667\n",
"2 901.157457 485.680014\n",
"3 639.080229 402.997356\n",
"4 750.875606 495.560775"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%matplotlib inline\n",
"\n",
"from __future__ import print_function\n",
"import patsy\n",
"import numpy as np\n",
"import pandas as pd\n",
"import statsmodels.api as sm\n",
"import statsmodels.formula.api as smf\n",
"import matplotlib.pyplot as plt\n",
"from statsmodels.regression.quantile_regression import QuantReg\n",
"\n",
"data = sm.datasets.engel.load_pandas().data\n",
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Least Absolute Deviation\n",
"\n",
"The LAD model is a special case of quantile regression where q=0.5"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" QuantReg Regression Results \n",
"==============================================================================\n",
"Dep. Variable: foodexp Pseudo R-squared: 0.6206\n",
"Model: QuantReg Bandwidth: 64.51\n",
"Method: Least Squares Sparsity: 209.3\n",
"Date: Sat, 10 Apr 2021 No. Observations: 235\n",
"Time: 01:00:05 Df Residuals: 233\n",
" Df Model: 1\n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"Intercept 81.4823 14.634 5.568 0.000 52.649 110.315\n",
"income 0.5602 0.013 42.516 0.000 0.534 0.586\n",
"==============================================================================\n",
"\n",
"The condition number is large, 2.38e+03. This might indicate that there are\n",
"strong multicollinearity or other numerical problems.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib/python3/dist-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
" return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
]
}
],
"source": [
"mod = smf.quantreg('foodexp ~ income', data)\n",
"res = mod.fit(q=.5)\n",
"print(res.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Visualizing the results\n",
"\n",
"We estimate the quantile regression model for many quantiles between .05 and .95, and compare best fit line from each of these models to Ordinary Least Squares results. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Prepare data for plotting\n",
"\n",
"For convenience, we place the quantile regression results in a Pandas DataFrame, and the OLS results in a dictionary."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib/python3/dist-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
" return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n",
"/usr/lib/python3/dist-packages/ipykernel_launcher.py:4: DeprecationWarning: \n",
".ix is deprecated. Please use\n",
".loc for label based indexing or\n",
".iloc for positional indexing\n",
"\n",
"See the documentation here:\n",
"http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
" after removing the cwd from sys.path.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" q a b lb ub\n",
"0 0.05 124.880098 0.343361 0.268632 0.418090\n",
"1 0.15 111.693660 0.423708 0.382780 0.464636\n",
"2 0.25 95.483539 0.474103 0.439900 0.508306\n",
"3 0.35 105.841294 0.488901 0.457759 0.520043\n",
"4 0.45 81.083647 0.552428 0.525021 0.579835\n",
"5 0.55 89.661370 0.565601 0.540955 0.590247\n",
"6 0.65 74.033435 0.604576 0.582169 0.626982\n",
"7 0.75 62.396584 0.644014 0.622411 0.665617\n",
"8 0.85 52.272216 0.677603 0.657383 0.697823\n",
"9 0.95 64.103964 0.709069 0.687831 0.730306\n",
"{'a': 147.47538852370576, 'b': 0.4851784236769231, 'lb': 0.45687381301842284, 'ub': 0.5134830343354233}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib/python3/dist-packages/ipykernel_launcher.py:10: DeprecationWarning: \n",
".ix is deprecated. Please use\n",
".loc for label based indexing or\n",
".iloc for positional indexing\n",
"\n",
"See the documentation here:\n",
"http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
" # Remove the CWD from sys.path while we load stuff.\n"
]
}
],
"source": [
"quantiles = np.arange(.05, .96, .1)\n",
"def fit_model(q):\n",
" res = mod.fit(q=q)\n",
" return [q, res.params['Intercept'], res.params['income']] + \\\n",
" res.conf_int().ix['income'].tolist()\n",
" \n",
"models = [fit_model(x) for x in quantiles]\n",
"models = pd.DataFrame(models, columns=['q', 'a', 'b','lb','ub'])\n",
"\n",
"ols = smf.ols('foodexp ~ income', data).fit()\n",
"ols_ci = ols.conf_int().ix['income'].tolist()\n",
"ols = dict(a = ols.params['Intercept'],\n",
" b = ols.params['income'],\n",
" lb = ols_ci[0],\n",
" ub = ols_ci[1])\n",
"\n",
"print(models)\n",
"print(ols)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### First plot\n",
"\n",
"This plot compares best fit lines for 10 quantile regression models to the least squares fit. As Koenker and Hallock (2001) point out, we see that:\n",
"\n",
"1. Food expenditure increases with income\n",
"2. The *dispersion* of food expenditure increases with income\n",
"3. The least squares estimates fit low income observations quite poorly (i.e. the OLS line passes over most low income households)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"x = np.arange(data.income.min(), data.income.max(), 50)\n",
"get_y = lambda a, b: a + b * x\n",
"\n",
"fig, ax = plt.subplots(figsize=(8, 6))\n",
"\n",
"for i in range(models.shape[0]):\n",
" y = get_y(models.a[i], models.b[i])\n",
" ax.plot(x, y, linestyle='dotted', color='grey')\n",
" \n",
"y = get_y(ols['a'], ols['b'])\n",
"\n",
"ax.plot(x, y, color='red', label='OLS')\n",
"ax.scatter(data.income, data.foodexp, alpha=.2)\n",
"ax.set_xlim((240, 3000))\n",
"ax.set_ylim((240, 2000))\n",
"legend = ax.legend()\n",
"ax.set_xlabel('Income', fontsize=16)\n",
"ax.set_ylabel('Food expenditure', fontsize=16);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Second plot\n",
"\n",
"The dotted black lines form 95% point-wise confidence band around 10 quantile regression estimates (solid black line). The red lines represent OLS regression results along with their 95% confindence interval.\n",
"\n",
"In most cases, the quantile regression point estimates lie outside the OLS confidence interval, which suggests that the effect of income on food expenditure may not be constant across the distribution."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"n = models.shape[0]\n",
"p1 = plt.plot(models.q, models.b, color='black', label='Quantile Reg.')\n",
"p2 = plt.plot(models.q, models.ub, linestyle='dotted', color='black')\n",
"p3 = plt.plot(models.q, models.lb, linestyle='dotted', color='black')\n",
"p4 = plt.plot(models.q, [ols['b']] * n, color='red', label='OLS')\n",
"p5 = plt.plot(models.q, [ols['lb']] * n, linestyle='dotted', color='red')\n",
"p6 = plt.plot(models.q, [ols['ub']] * n, linestyle='dotted', color='red')\n",
"plt.ylabel(r'$\\beta_{income}$')\n",
"plt.xlabel('Quantiles of the conditional food expenditure distribution')\n",
"plt.legend()\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
}

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/srv/reproducible-results/rbuild-debian/tmp.FTzzuDJkiI/dbd-tmp-gqMLHgR/diffoscope_1mz05yxs/tmpofxv9ex4/40a3992f-ffcd-46a2-a54e-0c640d91be7c vs.

Similarity: 0.0%
Differences: {
"replace": {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Recursive least squares\n",
"\n",
"Recursive least squares is an expanding window version of ordinary least squares. In addition to availability of regression coefficients computed recursively, the recursively computed residuals the construction of statistics to investigate parameter instability.\n",
"\n",
"The `RLS` class allows computation of recursive residuals and computes CUSUM and CUSUM of squares statistics. Plotting these statistics along with reference lines denoting statistically significant deviations from the null hypothesis of stable parameters allows an easy visual indication of parameter stability."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
" from pandas.core import datetools\n"
]
},
{
"ename": "ModuleNotFoundError",
"evalue": "No module named 'pandas_datareader'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-1-836971053788>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mstatsmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapi\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas_datareader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDataReader\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_printoptions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msuppress\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pandas_datareader'"
]
}
],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"import pandas as pd\n",
"import statsmodels.api as sm\n",
"import matplotlib.pyplot as plt\n",
"from pandas_datareader.data import DataReader\n",
"\n",
"np.set_printoptions(suppress=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Example 1: Copper\n",
"\n",
"We first consider parameter stability in the copper dataset (description below)."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"This data describes the world copper market from 1951 through 1975. In an\n",
"example, in Gill, the outcome variable (of a 2 stage estimation) is the world\n",
"consumption of copper for the 25 years. The explanatory variables are the\n",
"world consumption of copper in 1000 metric tons, the constant dollar adjusted\n",
"price of copper, the price of a substitute, aluminum, an index of real per\n",
"capita income base 1970, an annual measure of manufacturer inventory change,\n",
"and a time trend.\n",
"\n"
]
}
],
"source": [
"print(sm.datasets.copper.DESCRLONG)\n",
"\n",
"dta = sm.datasets.copper.load_pandas().data\n",
"dta.index = pd.date_range('1951-01-01', '1975-01-01', freq='AS')\n",
"endog = dta['WORLDCONSUMPTION']\n",
"\n",
"# To the regressors in the dataset, we add a column of ones for an intercept\n",
"exog = sm.add_constant(dta[['COPPERPRICE', 'INCOMEINDEX', 'ALUMPRICE', 'INVENTORYINDEX']])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, construct and fir the model, and print a summary. Although the `RLS` model computes the regression parameters recursively, so there are as many estimates as there are datapoints, the summary table only presents the regression parameters estimated on the entire sample; except for small effects from initialization of the recursiions, these estimates are equivalent to OLS estimates."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Statespace Model Results \n",
"==============================================================================\n",
"Dep. Variable: WORLDCONSUMPTION No. Observations: 25\n",
"Model: RecursiveLS Log Likelihood -153.737\n",
"Date: Sat, 10 Apr 2021 AIC 317.474\n",
"Time: 01:00:05 BIC 323.568\n",
"Sample: 01-01-1951 HQIC 319.164\n",
" - 01-01-1975 \n",
"Covariance Type: nonrobust \n",
"==================================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"----------------------------------------------------------------------------------\n",
"const -6513.9911 2367.692 -2.751 0.006 -1.12e+04 -1873.400\n",
"COPPERPRICE -13.6553 15.035 -0.908 0.364 -43.123 15.813\n",
"INCOMEINDEX 1.209e+04 762.603 15.853 0.000 1.06e+04 1.36e+04\n",
"ALUMPRICE 70.1440 32.668 2.147 0.032 6.116 134.172\n",
"INVENTORYINDEX 275.2797 2120.309 0.130 0.897 -3880.449 4431.009\n",
"===================================================================================\n",
"Ljung-Box (Q): 14.53 Jarque-Bera (JB): 1.91\n",
"Prob(Q): 0.75 Prob(JB): 0.39\n",
"Heteroskedasticity (H): 3.48 Skew: -0.74\n",
"Prob(H) (two-sided): 0.12 Kurtosis: 2.65\n",
"===================================================================================\n",
"\n",
"Warnings:\n",
"[1] Parameters and covariance matrix estimates are RLS estimates conditional on the entire sample.\n"
]
}
],
"source": [
"mod = sm.RecursiveLS(endog, exog)\n",
"res = mod.fit()\n",
"\n",
"print(res.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The recursive coefficients are available in the `recursive_coefficients` attribute. Alternatively, plots can generated using the `plot_recursive_coefficient` method."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 2.88890056e+00 4.94425543e+00 1.50526900e+03 1.85654931e+03\n",
" 1.59802075e+03 2.17199412e+03 -8.89363226e+02 1.22182180e+02\n",
" -4.18424888e+03 -6.24271256e+03 -7.11143788e+03 -6.40037719e+03\n",
" -6.09045022e+03 -7.15496361e+03 -6.29092382e+03 -5.80525707e+03\n",
" -6.21931693e+03 -6.68449506e+03 -6.43013706e+03 -5.95757655e+03\n",
" -6.40705871e+03 -5.98349196e+03 -5.22471620e+03 -5.28662076e+03\n",
" -6.51399113e+03]\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 5 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"print(res.recursive_coefficients.filtered[0])\n",
"res.plot_recursive_coefficient(range(mod.k_exog), alpha=None, figsize=(10,6));"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The CUSUM statistic is available in the `cusum` attribute, but usually it is more convenient to visually check for parameter stability using the `plot_cusum` method. In the plot below, the CUSUM statistic does not move outside of the 5% significance bands, so we fail to reject the null hypothesis of stable parameters at the 5% level."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0.27818974 0.51898929 1.05399673 1.94931413 2.44815035 3.37265218\n",
" 3.04781224 2.47334905 2.96190448 2.58230697 1.49436004 -0.50006285\n",
" -2.01110381 -1.75501007 -0.90601698 -1.41033745 -0.80380935 0.71256207\n",
" 1.19154001 -0.93367759]\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"print(res.cusum)\n",
"fig = res.plot_cusum();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Another related statistic is the CUSUM of squares. It is available in the `cusum_squares` attribute, but it is similarly more convenient to check it visually, using the `plot_cusum_squares` method. In the plot below, the CUSUM of squares statistic does not move outside of the 5% significance bands, so we fail to reject the null hypothesis of stable parameters at the 5% level."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"res.plot_cusum_squares();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Quantity theory of money\n",
"\n",
"The quantity theory of money suggests that \"a given change in the rate of change in the quantity of money induces ... an equal change in the rate of price inflation\" (Lucas, 1980). Following Lucas, we examine the relationship between double-sided exponentially weighted moving averages of money growth and CPI inflation. Although Lucas found the relationship between these variables to be stable, more recently it appears that the relationship is unstable; see e.g. Sargent and Surico (2010)."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'DataReader' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-7-d6b700d181ce>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mstart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'1959-12-01'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'2015-01-01'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mm2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'M2SL'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'fred'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mcpi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'CPIAUCSL'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'fred'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'DataReader' is not defined"
]
}
],
"source": [
"start = '1959-12-01'\n",
"end = '2015-01-01'\n",
"m2 = DataReader('M2SL', 'fred', start=start, end=end)\n",
"cpi = DataReader('CPIAUCSL', 'fred', start=start, end=end)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'm2' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-8-d105c7836024>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mseries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mseries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mn_window\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mn_window\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mm2_ewma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mewma\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mm2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'M2SL'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'QS'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdiff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.95\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 13\u001b[0m \u001b[0mcpi_ewma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mewma\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcpi\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'CPIAUCSL'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'QS'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdiff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.95\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'm2' is not defined"
]
}
],
"source": [
"def ewma(series, beta, n_window):\n",
" nobs = len(series)\n",
" scalar = (1 - beta) / (1 + beta)\n",
" ma = []\n",
" k = np.arange(n_window, 0, -1)\n",
" weights = np.r_[beta**k, 1, beta**k[::-1]]\n",
" for t in range(n_window, nobs - n_window):\n",
" window = series.iloc[t - n_window:t + n_window+1].values\n",
" ma.append(scalar * np.sum(weights * window))\n",
" return pd.Series(ma, name=series.name, index=series.iloc[n_window:-n_window].index)\n",
"\n",
"m2_ewma = ewma(np.log(m2['M2SL'].resample('QS').mean()).diff().ix[1:], 0.95, 10*4)\n",
"cpi_ewma = ewma(np.log(cpi['CPIAUCSL'].resample('QS').mean()).diff().ix[1:], 0.95, 10*4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After constructing the moving averages using the $\\beta = 0.95$ filter of Lucas (with a window of 10 years on either side), we plot each of the series below. Although they appear to move together prior for part of the sample, after 1990 they appear to diverge."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'm2_ewma' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-9-f2d187623c46>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m13\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mm2_ewma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'M2 Growth (EWMA)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcpi_ewma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'CPI Inflation (EWMA)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'm2_ewma' is not defined"
]
},
{
"data": {
"image/png": 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/vBXNBWXGHoIzmrGHjib5x6p6JMl/J/lwd5vdJsnMPfShJP9eVf+ayY3D7/GPUVaqqm9m8g+HHdN7ST6e5MVJ0t1fyuTekuuTLCd5Nsl71zymHgMAgPF4szAAAAxIEAAAgAEJAgAAMCBBAAAABiQIAADAgAQBAAAYkCAAAAADEgQAAGBA/wsj9K/znhC5nQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 936x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(13,3))\n",
"\n",
"ax.plot(m2_ewma, label='M2 Growth (EWMA)')\n",
"ax.plot(cpi_ewma, label='CPI Inflation (EWMA)')\n",
"ax.legend();"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'cpi_ewma' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-10-df40047925d9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mendog\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcpi_ewma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mexog\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_constant\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mm2_ewma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mexog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'const'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'M2'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mmod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRecursiveLS\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mendog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexog\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'cpi_ewma' is not defined"
]
}
],
"source": [
"endog = cpi_ewma\n",
"exog = sm.add_constant(m2_ewma)\n",
"exog.columns = ['const', 'M2']\n",
"\n",
"mod = sm.RecursiveLS(endog, exog)\n",
"res = mod.fit()\n",
"\n",
"print(res.summary())"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"res.plot_recursive_coefficient(1, alpha=None);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The CUSUM plot now shows subtantial deviation at the 5% level, suggesting a rejection of the null hypothesis of parameter stability."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"res.plot_cusum();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Similarly, the CUSUM of squares shows subtantial deviation at the 5% level, also suggesting a rejection of the null hypothesis of parameter stability."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"res.plot_cusum_squares();"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
}

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/srv/reproducible-results/rbuild-debian/tmp.FTzzuDJkiI/dbd-tmp-gqMLHgR/diffoscope_1mz05yxs/tmp0nac3z_n/bb023cf4-87bb-4c0e-ae37-25b65d773031 vs.

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Differences: {
"replace": {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Regression diagnostics"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This example file shows how to use a few of the ``statsmodels`` regression diagnostic tests in a real-life context. You can learn about more tests and find out more information about the tests here on the [Regression Diagnostics page.](http://www.statsmodels.org/stable/diagnostic.html) \n",
"\n",
"Note that most of the tests described here only return a tuple of numbers, without any annotation. A full description of outputs is always included in the docstring and in the online ``statsmodels`` documentation. For presentation purposes, we use the ``zip(name,test)`` construct to pretty-print short descriptions in the examples below."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Estimate a regression model"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: Lottery R-squared: 0.348\n",
"Model: OLS Adj. R-squared: 0.333\n",
"Method: Least Squares F-statistic: 22.20\n",
"Date: Sat, 10 Apr 2021 Prob (F-statistic): 1.90e-08\n",
"Time: 01:00:09 Log-Likelihood: -379.82\n",
"No. Observations: 86 AIC: 765.6\n",
"Df Residuals: 83 BIC: 773.0\n",
"Df Model: 2 \n",
"Covariance Type: nonrobust \n",
"===================================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"-----------------------------------------------------------------------------------\n",
"Intercept 246.4341 35.233 6.995 0.000 176.358 316.510\n",
"Literacy -0.4889 0.128 -3.832 0.000 -0.743 -0.235\n",
"np.log(Pop1831) -31.3114 5.977 -5.239 0.000 -43.199 -19.424\n",
"==============================================================================\n",
"Omnibus: 3.713 Durbin-Watson: 2.019\n",
"Prob(Omnibus): 0.156 Jarque-Bera (JB): 3.394\n",
"Skew: -0.487 Prob(JB): 0.183\n",
"Kurtosis: 3.003 Cond. No. 702.\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
" from pandas.core import datetools\n"
]
}
],
"source": [
"%matplotlib inline\n",
"\n",
"from __future__ import print_function\n",
"from statsmodels.compat import lzip\n",
"import statsmodels\n",
"import numpy as np\n",
"import pandas as pd\n",
"import statsmodels.formula.api as smf\n",
"import statsmodels.stats.api as sms\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Load data\n",
"dat = statsmodels.datasets.get_rdataset(\"Guerry\", \"HistData\", cache=True).data\n",
"\n",
"# Fit regression model (using the natural log of one of the regressaors)\n",
"results = smf.ols('Lottery ~ Literacy + np.log(Pop1831)', data=dat).fit()\n",
"\n",
"# Inspect the results\n",
"print(results.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Normality of the residuals"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Jarque-Bera test:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[('Jarque-Bera', 3.3936080248431764),\n",
" ('Chi^2 two-tail prob.', 0.18326831231663282),\n",
" ('Skew', -0.4865803431122344),\n",
" ('Kurtosis', 3.003417757881633)]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"name = ['Jarque-Bera', 'Chi^2 two-tail prob.', 'Skew', 'Kurtosis']\n",
"test = sms.jarque_bera(results.resid)\n",
"lzip(name, test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Omni test:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[('Chi^2', 3.7134378115971884), ('Two-tail probability', 0.15618424580304768)]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"name = ['Chi^2', 'Two-tail probability']\n",
"test = sms.omni_normtest(results.resid)\n",
"lzip(name, test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Influence tests\n",
"\n",
"Once created, an object of class ``OLSInfluence`` holds attributes and methods that allow users to assess the influence of each observation. For example, we can compute and extract the first few rows of DFbetas by:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[-0.00301154, 0.00290872, 0.00118179],\n",
" [-0.06425662, 0.04043093, 0.06281609],\n",
" [ 0.01554894, -0.03556038, -0.00905336],\n",
" [ 0.17899858, 0.04098207, -0.18062352],\n",
" [ 0.29679073, 0.21249207, -0.3213655 ]])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from statsmodels.stats.outliers_influence import OLSInfluence\n",
"test_class = OLSInfluence(results)\n",
"test_class.dfbetas[:5,:]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Explore other options by typing ``dir(influence_test)``\n",
"\n",
"Useful information on leverage can also be plotted:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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XXuH444/nsssu49VXX6WxsZGPf/zjJY7YzMws4WSfhzlz5rDzzjvz3ve+F4Dbb7+dcePGcdJJJzFo0CCmTZvGY489xp///OcSR2pmZuZkn5eZM2dyxhlnIAmAJUuWcOCBB27ZPmTIEPbcc0+WLPG6PmZmVnpO9j307LPPct999zF58uQtZevWrWOHHXZot98OO+zA66+/3tfhmZmZbcXJvod+8pOfcNhhhzFmzJgtZUOHDmXt2rXt9lu7di3bbbddX4dnZma2FSf7Hvrxj3/crlYPMG7cOB577LEtz9944w1WrFjBuHHj+jo8MzOzrTjZ98Af/vAHmpqatozCb/PRj36UJ598kjlz5rBhwwamT5/OO9/5Tvbdd98SRWpmZvYWJ/semDlzJscff/xWzfPDhw9nzpw5fPWrX2XYsGE89NBDzJo1q0RRmpmZtaeIKHUMBdHY2BgLFy4sdRhmZmZ9RtKiiGjsbj/X7M3MzKqck72ZmVmVc7I3MzOrck72ZmZmVW5gqQOoZHMXNzFj/jKeb25hRH0dUyaOZdL4hlKHZWZm1o6TfZ7mLm5i6u1P0LKxFYCm5ham3v4EgBO+mZmVFTfj52nG/GVbEn2blo2tzJi/rEQRmZmZZedkn6fnm1t6VG5mZlYqTvZ5GlFf16NyMzOzUnGyz9OUiWOpq61pV1ZXW8OUiWNLFJGZmVl2HqCXp7ZBeB6Nb2Zm5c7JvhcmjW9wcjczs7LnZnwzM7Mq52RvZmZW5ZzszczMqpyTvZmZWZVzsjczM6tyRU32ko6StEzSckkXZtl+uKRHJG2SdGJG+bskPShpiaTHJX28mHGamZlVs6Ile0k1wHXA0cD+wCmS9u+w23PAJ4GfdShfD5wREeOAo4BrJNUXK1YzM7NqVsz77A8ClkfEMwCSZgHHAUvbdoiIlem2zZkvjIi/ZDx+XtJLwHCguYjxmpmZVaViNuM3AKsynq9Oy3pE0kHANsCKAsVlZmbWr5T1AD1JuwE/AT4VEZuzbD9L0kJJC19++eW+D9DMzKwCFDPZNwG7ZzwfmZblRNL2wK+Br0bEH7PtExE/iIjGiGgcPnx4r4I1MzOrVsVM9g8De0saI2kb4GRgXi4vTPf/BfDjiJhdxBjNzMyqXtGSfURsAs4F5gNPAbdGxBJJ0yUdCyBpgqTVwEnA9yUtSV/+MeBw4JOSHk1/3lWsWM3MzKqZIqLUMRREY2NjLFy4sNRhmJmZ9RlJiyKisbv9ynqAnpmZmfWek72ZmVmVc7I3MzOrck72ZmZmVc7J3szMrMo52ZuZmVU5J3szM7Mq52RvZmZW5ZzszczMqpyTvZmZWZVzsjczM6tyTvZmZmZVzsnezMysyjnZm5mZVTknezMzsyrnZG9mZlblnOzNzMyqnJN9jo444ggGDRrE0KFDGTp0KGPHjt2y7eWXX+bUU09lhx12YNiwYZx22mkljNTMzKy9gaUOoJJce+21fPrTn96q/Pjjj2fChAk899xzDB48mCeffLIE0ZmZmWXnZN9Ld955J6tWrWLBggXU1NQAMH78+BJHZWZm9hY34/fA1KlT2WmnnTj00ENZsGABAH/84x8ZO3YskydPZscdd2TChAncd999pQ3UzMwsg5N9jq666iqeeeYZmpqaOOuss/jIRz7CihUrWL16NXfeeSfvf//7efHFF/nCF77AcccdxyuvvFLqkM3MzAAn+5wdfPDBbLfddmy77bZMnjyZQw89lDvuuIO6ujpGjx7NmWeeSW1tLSeffDK77747DzzwQKlDNjMzA5zs8yaJiOCd73wnkrbaZmZmVi6c7HPQ3NzM/Pnz2bBhA5s2beKnP/0pv/vd7zjqqKP46Ec/ypo1a5g5cyatra3Mnj2b1atXc+ihh5Y6bDMzM8Cj8XOyceNGLrroIv785z9TU1PDvvvuy9y5c9lnn30AmDdvHmeffTbnnHMO++67L7/85S/ZaaedShy1mZlZQhFR6hgKorGxMRYuXFjqMMzMzPqMpEUR0djdfm7GNzMzq3JO9mZmZlXOyd7MzKzKeYBenuYubmLG/GU839zCiPo6pkwcy6TxDaUOy8zMbCtO9nmYu7iJqbc/QcvGVgCamluYevsTAE74ZmZWdtyMn4cZ85dtSfRtWja2MmP+shJFZGZm1jkn+zw839zSo3IzM7NScrLPw4j6uh6Vm5mZlZKTfR6mTBxLXW1Nu7K62hqmTBxboojMzMw6V9RkL+koScskLZd0YZbth0t6RNImSSd22DZZ0tPpz+RixtlTk8Y38PXjD6Chvg4BDfV1fP34Azw4z8zMylLRRuNLqgGuAz4ErAYeljQvIpZm7PYc8Engix1e+zbgUqARCGBR+to1xYq3pyaNb3ByNzOzilDMmv1BwPKIeCYi3gRmAcdl7hARKyPicWBzh9dOBO6KiFfTBH8XcFQRYzUzM6taxUz2DcCqjOer07KCvVbSWZIWSlr48ssv5x2omZlZNavoAXoR8YOIaIyIxuHDh5c6HDMzs7JUzGTfBOye8XxkWlbs15qZmVmGYib7h4G9JY2RtA1wMjAvx9fOB/5F0jBJw4B/ScvMzMysh4qW7CNiE3AuSZJ+Crg1IpZImi7pWABJEyStBk4Cvi9pSfraV4HLSC4YHgamp2VmZmbWQ4qIUsdQEI2NjbFw4cJSh2FmZtZnJC2KiMbu9qvoAXpmZmbWPSd7MzOzKudkb2ZmVuWc7Avg6aefZtCgQZx++ukALFiwgAEDBjB06NAtPzNnzixxlGZm1l8VbW78/uScc85hwoQJ7cpGjBjB6tWrSxSRmZnZW1yz76VZs2ZRX1/PkUceWepQzMzMsnKy74W1a9dyySWXcPXVV2+17aWXXmKXXXZhzJgxnH/++bzxxhsliNDMzMzJvlcuvvhizjzzTEaOHNmufN999+XRRx/lhRde4N5772XRokVccMEFJYrSzMz6Oyf7PD366KPcfffdnH/++Vtt23XXXdl///0ZMGAAY8aM4Rvf+AZz5swpQZRmZmYeoJe3BQsWsHLlSkaNGgXAunXraG1tZenSpTzyyCPt9pXE5s2bSxGmmZmZp8vN1/r161m7du2W59/85jdZuXIl119/PU8++SRvf/vbGTVqFKtXr+aMM85g9OjR3HjjjX0Wn5mZVb9cp8t1zT5PgwcPZvDgwVueDx06lEGDBjF8+HAWL17M6aefzpo1a9hxxx356Ec/yhVXXFHCaM3MrD9zzd7MzKxCeSEcMzMzA5zszczMqp6TvZmZWZXzAL0Cmru4iRnzl/F8cwsj6uuYMnEsk8Y3lDosMzPr55zsC2Tu4iam3v4ELRtbAWhqbmHq7U8AOOGbmVlJuRm/QGbMX7Yl0bdp2djKjPnLShSRmZlZwsm+QJ5vbulRuZmZWV9xsi+QEfV1PSo3MzPrK072BTJl4ljqamvaldXV1jBl4tgSRWRmZpbwAL0CaRuE59H4ZmZWbpzsC2jS+AYndzMzKztuxjczM6tyTvZmZmZVzsnezMysyjnZm5mZVbmckr2kXST9UNL/pc/3l3RmcUMzMzOzQsi1Zn8TMB8YkT7/C/D5YgRkZmZmhZVrst8pIm4FNgNExCagteuXmJmZWTnINdm/IWlHIAAkvQd4rWhRmZmZWcHkOqnOBcA8YE9JDwDDgROLFpWZmZkVTE7JPiIekfQ+YCwgYFlEbCxqZGZmZlYQOSV7Scd3KNpH0mvAExHxUuHDMjMzs0LJtc/+TOAG4LT0539ImvYfkPSJzl4k6ShJyyQtl3Rhlu3bSvp5uv0hSaPT8lpJMyU9IekpSVN7+LnMzMwslWuy3wzsFxEnRMQJwP7ABuBg4MvZXiCpBrgOODrd/xRJ+3fY7UxgTUTsBXwbuCotPwnYNiIOAN4NfKbtQsDMzMx6JtdkPzoi/pbx/CVgbES8CnTWd38QsDwinomIN4FZwHEd9jkOmJk+ng0cKUkko/6HSBoI1AFvAmtzjNXMzMwy5Doa/35JvwJuS5+fAPxO0hCguZPXNACrMp6vJmkJyLpPRGxKxwHsSJL4jwNeAAYD56cXFmZmZtZDuSb7c0gS/KHp8x8DcyIigPcXIa6DSCbtGQEMI7nYuDsinsncSdJZwFkAo0aNKkIYZmZmlS+nZvxIzI6I89Of2Wmi70oTsHvG85FpWdZ90ib7HYC/A6cCv4mIjelo/weAxixx/SAiGiOicfjw4bl8lLJ1+umns9tuu7H99tuzzz77cMMNN5Q6JDMzqxK5LoTzHkkPS1on6U1JrZK660N/GNhb0hhJ2wAnk0zMk2keMDl9fCJwb3oR8RzwgfS9hwDvAf6c20eqTFOnTmXlypWsXbuWefPmcdFFF7Fo0aJSh2VmZlUg1wF61wKnAE+TDJj7NPDdrl6Qzp9/LskCOk8Bt0bEEknTJR2b7vZDYEdJy0lu5Wu7Pe86YKikJSQXDTdGxOO5f6zKM27cOLbddlsAJCGJFStWlDgqMzOrBrn22RMRyyXVREQrcKOkP+TwmjuAOzqUXZLxeAPJbXYdX7cuW3m1O/vss7nppptoaWlh/PjxfPjDHy51SGZmVgVyrdmvT5viH5X0DUnnA0OKGFe/9L3vfY/XX3+d+++/n+OPP35LTd/MzKw3ck32n0j3PRd4g2RQ3QnFCqo/q6mp4bDDDmP16tVcf/31pQ7HzMyymDVrFvvttx9Dhgxhzz335P7772fp0qU0NjYybNgwhg0bxgc/+EGWLl1a6lCBHJrx05nwroiI00lmzfta0aMyNm3a5D57M7MydNddd/HlL3+Zn//85xx00EG88MILAAwZMoTZs2ezxx57sHnzZq677jpOPvlkHn+89EPOuq3Zp330w9NmfCuCl156iVmzZrFu3TpaW1uZP38+t9xyC0ceeWSpQzMzsw4uvfRSLrnkEt7znvcwYMAAGhoaaGhooL6+ntGjRyOJiKCmpobly5eXOlwg9wF6K0kWvZlH0owPQERcXYyg+htJXH/99Xz2s59l8+bN7LHHHlxzzTUce+yx3b/YzMz6TGtrKwsXLuTYY49lr732YsOGDUyaNIkZM2ZQV1cHQH19PevWrWPz5s1Mnz69xBEnck32z6c/A4DtihdO/zR8+HDuu+++UodhZmbd+Nvf/sbGjRuZPXs2999/P7W1tRx33HFcfvnlXHHFFQA0NzfzxhtvMHPmTPbYY48SR5xQ9xPhZewsDY6I9UWMJ2+NjY2xcOHCUodhZmZVbM2aNbztbW/jpptuYvLkZE64OXPmcPnll7N48eJ2+27evJnhw4fz1FNPsfPOOxclHkmLImKrGWY7ynUGvUMkLSWdxU7SgZK+18sYzczMKsqwYcMYOXIkyQKticzHmTZv3sz69etpauo4U3zfy/XWu2uAiSTz1hMRjwGHFysoMzOzcvWpT32K7373u7z00kusWbOGb3/72xxzzDHcddddLF68mNbWVtauXcsFF1zAsGHD2G+//Uodcs7JnohY1aGotcCx9GtzFzdx6JX3MubCX3Polfcyd3HprwTNzGxrF198MRMmTGCfffZhv/32Y/z48Xz1q1+lubmZU045hR122IE999yTFStW8Jvf/IZBgwaVOuTc+uwlzQauJpkj/2DgPKAxIk4ubni5q+Q++7mLm5h6+xO0bHzr+qmutoavH38Ak8Y3lDAyMzMrZwXtswc+S7KmfQPJsrTvSp9bAcyYv6xdogdo2djKjPnLShSRmZlVk1xvvVNEnFbUSPqx55tbelRuZmbWE7nW7B+QdKekMyXVFzWifmhEfV2Pys3MzHoip2QfEfsAFwHjgEck/UrS6UWNrB+ZMnEsdbU17crqamuYMnFsiSIyM7Nq0pP17P8E/EnSf5IM1psJ3FyswPqTtkF4M+Yv4/nmFkbU1zFl4lgPzjMzqwBzFzeV/d/vnJK9pO2BjwInA3sCvwAOKmJc/c6k8Q1l98thZmZd63g3VVNzC1NvfwKgrP6m59pn/xjJCPzpEbFPRHw5IhYVMS4zM7OyVyl3U+XajP/2iAhJg4sajZmZWQWplLupcq3Zv8dz45uZmbVXKXdTeW58MzOzPFXK3VQ9GY2/qsPKPp4b38zM+rVKuZsq12S/StI/AyGplmRu/KeKF5Z1pRJu8zCyMdxJAAAeHUlEQVQz6y8q4W6qXJP9Z4H/4q258e8Ezi5WUNa5SrnNw8zMykeuM+i9EhGnRcQuEbFzRJwOnFHk2CyLSrnNw8zMykfO69lncUHBorCcdXY7R1OZ3eZhZmblozfJXt3vYoXW2e0cImniNzMz66g3yT4KFoXlbMrEsVmvsgLclG9mZll1mewlvS5pbZaf14ERfRSjZZg0vqHTq6xym7HJzMzKQ5ej8SNiu74KxHLXUF+XtY++3GZsMjOz8tCbZnwrkUqZscnMzMpDzjPoWfmolBmbzMysPDjZV6hKmLHJzMzKg5vxzczMqpxr9tanPK+/mVnfc7K3PuN5/c3MSqOozfiSjpK0TNJySRdm2b6tpJ+n2x+SNDpj2zslPShpiaQnJA0qZqxWfJ7X38ysNIqW7CXVANcBRwP7A6dI2r/DbmcCayJiL+DbwFXpawcCNwOfjYhxwBHAxmLFan2js0l/PBmQmVlxFbMZ/yBgeUQ8AyBpFnAcsDRjn+OAaenj2cC1kgT8C/B4RDwGEBF/L2Kc1kdGlOFkQB5DYGb9QTGb8RuAVRnPV6dlWfeJiE3Aa8COwD5ASJov6RFJX8r2BpLOkrRQ0sKXX3654B/ACqvcJgNqG0PQ1NxC8NYYAi8oZGbVplwH6A0EDgMmAOuBeyQtioh7MneKiB8APwBobGwsi4V5XFPsXLlNBtTVGAJ/Z2ZWTYqZ7JuA3TOej0zLsu2zOu2n3wH4O0krwO8i4hUASXcA/wTcQxnzaPPuldNkQB5DYGb9RTGb8R8G9pY0RtI2wMnAvA77zAMmp49PBO6NiADmAwdIGpxeBLyP9n39ZcmjzStLZ2MFvKCQmVWboiX7tA/+XJLE/RRwa0QskTRd0rHpbj8EdpS0HLgAuDB97RrgapILhkeBRyLi18WKtVBcU6ws5TaGwMysWIraZx8RdwB3dCi7JOPxBuCkTl57M8ntdxWjHEebW+fKbQyBmVmxlOsAvYo0ZeLYdn324JpiuSunMQRmZsXiZF9ArimamVk5crIvMNcUzcys3HiJWzMzsyrnZG9mZlblnOzNzMyqnJO9mZlZlfMAvRLw/PlmZtaXnOz7mOfPNzOzvuZm/D7m+fPNzKyvOdn3Mc+fb2Zmfc3Jvo95pTUzM+trTvZ9aO7iJta/uWmrcs+fb2ZmxeQBen2k48C8NvV1tUw7dpwH55mZWdE42RdQV7fUZRuYBzBk24Fb9vEteWZmVgxO9gXS3S113Q3M8y15ZmZWLO6zL5DubqnrbmCeb8kzM7NicbIvkO5q7lMmjqWutqbdtsyBeb4lz8zMisXJvkC6q7lPGt/A148/gIb6OgQ01Nfx9eMP2NJE71vyzMysWNxnXyBTJo7darR9x1vqJo1v6LT/PZfXm5mZ5cM1+wKZNL6BE97dQI0EQI3ECe/uPLlne31XNX8zM7N8uWZfIHMXNzFnUROtEQC0RjBnURONe7ytRwm/0pK7bxc0Myt/Tva91JbsmrIMpGsbTV/uyS/fhO3bBc3MKoOb8XuhLdllS/Rtyn00feZnCN5K2HMXN3X7Wt8uaGZWGVyz74Vp85ZknRUvU66j6UvVHN5Vwu7u/X27oJlZZXDNPk9zFzfR3LKxy31yHU3fm9p1b/UmYft2QTOzyuBkn6fumqp7Mpq+lM3hvUnY3U0UZGZm5cHN+HnqquZ7zcff1aMm+FI2h/fm/v7MRX48Gt/MrHw52edpRH1d1oF5wwbX9jjZdXasvmgO723CrsTbBc3M+hsn+zx1ViO+9CPjCnasvmoOd8I2M6tuTvZ5KmQTdl82h3sSHDOz/keRzvhW6RobG2PhwoWlDqOsdZwEB5IWBE/La2ZWmSQtiojG7vZzzb7Ayrnm3Jt76s3MrHI52RdQX0wf25uLCU+CY2bWPznZ90LHxLv+zU1FrTn39mKilKP+zcysdIo6qY6koyQtk7Rc0oVZtm8r6efp9ockje6wfZSkdZK+WMw48zF3cRNTZj/Wbta7Neuzz6hXqJpzbyff8SQ4Zmb9U9GSvaQa4DrgaGB/4BRJ+3fY7UxgTUTsBXwbuKrD9quB/ytWjL3xtf9dwsbW3AY3Fqrm3Ntm+EnjG/j68QfQUF+H6Nksf2ZmVrmK2Yx/ELA8Ip4BkDQLOA5YmrHPccC09PFs4FpJioiQNAn4K/BGEWPMW2e1+I4KWXOuH1yb9X17cjHhe+rNzPqfYib7BmBVxvPVwMGd7RMRmyS9BuwoaQPwZeBDQNk14Xenob6u4KPx5y5uYt2GTVuV19bIzfA5Kuc7JczMiqlcB+hNA74dEeskdbqTpLOAswBGjRrVN5Gl6utqs656V19XywMXfqDg7zdj/jI2bt6622DINgOdsHLQF3dKmJmVq2IO0GsCds94PjIty7qPpIHADsDfSVoAviFpJfB54CuSzu34BhHxg4hojIjG4cOHF/4TdGHaseOoHdD+QqR2gJh2bM+ny81FZ/3yr3WzzK4lSrmyoJlZqRWzZv8wsLekMSRJ/WTg1A77zAMmAw8CJwL3RjKl33vbdpA0DVgXEdcWMdYe68kUt4VoPvZtc73jOQbMrD8rWs0+IjYB5wLzgaeAWyNiiaTpko5Nd/shSR/9cuACYKvb88pVrgm8rfk48xa9qbc/wdzFHRs5uubb5nqns4siXyyZWX/gufHz0JM55g+98t6sNfKG+roe9+17gFn+vC6AmVUjz41fRD2ZY76Qzce+bS5/fbmyoJlZuXGyz0NPErj72suHL5bMrL8q6nS51aon/b/9qa997uImDr3yXsZc+GsOvfLeHo9LMDOz4nCyz0NPEnh/maK2UAMRzcys8NyMn4ee9v/2h+bjnoxjKBce8Ghm/YWTfZ7ySeDVnFwq7T52z6hnZv2Jm/H7SLYlcafMfqxqmrkr7T52z6hnZv2Jk30fybYk7sbW4Gv/u6REERVWpQ1ErLSWCDOz3nAzfh/pbEnczPJKbuavtPvYfUukmfUnTvZlohr6kCtpIOKUiWOzzqhXri0RZma94Wb8PlJfV9tlufuQ+1Z/uSXSzAxcs+8z044dx5TbHmu3Jn3mkrjuQ+57ldQSYWbWG67Z56mns8VNGt/AjJMObFeTnHHSgVuSTWd9xfWDaz0rnVkvDR06tN1PTU0Nn/vc57Zsv+GGG9hrr70YOnQoRx11FM8//3wJozUrPK96l4dirKCW7Zi1NYKgXWuAV2oz651169ax6667cscdd3D44YezYMECPvaxj/Hb3/6Wvffem/POO4+lS5dy3333lTpUs27luuqda/Z5KEb/erY+5CHbDGyX6AvxPmb93Zw5c9h5551573vfC8CvfvUrTjrpJMaNG8c222zDxRdfzO9+9ztWrFhR4kjNCsd99nnorB89261cPdGxD3nMhb/O6f0r+ZY9s742c+ZMzjjjDCRtKcts4Wx7/OSTT7Lnnnv2eXxmxeCafR46618XFLRPPZdZ6UqxAI1Xt7NK9eyzz3LfffcxefLkLWVHHXUUt956K48//jgtLS1Mnz4dSaxfv76EkZoVlpN9Hjq7FzugoE3sucxK19e37PniwirZT37yEw477DDGjBmzpeyDH/wgX/va1zjhhBMYPXo0o0ePZrvttmPkyJEljNSssJzsC6y7W+V6krhyuRe8L2/Zm7u4iS/c+ljVX1xY9frxj3/crlbf5pxzzuHpp5/mb3/7GyeccAKbNm3iHe94RwkiNCsO99nnYdq8zuezHyAx5sJfb+k7h7emkK0fXMu6DZu2DLrLZZa87u4F76tpX9uSbmsnd28Uaz6ASlw618rTH/7wB5qamjjppJPalW/YsIHly5czbtw4Vq1axVlnncV5553HsGHDShSpWeG5Zp+H5pbs89wDtEa8tardbY+1W+luzfqNBR9d31cL0GRLupmKNad8Z4MePdmQ9dTMmTM5/vjj2W677dqVb9iwgVNPPZWhQ4dy0EEHccghh3DZZZeVKEqz4nDNvog6JvbONDW3cOiV9+Y1mj7fBWh6OoK/q+RarDnl5y5uQiRjITrygjXWU9///vezltfX1/P444/3cTRmfcvJPg/DBtd2uopdvtpqsPksgNPTaV/zWXSns+6CGqlok/zMmL8sa6IXnQ+SNDOzrbkZPw+XfmRcUY9f7Ilz8hnB31l3wbc+dmDR+s47a00IKmclwEI6/fTT2W233dh+++3ZZ599uOGGG7bap+22sbvvvrsEEZpZuXKyz8Ok8Q0Mru3+1NUOUDLlbR6K2Sedzwj+UqwS11lTfUM/bcKfOnUqK1euZO3atcybN4+LLrqIRYsWbdm+YsUKbrvtNnbbbbcSRmlm5cjN+HnaZmAN6zdu3qpcaSdzttH4nTWFZ1PMPul8R/D39SpxXnO+vXHj3mpRkoQkVqxYwbvf/W4guX3sqquu4uyzzy5ViBXFM09af+Jkn6fXOhuRH/DXK/+1XVHmH5BDr7w3p4RfzIRWKUk038GH1ezss8/mpptuoqWlhfHjx/PhD38YgNtuu41tt912y3PrWj7jVswqmZN9nvKtHWdLtB0NG1xbkD84ndVcKimJes359r73ve/x3e9+lwcffJAFCxaw7bbb8vrrr/OVr3yFu+66q9ThVQzP32D9jZN9nvKtHWcm2qbmlq1uLaurrSnIAMDuai5OopWrpqaGww47jJtvvpnrr7+eZ599lk984hOMHj261KFVjL6cedKsHHiAXp56M2Bt0vgGHrjwA6y88l/59sff1aNj5Drdbl/PmW99b9OmTaxYsYJ77rmH73znO+y6667suuuurFq1io997GNcddVVpQ6xbOWyyJRZNXHNvhcKUTvuyTF60s9YaTUXD5bq2ksvvcS9997LMcccQ11dHXfffTe33HILt9xyC5dccgkbN741hmTChAlcffXVHH300SWMuLxVyrgVs0Jxsq8gPelnLMac+cVKyB4s1T1JXH/99Xz2s59l8+bN7LHHHlxzzTUce+yxW+1bU1PDsGHDGDp0aAkirQyVNG7FrBCc7CtIT2rrha65FDMhe7BU94YPH859992X074rV64sbjBVwuNWrD9xn32RFGMN9p70MxZ6EpxijgGotC4HM7NK45p9EWSrBX/+549y/q2PctrBo7h80gF5HbentfVC1lyKmZD7apleM7P+yjX7IuhsOdgIuPmPz3HR3Cd6fMy2/vKWja3UKJmCty+mrG1TzNHLfbVMr5lZf1XUZC/pKEnLJC2XdGGW7dtK+nm6/SFJo9PyD0laJOmJ9N8PFDPOQuuutnvLQ6t6dLy2loK22m9rxJZk2Fd9jsVMyKWYd9/MrD8pWjO+pBrgOuBDwGrgYUnzImJpxm5nAmsiYi9JJwNXAR8HXgE+EhHPS3oHMB+omL/83c2B3xqdr3OfbcR7OQxgK/boZQ+W6jnfrmhmuSpmn/1BwPKIeAZA0izgOCAz2R8HTEsfzwaulaSIWJyxzxKgTtK2EfGPIsZbMN1NidvWDN9RZyPeOztOXw9gc0IuH75d0cx6opjJvgHIbK9eDRzc2T4RsUnSa8COJDX7NicAj2RL9JLOAs4CGDVqVOEi74Gualdfuf3xrCvjnXLw7lmP1VkNvkbK2hrgAWz9Vzm09phZ5SjrAXqSxpE07X8m2/aI+EFENEZE4/Dhw/s2ONr3pQdv1a7mLm5i0vgGll52NKe/Z9SWmnyNxOnv6Xw0fmc19bY++kwewNa/+XZFM+uJYtbsm4DMKuzItCzbPqslDQR2AP4OIGkk8AvgjIhYUcQ485ZL7erySQfkfKtdZ3399XW1W44Nyap4l35k3FY1OPfh9h9tvytrF/0vbzx5D2++vJIh+72PA0/7CgBvvvkmp556KgsXLuTZZ5/lt7/9LUcccURpgzazkilmzf5hYG9JYyRtA5wMzOuwzzxgcvr4RODeiAhJ9cCvgQsj4oEixtgrha5dZRvxXjtAvPHmJppb3pr7fEOWroGuWhms+rT9rgwcuiM7HPJxhh7wIWoGqF1rT9vKeLvuumsJIzWzclC0mn3aB38uyUj6GuBHEbFE0nRgYUTMA34I/ETScuBVkgsCgHOBvYBLJF2Slv1LRLxUrHh7au7iJrZanzaVb196thHv69/cxJr1G9vt17KxlWnzlrSrtReqD9etA5Vhy+/KkG14vrmFbZtXsvfQN7eUb7PNNnz+858Hkrnyzax/K+oMehFxB3BHh7JLMh5vAE7K8rrLgcuLGVtvzF3cxBdue4xsd9DVdqhd9VTHEe9jLvx11v2aWzZuGRsAhWllqLYR3tV+4ZL5u3LRRQ+yevXqEkdkZuWqrAfolauv/e8SWjdnv1d+6KCBBU0oXbUSZM5LX4gZ7oo5/31fc7eGmdlbnOzz0LFZPVNzF9vy0VUrQWatvRAz3FXTCO9qunAxM+stJ/sCK/S975PGNzBscG2371WIKWeLOf99X6umCxczs95yss9D261w2TQ1txRsSds2l35kXE619knjG3jgwg/w1yv/lQcu/ECPuxOqaUGaarpw6cqmTZvYsGEDra2ttLa2smHDBjZt2gTAP/7xDzZs2AAkt+Jt2LCB6GKqZjOrXk72eZh27DhqB2Sf8hay9w/3Zn37vlwoZlDtW78S9XW1FbsgTU8vXHrz/ZTS5ZdfTl1dHVdeeSU333wzdXV1XH55MrZ17Nix1NXV0dTUxMSJE6mrq+PZZ58tccRmVgqqliv9xsbGWLhwYZ+930Vzn+Cnf3wu2513WzTU1/HAhR/YapQ7JImnnBJpOcbY29H0ub6+HD+7mVkuJC2KiMbu9ivqrXfV7Ld/frnLRA9v9Q9Xwjzm5RZjIW4DzHXhnnL77GZmheZkn6dcBnq19Q+X82CxttpvZ0vy5hpjoe9p78sEXM7fj5lZIbjPPk/dDfTK7B8u18FimfeidyaXGItxT3tfJuBy/X7MzArFyT5PUyaOpbYm+yC9jgPo8hnl3hcDxrLVnnsSY1fH6e097X2ZgKvpLoRKHWhoZsXlZvze6NBpXztAzDjpwK2ambPNed9VM3dfTVvbVS25s5X1enKc3tTCp0wcm3XQXDEScE+/n3JVbdMdm1nhONnnacb8ZWzsMGXuxs3RaZ9yroPF2o7dF/3VnS2pC9lX1uvpcXpTC+/rBNyT76dceaChmXXGyT5PxexT7qv+6my15zY9SRLFqoVXQwLuSx5oaGadcZ99norZp9xX/dVtk/V0Jtck0ZeT/ljnPNDQzDrjZJ+nYg7q6ssBY5PGN9BQgCTR26l6rfeqaaChmRWWk32eilmb7euaspNEdXALi5l1xtPlGlD4SXHMzKz4PF2u9YgHw5mZVS8345uZmVU5J3szM7Mq52RvZmZW5ZzszczMqpyTvZmZWZVzsjczM6tyTvZmZmZVzsnezMysyjnZm5mZVTknezMzsyrnZG9mZlblnOzNzMyqnJO9mZlZlXOyNzMzq3JO9mZmZlXOyd7MzKzKKSJKHUNBSHoZeLYEb70T8EoJ3rea+Bz2ns9h7/kc9p7PYWH05DzuERHDu9upapJ9qUhaGBGNpY6jkvkc9p7PYe/5HPaez2FhFOM8uhnfzMysyjnZm5mZVTkn+977QakDqAI+h73nc9h7Poe953NYGAU/j+6zNzMzq3Ku2ZuZmVU5J/s8STpK0jJJyyVdWOp4Ko2k3SX9VtJSSUsknVfqmCqVpBpJiyX9qtSxVCpJ9ZJmS/qzpKckHVLqmCqNpPPT/8tPSrpF0qBSx1TuJP1I0kuSnswoe5ukuyQ9nf47rBDv5WSfB0k1wHXA0cD+wCmS9i9tVBVnE/CFiNgfeA9wjs9h3s4Dnip1EBXuv4DfRMS+wIH4fPaIpAbgP4DGiHgHUAOcXNqoKsJNwFEdyi4E7omIvYF70ue95mSfn4OA5RHxTES8CcwCjitxTBUlIl6IiEfSx6+T/HFtKG1UlUfSSOBfgRtKHUulkrQDcDjwQ4CIeDMimksbVUUaCNRJGggMBp4vcTxlLyJ+B7zaofg4YGb6eCYwqRDv5WSfnwZgVcbz1ThR5U3SaGA88FBpI6lI1wBfAjaXOpAKNgZ4Gbgx7Q65QdKQUgdVSSKiCfgm8BzwAvBaRNxZ2qgq1i4R8UL6+EVgl0Ic1MneSkrSUGAO8PmIWFvqeCqJpGOAlyJiUaljqXADgX8Cro+I8cAbFKjptL9I+5WPI7lwGgEMkXR6aaOqfJHcLleQW+ac7PPTBOye8XxkWmY9IKmWJNH/NCJuL3U8FehQ4FhJK0m6kj4g6ebShlSRVgOrI6KtZWk2SfK33H0Q+GtEvBwRG4HbgX8ucUyV6m+SdgNI/32pEAd1ss/Pw8DeksZI2oZkIMq8EsdUUSSJpI/0qYi4utTxVKKImBoRIyNiNMnv4L0R4dpUD0XEi8AqSWPToiOBpSUMqRI9B7xH0uD0//aReJBjvuYBk9PHk4FfFuKgAwtxkP4mIjZJOheYTzLq9EcRsaTEYVWaQ4FPAE9IejQt+0pE3FHCmKz/+hzw0/Ti/RngUyWOp6JExEOSZgOPkNxpsxjPptctSbcARwA7SVoNXApcCdwq6UySlVw/VpD38gx6ZmZm1c3N+GZmZlXOyd7MzKzKOdmbmZlVOSd7MzOzKudkb2ZmVuWc7M2KQFJI+lbG8y9KmtbHMdwk6cT08Q29XWhI0ujM1bkKRdJ0SR/MUn5Eb1byk7RS0k6dbFP677S259nK0n9/mq5w+WS6SlltvjGZlYqTvVlx/AM4vrNk0510MZGCiYhPR0TRJ4rJJ+6IuCQi7i5GPF14l6TvAG+TNAm4opMygJ8C+wIHAHXAp/s4VrNe86Q6ZsWxiWRSkfOBr2ZuSBf++RGwE8kCLJ+KiOck3USyAtZ44BFJr5PMNb4bsA9wAclywEeTTM/8kYjYKOkS4CMkiegPwGeiwwQakhYAXySZt3x6WlwHbBMRYyS9G7gaGAq8AnwyIl5Iy38ErAd+n+2DSjqCZDKQF4B3Afun86L/B7ANyQJHZ6e7/xBoJJnv+0cR8e30c/8qImZLOopkcZ9XSCZoaXuPacC6iPhm+vxJ4JiIWClpLsn01YOA/4qIdpO5pIva3EoyrXUNcFlE/FxSC/AgUBsR/57uu1VZ5kRPkv6UHsesorhmb1Y81wGnpUuoZvouMDMi3klSa/xOxrZ9gA9GxBfS53uSLGF7HHAz8NuIOABoScsBro2ICek64nXAMZ0FFBHzIuJdEfEu4DHgm2mz9HeBEyOiLbm31WpvBD4XEYd081kPAr4aEftL2g/4OHBo+j6twGkkFwINEfGO9DPcmHkASYOA/yG5cHkvsGs379nm39K4G4H/kLRjh+1HAc9HxIHpOfqNpHcB/w78BJgv6fJsZR3iqyWZ9fE3OcZlVjac7M2KJF3F78ckNdxMhwA/Sx//BDgsY9ttEdGa8fz/0oVFniCplbYlmieA0enj90t6SNITwAeAcd3FJulLQEtEXAeMBd4B3JVOXXwRMFJSPVCfrrndFmtn/hQRf00fHwm8G3g4Pd6RwNtJpqF9u6TvpjX4jqsc7kuymMrTactErov6/Iekx4A/ktTw9+6w/QngQ5KukvTeiHgNeCwizgNejYi5wMWdlGX6HvC7iLg/x7jMyoab8c2K6xqS5ugbu9sx9UaH5/8AiIjNkjZmNM9vBgamteHvAY0RsSpt7h7U1Rukg+FOAg5vKwKWdKy9p8k+V5lxi6TlYmqW9z4QmAicQzLn97/lePxNtK+cDEqPdwTJimuHRMT6tLui3eePiL9I+ifgw8DXJd0ZEdPTbdPSfyNj/63KJF0KDAc+k2O8ZmXFNXuzIoqIV0n6i8/MKP4DySp1kDRv96am2JbYXpE0FDixq50l7UHSvXBSRLSkxcuA4ZIOSfeplTQuIpqBZkltLQ+n5RjTPcCJknZOj/c2SXukgxUHRMQcklpzx2Vk/wyMlrRn+vyUjG0r2/ZPE/eYtHwHYE2a6PclGdPQ8TOPANZHxM3AN7O8b5ckfZrkAuWUiNjck9ealQvX7M2K71vAuRnPPwfcKGkK6QC9fA8cEc2S/oekqXolyfLLXfkksCMwN72z7PmI+HB6i9530vEFA0laJJaksf1I0nqSVR5ziWmppIuAOyUNADaS1ORbSD53WyVjaofXbZB0FvBrSa+QDAh8R7p5DnCGpMXAQuAvaflvgM9KepzkouWPWUI6AJghaXMay7/n8jky/DfJ6mMPpufs9raWAbNK4VXvzMzMqpyb8c3MzKqck72ZmVmVc7I3MzOrck72ZmZmVc7J3szMrMo52ZuZmVU5J3szM7Mq52RvZmZW5f4/h4upveQduLMAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from statsmodels.graphics.regressionplots import plot_leverage_resid2\n",
"fig, ax = plt.subplots(figsize=(8,6))\n",
"fig = plot_leverage_resid2(results, ax = ax)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Other plotting options can be found on the [Graphics page.](http://www.statsmodels.org/stable/graphics.html)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Multicollinearity\n",
"\n",
"Condition number:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"702.1792145490056"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.linalg.cond(results.model.exog)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Heteroskedasticity tests\n",
"\n",
"Breush-Pagan test:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib/python3/dist-packages/ipykernel_launcher.py:3: DeprecationWarning: `het_breushpagan` is deprecated, use `het_breuschpagan` instead!\n",
"Use het_breuschpagan, het_breushpagan will be removed in 0.9 \n",
"(Note: misspelling missing 'c')\n",
" This is separate from the ipykernel package so we can avoid doing imports until\n"
]
},
{
"data": {
"text/plain": [
"[('Lagrange multiplier statistic', 4.893213374094033),\n",
" ('p-value', 0.0865869050235188),\n",
" ('f-value', 2.5037159462564795),\n",
" ('f p-value', 0.08794028782672685)]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"name = ['Lagrange multiplier statistic', 'p-value', \n",
" 'f-value', 'f p-value']\n",
"test = sms.het_breushpagan(results.resid, results.model.exog)\n",
"lzip(name, test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Goldfeld-Quandt test"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[('F statistic', 1.1002422436378145), ('p-value', 0.38202950686925213)]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"name = ['F statistic', 'p-value']\n",
"test = sms.het_goldfeldquandt(results.resid, results.model.exog)\n",
"lzip(name, test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Linearity\n",
"\n",
"Harvey-Collier multiplier test for Null hypothesis that the linear specification is correct:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[('t value', -1.0796490077794014), ('p value', 0.2834639247554071)]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"name = ['t value', 'p value']\n",
"test = sms.linear_harvey_collier(results)\n",
"lzip(name, test)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
}

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/srv/reproducible-results/rbuild-debian/tmp.FTzzuDJkiI/dbd-tmp-gqMLHgR/diffoscope_1mz05yxs/tmprelhfv9k/3051aa24-3e6e-4f49-9835-aa50ae1a999c vs.

Similarity: 0.0%
Differences: {
"replace": {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Regression Plots"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
" from pandas.core import datetools\n"
]
}
],
"source": [
"%matplotlib inline\n",
"\n",
"from __future__ import print_function\n",
"from statsmodels.compat import lzip\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import statsmodels.api as sm\n",
"from statsmodels.formula.api import ols"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Duncan's Prestige Dataset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load the Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can use a utility function to load any R dataset available from the great <a href=\"http://vincentarelbundock.github.com/Rdatasets/\">Rdatasets package</a>."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"prestige = sm.datasets.get_rdataset(\"Duncan\", \"car\", cache=True).data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>type</th>\n",
" <th>income</th>\n",
" <th>education</th>\n",
" <th>prestige</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>accountant</th>\n",
" <td>prof</td>\n",
" <td>62</td>\n",
" <td>86</td>\n",
" <td>82</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pilot</th>\n",
" <td>prof</td>\n",
" <td>72</td>\n",
" <td>76</td>\n",
" <td>83</td>\n",
" </tr>\n",
" <tr>\n",
" <th>architect</th>\n",
" <td>prof</td>\n",
" <td>75</td>\n",
" <td>92</td>\n",
" <td>90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>author</th>\n",
" <td>prof</td>\n",
" <td>55</td>\n",
" <td>90</td>\n",
" <td>76</td>\n",
" </tr>\n",
" <tr>\n",
" <th>chemist</th>\n",
" <td>prof</td>\n",
" <td>64</td>\n",
" <td>86</td>\n",
" <td>90</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" type income education prestige\n",
"accountant prof 62 86 82\n",
"pilot prof 72 76 83\n",
"architect prof 75 92 90\n",
"author prof 55 90 76\n",
"chemist prof 64 86 90"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prestige.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"prestige_model = ols(\"prestige ~ income + education\", data=prestige).fit()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: prestige R-squared: 0.828\n",
"Model: OLS Adj. R-squared: 0.820\n",
"Method: Least Squares F-statistic: 101.2\n",
"Date: Sat, 10 Apr 2021 Prob (F-statistic): 8.65e-17\n",
"Time: 01:00:09 Log-Likelihood: -178.98\n",
"No. Observations: 45 AIC: 364.0\n",
"Df Residuals: 42 BIC: 369.4\n",
"Df Model: 2 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"Intercept -6.0647 4.272 -1.420 0.163 -14.686 2.556\n",
"income 0.5987 0.120 5.003 0.000 0.357 0.840\n",
"education 0.5458 0.098 5.555 0.000 0.348 0.744\n",
"==============================================================================\n",
"Omnibus: 1.279 Durbin-Watson: 1.458\n",
"Prob(Omnibus): 0.528 Jarque-Bera (JB): 0.520\n",
"Skew: 0.155 Prob(JB): 0.771\n",
"Kurtosis: 3.426 Cond. No. 163.\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
]
}
],
"source": [
"print(prestige_model.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Influence plots"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Influence plots show the (externally) studentized residuals vs. the leverage of each observation as measured by the hat matrix.\n",
"\n",
"Externally studentized residuals are residuals that are scaled by their standard deviation where \n",
"\n",
"$$var(\\hat{\\epsilon}_i)=\\hat{\\sigma}^2_i(1-h_{ii})$$\n",
"\n",
"with\n",
"\n",
"$$\\hat{\\sigma}^2_i=\\frac{1}{n - p - 1 \\;\\;}\\sum_{j}^{n}\\;\\;\\;\\forall \\;\\;\\; j \\neq i$$\n",
"\n",
"$n$ is the number of observations and $p$ is the number of regressors. $h_{ii}$ is the $i$-th diagonal element of the hat matrix\n",
"\n",
"$$H=X(X^{\\;\\prime}X)^{-1}X^{\\;\\prime}$$\n",
"\n",
"The influence of each point can be visualized by the criterion keyword argument. Options are Cook's distance and DFFITS, two measures of influence."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(12,8))\n",
"fig = sm.graphics.influence_plot(prestige_model, ax=ax, criterion=\"cooks\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As you can see there are a few worrisome observations. Both contractor and reporter have low leverage but a large residual. <br />\n",
"RR.engineer has small residual and large leverage. Conductor and minister have both high leverage and large residuals, and, <br />\n",
"therefore, large influence."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Partial Regression Plots"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since we are doing multivariate regressions, we cannot just look at individual bivariate plots to discern relationships. <br />\n",
"Instead, we want to look at the relationship of the dependent variable and independent variables conditional on the other <br />\n",
"independent variables. We can do this through using partial regression plots, otherwise known as added variable plots. <br />\n",
"\n",
"In a partial regression plot, to discern the relationship between the response variable and the $k$-th variabe, we compute <br />\n",
"the residuals by regressing the response variable versus the independent variables excluding $X_k$. We can denote this by <br />\n",
"$X_{\\sim k}$. We then compute the residuals by regressing $X_k$ on $X_{\\sim k}$. The partial regression plot is the plot <br />\n",
"of the former versus the latter residuals. <br />\n",
"\n",
"The notable points of this plot are that the fitted line has slope $\\beta_k$ and intercept zero. The residuals of this plot <br />\n",
"are the same as those of the least squares fit of the original model with full $X$. You can discern the effects of the <br />\n",
"individual data values on the estimation of a coefficient easily. If obs_labels is True, then these points are annotated <br />\n",
"with their observation label. You can also see the violation of underlying assumptions such as homooskedasticity and <br />\n",
"linearity."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(12,8))\n",
"fig = sm.graphics.plot_partregress(\"prestige\", \"income\", [\"income\", \"education\"], data=prestige, ax=ax)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x1008 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fix, ax = plt.subplots(figsize=(12,14))\n",
"fig = sm.graphics.plot_partregress(\"prestige\", \"income\", [\"education\"], data=prestige, ax=ax)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As you can see the partial regression plot confirms the influence of conductor, minister, and RR.engineer on the partial relationship between income and prestige. The cases greatly decrease the effect of income on prestige. Dropping these cases confirms this."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: prestige R-squared: 0.876\n",
"Model: OLS Adj. R-squared: 0.870\n",
"Method: Least Squares F-statistic: 138.1\n",
"Date: Sat, 10 Apr 2021 Prob (F-statistic): 2.02e-18\n",
"Time: 01:00:11 Log-Likelihood: -160.59\n",
"No. Observations: 42 AIC: 327.2\n",
"Df Residuals: 39 BIC: 332.4\n",
"Df Model: 2 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"Intercept -6.3174 3.680 -1.717 0.094 -13.760 1.125\n",
"income 0.9307 0.154 6.053 0.000 0.620 1.242\n",
"education 0.2846 0.121 2.345 0.024 0.039 0.530\n",
"==============================================================================\n",
"Omnibus: 3.811 Durbin-Watson: 1.468\n",
"Prob(Omnibus): 0.149 Jarque-Bera (JB): 2.802\n",
"Skew: -0.614 Prob(JB): 0.246\n",
"Kurtosis: 3.303 Cond. No. 158.\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
]
}
],
"source": [
"subset = ~prestige.index.isin([\"conductor\", \"RR.engineer\", \"minister\"])\n",
"prestige_model2 = ols(\"prestige ~ income + education\", data=prestige, subset=subset).fit()\n",
"print(prestige_model2.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For a quick check of all the regressors, you can use plot_partregress_grid. These plots will not label the <br />\n",
"points, but you can use them to identify problems and then use plot_partregress to get more information."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x576 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12,8))\n",
"fig = sm.graphics.plot_partregress_grid(prestige_model, fig=fig)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Component-Component plus Residual (CCPR) Plots"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The CCPR plot provides a way to judge the effect of one regressor on the <br />\n",
"response variable by taking into account the effects of the other <br />\n",
"independent variables. The partial residuals plot is defined as <br />\n",
"$\\text{Residuals} + B_iX_i \\text{ }\\text{ }$ versus $X_i$. The component adds $B_iX_i$ versus <br />\n",
"$X_i$ to show where the fitted line would lie. Care should be taken if $X_i$ <br />\n",
"is highly correlated with any of the other independent variables. If this <br />\n",
"is the case, the variance evident in the plot will be an underestimate of <br />\n",
"the true variance."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(12, 8))\n",
"fig = sm.graphics.plot_ccpr(prestige_model, \"education\", ax=ax)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As you can see the relationship between the variation in prestige explained by education conditional on income seems to be linear, though you can see there are some observations that are exerting considerable influence on the relationship. We can quickly look at more than one variable by using plot_ccpr_grid."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x576 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12, 8))\n",
"fig = sm.graphics.plot_ccpr_grid(prestige_model, fig=fig)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Regression Plots"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The plot_regress_exog function is a convenience function that gives a 2x2 plot containing the dependent variable and fitted values with confidence intervals vs. the independent variable chosen, the residuals of the model vs. the chosen independent variable, a partial regression plot, and a CCPR plot. This function can be used for quickly checking modeling assumptions with respect to a single regressor."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x576 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12,8))\n",
"fig = sm.graphics.plot_regress_exog(prestige_model, \"education\", fig=fig)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Fit Plot"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The plot_fit function plots the fitted values versus a chosen independent variable. It includes prediction confidence intervals and optionally plots the true dependent variable."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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DD+j973+/Zs+era997Wtas2aNXnrpJb344os66qijJElnnHFGw2oqVyNasq+VdGzRutskHeDuB0r6k6SLJMnM9pN0mqT9w8dcaWbbNKBGAAAASFJ3t9TWVnpbW1uwPUKbNm0acZu7a//999eqVau0atUq/fGPf9Stt94a6fPXS91Dtrv/RtILRetudffN4eJvJU0P758o6Tp33+TuT0l6XNKh9a4RAAAAoUwm6HtdHLTb2mrqkz2S8ePH6+WXXx5c3m677QaX9957bw0MDAx2CXnjjTf04IMPavvtt9eUKVN05513SpJ6enoirSkKSeiTfbakX4T3p0l6umDbmnAdAAAAGqU4aEcQsEeaCv3DH/6wLr30Uh188MF64okndNZZZ+ncc8/V7Nmz9eabb2rp0qW64IILdNBBB2n27Nm66667JEnd3d369Kc/rSOOOEKTJk2quq56MXev/5OYtUvqdfcDitYvkDRH0kfc3c3s25J+6+4/DLdfLekX7r60xDHnS5ovSTNmzHh3f39/fV8EAABAE3v44Ye17777VvagbDbog93dXXML9mOPPaa99tqr7PXVHCtqpd4zM1vp7nPGemxsQ/iZ2VmSOiQd41uT/lpJexTsNj1cN4y7L5K0SJLmzJlT/28KAAAArSaTkfr64q6iKcXSXcTMjpX0ZUknuHvh5avLJJ1mZtua2SxJe0n6XRw1AgAAANWqe0u2mS2RdLSkXcxsjaSvKhhNZFtJt5mZFHQROdfdHzSzH0t6SNJmSZ929zfrXSMAAAAQpbqHbHefV2L11aPsv1DSwvpVBAAA0JrcXWEDJ8ZQ63WLSRhdBAAAAHU2ceJEPf/88zWHx1bg7nr++ec1ceLEqo8R24WPAAAAaJzp06drzZo1GhgYiOX5169fr82bN5e9vppjRWnixImaPn362DuOoCFD+NXbnDlzfMWKFXGXAQAAgBGccMIJWrZsWdnrqzlWI5Q7hB/dRQAAAFJuyZIlcZfQcgjZAAAAKUfIbjxCNgAAABAxQjYAAAAQMUI2AAAAEDFCNgAAABAxQjYAAAAQMUI2AAAAEDFCNgAAABAxQjYAAEg1xohGHAjZAAAg1QjZiAMhGwAAAIgYIRsAAACIGCEbAAAgAnRLQSFCNgAAQAQI2ShEyAYAAAAiRsgGAABoYbTA1wchGwAAoIURsuuDkA0AAABEjJANAAAARIyQDQAAUEd0x2hNhGwAAIA6ImS3JkI2AAAAEDFCNgAAABAxQjYAAAAQMUI2AAAAEDFCNgAAQATWrl0bdwlIEEI2AABABAjZKETIBgAANWGIOmA4QjYAAKgJITvw2muvxV0CEoSQDQAAEIGNGzfGXQIShJANAAAARIyQDQAAAESMkA0AAABEjJANAADQRLjQtDkQsgEAAJoIIbs5ELIBAACAiBGyAQAAgIgRsgEAAICIEbIBAACAiNU9ZJvZNWa23sweKFi3k5ndZmaPhf/uGK43M7vczB43s/vN7F31rg8AAACIWiNasq+VdGzRugsl3e7ue0m6PVyWpOMk7RXe5kv6TgPqAwAAACJV95Dt7r+R9ELR6hMlLQ7vL5Z0UsH6//DAbyVNMbO317tGAAAAIEpx9cnezd3XhfeflbRbeH+apKcL9lsTrgMAAMAoGD87WWK/8NHdXZJX+jgzm29mK8xsxcDAQB0qAwAAaB6E7GSJK2Q/l+8GEv67Ply/VtIeBftND9cN4+6L3H2Ou8+ZOnVqXYsFAAAAKhFXyF4m6czw/pmSbipY/7fhKCOHS/qfgm4lAAAAQFMYX+8nMLMlko6WtIuZrZH0VUmXSPqxmX1cUr+kj4a73yLpQ5Iel5ST1FXv+gAAAICo1T1ku/u8ETYdU2Jfl/Tp+lYEAAAA1FfsFz4CAAAgvXp6pPZ26eabb1R7e7DcCgjZAAAAqIueHmn+fKm/X5LGqb8/WK4paGezumr5cimbjajK+iBkAwAAoC4WLJByuaHrcrlgfVWyWamjQ7tu3Ch1dCQ6aBOyAQAAUBerV1e2flRhwB5M7blcooM2IRsAAAB1MWNGZetHVByw8xIctAnZAAAAqIuFC6W2tqHr2tqC9RXp6hoesPNyuWB7whCyAQAAUBedndKiRdLMmZK0RTNnBsudnRUeqLt7eFrPa2sLticMIRsAAAB109kp9fVJxx9/kvr6qgjYkpTJSL29pZvFe3uD7QlDyAYAAEDyFQftBAdsiZANAACAZhEG7fWTJiU6YEuEbAAAAMSlmollMhmdM3duogO2RMgGAABoSQ2d7rxUmG6iiWWqQcgGAABoMXWZ7nwkpcJ0k00sUw1CNgAATW7JkiVxl5BIDW2pbTKRT3c+klJh+rjjglsTTSxTDUI2AABNjpA9XENbaptQpNOdj2SkWRo3bQpupSR0YplqELIBAEDq1NpSm/YvLpFNdz6a0WZpHElCJ5apBiEbAACkTq0ttWkP2ZFNdz6a0WZp3Hbb4FZcQMKH5asEIRsAAKROQ1pqm1hk052PZrRZGn/xi+DWJBPLVIOQDQAAUqchLbWh/AWWr7++MXEXWI528Wck052PZbRZGquYWKaZLmYlZAMAgNRpSEutkn2BZWJqGy1MVzCxTGJeT5kI2QAAIJUa0VLbsKHwqpCv7Whl9ZTadbSy8dUWwSyNSX6vSyFkAwAAVKkhQ+FVafXqIGD3qkPt6levOnS0somoTaq860eS3+tSCNkAAABVSvIFlqfuGgTsyQqafycrp1516NRd45/spZquH0l+r0shZAMAAFSpkRdYViSb1Q82bA3YeZOV0w82xD+rYjVdPxL7Xo+AkA0AAFClzk7pzDOlbbaRJNc22wTLdRmpoxJdXRr/eumJYMa/Hv+sitV0/WjUxaxRIWQDAABUqadHWrxYevNNSTK9+WawHPuIF6NNBJOAWRWr7frRkGEHI0LIBuos7bOGAUCaVHoxXmJHvBhtIpgETPrSbF0/qkHIBuqMkI204txG2lRzMV6iR7wYbSKYmDVb149qELKBFkZIQi04f5A21bRKJ37EiypmVWyUZur6UQ1CNtDCCEkAiqXucyGb1VXLl5c1mkY1rdJN0e0hgolgUDlCNgAAGJSqkJ3NSh0d2nXjRqlj7GHrqmmVboVuD6gOIRsAAKRPGLAH+3/kcmMG7WpbpfPdHiZMmJTKbg+oDiEbAACkS3HAzhsjaNMqjSgRsgEAQLp0dQ0P2Hm50SdiqfpivGxWf3r99dhnUkRyELIBAEC6NHoilrDlfKZUVt9vtAZCNgAASJdGTsRSRd9vtAZCNgAASJ9GTMRSZd/vRKlgiENUhpANAACqUukU5A1X74lYauj7nQgVDnGIyhCyAQBAxaqZgjwW9ZyIpdF9v6NEN5e6I2QDAICKVTMFeeqELeWbJwwN2psn1KFrSpTS0M2lCRCyAQBAxaqZgjyNep7J6Hjr1asKgvaratPx1queZ8KAncQ+z83ezaVJELIBAEDFqpmCPI0WLJB+uSmjDvWqTzPVoV79clMmaNFPap/nZu7m0kQI2QCAiiT+Yjc0RLVTkKdNvuX+DmU0S326Q0EL9jv6E9znuZFDHLYwQjYAoGxNc7Eb6i7tU5CvXbu2rP1Ktdwfrax6LeF9nhsxxGGLizVkm9nfm9mDZvaAmS0xs4lmNsvM7jGzx83sejObEGeNAICtuNgNhaqegrwJlBuyS7XoX2tdavMm6PNc7yEOW1xsIdvMpkn6rKQ57n6ApG0knSbp3yV90933lLRB0sfjqhEAMBQXuwFDlWrRf+yiJurzXM8hDltc3N1FxkuaZGbjJbVJWifpryUtDbcvlnRSTLUBAIpwsRswXGen1NedVZ+2UV93VnMX0ucZMYZsd18r6euSVisI1/8jaaWkF919c7jbGknT4qkQAFCMi90wTBKHqGu0cBSRmdLWPtf0eW55cXYX2VHSiZJmSdpd0mRJx1bw+PlmtsLMVgwMDNSpSgDAkiVLBu+n/WI3VCipQ9Q10mgzJ9LnuaXF2V1krqSn3H3A3d+Q9FNJR0qaEnYfkaTpkkpeeeDui9x9jrvPmTp1amMqBoAWVBiypXRf7IYKMC13eTMn0ue5ZcUZsldLOtzM2szMJB0j6SFJWUknh/ucKemmmOoDAAClMC13gJkTMYo4+2Tfo+ACx99L+mNYyyJJF0j6vJk9LmlnSVfHVSMAAM2s+FeIyBAuA93d0rbblt627bbJGkUEDRfr6CLu/lV338fdD3D3M9x9k7s/6e6Huvue7n6Ku2+Ks0YAAJpV3UI203IDY4p7CD8ASKy6BRSg2TEtd6CrS9o0Qlvgpk2t06KPkgjZADACQjYwCoaoi6dFnyETmwYhGwCK9PRI7e3SzTffqPb2YBlACa0+RF2DW/SXL8gqd0wwZGLumA4tX0DQTjJCNgAU6OmR5s+X+vslaZz6+4NlgjYwglYfoq5BLfrLF2R1xL92qM2DC07bPKcj/pWgnWSEbAAosGBB6VHJFiyIpx4ATSAM2v1SfVr0s1m95986NFlDP5wmK6f3/FsLDZnYZAjZAFBg9erK1gOAJCmT0V9OmFCfFv2ursEW7GJt3kJDJjYZQjYAFJgxo7L1AFB33d3KWekLLHPGkIlJRcgGgAILF5a+hmnhwnjqAQBlMrrrol69qqEfTq+qTXdd1IIXnDYJQjYAFOjslBYtkmbOlKQtmjkzWO7sjLsyAK1s7sKM7v6H3sEW7Zy16e5/6NXchQTspCJkA0CRzk6pr086/viT1NdHwAaQDHMXZtR2ezBkYtvtBOykI2QDAAA0ixJDJjK2fzIRsgEAAJpUfmz/Wf1ZPaV3aFZ/lrH9E4KQDQAA0KQWLJAOzWXVqw61q1+96tChuSxj+ycAIRsAAKBJvaM/CNj5iWomK6dedegd/UxQEzdCNgAAQDPKZtVrpWeC7DVmgowbIRsAAKAZMRNkohGyAQAAmlF39/DZs/LamAkyboRsAACAZpTJSL29paep7WUmyLgRsgEAAJpVcdAmYCcGIRtArJYsWRJ3CQDQ3MKgvX7SJAJ2ghCyAcQqCSE7CTUAQE1KzASJeBGyAbQ8QjYAIGqEbAAAACBihGwAAAAgYoRsAAAAIGKEbAAAADSVefPmxV3CmAjZQJ309Ejt7dLNN9+o9vZgGQAA1K4ZQvb4uAsA0qinR5o/X8rlJGmc+vuDZUnq7IyzMgAA0Ai0ZAN1sGBBPmBvlcsF65FuDAeIhstmddXy5VI2G3clAAoQsoE6WL26svVID0I2GiqblTo6tOvGjVJHB0EbSBBCNlAHM2ZUth4AKhYG7MGfzXI5gjaQIIRsoA4WLpTa2oaua2sL1gOVoGUcJRUH7DyCNpAYhGygDjo7pUWLpJkzJWmLZs4MlrnoEZUiZKOkrq7hATsvlwu2o3rZrO4dGODLCmpCyAbqpLNT6uuTjj/+JPX1EbABRKi7e/jPZXltbcF2VCf8lWCPLVv4VQA1IWQDANBsMhmpt7d0v7Te3mA7Kkc/91g0w5jX1SBkAwDQjIqDNgG7NvRzjw0hGwAAJEsYtNdPmkTArhX93OsurWF6JIRsAACaWSajc+bOJWDXin7udUfIBgAAaDX0c0fECNkAAAAS/dwRKUI2AABAXhi0nx43joCNmhCyAQAACmUyOmTqVAI2akLIBoowwx5QhmxWVy1fzrBmADCCWEO2mU0xs6Vm9oiZPWxmR5jZTmZ2m5k9Fv67Y5w1ovUQsoExhOMJ77pxI+MHA8AI4m7J/pakX7r7PpIOkvSwpAsl3e7ue0m6PVwGEoEAjpbHjHjpxi8UqdVqw+clQWwh28x2kHSUpKslyd1fd/cXJZ0oaXG422JJJ8VTITAcIRstjRnx0o1fKFKNkN14cbZkz5I0IKnbzP5gZleZ2WRJu7n7unCfZyXtFluFQBPhCwDqjhnx0otfKIDIxRmyx0t6l6TvuPvBkl5VUdcQd3dJXurBZjbfzFaY2YqBgYG6FwskHSEbdceMeOnELxRD9PRI7e3Sc889o/b2YBmoRpwhe42kNe5+T7i8VEHofs7M3i5J4b/rSz3Y3Re5+xx3nzN16tSGFAykCn0vUSlmxEsnfqEY1NMjzZ8v9fdL0jj19wfLBG1UI7aQ7e7PSnrazPYOVx3W9Fw8AAAgAElEQVQj6SFJyySdGa47U9JNMZQHRCOpQZa+l6gWM+KlD79QDFqwoHSD/oIF8dSD5hb36CKfkdRjZvdLmi3pXyVdIun9ZvaYpLnhMtB8khpk6XuJWoVBe/2kSQTsNOAXikGrV1e2HhhNrCHb3VeFXT4OdPeT3H2Duz/v7se4+17uPtfdX4izRqAqSQ2y9L1EVDIZnTN3bksFsFTjFwpJ0owZla0HRhN3SzaQPg0OsvmLdG6++caxL9Kh7yWAEfQ8k9G87XrVpxmat12vep5prYAtSQsXlm7QX7gwnnrQ3AjZQNQaGGQrvkiHvpcASsh/llz3XEaz1K/rnsu05AV/nZ3SokXSzJmStEUzZwbLnZ1xV4ZmRMgGotbAIFvxRTr0vQRQQtov+KtkIpbOTqmvT9ptt93V10fARvUI2UDUGhhkq7pIh76XAIqk/YI/ZjtEHAjZQD00KMhWfZEOo0MAKMAFf0D0yg7ZZvZeM+sK7081s1n1KwtIgQYE2Zou0mF0CAAhLviLxsSJE+MuAQlSVsg2s69KukDSReGqt0j6Yb2KAlKjzkGWi3QARIHPkmhMmjQp7hKQIOW2ZP8vSSdIelWS3P0ZSdvVqygA5ctfpHP88SdxkQ6AqvFZAkSr3JD9uru7JJckM5tcv5IAAADQKFwYWh/lhuwfm9n3JE0xs09IWi7p+/UrCwAAAI1AyK6PskK2u39d0lJJP5G0t6SvuPsV9SwMQLpVNFMl4pPN6qrlyyOfqRQA0m58uTu6+22SbqtjLQBaRH52uWDyi60zVUr0A02UbFbq6NCuGzdKHR0M94jWkc3q3oGB4L+BCM55WopbU7mji7xsZi8V3Z42s5+Z2TvqXSSAdEn77HKpEAbswT9ULhcs06KNtAvP/T22bInsnCdkt6Zy+2R/Q9KXJE2TNF3SFxX0yb5O0jX1KQ1AWqV9drmmVxyw8wjaSLuIv1zmu8WNGye6xbWgckP2se7+PXd/2d1fcvdFkj7k7tdL2rGO9QFIIWaXS7iuruEBOy+XC7YDaRPxl8t8t7j+fsldg93iCNqto9yQvcXMPmpm48LbRwu2eT0KA5BezC6XcN3dw/9AeW1twXYgbSL+ckm3OJQbsjslnSFpvaTnwvsfM7NJks6rU20AUorZ5RIukwkuciz1TYiLH5sHI8NUJuIvl3SLQ7lD+D3p7se7+y7uPjW8/7i7b3T3O+tdJID0Sfzscq0eUIqDNgG7uRSPDNOq53ElIvhyOW3atMH7dIvDqCHbzL4c/nuFmV1efGtMiQDQYASUQBg61k+aRMBuJowMU70av1wWhmy6xWGsluyHw39XSFpZ4gYA6UJAGSqT0Tlz5xKwm0VMI8Okaoi6MGg/PW5cTV8uC7vFmYlucS1o1Mlo3P3m8G7O3W8o3GZmp9StKgCIw1gBhdZcJF05F+/19UX+tKkK2ZKUyeiQqVP1bIX/vRe/D52dhOpWVu6FjxeVuQ4AmhdD16HZMTJMrFL3ZQM1GbUl28yOk/QhSdOK+mBvL2lzPQsDgIbr7i7dki0RUNAc8n2Ki89jLlwFGm6sluxnFPTHfk1D+2Ivk/TB+pYGAA3G0HVIA0aGARJh1JDt7ve5+2JJe7r74vD+MkmPu/uGhlQIAI1EQEEaMDIMELty+2TfZmbbm9lOku6T1G1m36hjXQAQHwIK0oCRYYBYlRuyd3D3lyR9RFK3u79b0tz6lQUAMSOgAEgoLrBsDuWG7PFm9nZJH5XUW8d6AAAAMApCdnMoN2RfLOlXkp5w93vN7B2SHqtfWQAAAEDzGnUIv7xwIpobCpaflPQ39SoKAAAAaGZltWSb2V+a2e1m9kC4fKCZ/WN9SwPKt2TJkorWAwAA1FO53UW+r2CGxzckyd3vl3RavYoCKpXkkL127doRtyWhvlbW0yO1t0s333yj2tuDZQAAolBuyG5z998VrWPGR6AMhOxk6umR5s+X+vslaZz6+4NlgjYAIArlhuw/m9lfSHJJMrOTJa2rW1VAjEYLxUiPBQuGz56eywXrAQCoVbkh+9OSvidpHzNbK+lzks6tW1VoaXG37o4WshsVwON+D1rB6tWVrQcAoBJjhmwzGydpjrvPlTRV0j7u/l537697dWhJSQ6YhOyIZbO6avlyKZtt+FPPmBH8e7SyekrtOlrZIesBAKjFmCHb3bdIOi+8/6q7v1z3qgCkXzYrdXRo140bpY6OhgfthQulY7fNqlcdale/etWhY7fNauHChpYBAJFhkppkKbe7yG1m9kUz28PMdsrf6loZgIo0VV/yMGAPdorO5RoetDt3z+pm79BkBTVMVk43e4c6d298qzoARIGQnSzlhuyzJf2dpF9LWlFwA5AQTROyiwN2XiODdljD+NeH1jD+9caHfQBAOpUbsveT9P8k3SdplaQrJO1fr6IApFhX1/CAnZfLBdtboQYAQKqVG7IXS9pX0uUKAvZ+4ToAqEx3t9TWVnpbW1uwvRVqAACkWrkhe293P8fds+HtE5L2rmdhiEfLjGqB+GQyUm/v8JDb1hasz2RaowYAQKqVG7L/YGaH5xfM7DBJ/12fkhCnZg3ZTdMfGYHikBtHuI24BqZoRxw474DkKjdkHybpLjPrM7M+SXdLOtrM/mhm99etOqBMhOwmFIbc9ZMmxdd6HFENTNGOOHDeAclWbsg+VtIsSX8V3mZJOk5Sh6TjaynAzLYxsz+YWW+4PMvM7jGzx83sejObUMvxASRYJqNz5s6Nt3tGBDXkp2gvnNiGKdpRb/nzrhDnHZAcZYVsd+8f7VZjDedLerhg+d8lfdPd95S0QdLHazw+ANTV6tVBwC6c2OZoZZmiHXU10vnFeQckQ7kt2XVhZtMlfVjSVeGySfprSUvDXRZLOime6gCgPKfuGgTswoltetWhU3dlvG3Uz4wZla0H0FixhmxJl0n6sqQt4fLOkl50983h8hpJ0+IoDADKks3qBxu2Buy8ycrpBxtqnNgmm9VVy5czOQ5KWriw9AA5CxdyQSSQBLGFbDPrkLTe3VdW+fj5ZrbCzFYMDAxEXB0AlKmra9jMkXnjX69hYptwVspdN25kFkqU1NkpLVokzZwpSVs0c2awLHFBJJAEcbZkHynphHC0kusUdBP5lqQpZjY+3Ge6pJLDRrj7Inef4+5zpk6d2oh6AWC4ekxsUzz1fCOnnEdT6eyU+vqk448/SX19wTIXRALJEFvIdveL3H26u7dLOk3Sf7p7p6SspJPD3c6UdFNMJQKpxU/JEYp6YpvigJ1H0EaZuCASSIa4+2SXcoGkz5vZ4wr6aF8dcz1AqjC2bh1EObFNV9fwgJ2Xq6H7CVoGF0QCyZCIkO3ud7h7R3j/SXc/1N33dPdT3H1T3PUBacJPyXUS1eQ69eh+gtSbN2/e4P3RLogE0DiJCNkAGoefkusoisl1ou5+gpZQGLJHuiCyszO28oCWRMhGLJYsWRJ3CS2Ln5KbQJTdT9CSSl0QCaCxCNmIBSE7PvyU3CSi6n4CAIgFIRuJs3ZtyVEbERF+Sm4iUXQ/AQDEgpCNmkXdKk3Irj9+SgaA0U2bxoTTqA0hGzWj6wcAIG0I2agVIRsAAACIGCEbKJTN6t6BAWbVA8TMoABQC0I2kBdOZ73Hli2lp68mgKOFMDMoANSGkA1IgwF7cCrEXG5o0B4rgMeNLwCIGDODAkBtCNlIpYp+5i4O2Hn5oP2Nb4wewOOW9C8AaErMDAoAtSFkI3Uq/pm7q2t4wM7L5aQvfnHkAB53oB2rBR6oEjODAkBtCNlInYp/5u7uHj4FYp6Z5F56Wy4XBPS4jNUCT9BGDZgZFABqQ8hG6lT8M3c4fXXJRPH1r48cwNvagoAel7Fa4OP8AoCmV87MoPPmzYurPABIPEI2Uqeqn7mLg3ZbW7D8+c+PHMB7e+Od7nq0Fvi4vwAgFcaaGZSQDQAjI2Qjdar+mTsM2k+PGzc0QI8UwOMM2KXqyktKfQAAtDBCNlKnnJ+5R5TJ6JCpU4cH1JECeNyS+gUAQNPiFwogGoRspNJYP3NXZaQAHrekfgEA0JQI2UA0CNlAGiT1CwAAAC2KkA0AAABEjJANhPKzRD733DNjzxIJAAAwCkJ2jZYsWRJ3CYhAxbNEAgAAjIKQXSNCdjpUPEskAADAKAjZgKqYJRIAAGAUhGxAVc4SCQAAMAJCNqAaZolscoyHCwBAfRCyAdU4S2QTI2QDAFAf4+MuAEiKzs7g9ra37a6+vmfjLgcAADQxWrLR1JphbOtp06bFXQIAAGgwWrLRtPJjWwdD720d21pKVjcPQjYAAK2Hlmw0Lca2BgAASUXIRtNibGsAAJBUhGw0Lca2BgAASUXIRtNq1bGtgUZjqEcAqBwhG4lR6UghrTq2NdBohGwAqByjiyARqh0phLGtAQBAEtGSjURgpBAAAJAmhGwkAiOFAACANCFkIxEYKQQAAKQJIRuJwEghAIAk4YJf1IqQjURgpBAkEf+TRbPjHK4e7x1qxegiSAxGCkHS8D9ZNDvOYSA+tGQDAAAAEYstZJvZHmaWNbOHzOxBMzs/XL+Tmd1mZo+F/+4YV40AAABANeJsyd4s6Qvuvp+kwyV92sz2k3ShpNvdfS9Jt4fLQKwqnY0SAAC0tthCtruvc/ffh/dflvSwpGmSTpS0ONxtsaST4qkQTSOb1b0DA1I2W5fD52ej7O+XCmejJGgDAICRJKJPtpm1SzpY0j2SdnP3deGmZyXtFlNZaAbZrNTRoT22bJE6OoYG7WxWVy1fXnP4rmk2ytFqiKg+AACQPLGHbDN7q6SfSPqcu79UuM3dXZKP8Lj5ZrbCzFYMDAw0oFIkThiwBxNwLrc1aIfbdt24cXj4rlDVs1GOVkOE9QEAgOSJNWSb2VsUBOwed/9puPo5M3t7uP3tktaXeqy7L3L3Oe4+Z+rUqY0pGMlRHLDzcjnpuOOCW6nwXYWddqpsfcn6SnwBiKo+AACQPHGOLmKSrpb0sLt/o2DTMklnhvfPlHRTo2tDE+jqGh6w8zZtCm6Fyg2yo/TvPlpZPaV2Ha2xj1H2F4BK6wMAAE0hzpbsIyWdIemvzWxVePuQpEskvd/MHpM0N1xGqyj3Isbu7uHzsI8llwvC+SjPXap/9wsvBAG7Vx1qV7961aGjldULL4xwnEq/ABTXR19tAACaXpyji9zp7ubuB7r77PB2i7s/7+7HuPte7j7X3UeKMkib0S5iLJbJSL29w4P2ttsGt1La2oJwPspzl+rCcequQcCerGDbZOXUqw6duusI9Y32BWCs+j77WfpqAwCQArFf+AhIqq6fcnHQbmuTfvGL4FYcctvagn0zmbGfOy/s3vHD548bDNh5k5XTDzaMUN9IXwDGqu9f/kX63/+bvtoAAKQAIRvxGy3klhm0nx43bmuILhW+RwrY0pjdO7bZXLp7x/jXR+l+MloNpbYVB+xK3gOp7mOFAwCAyhCyEb/RQu5Y/aglKZPRIVOnDg3RYZBdP2nS6AFbqq17x0jdT8aqoXjb5ZdX/x5U0s0GAAA0BCEb8Rst5I4RZEed7jyT0Tlz544esMP9qureMVZ4H6uGwm3VvgcMB5ha+XP75ptvHH5uAwASj5CN+I0WckcJspFOd15p945yAnYtz59XbV9ygnZTi/TcBgDEgpBdpVS2MsU5dFwVQbam6c5HqWFI/+5ytkUlyr7k5XSzQWJFfm4DABqOkF2FVLYyJWGa7wqDbH5a8+JJYsac7nyMGob17y5nW1Si6ks+Vn/xBJk3b17cJSSihkIjncM1ndsAgIYiZFchda1MSerXW0GQnTGj9CQxM2Y0oM56iqIveb1a2+sgCQE3CTUUGukcbvpzGwBaCCG7CqlqZWrifr1XdZaeJOaqzuTWHLlG9BdHwy1cWPq708KF8dQDAKgcIbsKqWplatZ+vdms5l7WUXKSmLmXJfvLQeQa0V8cDdXZKS1aJM2cKUlbNHNmsNzZGXdlAIByEbKrkKpWpmbt19usXw7qpRH9xdFQnZ1SX590/PEnqa+PgA0AzYaQXYVUtTJlMlr+uV69qqFB+1W1afnnEtwq2qxfDgAAQEsgZFcpTa1M5/Rk1KGtQftVtalDvTqnJ6EBW0rNRX8AACCdCNnQ6tXSHQqCdp9mqkO9ukOZ5F/IyUV/AAAgoQjZGLxg8w5lNEt9ukOZIesTjYv+kDBJGw4QABAPQjaa/0JOLvpDghCyAQASIRtK2YWcNejpkdrbpeeee0bt7U0+gycAAIgVIRuS0nUhZzV6eqT586X+fkkap/7+YJmgDQAAqkHIRkP19Ejz3pbV5Tffp3lvyyYmxC5YUHrSywUL6vN8+Vbzm2++seZWc1rg043uJwDQnMbHXQBaR0+P9MOPZ7V0UzBT41XPdejkj/dKysTecj7SSCr1GGEl32oehPqtreZS5b8gRHksJBMhGwCaEy3ZaJjeL2wN2FIwBfrSTR3q/ULBFOjZrO4dGGj4tOgjjaRSjxFWomw1b3QLPAAAKA8hG7XJZnXV8uVjh+JsVlc9tzVg5+VbtJXNBreODu2xZYvU0dHQoN3IEVaibDVvZAs8AAAoHyEb1QtD8a4bN44diru6hgXsvMnKSfPmBcfIN8vmcg0N2o0cYSXKVvNGtsADAIDyEbJRnTBglx2Ku7u1eUJbyU1vjt9WevHF0v0eGhy0+/qk3Xbbva4jrETZat70Y5wDAJBShGxUrjhg540WijMZjf9l77CgvXlCm7bZeYq0aVPp58rlpK6uiApPhihbzRnjHACAZCJko3JdXcMDdt5ooTgM2oNNr21twfKSJSO2cm+e0CZ1d0dQdLJEOS55o1rgAQBA+QjZqFx39/A+CnltY4TiTEbq7dX6SZOk3t5gOZPRGTv26lUNPearatMZO/Y27XTpUY6FDQAAmgshG5ULg3LJzsC9ZYTiTEbnzJ07ZL/r12fUoa1B+1W1qUO9un598wZsZpAEAKB1EbJRneKgXW7AHsGMGdIdCoJ2n2aqQ726Q5mmHSWD8asBAGhthGxUr1TXjyrlR8m4QxnNUp/uUKapR8modvxqupgAAJAOhGzUpkTXj2qkbZSMasavposJAADpQchGYqRplIxqxq+miwkAAOlByAaKTJs2reZjVNMyzxTpAACkByEbKDJayK4kgFc6FnatU6RH8eUAAABEg5ANVKCeQbbWKdIJ2QAAJAchG0iItF38CQBAKyNkAwkS5XTrAAAgPoRsVI0xnQEAAEojZNcim9VVy5dL2WzclTQcYzoDAACMjJBdrWxW6ujQrhs3Sh0dLRe0GdMZAABgZITsaoQBezBl5nItF7QZ0xkAAGBkhOxKFQfsvBYL2rWO6QwAAJBmhOxKdXUND9h5uVywvQXUOqYzAABAmhGyK9XdPTxd5rW1BdtbAGM6AwAAjCyxIdvMjjWzR83scTO7MO56BmUyUm9v6Wbc3t5ge4tgTGcAAIDSEhmyzWwbSf9P0nGS9pM0z8z2i7eqAsVBuwUDNgAAAEaWyJAt6VBJj7v7k+7+uqTrJJ0Yc01DhUF7/aRJBGwAAAAMkdSQPU3S0wXLa8J1yZLJ6Jy5cwnYAAAAGCKpIXtMZjbfzFaY2YqBgYG4ywEAAAAGJTVkr5W0R8Hy9HDdIHdf5O5z3H3O1KlTG1ocAAAAMJqkhux7Je1lZrPMbIKk0yQti7kmAAAAoCzj4y6gFHffbGbnSfqVpG0kXePuD8ZcFgAAAFCWRIZsSXL3WyTdEncdAAAAQKWS2l0EAAAAaFqEbAAAACBihGwAAAAgYoRsAAAAIGKEbAAAACBihGwAAAAgYoRsAAAAIGKEbAAAACBihGwAAAAgYoRsAAAAIGKEbAAAACBihGwAAAAgYoRsAAAAIGKEbAAAACBihGwAAAAgYoRsAAAAIGKEbAAAACBihGykwrRp00qunzdvXoMrAQAAIGQjJZIcskerIQn1AQCA6BGygTojZAMA0HoI2UARgi8AAKgVIRuJM1LXj0ZJQsszQR8AgOZGyEbixB2yR0PIBgAA5SBkAwAAABEjZAMAAAARI2QDKUEXEwAAkoOQDaQEIRsAgOQgZAMAAAARI2QDAAAAESNkYwi6HAAAANSOkI0hmjVkN2vdAAAgnQjZSAVCNgAASBJCNgAAABAxQjYAAAAQMUI2AAAAEDFCNgAAABAxQjYAAAAQMUI2AAAAEDFCNgAAABAxQjYAAAAQMUI2AAAAEDFCNgAAABAxQjZiwTToAAAgzWIJ2WZ2qZk9Ymb3m9nPzGxKwbaLzOxxM3vUzD4YR32oP0I2AABIs7hasm+TdIC7HyjpT5IukiQz20/SaZL2l3SspCvNbJuYakRMCOAAAKDZxRKy3f1Wd98cLv5W0vTw/omSrnP3Te7+lKTHJR0aR40oX9ShmJANAACaXRL6ZJ8t6Rfh/WmSni7YtiZchwQjFAMAAAw1vl4HNrPlkt5WYtMCd78p3GeBpM2Seqo4/nxJ8yVpxowZNVQKAAAARKtuIdvd54623czOktQh6Rh393D1Wkl7FOw2PVxX6viLJC2SpDlz5nipfQAAAIA4xDW6yLGSvizpBHfPFWxaJuk0M9vWzGZJ2kvS7+KoEQAAAKhW3Vqyx/BtSdtKus3MJOm37n6uuz9oZj+W9JCCbiSfdvc3Y6oRAAAAqEosIdvd9xxl20JJCxtYDgAAABCpJIwuAgAAAKQKIRsAAACIGCEbAAAAiBghGwAAAIgYIRsAAACIGCEbAAAAiBghGwAAAIgYIRsAAACIGCEbAAAAiBghGwAAAIgYIRsAAACIGCG7RvPmzYu7BAAAACQMIbtGhGwAAAAUI2QDAAAAESNkAwAAABEjZAMAAAARI2QDAAAAESNkAwAAABEjZAMAAAARI2QDAAAAESNkAwAAABEjZAMAAAARI2QDAAAAESNkAwAAABEjZAMAAAARI2QDAAAAESNkAwAAABEjZAMAAAARI2QDAAAAESNkAwAAABEjZAMAAAARM3ePu4aamdmApP6460BkdpH057iLQCJwLiCPcwES5wG2ivNcmOnuU8faKRUhG+liZivcfU7cdSB+nAvI41yAxHmArZrhXKC7CAAAABAxQjYAAAAQMUI2kmhR3AUgMTgXkMe5AInzAFsl/lygTzYAAAAQMVqyAQAAgIgRshEbM9vDzLJm9pCZPWhm54frdzKz28zssfDfHeOuFY1hZtuY2R/MrDdcnmVm95jZ42Z2vZlNiLtG1J+ZTTGzpWb2iJk9bGZH8LnQmszs78P/PzxgZkvMbCKfC63BzK4xs/Vm9kDBupKfAxa4PDwn7jezd8VX+VaEbMRps6QvuPt+kg6X9Gkz20/ShZJud/e9JN0eLqM1nC/p4YLlf5f0TXffU9IGSR+PpSo02rck/dLd95F0kIJzgs+FFmNm0yR9VtIcdz9A0jaSThOfC63iWknHFq0b6XPgOEl7hbf5kr7ToBpHRchGbNx9nbv/Prz/soL/kU6TdKKkxeFuiyWdFE+FaCQzmy7pw5KuCpdN0l9LWhruwrnQAsxsB0lHSbpaktz9dXd/UXwutKrxkiaZ2XhJbZLWic+FluDuv5H0QtHqkT4HTpT0Hx74raQpZvb2xlQ6MkI2EsHM2iUdLOkeSbu5+7pw07OSdoupLDTWZZK+LGlLuLyzpBfdfXO4vEbBlzCk2yxJA5K6w65DV5nZZPG50HLcfa2kr0tarSBc/4+kleJzoZWN9DkwTdLTBfsl4rwgZCN2ZvZWST+R9Dl3f6lwmwfD3zAETsqZWYek9e6+Mu5aELvxkt4l6TvufrCkV1XUNYTPhdYQ9rc9UcEXr90lTdbw7gNoUc3wOUDIRqzM7C0KAnaPu/80XP1c/mee8N/1cdWHhjlS0glm1ifpOgU/B39LwU9+48N9pktaG095aKA1kta4+z3h8lIFoZvPhdYzV9JT7j7g7m9I+qmCzwo+F1rXSJ8DayXtUbBfIs4LQjZiE/a5vVrSw+7+jYJNyySdGd4/U9JNja4NjeXuF7n7dHdvV3Bh03+6e6ekrKSTw904F1qAuz8r6Wkz2ztcdYykh8TnQitaLelwM2sL/3+RPxf4XGhdI30OLJP0t+EoI4dL+p+CbiWxYTIaxMbM3ivpvyT9UVv74f6Dgn7ZP5Y0Q1K/pI+6e/HFD0gpMzta0hfdvcPM3qGgZXsnSX+Q9DF33xRnfag/M5ut4ALYCZKelNSloFGIz4UWY2b/LOlUBaNR/UHSOQr62vK5kHJmtkTS0ZJ2kfScpK9KulElPgfCL2HfVtCdKCepy91XxFF3IUI2AAAAEDG6iwAAAAARI2QDAAAAESNkAwAAABEjZAMAAAARI2QDAAAAESNkA0ATM7OzzOzbER/zJDPbr2D5YjObG+VzAEDaEbIBAMVOkjQYst39K+6+PMZ6AKDpELIBIMHM7GNm9jszW2Vm3zOzbcysy8z+ZGa/VjDNdH7fa83s5ILlVwruX2BmfzSz+8zsknDdJ8zs3nDdT8KZ9d4j6QRJl4bP+ReFxzWzY8zsD+GxrjGzbcP1fWb2z2b2+3DbPg16iwAgkQjZAJBQZravgtnujnT32ZLelPQxSf+sIFy/XwUtzqMc5zhJJ0o6zN0PkvR/wk0/dfdDwnUPS/q4u9+lYIriL7n7bHd/ouA4EyVdK+lUd3+npPGSPlXwVH9293dJ+o6kL1b/ygGg+RGyASC5jpH0bkn3mtmqcPnvJd3h7gPu/rqk68s4zlxJ3e6ek6SC6cgPMLP/MrM/SuqUtP8Yx9lb0lPu/qdwebGkowq2/zT8d6Wk9jLqAoDUImQDQHKZpMVhi/Jsd99b0j+Nsv9mhZ/rZv+/fTtmxSiK4zj+/atHUhZK2RTvwfi8CSYZnt1qY/AulMWovAF5GEwiUQax2bwAKQaO4Rx1sihOebq+n+Xe07333znbr3//G2PA+Df194D10pXeBiZ+ud/XcsI7CcIAAADiSURBVH0jd7kl6d8yZEvS6DoBliNiFiAipoFroB8RMxHRA1aq9x/InW/Ic9W9cj8EBhExWdUBmAIeS53Vqs5TefbVPTAfEYtlvQac/vx4ktRdhmxJGlEppVtgEziKiBtyWJ4jd7PPgGPgqvpklxzAL4Al4LnUOSTPWV+WsZPPeekt4LzUvavq7AMb5QfHhWo/L8AAOCgjJu/ATsszS1JXRErpr/cgSZIkdYqdbEmSJKkxQ7YkSZLUmCFbkiRJasyQLUmSJDVmyJYkSZIaM2RLkiRJjRmyJUmSpMYM2ZIkSVJjH7BljE3V2lknAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(12, 8))\n",
"fig = sm.graphics.plot_fit(prestige_model, \"education\", ax=ax)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Statewide Crime 2009 Dataset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compare the following to http://www.ats.ucla.edu/stat/stata/webbooks/reg/chapter4/statareg_self_assessment_answers4.htm\n",
"\n",
"Though the data here is not the same as in that example. You could run that example by uncommenting the necessary cells below."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#dta = pd.read_csv(\"http://www.stat.ufl.edu/~aa/social/csv_files/statewide-crime-2.csv\")\n",
"#dta = dta.set_index(\"State\", inplace=True).dropna()\n",
"#dta.rename(columns={\"VR\" : \"crime\",\n",
"# \"MR\" : \"murder\",\n",
"# \"M\" : \"pctmetro\",\n",
"# \"W\" : \"pctwhite\",\n",
"# \"H\" : \"pcths\",\n",
"# \"P\" : \"poverty\",\n",
"# \"S\" : \"single\"\n",
"# }, inplace=True)\n",
"#\n",
"#crime_model = ols(\"murder ~ pctmetro + poverty + pcths + single\", data=dta).fit()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib/python3/dist-packages/numpy/lib/npyio.py:2279: VisibleDeprecationWarning: Reading unicode strings without specifying the encoding argument is deprecated. Set the encoding, use None for the system default.\n",
" output = genfromtxt(fname, **kwargs)\n"
]
}
],
"source": [
"dta = sm.datasets.statecrime.load_pandas().data"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: murder R-squared: 0.813\n",
"Model: OLS Adj. R-squared: 0.797\n",
"Method: Least Squares F-statistic: 50.08\n",
"Date: Sat, 10 Apr 2021 Prob (F-statistic): 3.42e-16\n",
"Time: 01:00:14 Log-Likelihood: -95.050\n",
"No. Observations: 51 AIC: 200.1\n",
"Df Residuals: 46 BIC: 209.8\n",
"Df Model: 4 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"Intercept -44.1024 12.086 -3.649 0.001 -68.430 -19.774\n",
"urban 0.0109 0.015 0.707 0.483 -0.020 0.042\n",
"poverty 0.4121 0.140 2.939 0.005 0.130 0.694\n",
"hs_grad 0.3059 0.117 2.611 0.012 0.070 0.542\n",
"single 0.6374 0.070 9.065 0.000 0.496 0.779\n",
"==============================================================================\n",
"Omnibus: 1.618 Durbin-Watson: 2.507\n",
"Prob(Omnibus): 0.445 Jarque-Bera (JB): 0.831\n",
"Skew: -0.220 Prob(JB): 0.660\n",
"Kurtosis: 3.445 Cond. No. 5.80e+03\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"[2] The condition number is large, 5.8e+03. This might indicate that there are\n",
"strong multicollinearity or other numerical problems.\n"
]
}
],
"source": [
"crime_model = ols(\"murder ~ urban + poverty + hs_grad + single\", data=dta).fit()\n",
"print(crime_model.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Partial Regression Plots"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x576 with 5 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12,8))\n",
"fig = sm.graphics.plot_partregress_grid(crime_model, fig=fig)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(12,8))\n",
"fig = sm.graphics.plot_partregress(\"murder\", \"hs_grad\", [\"urban\", \"poverty\", \"single\"], ax=ax, data=dta)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Leverage-Resid<sup>2</sup> Plot"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Closely related to the influence_plot is the leverage-resid<sup>2</sup> plot."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(8,6))\n",
"fig = sm.graphics.plot_leverage_resid2(crime_model, ax=ax)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Influence Plot"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(8,6))\n",
"fig = sm.graphics.influence_plot(crime_model, ax=ax)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Using robust regression to correct for outliers."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Part of the problem here in recreating the Stata results is that M-estimators are not robust to leverage points. MM-estimators should do better with this examples."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from statsmodels.formula.api import rlm"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Robust linear Model Regression Results \n",
"==============================================================================\n",
"Dep. Variable: murder No. Observations: 51\n",
"Model: RLM Df Residuals: 46\n",
"Method: IRLS Df Model: 4\n",
"Norm: TukeyBiweight \n",
"Scale Est.: mad \n",
"Cov Type: H1 \n",
"Date: Sat, 10 Apr 2021 \n",
"Time: 01:00:15 \n",
"No. Iterations: 50 \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"Intercept -4.2986 9.494 -0.453 0.651 -22.907 14.310\n",
"urban 0.0029 0.012 0.241 0.809 -0.021 0.027\n",
"poverty 0.2753 0.110 2.499 0.012 0.059 0.491\n",
"hs_grad -0.0302 0.092 -0.328 0.743 -0.211 0.150\n",
"single 0.2902 0.055 5.253 0.000 0.182 0.398\n",
"==============================================================================\n",
"\n",
"If the model instance has been used for another fit with different fit\n",
"parameters, then the fit options might not be the correct ones anymore .\n"
]
}
],
"source": [
"rob_crime_model = rlm(\"murder ~ urban + poverty + hs_grad + single\", data=dta, \n",
" M=sm.robust.norms.TukeyBiweight(3)).fit(conv=\"weights\")\n",
"print(rob_crime_model.summary())"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#rob_crime_model = rlm(\"murder ~ pctmetro + poverty + pcths + single\", data=dta, M=sm.robust.norms.TukeyBiweight()).fit(conv=\"weights\")\n",
"#print(rob_crime_model.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There isn't yet an influence diagnostics method as part of RLM, but we can recreate them. (This depends on the status of [issue #888](https://github.com/statsmodels/statsmodels/issues/808))"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"weights = rob_crime_model.weights\n",
"idx = weights > 0\n",
"X = rob_crime_model.model.exog[idx.values]\n",
"ww = weights[idx] / weights[idx].mean()\n",
"hat_matrix_diag = ww*(X*np.linalg.pinv(X).T).sum(1)\n",
"resid = rob_crime_model.resid\n",
"resid2 = resid**2\n",
"resid2 /= resid2.sum()\n",
"nobs = int(idx.sum())\n",
"hm = hat_matrix_diag.mean()\n",
"rm = resid2.mean()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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iIiJyXavQaxJM01wKLD2v7flz3ncsZbnvgaYVWVt56dU8SKFARERERG4oFTncSERERERErkMKCSIiIiIiYkchQURERERE7CgkiIiIiIiIHYUEERERERGxo5AgIiIiIiJ2FBJERERERMSOQoKIiIiIiNhRSBARERERETsKCSIiIiIiYkchQURERERE7CgkiIiIiIiIHYUEERERERGxo5AgIiIiIiJ2FBJERERERMSOQoKIiIiIiNhRSBARERERETsKCSIiIiIiYkchQURERERE7CgkiIiIiIiIHYUEERERERGxo5AgIiIiIiJ2FBJERERERMSOQoKIiIiIiNhRSBARERERETsKCSIiIiIiYkchQURERERE7CgkiIiIiIiIHYUEERERERGxo5AgIiIiIiJ2FBJERERERMSOQoKIiIiIiNhRSBARERERETsKCSIiIiIiYkchQURERERE7CgkiIiIiIiIHYUEERERERGxo5AgIiIiIiJ2FBJERERERMSOQoKIiIiIiNhRSBARERERETsKCSIiIiIiYkchQURERERE7CgkiIiIiIiIHYUEERERERGxo5AgIiIiIiJ2FBJERERERMSOQoKIiIiIiNhRSBARERERETsKCSIiIiIiYkchQURERERE7CgkiIiIiIiIHYUEERERERGxo5AgIiIiIiJ2FBJERERERMSOQoKIiIiIiNhRSLgCoaGhLF++vLLLKFcvvfQSQ4YMuSrLl3VbIiIiIlKxFBLKWVJSElFRUQA8/PDD/O1vf7PmFRYWUq1atRLb1qxZc8XbnD59Oh06dCh1/iOPPMKgQYMuaN+yZQsuLi5kZ2fz3HPPMXXq1Cuu4XKWv5y+8fHxTJ8+/YrrEhEREZHLp5BQgSIiIkhOTramN2zYQO3atVm1apVdG0CLFi0qrI64uDgWLFjA8ePH7dpnzpxJ9+7dqV69+kWXLyoqqrDaREREROTao5BwhdavX0/jxo3x8fFh8ODBnDx58oI+ERER/PLLL2RlZQGwatUq+vfvz/Hjx+3a2rZti7OzMwBr1qyhXbt2eHt706xZM5KSkqz1TZ8+nbp16+Lh4UGdOnWYPXs2v/zyC4888gg//PAD7u7ueHt7X1BH27ZtCQoK4rPPPrPaTp8+zccff2ydYUhISCA2NhaAPXv2YBgGH3zwAbVr1+aOO+4AYMaMGYSEhODr68vYsWPthl2VtPxHH31E7dq18fPzY/z48da2z+0L0LdvXwICAvDy8iIiIoJt27Zd5l9DRERERMqTQsIVmj17NomJiezatYvU1FTGjRsHQFRUlPXD3mazERISYp05SE5OJjw8nHbt2tm1RUREAJCRkUG3bt0YPXo02dnZTJo0iT59+nDo0CGOHz/OsGHDWLZsGXl5eXz//ffceuutNGrUiHfffZe2bdty7NgxcnJySqx30KBBzJgxw5pevnw5hYWFdO3atdR9XLlyJb/88guJiYls376dRx99lNmzZ7N//35yc3PJyMi46Ge0evVqUlJSWLFiBS+++CK//PJLif3uvvtuduzYwcGDB/nLX/7CgAEDrHnTp08nPj7+otsRERERkfKlkHCFhg4dis1mo3r16owaNYo5c+aU2C8yMpLk5GSKi4tZt24dbdq0ITw83Gr77rvviIyMBGDWrFl07dqVrl274uDgwF133UXLli1ZunQpAA4ODvz888/k5+dTq1YtmjRpcsn1Dhw4kJUrV5Keng6cOSsQExNjncEoSUJCAtWqVcPNzY1PP/2UHj160KFDB6pUqcKLL76IYRgX3eaYMWNwc3OjWbNmNGvWjC1btpTY74EHHsDDwwMXFxcSEhLYsmULubm5l7xvIiIiIlK+FBKukM1ms96HhISQmZlZYr+z1yVs3bqVunXrUrVqVTp06GC15efn07p1awD27t3L/Pnz8fb2tl6rV69m//79VKtWjXnz5vHuu+9Sq1YtunXrxq+//nrJ9dauXZuIiAhmzZrFsWPHWLhwYYkXM5e2j5mZmXbTVatWxdfX96LLBwQE2PU/duzYBX1Onz7NiBEjCAsLw9PTk9DQUABrOJaIiIiIXH0KCVdo37591vu0tDQCAwNL7BcREcGWLVtYsmQJ4eHhADRp0oR9+/axZMkSWrVqhaurK3DmR/nAgQPJycmxXsePH2fEiBEAdO7cma+//pr9+/fTsGFDHnzwQYA/PKJ/VlxcHDNnzuSzzz6jTp06f3ix9LnrrVWrlnUWAiA/P5/Dhw9f0nYv5uOPP2bRokUsX76c3Nxc9uzZA4BpmmVet4iIiIhcGYWEKzRlyhTS09PJzs5m/PjxREdHl9ivXr161KxZkzfeeMMKCYZh0Lp1a9544w3regSA2NhYFi9eTGJiIqdPn+bkyZMkJSWRnp7OgQMHWLRoEcePH8fFxQV3d3ccHM78+WrWrEl6ejoFBQUXrblPnz6kpaUxZswY4uLiLmt/77vvPhYvXsz3339PQUEBCQkJ5fJDPi8vDxcXF3x9fTlx4gTPPfdcmdcpIiIiImWjkHCFYmJi6NSpE3Xr1iUsLIzRo0eX2jciIoJDhw7Rvn17qy08PJyDBw/ahQSbzcaiRYt46aWXqFGjBjabjYkTJ1JcXExxcTH/93//R2BgINWrV2flypW88847ANxxxx00adKEgIAA/Pz8Sq2jWrVq9OnTh/T0dLuLgy9FkyZN+Ne//kX//v2pVasW7u7u+Pv74+LiclnrOd+gQYMICQkhKCiIxo0b06ZNmzKtT0RERETKzrhRhnW0bNnSPPvMAal4x44dw9vbmx07dlCnTp3KLkfkT+PswxrPvT2yiIhcXYZhbDRNs2Vl11GRnCq7gOvZwk0ZTExMITMnn0BvN4Z3bkCv5kGVXVaFWbx4MXfeeSemafLMM8/QtGlT60JjEREREblxVOhwI8MwuhiGkWIYxk7DMEaUMP8pwzC2G4bxk2EYKwzDCDlnXpxhGDt+f13eAPqrYOGmDEYu2EpGTj4mkJGTz8gFW1m46eLPDrieLVq0iMDAQAIDA9mxYwdz58695IumRUREROT6UWEhwTAMR2AKcDfQGLjfMIzG53XbBLQ0TfMW4FPg1d+XrQ6MAVoDtwFjDMPwqahar8TExBTyC0/bteUXnmZiYkolVVTxpk6dSk5ODrm5uaxYsYIGDRpUdkkiIiIiUgEq8kzCbcBO0zR3m6ZZAMwFep7bwTTNb03TPPH75Bog+Pf3nYGvTdPMNk3zCPA10KUCa71smTn5l9UuIiIiInK9qMiQEATsO2c6/fe20vwVWHY5yxqG8ZBhGBsMw9hw6NChMpZ7eQK93S6rXURERETkenFN3ALVMIxYoCUw8XKWM03zfdM0W5qm2bJGjRoVU1wphndugJuzo12bm7MjwztrCI6IiIiIXN8qMiRkALZzpoN/b7NjGEZHYBRwj2mapy5n2crUq3kQL/duSpC3GwYQ5O3Gy72b3tB3NxIRERGRP4eKvAXqeqC+YRh1OPMDvz8Qc24HwzCaA+8BXUzTPHjOrETgpXMuVu4EjKzAWq9Ir+ZBCgUiIiIicsOpsJBgmmaRYRhDOfOD3xH40DTNbYZhvAhsME3zc84ML3IH5v9+K8000zTvMU0z2zCMsZwJGgAvmqaZXVG1ioiIiIjI/1Tow9RM01wKLD2v7flz3ne8yLIfAh9WXHUiIiIiIlKSa+LCZRERERERuXYoJIiIiIiIiB2FBBERERERsaOQICIiIiIidhQSRERERETEjkKCiIiIiIjYUUgQERERERE7CgkiIiIiImJHIUFEREREROwoJIiIiIiIiB2FBBERERERsaOQcIMJDQ1l+fLllV3GFXN3d2f37t2VXYaIiIjIn5pCwp9EUlISUVFRADz88MP87W9/s+YVFhZSrVq1EtvWrFlzVes8duwYdevWLfN6pk+fTnx8fNkLEhEREfkTUkj4E4qIiCA5Odma3rBhA7Vr12bVqlV2bQAtWrS46vWJiIiISOVSSLgBrV+/nsaNG+Pj48PgwYM5efKk3fyIiAh++eUXsrKyAFi1ahX9+/fn+PHjdm1t27bF2dmZbt268a9//ctuHbfccgv/+c9/APj+++9p1aoVXl5etGrViu+//97qFxUVxejRo2nXrh3u7u706NGDw4cPM2DAADw9PWnVqhV79uyx+huGwc6dOwGIj4/nscceo1u3bnh4eNC6dWt27dpl9f3qq69o0KABXl5ePProo0RGRjJ16tTy+yBFRERE/qQUEm5As2fPJjExkV27dpGamsq4ceOIiooiKSkJAJvNRkhIiHXmIDk5mfDwcNq1a2fXFhERAUBcXByzZs2y1r9lyxYyMjLo1q0b2dnZdOvWjWHDhnH48GGeeuopunXrxuHDh63+c+fOZebMmWRkZLBr1y7atm3L4MGDyc7OplGjRrzwwgul7svcuXMZM2YMR44coV69eowaNQqArKws7rvvPl5++WUOHz5MgwYN7MJJfHw806dPL5fPU0REROTPRiHhBjR06FBsNhvVq1dn1KhRzJkz54I+kZGRJCcnU1xczLp162jTpg3h4eFW23fffUdkZCQA99xzD6mpqezYsQOAmTNnEh0dTZUqVViyZAn169dn4MCBODk5cf/999OwYUMWL15sbWvw4MGEhYXh5eXF3XffTVhYGB07dsTJyYm+ffuyadOmUvfl3nvv5bbbbsPJyYkBAwawefNmAJYuXUqTJk3o3bs3Tk5ODBs2jICAgPL8GEVERET+tBQSbkA2m816HxISQmZm5gV9zl6XsHXrVurWrUvVqlXp0KGD1Zafn0/r1q0BcHV1JTo6mlmzZlFcXMycOXMYOHAgAJmZmYSEhNitOyQkhIyMDGu6Zs2a1ns3N7cLpo8dO1bqvpz7w79q1apW38zMTLv9NAyD4ODgi38wIiIiInJJFBJuQPv27bPep6WlERgYeEGfiIgItmzZwpIlSwgPDwegSZMm7Nu3jyVLltCqVStcXV2t/nFxccyePZsVK1ZQtWpV2rZtC0BgYCB79+61W3daWhpBQUEVsWuWWrVqkZ6ebk2bpmk3LSIiIiJXTiHhBjRlyhTS09PJzs5m/PjxREdHX9CnXr161KxZkzfeeMMKCYZh0Lp1a9544w3reoSz2rZti4ODA08//bR1FgGga9eupKam8vHHH1NUVMS8efPYvn073bt3r9B97NatG1u3bmXhwoUUFRUxZcoUfvvttwrdpoiIiMifhULCDSgmJoZOnTpRt25dwsLCGD16dIn9IiIiOHToEO3bt7fawsPDOXjw4AUhAWDQoEFs3bqV2NhYq83X15cvvviC1157DV9fX1599VW++OIL/Pz8yn/HzuHn58f8+fN59tln8fX1Zfv27bRs2RIXF5cK3a6IiIjIn4FhmmZl11AuWrZsaZ69t79UjBkzZvD++++zevXqyi7lAsXFxQQHBzN79mxuv/32yi5HpMKcfSji2buViYjI1WcYxkbTNFtWdh0VyamyC5CKsXBTBhMTU8jMySfQ243hnRvQq/mVXydw4sQJ3n77bR599NFyrLJsEhMTad26NW5ubkycOBHTNGnTpk1llyUiIiJy3dNwoxvQwk0ZjFywlYycfEwgIyefkQu2snBTxh8uW5LExERq1KhBzZo1iYmJKd9iy+CHH34gLCwMPz8/Fi9ezMKFC3Fzc6vsskRERESuexpudANqP+EbMnLyL2gP8nbjuxF3VEJFIlJeNNxIRKTy/RmGG+lMwg0os4SAcLF2EREREZFzKSTcgAK9Sx5yU1q7iIiIiMi5FBJuQMM7N8DN2dGuzc3ZkeGdG1RSRSIiIiJyPdHdjW5AZ+9i1P+OFnh3fpywW9uW+e5GIiIiIvLnoTMJN6hezYMI8HJl1pDWfDfiDruAkJSURFRUFGlpabi7u1svwzCoVq2aNb1q1aqrXveXX35Jly5drvp2RUREROR/dCbhT6x27docO3bMmjYMgy1btlCvXr1KrEpEREREKpvOJNzg1q9fT+PGjfHx8WHw4MGcPHnyspbPz8/n73//OzabjYCAAB5//HFOnToFwAsvvEBERATFxcUAvP7669x6660UFBRQVFREnz59qFmzJt7e3tx+++2kpKRY6120aBENGzZFjSUlAAAgAElEQVTEw8MDm83Gm2++WX47LSIiIiJlopBwg5s9ezaJiYns2rWL1NRUxo0bR1RU1CXfY/2pp54iPT2drVu3kpKSQmpqKhMmTABg1KhRnDp1ildffZVt27bx4osvMnv2bKpUqQJAz5492bVrF7/99hsNGzYkLi7OWu8DDzzAjBkzyMvLY/PmzYSHhwPQpUsXvvzyy/L9EERERETksigk3OCGDh2KzWajevXqjBo1ijlz5lzyskVFRXzwwQe88cYbeHt74+XlxYgRI5g7dy4ATk5OzJo1i1deeYXevXvz/PPP06RJE2veoEGDcHd3x9XVlTFjxrBu3TrrTIajoyPbtm0jLy8PX19fmjdvXv47LyIiIiJXRCHhBmez2az3ISEhZGZmXvKymZmZFBYW0qRJE7y9vfH29qZXr14cPHjQ6lO/fn3atWvH/v37efjhh632oqIinn76aerWrYunpycNGzbENE0OHz4MnBlu9Nlnn1G7dm3uuOMO1q9fXw57KyIiIiLlQSHhBrdv3z7rfVpaGoGBgZe8bK1atXBycsLT05NPP/2UnJwccnNzrR/6AAsWLGDr1q20bduWkSNHWu3Tpk3j66+/5ttvvyU3N5dff/0VANM0AWjbti1ffPEFBw4coFOnTsTExJR1V0VERESknCgk3OCmTJlCeno62dnZjB8/nujo6Ete1tnZmQceeIDs7Gxyc3MxTZN9+/bx9ddfA2cCQv/+/Zk2bRq7d+9m2rRprFixAoC8vDxcXV2ZN28eLVq0YPTo0dZ6jx8/zty5czl69CjOzs54eHjg4FC5/xSjoqKYOnWqXZthGJVUjYiIiEjlUki4wcXExNCpUyfq1q1LWFiY3Y/1SzF58mQcHR159NFH8fLyokuXLuzcuROAV155BX9/f+68804eeOABateuzeDBg8nJyeGvf/0rNWrUYOTIkezdu5cOHTrYrffDDz8kJCQELy8vZsyYwYwZM+zmFxUVlW3HRUREROSKKSSUQWhoKMuXL6/sMkq1Z88eRo4cyfbt28nJyeGjjz6iatWqpfY3TfOCZyS4ubnh4+PD3//+d4KDg8nMzLQuQH7llVes/gMHDuTXX39l1apV1kXOEydOxNHRkZSUFOLj48nJyWHMmDHUq1ePbdu28dhjj3HkyBHWrl1LXFwc9erV48knn8TX15eEhASmT59O+/btGTJkCIZhULduXb7//numT5+OzWbD39+fjz76yKo1NzeXQYMGUaNGDUJCQhg3bpx1e9bp06fToUMHnnnmGXx8fKhTpw7Lli0DztyladWqVQwdOhR3d3eGDh1a3n8KuUrK+zt599132/0bu1yPPPIIY8eOveLlX3rpJYYMGXLFy5+rSZMml3xXMxEREUzTvCFeLVq0MK+2kJAQ8+uvvy5x3rfffmtGRkZa04B58803m6dPn7baRo0aZcbFxVVojYD59sJVZruXV5ih//jCbPfyCjP6oSfNAQMGXPI6QkJCzCZNmphpaWnm4cOHzXbt2pmjRo26oF/Hjh3NsWPHWtMjRowwe/bsaU336tXLfOihh8xjx46ZBw4cMFu1amW+++67pmmaZoMGDUwHBwfzzTffNAsLC80TJ06Y06ZNMx0dHc0PP/zQLCoqMkeNGmXabDbz0UcfNU+ePGkmJiaa7u7uZl5enmmapjlw4EDznnvuMY8ePWr+97//NevXr29OnTrVnDZtmtm+fXvTycnJfP/9982ioiLz7bffNmvVqmUWFxebpmmakZGR5r///e8r+ozl2lHad7Kk72ONGjXMwsJCq62goMCsUaOGeeZ/i9euyMhIu325EmPGjDHHjBlTLvWIiPwZARvMa+D3b0W+dCbhKsrMzLRuH3o1TfoqlYycfEwgIyef5b8cZF/2ictax6XcSjUuLo6ZM2cCUFxczOzZs61nIxw4cIClS5cyefJkqlWrhr+/P08++aTd5+Ht7c3jjz+Ok5MTbm5uANSpU4fBgwfj6OhIdHQ0+/bt4/nnn8fFxYVOnTpRpUoVUlNTOX36NHPnzuXll1/Gw8OD0NBQnn76aaseOHN3pwcffBBHR0fi4uLYv38/Bw4cuNyPU24QPj4+1tkkgGXLluHj41OJFYmIiFw7FBLK6HKeaPzss88yZsyYUsfbr1mzhnbt2uHt7U2zZs2soQHffvstTZs2tfrdddddtGrVypoODw9n4cKFpW73ZNFpu+mi4mK27z9qTT/xxBPYbDY8PT1p0aIFq1atsuYlJCRw6NAh5s2bh4eHB02bNsU0TdLS0vD398dms/HVV18B0Lt3b3bt2sWgQYNo3Lgx6enpfPjhh2RnZ7N3714KCwvx8vLCwcEBwzCIjY21uyVr1apVad++PR4eHnTq1Im8vDxq1qzJnj17MAwDZ2dnAKKjoxk1ahTt27cnOzub1NRUdu/eTWFhIbfffjtBQUGMHj2a4OBgMjIyrPUHBATYbQvg2LFjpX5ucn261O/kwIED7a6FmTFjBoMGDbLrc+4F7Tt37iQyMhIvLy/8/PysmwCYpsmTTz6Jv78/np6eNG3alJ9//hmA+Ph46zqgrKwsunfvjre3N9WrVyc8PNwaDvfKK68QFBSEh4cHDRo0sG4AkJCQQGxsLID1PcjMzOSHH36gVq1aTJo0yao1ISGB++67j+joaDw8PPjLX/7Cli1brPnX+vBIERG5tigkXKHQ0FBOnjxZ4hONgRKfaty7d288PT2ZPn36BevLyMigW7dujB49muzsbCZNmkSfPn04dOgQbdq0YceOHWRlZVFYWMhPP/1EZmYmeXl55Ofns2HDBuuJxZcqv+B/waFVq1Zs3ryZ7OxsYmJi6Nu3r90PqxMnTtC0aVOOHDlC8+bNeeCBB/Dw8CAjI4Pnn3/eej5C1apVqVGjBp999hkNGzbkwQcfpEqVKgwbNgybzYaTkxN33XUXx44do6ioiHXr1tk9H+HQoUNMmzaNgwcPUlBQcNEnL8+cOZP333+foKAgAgICGD58OA4ODixZsoRNmzbx1VdfMWfOHIKCgoiPj//Dcd26k9GN41KfMt6rVy+Sk5PJycnhyJEjrFq1ip49e5a63n/+85906tSJI0eOkJ6ezuOPPw7AV199RXJyMqmpqeTm5vLJJ5/g6+t7wfKvvfYawcHBHDp0iAMHDvDSSy9hGAYpKSm89dZbrF+/nry8PBITEwkNDS21jpycHG677Ta++uorXnnlFbsf/osWLaJv377Wd7lXr14UFhZesI6EhAQSEhIu/kGKiMifmkJCGZU0DCcpKYmoqCirz9kfoKdPn2bs2LGMHTuW48eP8/rrr1sXRc6aNYuuXbvStWtXHBwcuOuuu2jZsiVLly7Fzc2NVq1akZyczMaNG2nWrBnt27fnu+++Y82aNdSvX58WLVqUepRw//QnSJscbb1y13yKWxVHa35sbCy+vr44OTnx9NNPc+rUKVJSUqz5rq6ufPvtt/z222906dKFrKwsHnroIZydnenfvz979uzh7bffBqBmzZoUFxfzzTffMGTIEMaOHcsnn3yCv78/TZo04ccff2Tz5s0YhoG3tzebNm2ytlOzZk1uuukm3Nzc6NevH2lpaaV+7vHx8dbTnY8ePcqyZcvo168f48ePx83NjdjYWBYsWGAdhf0jNWvWZPfu3ZfUV65tl/qUcVdXV3r06MG8efOYN28e99xzD66urqWu19nZmb1795KZmYmrq6t1xy5nZ2fy8vL49ddfMU2TRo0aUatWrRKX379/P3v37sXZ2Znw8HAMw8DR0ZFTp06xfft2CgsLCQ0NJSwsrNQ6QkNDcXR0pGnTpgwePNhu/1q0aMF9992Hs7MzTz31FCdPnmTNmjWX+tGJiIhYFBLK6HKeaLxy5Uq6du1KcHAwTz75pN0Pkr179zJ//nzrycbe3t6sXr2a/fv3AxAZGUlSUhLJyclERkYSFRXFypUrWblyJZGRkRetsc6Qt6j993nWy7ddPxrX8rTmT5o0iUaNGuHl5YW3tze5ublkZWVZ8x0dHa1bqT700EO4ubnx/PPPA1jXDpw98+Dl5UW1atUIDg6mVatWhISEUFhYSFZWFl9//TXBwcFERkbi5OREu3bt7B72VqVKFet91apVLzp069zP/bfffqOwsJClS5eyZMkSvLy8+Pvf/467uzsPPPDART+bs5544gk+/fRTfHx8GDZs2CUtI9emy/lODho0yLoF7/lDjc736quvYpomt912G02aNOHDDz8E4I477mDo0KE89thj+Pv789BDD3H06NELlh8+fDj16tWzbkk8YcIEAOrVq8fkyZNJSEjA39+f/v37X7RmFxeXUvfv3H13cHCw7kgmIiJyuRQSyuDUqVMMGTLEGvu8c+fOiz7R+Ox1A+PHj2fmzJk0bNjQmmez2ejTpw8RERE4ODjg5+fH5MmTGTFiBHAmRMyYMYM333yT8ePH89prr7FkyRJWrlzJli1bSEtLo0ePHri7u/Pqq68C0LdvXwAypw/jyLyRFB7aS5C3Gx0b+ZOesoXHHnuMtm3b8uyzz+Lk5MSGDRvIycnBy8uLt956C5vNxssvv0yVKlXo0KED27dvZ+HChfj5+ZV6K1XDMBgyZAjbt28Hzjzl2dnZGT8/P/z8/Fi/fj2FhYXs3r0bX19fTp06BZy5XuAf//iH3bpq1qzJ6tWrrel69epZT2w+e3YmPT2dHj164OLiwuHDhykoKKC4uJji4mIOHjxoPaQtPj7ebl1gf8vXtm3bkpqaypEjR3jzzTdL/RvKte9ynjIeHh5uXcB+/rM8zhcQEMC///1vMjMzee+993j00UetZ4YMGzaMjRs3sn37dlJTU5k4ceIFy3t4ePDaa6+xe/duPv/8c/7v//7PuvYgJiaG1atXs3fvXgzDuOC7cK6z35mS9u/cfS8uLiY9Pf2ynrIuIiJylkJCGRw/fhwvLy+Sk5PZtm0bQ4cOJTo6usTxzwDr1q0jJyeHZs2acfr0abZt22bNi42NZf78+RQXF7Nv3z5mz57NM888w7x584AzISI3N5djx45x+PBh+vXrx6+//sratWv59NNPqV27NosXL+bYsWM8++yzwJl7vANsXL+OwT3voMbG9/luxB00/P0swty5c+nXrx8BAQHUr1+f5557jhdffJGjR4/SoEEDNm/ezD/+8Q/q1KlzwXUK5+vXr5/1ftasWWzfvp0TJ07w/PPPc9999+Ho6Mi3337L1q1bOX36NJ6enjg7O5fLk5Zr1apFp06dePrppzl69CjFxcXs2rWLlStXlnndcv25nKeMG4bB4sWL+fzzz//wupT58+eTnp4OnLkzkmEYODg4sH79etauXUthYSHVqlXD1dW1xH/XX3zxBTt37sQ0Tby8vHB0dMTBwYGUlBS++eYbTp06haurK25ubhf9Xuzdu9f6/8e0adPs9m/jxo0sWLCAoqIiJk+ejIuLC23atPmjj0xEROQCCgll4OHhQVxcHNHR0dYDyy72ROM777zTGv989913k5ubaze/qKiInJwcbDYb3bt3p0aNGsyfPx84MxTH09OT5s2b4+bmxsCBAzl9+jQhISH4+/uXuL2zQ22qVKlCQkICW7Zssdvmvffey7Bhw+jSpQtffvkl//nPf3B1dcVms9GxY0d8fX1xcHCgUaNGnDp1incWreKJuZtIP5JP+wnfsHBTRonbHThwIPHx8QQEBHDy5EnryPxvv/3Gfffdh6enJ40aNSIyMpKBAwde+gd+ETNmzKCgoMC6q819991nDdWSP5fLfcp4kyZNrOtbLmb9+vW0bt0ad3d37rnnHt544w3q1q3L0aNHefDBB/Hx8SEkJARfX1+GDx9+wfI7duygY8eOuLu707ZtWx599FFuv/12Tp06xYgRI/Dz8yMgIICDBw/y8ssvl1qHl5cX69at48477+SZZ56hU6dO1ryePXsyb948fHx8mDlzJgsWLLDuCiYiInI5jLPDN653LVu2NDds2HDVthcaGsqUKVPo1q0bANu2baNly5bk5+df0NcwDHbs2EFmZiYjR47ENE1eeeUVatWqRf369TFNk7Vr19K9e3cOHTpkLffuu+/y2Wef8fXXX5OQkMDOnTuZNWsWcOZ2iHXq1KGwsBAnJydCQ0OZOnUqHTt2BM5cJD1q1Cjmz5/PoUOHcHBwIDc3l507dxIWFkZ8fDzBwcHW3ZiSkpKIjY21jpROmjSJDz74gMzMTAzD4OjRowTHjMch+BarPjdnR17u3ZRezYOstqioKGJjY8vtKbEicsbZ73xERASGYVxwtvL8/0eIiEjFMQxjo2maLSu7joqkMwllcHb878JNGfSd9DlFrt4XPcJ+sfHPgYGBZGdnk5eXZ7WlpaURFBR0/mpKdP5QiY8//phFixaxfPlycnNz2bNnDwCXEgpXrVrFq6++yieffMKRI0fIycnBwaUap4qK7frlF55mYmJKKWsRqTwLN2XQfsI31Bmx5KLfSRERESmZU2UXcD2bMmUKTqEteHX5HtK+nkXVhuFk5OQzcsFWALsj7PC/8c9n35/LZrPRrl07Ro4cyaRJk0hNTeWDDz5g9uzZl1TL+bfwzMvLw8XFBV9fX06cOMFzzz13yfuVl5eHk5MTNWrUoKioiAkTJnD6VMlPaM7MufDMiUhlWrgpg5ELtpJfeOZZIBf7ToqIiEjJdCahDGJiYhgWdx873xqMk08AXu3OXEB4sSPsFxv/PGfOHPbs2UNgYCD33nsvL7zwgjV86I+MHDmScePG4e3tzaRJkxg0aBAhISEEBQXRuHHjy7p4sXPnznTp0oWbbrqJkJAQXF1dcfGqUWLfQG83u+mkpCQNNZJKNTExxQoIZ90IZ71CQ0MxTbPUC6wTEhI01EhERMqNrkkoozojllDSJ2gA/53Q7WqXU2HOPzoLJV+TIFLZbvTv5NkHNZZ0BzUREbk6dE2C/KHzj6T/Ufv1qlfzIF7u3ZQgbzcMIMjbTQFBrkl/lu+kiIhIRdI1CWV0e8MazF6TZnfk0s3ZkeGdG1RaTRWlV/MghQK55g3v3KDEs1434ndSRESkoigklMHCTRl8tjHDLiAYQJ8W+jEtUlnOfvcmJqaQmZNPoLcbwzs30HdSRETkMigklEFJF0iawLe/Hip5ARG5KnTWS0REpGx0TUIZlHb7T90WVERERESuZwoJZaALJEVERETkRqSQUAbDOzfAzdnRrk0XSIqIiIjI9U7XJJSBLpAUERERkRuRQkIZ6QJJEREREbnRaLiRiIiIiIjYUUgQERERERE7CgkiIiIiImJHIUGuqtDQUJYvX15u67v77rv56KOPyrTdVatW0aCB7kglIiIicpZCglwTkpKSiIqKsqYNw8Df35+ioiKrrbCwEH9/fwzDsNqWLVtGXFxcmbYdHh5OSkpKmdZxKfbs2UNoaGiFb0dERESkrCo0JBiG0cUwjBTDMHYahjGihPkRhmH8aBhGkWEY950377RhGJt/f31ekXWWh/I+Qi7g4+PDsmXLrOlly5bh4+NTiRWJiIiI/DlUWEgwDMMRmALcDTQG7jcMo/F53dKAeODjElaRb5rmrb+/7qmoOq+Gko6SN23alOLiYqtt9OjRxMfHV2gd10qQWb9+PY0bN8bT0xN3d3dOnjxZYr+BAwcyY8YMa3rGjBkMGjTIrk9UVBRTp061pv/973/TqFEjPDw8aNy4MT/++KM1b/Pmzdxyyy14eXkRHR1tbTcpKYng4GCr348//kjz5s3x8PCgb9++REdHM3r0aACOHDlC9+7dqVGjBj4+PnTv3p309HS7ev75z3/Svn17PDw86NSpE1lZWWX4tERERESuvoo8k3AbsNM0zd2maRYAc4Ge53YwTXOPaZo/AcUlreBGlpmZydy5cyu7jEty7pCf8jB79mwSExNJTk6mqKiIcePGERUVRVJSkl2/Xr16kZycTE5ODkeOHGHVqlX07Nmz5JUC8+fPJyEhgRkzZnD06FE+//xzfH19rfmffPIJX375Jf/973/56aefmD59+gXrKCgo4N577yU+Pp7s7Gzuv/9+/vOf/1jzi4uLGTx4MHv37iUtLQ03NzeGDh1qt46PP/6YadOmcfDgQQoKCpg0aRJwJqTt2bPn8j8wERERkausIkNCELDvnOn039sulathGBsMw1hjGEavkjoYhvHQ7302HDp0qCy1louzR8h9fHwYPHhwqUfIAZ599lnGjBlT6g/wNWvW0K5dO7y9vWnWrJn1A/rbb7+ladOmVr+77rqLVq1aWdPh4eEsXLjwkur98MMPadSoET4+PnTu3Jm9e/da8wzDYMqUKdSvX5/69etjmiZPPvkk/v7+eHp60rRpU37++WcATp06xTPPPEPt2rWpWbMmjzzyCPn5+QDcfPPNLF682FqvaZrs2bOHrKwsvL298fLyYs6cOSXW5+rqSo8ePZg3bx7z5s3jnnvuwdXVtdT9mTp1Ks8++yytWrXCMAzq1atHSEiINX/YsGEEBgZSvXp1evTowebNmy9Yx5o1aygqKmLYsGE4OzvTu3dvbrvtNmu+r68vffr0oWrVqnh4eDBq1ChWrlxpt47Bgwdz00034ebmRr9+/UrcjoiIiMi17Fq+cDnENM2WQAww2TCMsPM7mKb5vmmaLU3TbFmjRo2rX+F5zh4h37VrF6mpqYwbNw6gxKPkvXv3xtPTs8Sj2RkZGXTr1o3Ro0eTnZ3NpEmT6NOnD4cOHaJNmzbs2LGDrKwsCgsL+emnn8jMzCQvL4/8/Hw2bNhAeHj4H9a6aNEiXnrpJRYsWMChQ4cIDw/n/vvvt+uzcOFC1q5dy/bt2/nqq69ITk4mNTWV3NxcPvnkE+so/YgRI0hNTWXz5s3s3LmTjIwMXnzxRQAGDRrErFmzrHXm5+fj5+dH8+bNCQ0N5ZtvviEzM7PUOgcNGsSMGTNKHGp0vn379hEWdsE/E0tAQID1vmrVqhw7duyCPpmZmQQFBdldHG2z2az3J06c4OGHHyYkJARPT08iIiLIycnh9OnTl7UdERERkWtZRYaEDMB2znTw722XxDTNjN//uxtIApqXZ3EVYejQodhsNqpXr86oUaNKPUIOZ47Ujx07lrFjx1JQUGA3b9asWXTt2pWuXbvi4ODAXXfdRcuWLVm6dClubm60atWK5ORkNm7cSLNmzWjfvj3fffcda9asoX79+nZDbErz7rvvMnLkSBo1aoSTkxPPPfccmzdvtjubMHLkSKpXr46bmxvOzs7k5eXx66+/YpomjRo1olatWpimyfvvv8/rr79O9erV8fDw4LnnnrOGUsXGxrJ06VKOHj0KwPHjx/nLX/5ibSMtLY3AwMBS6wwPD2f//v0cOHCADh06XHSfbDYbu3bt+sN9v5hatWqRkZGBaZpW2759/zsh9tprr5GSksLatWs5evQoycnJAHb9RURERK53FRkS1gP1DcOoYxhGFaA/cEl3KTIMw8cwDJff3/sB7YHtFVZpOTn3iHNISMhFj5ADdO3aleDgYN577z279r179zJ//ny8vb2t1+rVq9m/fz8AkZGRJCUlkZycTGRkJFFRUaxcuZKVK1cSGRl5SbXu3buXJ554wlp/9erVMU2TjIz/5bhz9+eOO+5g6NChPPbYY/j7+/PQQw9x9OhRDh06xIkTJ2jRooW1ri5dunB2+FdgYCDt27fns88+Iycnh/z8fLZv3056ejrZ2dmMHz+e6OjoUus0DIPFixfz+eef2x3dL8mQIUOYNGkSGzduxDRNdu7caRd6LkXbtm1xdHTkrbfeoqioiEWLFrFu3Tprfl5eHm5ubnh7e5Odnc0LL7xwWesXERERuR5UWEgwTbMIGAokAr8An5imuc0wjBcNw7gHwDCMVoZhpAN9gfcMw9j2++KNgA2GYWwBvgUmmKZ5zYeEc484/9ER8rPGjx/PSy+9xIkTJ6w2m83GwIEDycnJsV7Hjx9nxIgzd5E9PyRERkZedkiw2Wy89957dtvIz8+nXbt2Vp/zf5QPGzaMjRs3sn37dlJTU5k4cSJ+fn64ubmxbds2az25ubl2Q2zi4uKYNWsW8+fPx8XFhbi4ODp16kTdunUJCwuz7hxUmiZNmtCkSZM/3Ke+ffsyatQoYmJi8PDwoFevXmRnZ1/S53FWlSpVWLBgAR988AHe3t7MmjWL7t274+LiAsDf/5+9+46K4nobOP6dZeksLE2R4gKiBNDYjYoFjb3FaOyaaBJT1JgYY9Rgiy3GEn01xaiJvRs7dg2i5mdJggU7GqTZAEFAOvf9gzBxBQ0aiSX3c86ew8zcmXl2Vs+ZO3Pv83z0kTpkqm7durRq1eqhji9JkiRJkvRMEEI8F5+aNWuKJ8lgMIjKlSuLmJgYkZiYKAIDA8XIkSOLbQuIixcvqsvNmjUTDg4O4o033hBCCBEdHS3Kli0rduzYIXJzc0VGRob4+eefRUxMjBBCiLS0NGFmZiacnZ1FVlaWEEIIV1dXYWlpKa5fv/7AGHfv3i2EEGL9+vUiICBARERECCGESE5OFmvWrLlvjEePHhWHDx8W2dnZIi0tTbRs2VKMGTNGCCHE4MGDRZcuXdRzx8bGih07dqj73rlzR+j1ehEQECAWL15csgv6FKlTp4748ccfn3QYkiSEEKJx48aicePGTzoMSZKk/zTgV/EU3P+W5udpnrj8zOnZs+dDPSEvNHHiRKMn3h4eHurEYmdnZzw8PJg2bZpaV8Ha2poaNWoQEBCAmZkZUDBMxmAwUKZMmQeeq/DtwKuvvsrw4cPp3r07tra2VK5c2ahw2b1u375N//79sbe3x2Aw4OjoyLBhwwD48ssv8fHxoW7dutja2tKsWTOjCsaWlpZ07tyZP/74g06dOgGwMTyOwCn78BoRQuCUfWwML/F0lVK3f/9+rl27Rm5uLosXL+bkyZPyjYEkSZIkSf8pinhOJlzWqlVL/Prrr086jPoAJkIAACAASURBVKeag4MD+/bto1q1av/6ucePH8+FCxdYtmwZG8PjGLn+FBk5f2UEsjQ14YtOVehY/WGy5JaOefPmMXr0aNLT0/H29uaLL76gbdu2TzosSQJQCzPemzFNkiRJ+vcoivKbKMjC+dzSPukAnjcbw+OYtvM88ckZuOotGdbS96m48d21axd5eXlUrFjxXz93UlISP/zwA0uXLgVg2s7zRh0EgIycPKbtPP9UXKt33nmHd95550mHIUmSJEmS9MSUeLiRoiiWiqL4lmYwz7rCJ+RxyRkIIC45g5HrTz3xoTTdu3fn3XffZf78+VhbW/+r554/fz4eHh60bt2aRo0aARCfnFFs2/utlyRJkiRJkv5dJXqToChKe2A6YAZ4KYpSDRgvhOhQmsE9a57WJ+SFNQuehP79+9O/f3+jda56S+KK6RC46i3/rbAkSZIkSZKkByjpm4RxQB0gGUAIcRzwKqWYnlnyCXnJDGvpi6WpidE6S1MThrWUL6okSZIkSZKeBiXtJOQIIVLuWfd8zHh+jO5+Eh773ZtkRB0vsv5JiYqKQlEUcnNz/7Vzenp6smfPniLrO1Z344tOVXDTW6IAbnrLp2bSsiRJkiRJklTyTsJpRVF6AiaKolRUFGUO8EspxvVMKukT8tDQUDVDCRSkJa1SpYqa4hRg1KhR9O3b95Fjud8Nekn17dsXMzMzdDodOp2OypUrM3LkSFJS7u0rPpqO1d04NKIpf0xpy6ERTY06CH379i1x+liARYsW/aNrJUmSJEmSJBkraSfhAyAAyAJWAreBj0orqGfV3U/IAZxszEr8hDw+Pv6xzB14nG8KPv30U1JTU7l58yYLFy7k8OHDBAYGkp6e/tjOIUmSJEmSJD19StRJEELcEUIECyFqCyFq/fl3ZmkH9ywqfELubm9J7wq5fNarOfb29vTr14/MzPtfsk8//ZSxY8fe9yZ/8+bNBAQEoNfrCQoK4uzZs+o2T09PvvzyS1588UWsra3p0aMH0dHRtG/fHhsbG6ZOnaq2Xb58OeXLl8fJyYlJkyaV6DtZWFhQu3ZtNm/eTGJiIgsXLgTg0qVLNG3aFEdHR5ycnOjVqxfJycnFHuPs2bN4eXmxcuVKdTkoKAi9Xk9AQACbN28GCmoULF++nKlTp2JjY0P79u0BmDJlChUqVECn0+Hv78+GDRtKFLskSZIkSZL08ErUSVAUZYuiKJvv+SxVFOVDRVEsSjvIZ9Xy5cvZuXMnly5d4sKFC0ycOBEoKIZ0byGkTp06YWtry6JFi4oc58KFC/To0YNZs2Zx8+ZN2rRpQ/v27cnOzlbbrFy5kpCQEJKTk1m5ciXly5dny5YtpKWl8emnn6rtDh48yPnz59m7dy/jx4836mz8HZ1OR/PmzTlw4AAAQghGjhxJfHw8Z8+eJSYmhnHjxhXZ7/fff6dly5bMmTOHHj16kJOTQ/v27WnRogU3btxgzpw59OrVi/Pnz/POO+/Qq1cvPv30U9LS0tiyZQsAFSpU4MCBA6SkpDB27Fh69+7N1atXgYLhScVdN0mSJEmSJOnRlHS40WUgDZj/5+c2cB2o9Oey9Ke75wIMGjQIDw8PHBwcCA4OVp+iF0dRFCZMmMCECROMbv4BVq9eTdu2bWnevDmmpqZ88sknJCYmUrNmTbXN4MGD8fDwwNLywZOkx44di6WlJVWrVqVq1aqcOHHiob6fq6srSUlJAPj4+NC8eXPMzc1xdnbm448/Zv/+/UbtDxw4QIcOHViyZAnt2rUD4PDhw6SlpTFixAjMzMxo2rQp7dq1e+D16dKlC66urmg0Grp160bFihU5evToQ8UuSZIkSZIklUxJOwnVhRA9hRBb/vz0BuoIIQYCNUoxvmeah4eH+vf169eJiopSlxVFwdraGhsbGwD69OlDamoq7u7ufP/990bHiY+Px2AwqMsajQYHBweysrKKPdeDuLi4qH9bWVmRlpb2UN8pLi4OBwcH9Tt1794dNzc3bG1t6d27NwkJCWrba9euMWfOHOrXr280UTs+Ph4PDw80mr/++RkMBuLi7l90bsmSJVSrVg29Xo9eryciIsLoXJIkSZIkSdLjU9JOgrOiKOULF/782+nPxezid5FiYmLUv2/cuIGZmZnR9hMnTqg36Z07d2bQoEFUrFiRyZMnc+fOHbWdq6srV65cUZeFECQlJWFubq6uUxTF6Nj3Lj8OaWlp7Nmzh4YNG5Kbm8tnn32GoiicOnWK27dvs2zZMoQwzow7ZMgQoqOjGTJkiNH3iYmJMcrmFB0djZubW7GxX7lyhf79+/P111+TmJhIcnIylStXLnIuSZIkSZIk6fEoaSdhKHBQUZSfFUUJBQ4AwxRFsQYWl1Zwz6pjx44RHx/PwIED6dq1K/Hx8SxbtgxnZ+f77tOxY0e+++47Vq1aha+vL4sXLyY7O5u33nqL2bNns27dOnr16kVmZiYzZszA1NQUnU6n7v/NN9/g4eGBra0tNWvWxMrKisuXL5OZmYmlpaU6RGjy5MlotVpu374NwMaNG/noo4JEVSEhIVSvXh1bW1vWrl1LWFgYAFlZWWzZsgWdToeiKEyZMoWmTZuSmprKnTt3aNu2Lba2tvTq1cvo7QYUvK3YsWMHYWFhjBgxAoCXXnoJKysrpk6dSk5ODqGhoWzZsoXu3bsDULZsWS5fvqweIz09HUVR1Ou3cOFCIiIi/tFvJEmSJEmSJN1fSbMbbQMqUpD29EPAVwgRIoRIF0LMKs0An0XLly+nbNmyfPbZZ2zduhVvb29q1KjBuXPnHrjfK6+8Qm5uLq+88gpJSUkcPHgQrVZLVFQUP/74I+vXr8fe3p4tW7bw4YcfGg3X8fX15fjx4yQlJdGzZ0/i4uKYMGECLi4ulCtXjiNHjgAQFhaGwWDg0KFDQMGk6MaNGwNgbW3NkiVLSE5OplmzZhw4cABLS0scHR0ZOnQoAI0aNeL8+fPs3LmT9957j5CQEI4fP463tze9e/cmMTGRmzdvAgVDm6pWrYper2f37t1s376d0aNHY2ZmxpYtW9i+fTtOTk4MGDCAJUuW8MILLwDw1ltvcebMGfR6PR07dsTf35+hQ4dSr149ypYty6lTpwgMDHy8P5okSZIkSZKkUko6ZENRlMqAP6BmMxJCLCmluB5arVq1xK+//vqkw8DT05MRI0bw3nvvAbBt2zY++OADLl26ZNROURQuXryIj4+P0XoXFxdmzJhBs2bNKF++PMnJyepk5JUrVzJv3jx+/vlnFi1axIIFCzh48GCxcdjb2xMaGkrVqlUZPXo0KSkpfPXVV7i7uzNy5EiuXr3KuHHjsLe3JzY2FkdHxyLH+Oijj1AUhZkzZxIVFYWXlxeXLl3C29sbgC+//JKIiAiWLl2q7tOyZUt69uzJG2+88egXUZKk+yqc33NvhjRJkiTp36Moym9CiFpPOo7SVNIUqGOBOX9+mgBTgQ6lGNcz7e5JxJF3LIiKjsVrRAiBU/axMfz+k3NzcnK4efMmDg4OXLlyhZycHMqVK6dO1n333Xe5ceNGsftOnz4dPz8/7Ozs0Ov1pKSkqBN7GzduTGhoKL///jtVqlShefPm7N+/n8OHD+Pj46N2EI4cOUKTJk1wdnbGzs6OuXPnFpkcfPd3u3LlCmvXrlXj0+v1HDx4UE1NKkmSJEmSJD2btCVs9xpQFQgXQvRTFKUssKD0wnq2FU5Y3hgex7SfDqGxcUAAcckZjFx/6r77bdq0Ca1WS506dcjOzsbc3JyEhAS02gf/TAcOHGDq1Kns3buXgIAANBoN9vb26sTe+vXrc/78eTZs2EDjxo3x9/cnOjqabdu2qUONAHr27MmgQYPYvn07FhYWfPTRR0U6CXdPKvbw8KBPnz7Mny+z4EqSJEmSJD1PSjpxOUMIkQ/kKopiC9wAvEsvrGfbN998Q2xsLJM3HOPmgVVYvdBQ3ZaRk8e0neeN2iclJbF8+XIGDhzI8OHDcXR0pFy5crRo0YKhQ4dy+/Zt8vPzuXTpUpE6BACpqalotVqcnZ3Jzc1l/Pjx6sRkKJg8XLNmTb755hu1U1C/fn3mzp1r1ElITU3FwcEBCwsLjh49yooVKx74PXv37s2WLVvYuXMneXl5ZGZmEhoaSmxs7CNdN0mSJEmSJOnpUNJOwq+KougpKJz2G/A7ICtZ3UfPnj1p0aIFv07tjdbeBbv63Yy2xydnAFC1alVsbGzw8fFhwYIFzJw5k/Hjx6vtlixZQnZ2Nv7+/tjb2/Paa68VO5SnZcuWtGrVikqVKmEwGLCwsChSN6Fx48bk5ORQp04ddTk1NZVGjRqpbb799lvGjBmDTqdj/PjxdO3a9YHf08PDg02bNjF58mScnZ3x8PBg2rRpRqlNJUmSJEmSpGfP305cVgrGl7gLIWL+XPYEbIUQJ0s9uofwpCYue3p6smDBApo1a1ZkW+CUfcT92SG4m5vekkMjmpb4HH83Sflxu9+kakmSnjw5cVmSJOnJkxOXAVHQi9h413LU09ZBeBpNnL+WE3P/KiAmhOD2r5u5+uNAfvu8He7u7nTp0oVTp+4/R+Fpt2jRIvr27fukw5AkSZIkSZIes5IONzqsKErtUo3kObIxPI55YX+Qe9ewm1t755H++xaCJ0wlJfkWFy5coGPHjoSEhJRaHEIIOfRHkiRJkiRJemgl7SQ0oaCjcElRlJOKopxSFEW+TfjTsWPH1HkD/fr148utp8jOy1O35yTFkfp7CL49RjHm3a6Ym5tjZWVFr1691CrEKSkpvP766zg7O2MwGJg4ceJ9b/B/+eUXateujZ2dHbVr1+aXX35RtwUFBREcHExgYKBadXnhwoX4+fmh0+nw9vbm+++/NzretGnTKFeuHK6urvz4449G2x4mLkmSJEmSJOn5UNIUqK1LNYpn3PLly9m5cyfW1ta0b9+es+cXoW/UB5eeLwKQeeUEJjpHUnWG+x7jgw8+ICUlhcuXL5OYmEiLFi0oV64cb731llG7pKQk2rZty+zZs+nRowdr166lbdu2REZGqvUOli5dyvbt2/H19UUIwfnz59XKz2FhYbRu3ZratWtTo0YNduzYwfTp09m7dy9eXl7079+/xHH17dtXDjeSJEmSJEl6DpXoTYIQ4grgATT98+87Jd33v2DQoEF4eHjg4OBAcHAwmefDjLbnZ6RiYu2Aq96y2P3z8vJYtWoVX3zxBTqdDk9PT4YOHWpUybhQSEgIFStWpE+fPmi1Wnr06MELL7zAli1b1DZ9+/YlICAArVaLqakpbdu2pUKFCiiKQuPGjWnRogUHDhwAYM2aNfTr14/KlStjbW3NuHHjHikuSZIkSZIk6fnxMBWXhwMj/1xlCiwrraCeNXenGzUYDIj0JCxNTdR1Gksd+em3GNbSt9j9ExISyMnJwWD4602DwWAgLq5odeb4+HijdsW1vTf96fbt26lbty4ODg7o9Xq2bdumFkmLj48vEv+jxCVJkiRJkiQ9P0r6NuBVoAOQDiCEiAd0pRXUs6awwjJAdHQ07m5ufNGpCm56SxTAUOUl8tMScM8rWuMAwMnJCVNTU65cuWJ0HDc3tyJtXV1djdoV1/buqshZWVl07tyZTz75hOvXr5OcnEybNm3UaszlypUrEv+jxCVJkiRJkiQ9P0raScj+MxWqAFAUxbr0Qnr2FFZYTkpKYtKkSXTr1o2O1d04NKIpf0xpy+/TX2fAgAH06NGD0NBQsrOzyczMZNWqVUyZMgUTExO6du1KcHAwqampXLlyha+++orevXsXOVebNm24cOECK1asIDc3l9WrV3PmzBnatWtXbGzZ2dlkZWXh7OyMVqtl+/bt7Nq1S93etWtXFi1axJkzZ7hz5w6ff/65uu1h4pIkSZIkSZKeHyXtJKxRFOV7QK8oSn9gDwXVlyX+qrDs7e1NhQoVGDVqVJE2s2fPZtCgQQwcOBC9Xk+FChXYsGED7du3B2DOnDlYW1vj7e1NgwYN6NmzJ2+++WaR4zg6OrJ161ZmzJiBo6MjU6dOZevWrTg5ORUbm06nY/bs2XTt2hV7e3tWrFhBhw4d1O2tW7fmo48+omnTpvj4+NC0qXGRt5LGJUmSJEmSJD0//rbistpQUZoDLQAF2CmE2F2agT2sJ1VxWZIk6d8kKy5LkiQ9ebLi8p8URfkYOCOEGCaE+ORp6yA8KZ6enuzZs4eN4XEETtmH14gQAqfsY2P4P5/YGxQUxIIFCx46lqfJ38VUkphDQ0Nxd3cv0fkWLVpEgwYNHipGSZIkSZIkqaiSDjfSAbsURTmgKMogRVHKlmZQzwJPT08yMzM5FJnAyPWniEvOQABxyRkM/mo5lWvVM2ovhMDb2xt/f/+/PXbr1q25du3aY4kzNDRUffIIBZOaq1SpYlQQbdSoUY9c72DRokUPvW9xMUVGRj5wn7snY5eGcePGGaV/lSRJkiRJ+i8raZ2Ez4UQAcBAoBywX1GUp+ux9ROy5tcYMnL+qq6cGX2Sazu/IyYpQ10nhMDV1ZWoqCguX77MsWPH7ns8IQQhISG4uLiUWszx8fGsWrXqvtuDgoLkUAZJkiRJkqT/sIctiHYDuAYkAmUefzjPnviLEcQveJ+YWd1ICJmFyMsBICv3r45DWFgYCQkJKIpCvXr1WLx4sdExgoKCCA4OJjAwECsrKy5fvmy0/erVq7z44otMmzYNgIULF+Ln54dOp8Pb25vvv/8egGPHjuHp6YlGo6F27do4OzvTqVOnIm8lTExM6N27N66urkyfPh0oyILUrl07nJ2dOXjwIO3bt8fOzo6qVasSGhrKokWLKFOmDCYmJnh5ebF8+XIAdu7cyd69e4GCQm/Vq1fH1tYWDw8PkpOTOXbsGP7+/tjb29OoUSPKly/PK6+8UiSN64kTJ6hXrx56vR4rKyvMzc3x8/Mr0qGaMmUKFSpUQKfT4e/vz4YNG4r8Jp988gn29vZ4eXmxffv2v36r+Hg6dOiAg4MDPj4+zJ8v595LkiRJkiQVSwjxtx9gABAKnAbGAf4l2e/f/NSsWVP8mwwGg9Dr9UIxMRWKmaWweqGxMHP1FdYBQcLco7Ko/8VetW2fPn2EVqsV9erVE61atRKOjo4iKytLCCFE48aNRfny5YWZmZkwMzMTZ8+eFQ0bNhSVKlUS8+fPFy+88IJQFEWYmZkJa2trAYjJkyeLyMhIsXHjRmEwGAQgzMzMhI+Pj1i9erXQaDTCxMRENGnSRN3esWNHkZGRIQAxb948YWtrK6ysrISNjY3w8fERHTp0EOvWrRMXL14UWq1WVK5cWXTo0EHs2rVLODg4CGtraxEeHi5sbGzEwYMHRUREhBBCiFq1aomVK1cKIYT4+eefxcmTJ0VeXp44ceKE0Gg0wsPDQ0RHR4tDhw4JjUYj+vTpIzIzM8WQIUOEiYmJ2L17twDEhg0bxP/+9z8xbNgwUatWLVGxYkUxZswYERAQINzc3NRruWbNGhEXFyfy8vLEqlWrhJWVlYiPjxdCCLFw4UKh1WrFvHnzRG5urvj2229FuXLlRH5+vhBCiIYNG4r3339fZGRkiPDwcOHk5CT27v3rd5KkZ0Hjxo1F48aNn3QYkiRJ/2nAr+IpuP8tzU9J3yR4AB8JIQKEEOOEEGceb1fl2ZSenk7P9z7G54PF5KXewMS2DFlx5/B6Y5paXfnOnTusXbsWKysrPvroI44ePUpOTg4hISHqca5fv86bb75Jeno6FSpUQKMp+FnOnDlDRkYGy5cvJysri6+++gpfX18GDhxIXl4ePXv2ZP78+bRv3x4TExOSk5Oxt7fHzMwMBwcHfvnlF3755RecnJwIDw9n0aJFAFy+fJn+/fvj6OhIVFQUWq2W8PBwOnfuzE8//UT37t1ZunQpYWFhNG/enOrVq5Ofn8+lS5do164de/fuJSAggIsXL3Lu3Dk1pWpQUBBVqlRBo9Hw4osvYm1tTcWKFfHw8GDPnj00aNCAQ4cOYW5uzoQJE9TvCVC5cmXq1q3LunXrmDBhAgMGDODkyZMMHjzY6Jp36dIFV1dXNBoN3bp1o2LFihw9elTdbjAY6N+/PyYmJrzxxhtcvXqV69evExMTw6FDh/jyyy+xsLCgWrVqvP322yxZsqRU/m1IkiRJkiQ9y0o6J2EkYKMoSj8ARVGcFUXxKtXInnKxsbFYWFjQo3VDpvaqj3ezPmTHnSE/LYkvOlWhY/WCqsTr169Xb+g7dOhAbm4utWrVMhpy5OLiQo0aNdBqtZiamqrrly9fjpubG6+99hoHDx5k1KhRbN68mUOHDvHyyy+Tk5NDly5d2LFjB2ZmZuTn53P69GkcHR3RarXk5+fj6uqKtbU1derU4fjx4wAcPXqUH374gZSUFCZNmkSDBg24du0a7777LhMnTmTZsmVUr16d5ORk9Ho9YWFhuLu7M3fuXDZv3szUqVM5d+4cK1asoGPHjlhZWQFw5MgRmjRpgrOzM3Z2dqSmpqqVnePj4/H29iY+Ph4Aa2trHB0d1e/6xx9/0K5dO/744w9ee+01PvvsMxISEjAYDEbXfcmSJVSrVg29Xo9eryciIoKEhASja1moMK60tDTi4+NxcHBAp/urULjBYCAu7p9nopIkSZIkSXrelDQF6lhgODDyz1WmwLLSCupZYWJiQkxMDB2ru7F2WAc0mbcp6+zIrCG91DZz584lJyeHefPmYWlpye3bt/n5558JCQlRb27Nzc2Lzd4zbtw4nJyc6NixI126dGHx4sUYDAY6d+6Mn58fgwYNIjk5mTZt2gBga2vLzZs3AcjLyzMqsGZhYUFaWhoAs2bN4rXXXiMvL4+ZM2eyePFicnJyOHfuHK+88gpWVlaEh4cDkJCQQHZ2NhcuXGD37t1cvXqVvLw8evTowcqVK+nZs6d6jsKOUExMDCkpKeh0OtLT0wEoV64cixYtwtnZGSh4w5KQkMDQoUMBGDt2LC+88ALly5dn3bp1TJ48GSEE0dHR6vGvXLlC//79+frrr0lMTCQ5OZnKlSurHZEHcXV1JSkpidTUVHVddHQ0bm5uf7uvJEmSJEnSf01Jhxu9CnQA0gGEEPEUpEX9T/L09EQIQV5eHt988w2xsbGcPn0ajUZDkyZN1HaxsbEcOnQIABsbGxwdHbGyskJRFGxsbFi5ciXnzp0zehJ+N1NTU/bu3UtYWBguLi60bNmS7OxssrKycHd3JyYmhu3bt7Nr1y71fKampuTn55OSkkLjxo2NjpeXVzCZ+ttvv+XixYuMHz8eCwsL9Ym7paUlrVq1IjMzkwEDBgCQmZnJ+vXr+eGHH0hPT8fGxoaAgACioqJISkqiefPm6vFTU1NxcHDAwsKCo0ePkp6ezvnz54mNjVXb1ahRg+zsbMaMGWOUhjU9PR1bW1u6devG6NGj+eabb8jOzmbOnDlGbRRFUTsaCxcuJCIiokS/mYeHB/Xr12fkyJFkZmZy8uRJfvjhB3r37l2i/SVJkiRJkv5LStpJyP5zkoYAUBTFuvRCenakp6dz9epVDAYD3bt3x8XFhVOnTqnbv/rqK6DgCX92djbm5ua8+eablC9fnkaNGrF48WKEEKSlpTFkyBAMBgMTJ05Un4wfOnQIS0tL3NzciIiIwM7OjrCwMGbPns3mzZtZt24dAwcOxMrKipSUFPLy8liwYAHXrl1Dq9VSs2ZN6tevT3R0NFu3buX69esA/Pjjjxw4cIBJkyZhZmZGSkoKUDAsp0+fPuTn53P48GGgYEjOJ598wvDhw3F1dcXBwYGUlBSSk5NJSUnBy8tLnevg7OzMxx9/jE6nY/z48ZiampKbm0uLFi1o1KgRAJs3b8bc3JzY2Fj1Zh9g+PDhrFixgm+//Zbo6GhiYmKIiIigT58+aht/f3+GDh1KvXr1KFu2LKdOnSIwMLDEv9fKlSuJiorC1dWVV199lc8//5xmzZo97M8uSZIkSZL0/CvJ7GbgE+B74DLQH/gf8MGTnnV99+ffzG5kMBiERqMRZcqUET4+PkKn0wlnZ2fRvXt3o6wjlpaWQqPRiF9++UXk5OSIiIgIUbt2beHv7y8K47WwsBBlypQRt2/fFn/88YeoWLGimtlo4cKFAhCmpqbCyspKmJmZCUVRxP79+4UQQgQEBAhTU1NhbW0tfHx8hKOjoxg+fLgQQgg3Nzeh0+lESEiIyMvLE7179xZmZmbixo0bIi4uTjg4OAgTExPh7e0tvvnmGwGInJwc8fPPPxtlExJCiLFjx4pevXoJIYSIiooSNjY2YsWKFSI7O1skJCSI8PBwIURB1pX58+er+y1cuFAEBgaqy4C4ePHi4/9BJOk/RGY3kiRJevKQ2Y3UjsR0YB3wE+ALjBFCzHnwXs83W1tbNBoNV6/fxKJSfWjwDj+F7CIhLQuAixcvkpmZiZ+fH/Xq1UOr1RIQEMBPP/1EZGQkU6dOJS8vj6ysLJo2bUr37t2pUqUKOTk56PV63n77bfVcZ86cIT09naioKIQQvPrqqxgMBtLT0xk1ahRpaWkEBwfj4+OjTk7+4IMPeOWVV2jTpg0ajYalS5cSFBTEtm3bUBSFW7ducfPmTS5dusSAAQMQQqDVav/2e69YsYJmzZrRo0cPTE1NcXR0pFq1avdtn5ieTeCUfXiNKMjmtPv046kkLUmSJEmSJJWev78r/JMQYjewuxRjeaakp6fT56Mx7Eh1J3rVWExzFXIz0shrPZaN4XFcO7IXe3t7o4myUDA2vm7duuzevZuAgACEEOzatYvt27dTo0YNmjdvzq+//lrsOYcPHw7A3r170ev1+Pv7c+nSJQD69u0LwJf/9y2BU/Zxak0Yaad2s37jZkxNCiZF5+Tk0KRJE2JiYnBwcMDe3v6hv3dMTAwVKlQoUdvfr9ziSkI6Gcl/VZ+evusC5cp7qdmfJEmSJEmSpKfPA98kKIqSqijK7WI+qYqi3P63gnwa6XQ6lJHwYAAAIABJREFUQo5Hk621wq5eV+5EHsHExoGMnDym7TxPQkICHh4exMbGFrnpL1euHAkJCTg5OaEoCk2aNKFOnTpotVoqV66sTjC+W15eHqtWrQIKJkF7enri7u6uzh2Av27K45IzMNE5Ye3fhPIfrWbRz6dJTk4mPT2dESNG4OHhQVJSEsnJyUXOU1yWpbt5eHioHZN7WVtbc+fOHXV50//OkH9P4qHM3ILrI0mSJEmSJD29HthJEELohBC2xXx0QgjbfyvIp5GJiQkxBzeQezsBxdya/PRkrF5oCEBccgZOTk7cunWLAQMG0KNHD0JDQ8nOziYzM5MTJ05w8eJFTExM8PLy4tSpU6SmpnLlyhU2bdqEubl5kfMlJCSQk5NjtM7CwsLoRn97xFUyrl4kI+o41gFNuHPpKEnnjzJ1+xkyMzMJDQ0lNjaWcuXK0bp1awYMGMCtW7fIyckhLCwMgLJly5KYmKhOZr5Xr1692LNnD2vWrCE0NNRoiFO1atVYv349d+7cITIykvijIUb7aqz15CZfI/6uNwuSJEmSJEnS06ek2Y2ke+Tl5WETEMSNNaO5vno0iqk5dvW7AWCiKDRt2pSYmBh69+7NoEGDGDhwIHq9Hk9PTy5cuKCm3nzppZcwNTXF29ubBg0a8PLLL2NtXTR5lJOTk1GhNShIT6rX69XlW3f+6kRobZ0p02k0KYfXcmRCJ8qWLUvnzp3VtKPbtm1jw4YN+Pr6UqZMGWbNmkVOTg6NGjUiMzMTb29v9Hq9WvysUPny5dm2bRszZsygQ4cOZGVlceLECQCGDBmCmZkZZcuW5Y033sClRnOjffWBPUkMmUnM/3VjzZo1Rts8PT2Jiop6mJ9AkiRJkiRJKiUlnpMgGXN1deWWfxOsq7bi5k8TMPcIQGNqAUCeEFSqVIn33nuP3r17s3TpUgYNGsS5c+fo168fNWvWVCcmm5mZ0bFjRyZOnAhAaGgou3cXTP0YN26cej4TExO6du1Keno6ZcuW5cqVK2g0Gj755BO1jW/D9hw5uEJdNnf1xaXnFNz0lkyqq2HcuHGUL19e3V6+fHmmT59O+/btgYL0pPb29ty8eZPExES13d1xADRs2JAjR44UuSZOTk5qzQaAjeFxjFx/ioycguFTuuptKFOnPVVv7GLz5s107dq15BdckiRJkiRJ+tfINwmPqGfPniT9NIa4799Ga++ivkUAcNNbAvD111/z9ttv07t3b2xsbGjVqhVBQUH89NNPj3TOOXPmYG1tjYeHB76+vvTs2ZM333wTRVEoU6YMH79cAUWB7KsXiJv/Hle+bMeVL9vxYZCh2OP16dOHJUuWqMtLlizh9ddfN2qzcOFC/Pz80Ol0eHt78/3336vbQkNDcXd3V5c9PT2ZPn06L774InZ2dqyc8jGft62Im94SBbC8epzMNUPZuPhbdu7cycmTJx/pOjyKcePGFSmcFhQUxIIFC0q0f9++fdV6EJIkSZIkSc87+SbhERQOi/Fr9TrD1p4g567ZuaYahWEtfQHQaDQMHz5czUpUnHtvPIOCgoiNjVWXd+/ejY+PDwD29vYsW7aM0NBQxo0bx5gxY9R29vb2mF07ib2VGWnnw7Cr04n0o+vITIrn5NaFTJw4kdDQUKNzdezYkTlz5pCcnIwQggMHDjBu3DhGjRqltilTpgxbt27F29ubsLAwWrduTe3atalRo0ax32fNmjXs2LEDCwsLAgMDaXJ8N4dGvEd4eDgtW/Zjy5YthISEsGfPHjp06MD58+cxNzeXQ40kSZIkSZKeIvJNwj91bzKgBycHemjHjh3D398fe3t7+vXrR2ZmZrHtCt8KWJmZ8NW44QRZxzHq4wFAQaVhMH4rALBhwwbat2/P6tWr+eGHHzA3N6d+/fpAwZCi/Px82rZty7p163B3d6ddu3ZoNBp++OEHAM6ePcuNGzfQ6/WUK1eOpKQkBgwYoFZmrlevHhMnTsTBwYHAwEACAgJ46aWX0Gg0uLu7k5SUhL29PQEBAUYZoBRFITIyUl3u27ev2nFJSEigXbt26PV6HBwc1DgB4uPj6dy5M87Oznh5eTF79mwAduzYweTJk1m9ejU2NjZUrVqV4OBgDhw4wKBBg7CxsWHQoEEIIRgyZAhlypTB1taWKlWqEBER8ci/naenJ3v27Hnk/Z82ixYtokGDBk86DEmSJEmS/gWyk/APjNt8mpw84xyfOXnisab4XL58OTt37uTSpUtcuHCBiRMnEhQUVOxbgbCwMPLz87G3t+fAgQO88sorAOrk48K3ArdvF2SvnTx5MnXr1mXJkiXMnDmT6tWrq3MNJk+ejKIozJ8/nzFjxpCeno6JiQlZWVnqOTUaDXq9noSEBP73v/+RkZHB0aNHAUhNTWXt2rWULVuW+Ph4GjRowP/+9z/0ej1Tpkxh7dq1ZGVlMX/+fDp06MCgQYNKdD1mzJiBu7s7N2/e5Pr162qc+fn5tG/fnqpVqxIXF8fevXuZNWsWO3fupFWrVnz22Wd069aNtLQ0Tpw4waRJk2jYsCFff/01aWlpfP311+zatYuwsDAuXLhASkoKa9aswdHRESi4QS6sRfE4hIaGEhQUpC7f2zGaPn065cqV4/Tp0//4PHcPCXsaPO5rKUmSJEnS4yc7CY9oY3gcyRl/ZROK/e5NMqIKUoE+SorP+93MDRo0CA8PDxwcHAgODlbfCtxr0qRJuLu7k56ezqZNm+jQoQMWFgUTqV1dXVEUBV9fXypUqKDWQmjQoAFpaWlcvXpVrW9Q2KFo2LAh2dnZfPDBB1hYWLBq1Spu3rxJ27Zt1SJsvr6+mJmZodVq8fT0RKfTqfMMtm7dio2NDX5+flhYWODl5cXo0aNJTk5mxIgRvPzyy2RlZdGrVy/69OmjZkj6O6amply9epUrV65gampKw4YNURSFY8eOcfPmTcaMGYOZmRne3t70799frS1R0mOnpqZy7tw5hBD4+flRrly5Eu//uEycOJFZs2axf/9+AgIC/vXzS5IkSZIkyU7CI3rQ2wLXPycuQ/FPjK2trbGxscHGxsYohWlxPDw81L8NBkORlKSFJkyYwMyZM0lPT2fjxo20atVKraHQrVvBpOr9+/dTt25dHBwc1OXExES2bNnCzp078fHxoU+fPgBMmTKF7OxscnJyGDx4sDpsaOvWrWoV6ZiYGBISEnBxccHW1pbk5GS1vkJMTIxRRef+/fszd+5cjhw5ghACJycnQkJCSE1NxcrKiszMTHJzcx94LQCGDRuGj48PLVq0wNvbmylTpgBw5coV4uPj0ev16mfy5Mlcv379b49ZqGnTpmq62jJlyvDOO++ob10eVUmHixUaNWoUCxYsICwsjEqVKqnrt27dSrVq1dDr9dSvX99o0ve9E8a7detGZmYm6enptG7dmvj4ePXfW3x8vNHwLSjaQY2JiaFTp044Ozvj6Oh437c8w4YNo0GDBqSkpODg4MCpU6fUbTdu3MDKyoqbN28+9DWTJEmSJOnJk52ER/SgtwWFE5fv58SJE6SlpZGWllZs1eO7xcTEqH9HR0fj6up637YNGzYkLy8PS0tLRo0aRZMmTQDUG8JBgwbxySefqDfOjRs3RghBQEAAderUYcaMGaxYUZBC9auvvuLo0aPMnj2befPmERERQZs2bXBzc2Pfvn0AzJw5E1NTUy5evMjt27fR6/UIUTD8ysPDg1u3bqmx1apVi/nz5zNo0CC+/PJLNm3adN9sQVZWVkaVm69du6b+rdPpmDFjBpcvX2bz5s189dVX7N27Fw8PD7y8vEhOTlY/qampbNu2DSi+knRx6wYPHsxvv/3GmTNnuHDhAtOmTbvv9S6J4oaLAcUOGRsxYgSrV68mLCwMb29vdX14eDhvvvkm33//PYmJibz77rtqjYpChRPG//jjD06ePMmiRYuwtrZm+/btuLq6qv/eHvTvBwrqf7Rr1w6DwUBUVBRxcXF0797dqE1+fj79+/fn5MmT7Nq1Czs7O7p3786yZcvUNitXruTll1/G2dm5yDlkpihJkiRJevrJTsIjuvttQaHsqxe49uMA+jWtXKKnxg9y9uxZrl27xsCBA6lUqRLLly9n0qRJdOvWjb59+/L+++/Tpk0btfDap59+yujRozl+/DhhYWG8+eab6nCjwiE32dnZODs7s3PnTry9vdmzZw+zZ89m3LhxbN26lcjISAwGA25ubpiYmKDRaGjWrBkrV67k+vXrLF26lJdfflnNbKTVahk2bBg2NjacO3cOZ2dn9c1Iu3btgILOQVZWFqmpqdjb23Ps2DFGjBhB586dWbt2rTqJ+m7VqlVjxYoV5OXlsWPHDvbv369uK4xTCIGdnZ0aZ506ddDpdHz55ZdkZGSQl5dHREQEx44dAwoqSUdFRamTnAvXXb58WV0+duwYR44cIScnB2traywsLNBo/tl/kZIOFwPYtWsXrVq1MqplATBv3jzeffddXnrpJUxMTHjjjTcwNzfn8OHDapvBgwerE8bbt2+vVsF+WEePHiU+Pp5p06ap1+Duyco5OTn06NGDpKQktmzZgpWVFQBvvPEGK1euVDuJS5cuVd9KSZIkSZL07JGdhEc0rKUvlqYmRuvunN3P14vX/e0k47+Tk5ND+/btsbS05PPPPycrK4vevXvj5OSkvhVYsWIFwcHB6tCfQgEBAcTExDBpylSc2g4BYPjs5QCMHj2arl270r17d/z9/dW/v/vuO9avX0+zZs2oXLkyAAMGDKBJkyZkZWUxYsQInJyccHFx4caNG3zxxRdAweTaFStWoNPp6N+/vzqsCQqe+O/evZstW7bg4uJCxYoV+fnnn0v0/f/v//6PLVu2oNfrWb58OR07dlS3Xbx4kWbNmmFjY0O9evXUOE1MTNi6dSvHjx/Hy8sLJycn3n77bXX4U5cuXQBwdHRUOzkffvgh69atw97ensGDB3P79m369++Pvb09BoMBR0dHhg0b9lC/3b1KOlwMCjpz69atY+zYsUbrr1y5wowZM4yGUsXExBgdy8XFRf3bysqKtLS0R4o3JiYGg8GAVlt8duTIyEg2bdrE2LFjMTMzU9e/9NJLWFlZERoayrlz54iMjKRDhw6PFIMkSZIkSU+erJPwiDpWdwMK5ibEJ2eg1Sj07f8eb7euDUBwcDAffPCBOrzkblWrVsXU1BSA119/XU3VWejw4cOkpaWRmJiIRqNh1KhR9OjRA19fX/XJraWlJWfPniUwMBAhhFG2mGnfLcLkhSZkuFTFMHwrMV8XFEgrW70Z16+PKxKPlZUViqIUW6vgxRdfVDMW3atRo0acO3fOaN348ePVvytXrszevXuL7HdvBWdPT0/1CTQUvH24X1afIUOGMGTIkGK3ubq63vdJvaOjIwcPHjRaV69ePS5cuGC07nEXeHuY4WKVKlViz549BAUFYWlpyYgRI4CCjkZwcDDBwcEPff7ihlRZW1vfdziXh4cH0dHR5ObmFttR8PPzY+DAgbRu3Zp9+/bh6/vX0Lo33niDZcuW4eLiwmuvvaa+yZIkSZIk6dmj3H1z9izT6XSiZs2aT+z8hw8fpmLFimrKzPT0dH7//XcaNmxo1G7//v34+voaPfkFiI2N5fLlyzRq1IgbN24QGxtL9erVOfxrOMJCh2XFeoj0RCpV8iX20EaSk5MxGAxq9p1z585hbm6Ol5cXR6NuYVmpProaBUN+Yr7tR37qTTz6zsD7j83cvn2by5cvc+fOHfLz88nPz8fZ2Rk/P79SvUaFnRBPT89SPc/T4vDhw2i1WqpUqYJGoyEiIgI7Ozuj+QaF9u/fT506dbC0tFTTtBoMBtzd3UlNTeX06dP4+/uj0+nIz88nOTkZOzs7tFothw8fxtfXV50oHhUVRUZGBn5+fty5c4fffvuNevXqqTf9V69eJSYmhurVqyOEICIigqysLOrVq4cQgt9++w17e3s8PT1RFIXU1FTs7Oy4du0aV69epXr16ly7do2oqCiqVq2KpWXB0LvMzEx+++03TExMeOGFF/52Ur70aAqHklWrVu0JRyJJkvTftX///t+EELWedBylSb5JeIzunkiamZlpNBzjYZiZmZGZmcnVfFtycnIQWTcxuX4Zc7cXSPRuRe6vPwP3n/Cs0TmTezvhrxX5eQDkmdoABfMd3NzccHV1RaPREBkZSU5OTnGHkv6hMmXKcPLkSbKysnBycsJgMPztPjY2Nrz44oucPHkSjUaDq6srlSpVIjIykjt37mBiYoKtrS12dnZ/eywrKyvKlCmjZpWqXbs2ZcuW5datWxw5cgQLCwtcXFzUNx6KolC5cmUiIyPVOQ9ly5Ytci4XFxeEEJw4cYJq1aphYWGBhYUFNjY2ZGRklCg2SZIkSZKeYkKI5+JTs2ZN8SQZDAZRuXJlERMTIxITE0VgYKAYOXJkkXaAGDp0qPDz8xN6vV707dtXZGRkiJkzZwozMzMhhBBZWVnCy8tLWLtVEpZ+DYWZu79AYyJc3/5OGIZvFc41Wojy5cuL+fPnCyGEiIyMFC4uLsLCwkI4OjoKW6+qQmNlJ8q99a3w+HidUEzNBSDMHcoJvV4vLCws1H13794tzMzMhLm5udDr9aJt27YiJiZGjbdx48YiODhY1KtXT1hbW4t27dqJhIQE0bNnT6HT6UStWrXEH3/8obYfPHiwcHd3FzqdTtSoUUOEhYWp28aOHSvGjh1bCldfelr069dPBAcHP+kwnmuNGzcWjRs3ftJhSJIk/acBv4qn4P63ND9y4vJj1LNnTzV/f4UKFYxy0d9t06ZNRdJiVqtWTU0XaWZmxpo1a0iPjyTz4lHyUq6jaM3Q6guGKN3JzjM6nhCCKlWq8OGHH3L27Fnc9RaYO7hyfdVnxM97B0wK3miMmfo1ly5donz58gwdOhSdTsfUqVNp2rQpHTt2JDo6GktLyyJ58VetWsXSpUuJi4vj0qVL1KtXj379+pGUlISfnx+ff/652rZ27docP36cpKQkevbsSZcuXdQsT+PGjSsyH+G/YGN4HIFT9uE1IoTAKfvYGB73pEMqFVFRUaxfv5633nrrSYciSZIkSdI/JDsJj0lUVBQjR47kzJkzJCcns3jxYnWS8d0MBgNDhw4tkhYzKCiI2NhYtd25c+fQWunwGLIGt3fmAwoZlwrSeVbt9RleXl5qWx8fH3bt2sWUKVNwdnZm0pgRuFhrqDvqJzwGLsHMwop3P/uCz97qhIODAzNnzsTJyYnU1FR27drF9u3bWbVqFTqdjuDgYKOUowD9+vWjQoUK2NnZ0bp1aypUqECzZs3QarV06dKF8PBwtW3v3r1xdHREq9UydOhQsrKyOH/+/oXnnncbw+MYuf4UcckZCCAuOYOR6089dx2F0aNHU7lyZYYNG2b0b1OSJEmSpGeT7CQ8ZiV5alyStJiLFy+mWZuOWJmboWjNsPKtT1rEPixNTYoUa7t+/Trdu3fHzc0NW1tbevfuTU56CodGNOWPKW1xsbOgff0qxZ7zzp07vPvuuxgMBmxtbWnUqBHJycnk5f31tqJs2bLq35aWlkWW7063OX36dPz8/LCzs0Ov15OSkkJCwl3zI/5jpu08T0aO8ZufjJy8B1bsfhZNmDCBtLS0R8rAJEmSJEnS00dOXH6MCp8aF94UFj41hr9SpsLfp8WMjY1l37592NjYoNm3jfSsXHKzMyEvh5FN3OhY3Y1Zd7X/7LPPUBSFU6dO4eDgwMaNG4sMGbrfOWfMmMH58+c5cuQILi4uHD9+XM1687AOHDjA1KlT2bt3LwEBAWg0Guzt7R/pWM+L+1XmflDFbkmSJEmSpCdNvkl4jEr61Pibb74hNjaWpKQktYry3ZYuXUqlSpU4f/48ZyNOEn3xDPFXLuNZ3oOU06FFzpuamoqNjQ12dnbExcUxbdq0Im3ud87U1FQsLS3R6/UkJSUZzS94WKmpqWi1WpydncnNzWX8+PHcvn37kY/3PCiuMveD1kuSJEmSJD0NZCfhMSrpU+O/m+C8ePFiBgwYgIuLi9HnvffeY/HixUWOP3bsWH7//Xfs7Oxo27YtnTp1KtLmfuf86KOPyMjIwMnJibp169KqVatH/fq0bNmSVq1aUalSJQwGAxYWFkZDq/6LiqvMXdyQMUmSJEmSpKdJqRZTUxSlFfB/gAmwQAgx5Z7tjYBZwItAdyHEuru2vQEU3j1PFEIUvTu+S61atcSvv/76OMN/aIFT9hFXTEfBTW/JoRFNn0BE0tNgY3icWpnbVW/JsJa+RsPPJOlhBAUFARAaGvpE45AkSfovUxTluS+mVmpvEhRFMQG+AVoD/kAPRVH872kWDfQFVtyzrwMwFngJqAOMVRTFvrRi/Sc8PT3Zs2cP8HBPjUsjLWZoaCju7u733f7ee+8xYcKEf3weyfh3/zsdq7upk8gPjWhapIMQFBTEggULHktcffv2vW/q3cchKioKRVHIzc197MdetGgRDRo0eOzHlSRJkiTp4ZXmcKM6QKQQ4rIQIhtYBbxydwMhRJQQ4iSQf8++LYHdQogkIcQtYDfw6ONg/iUdq7vxRacquOktyYw+ya01wXzRqQrb547n/fffV9utOxpFp5cqcHL1dDUt5oi14VhYWqlVbkvD3LlzGT169GM5lqIoREZGPpZjLVq0iL59+z6WYz0NQkND1ae9UHCtrK2tsbGxwc3NjY8//tgoe9TT4nH+po+DoihPOgRJkiRJ+s8qzU6CGxBz13Lsn+se276KoryjKMqviqL8evPmzUcO9HEqfGq86p16VC+vp2N1Nxo1akRYWJjaZvzCLZjonMmKPU3sd2+SEXWclJhz5OQJatas+QSj/8vDPCl/HA4ePFiqT8CftBMnTpCWlsb+/ftZvXo1P/7445MOSZIkSZIk6b6e6YnLQoh5QohaQohahdWKn4Rjx47h7++Pvb09/fr1UysMF2rUqBFnz55V6wXEnvsda79G5OdkIfILXqJkxp7GzNUXU1NTALp06YKDgwNarZZGjRpx+vRpNm3aRLVq1bCyskKr1aLVanFxcWH69OlG55sxYwZlypShXLlyLFy4UF1/91CUwqFJM2bMwNLSEhsbG6O2eXl5jB49GltbW2rXrs2oUaPUoSCNGjUCoGrVqtjY2PD+++9jYmKCubk5Go0GExMTypcvz4EDB9TjKYrC3LlzqVixInq9noEDBz6W1KgPO0QlKioKT0/Pf3xe+PvfvTg+Pj4EBgZy/Phxo/VXrlwhMDAQnU5HixYtjGpLbN68mYCAAPR6PUFBQZw9e1bdFh4eTo0aNdDpdHTr1q1IDFu3bqVatWro9Xrq16/PyZMnS/Tdjh49Sq1atbC1taVs2bJ8/PHHxbZbuHAhfn5+6HQ6vL29+f7779Vtd/8bK+7fY2JiIh06dMDW1pY6depw6dKlEsUmSZIkSVLpK81OQhxwd2ob9z/Xlfa+/7rly5ezc+dOLl26xIULF5g4cSJBQUHqxEIPDw8MBsNfN81Xz2LuEYC52wuQlwNAVsxpnCtWU4/ZunVrli1bRv369alRowavvfYar7/+OjNmzMDOzo6QkBBWr17N7t27adr0r0nR165dIyUlhbi4OH744QcGDhzIrVu3io27sG2XLl1o166dUdukpCQsLCy4du0aixcvNsqqVPhWpPDp+EsvvaTeKB45coRTp05hZ2dHUFAQERER6n5bt27l2LFjnDx5kjVr1rBz506goPPyLI5FL+53B4x++3udO3eOAwcO4OPjY7R+xYoVLFy4kBs3bpCdna12/C5cuECPHj2YNWsWN2/epE2bNrRv357s7Gyys7Pp2LEjffr0ISkpiS5duvDTTz+pxwwPD+fN/2fvzsNruvYGjn93BklkOpnIIDkRMUaqhiiNOqSmUuo1xCxUXW2vUnOIEopqqQ63Sg2NhpjubU0ltFREW2OJKkoIEkkaIk7mOev9Q+06kpCooKzP85znOWfvtfdae+/wrLXX8Hv1Vb744guuX7/OqFGj6NGjB/n5+fe8trFjxzJ27FgyMjK4cOECgYGBZaarUaMG3377LRkZGYSFhTFu3DiOHTum7r/b3+O///1vzM3NSU5O5ssvvyzVu/I0x9eQJEmSpEetKhsJR4C6iqLUVhSlGtAf2FrBY3cBnRRFsftzwnKnP7c9lkaPHo27uzv29vaEhISwbt26Uml0Oh3R0dGUlJRQfPU8Gm0jzGv5IIoLyE86S27cUa799D/1jfSrr75K9erVMTIyIjQ0lN9//x0PDw9efPFFTE1NuXTpEh07dsTX15dmzZqRn5/PZ599RnFxMcuXL2fSpEm8+OKLWFlZsWjRolKV8Pbt22NiYoKLiwvr1q3jm2++ITc3l+7du1NcXExOTg5+fn60atWK1q1bY21tTUnJnVNH/nLt2jVeffVV/Pz8aNSoEQcOHEAIwaRJk9Q02dnZNGjQAF/fm9GfbzUS7pSZmUn79u0ZM2YMQgjS09MZOnQoTk5OaLVa5syZQ0lJCWfOnOH111/nwIEDWFlZodFoANi+fTtNmzbFxsYGd3d3QkNDK/tIK6Qiz/2WZs2aYWlpScOGDWnXrh1vvvmmwf7hw4dTr149LCwsCAwMVHsaNmzYQLdu3ejYsSOmpqZMnDiR3Nxcfv75Zw4ePEhhYSFvv/02pqam9OnTBz8/P/Wcy5YtY9SoUTz33HMYGxsTFBSEmZlZhea9mJqacv78eVJTU7GysqJVq1ZlpuvWrRt16tRBURR0Oh2dOnUy6EEyNTVlxowZmJqa0rVrV6ysrDh79izFxcV8/fXXzJ49G0tLSxo3bkxQUNA9yyVJkiRJ0sNRZY0EIUQRMJqblfszwEYhxClFUWYritIDQFEUP0VRrgB9gS8URTn157FpwLvcbGgcAWb/ue2xdHssAK1WS1JSUqk0t+YlnDx5kob1vHm/nx/uDZshigrJO7UHExNTLpygE8MDAAAgAElEQVQ/x7lz55g9ezbBwcGMGDGCY8eOqcNjzp07x7hx45g6dSpbt25Fq9Wi0+k4cOAAc+fO5fTp07i4uPDrr79y+PBh5syZQ/Xq1csdBqPRaHjjjTcYNGgQkydPRqvVEhoayq35HXv27GHnzp1cvHiRtLQ0rl69Wu49KCgoQKvVqr+trKywtrY2qJB26tSJ2NhYrl69ipOTE2vXri11nuvXr/Piiy/i7+/Pp59+iqIovPXWW6SnpxMXF8e+ffsIDw9Xh7ksXbqU1q1bk5WVhV6vB8DS0pLw8HD0ej3bt29nyZIlbN68Gbg51+LSpUvlXkdlVOS533Ls2DGysrLYsGEDhw4dIjs722C/s7Oz+r169epkZWUBkJSUZHBfjYyMcHd3JzExkaSkJNzc3Awm+N6e9vLly3z44YdoNBr1k5CQcNdy3rJy5UrOnTtHgwYN8PPz49tvvy0zXWRkJK1atcLe3h6NRsOOHTsMhko5ODhgYvJXYPdb13bt2jWKiopK3UNJkiRJkh4PVTonQQixQwhRTwhRRwgx989tM4QQW//8fkQIUUsIYSmEcBBC+Nx27JdCCO8/P2Hl5fE4SEj4a451fHw8rq6updK0bduWEydOsH37dl544QV6NnXj+EevYkQJndu0oNVzLXF1dSUkJIQVK1awZcsWdu/eTXp6ulqpXbNmDYmJicyYMYM9e/bQvXt3XnrpJQIDA4mIiGDo0KEYGRnh5OTEzJkzWb169X1dz635Hf3798fV1RV7e3tq165dqmJ7u2rVqnH58mX1d3Z2NtnZ2WRmZqrb+vbti7W1NWZmZjz77LNcvXqV9PR0dX9SUhI6nY6+ffuqQ3eKi4tZv3497733HtbW1nh6ejJhwoS7Xlu7du3w9fXFyMiIZ555hgEDBrBv3777uhd3U5HnfjtFUQgMDKR169bMnj27Qnm4uroa3FchBAkJCbi5ueHi4kJiYqLBsJz4+Hj1u7u7OyEhIej1evWTk5PDgAED7plv3bp1WbduHVevXmXKlCn06dOn1PPPz8+nd+/eTJw4kZSUFPR6PV27dq3QMCEnJydMTExK3UNJkiRJkh4P/+iJy4+LxYsXc+XKFdLS0pg7dy79+vUrlcbb25uaNWvyySef8MILLwA3K41mZmZER0erk4G1Wi03btzAzMwMBwcHcnJymDZtGgBNmzZlzZo1fPzxx0RGRvLjjz8SHR2NkZERSUlJ1KxZU83vXm+278bY2Jjq1asTGRlJTk4Ov//+O6dPnzZYtrNmzZrExcWpv52cnAgLCyMmJob8/HymTZtG7dq1cXBwUNMsWLCAOnXqYGNjw//+dzNu3u1vnbdv305ubi6vv/66ui01NZXCwkKDt8xarZbExPKnqBw6dIj27dvj5OSEra0tS5cuNcjnQanIcy9LcHAwy5cv548//rhn2sDAQLZv386ePXsoLCzkww8/xMzMjOeff57WrVtjYmLCp59+SmFhId988w2HDx9Wjx05ciRLly7l0KFDCCHIzs5m+/btBg238qxZs4Zr165hZGSkDuMyMjL876KgoID8/Hy1wh8ZGcl3331XoXtgbGxMr169CA0NJScnh9OnT5cZTVySJEmSpEdDNhIegIEDB9KpUye8vLyoU6dOuUt5tm3blmvXruHv769uMzMzQ6/Xq42E+Ph4atWqhVarxc3NjUaNGpUaD7569Wp69epFYmIiP//8MxEREbi6upKSkqKmuf3Ntrm5OTk5Oeq+OyunZa1Hb29vT3Z2Ns7OzgwZMoTGjRsbVBJDQ0MJCgpCo9Fw+PBhNBoN7777Lr1798bFxYULFy7g4eGhNogAdu/erfaO9OnTBzCcnDpy5Ei6dOlC165d1bfWjo6OmJqaGrxNj4+Px83NrdyyDxw4kB49epCQkEB6ejqvv/56lUyCrehzv5Ovry9t27ZlwYIF90xbv3591qxZw1tvvYWjoyPbtm1j27ZtVKtWjWrVqvHNN9+watUq7O3t2bBhA7169VKPbdGiBcuXL2f06NHY2dnh7e3NqlWrKlTGnTt34uPjg5WVFWPHjmX9+vVYWFgYpLG2tubTTz8lMDAQOzs71q5dS48ePSp0foDPPvuMrKwsnJ2dGTZsGMOHD6/wsZIkSZIkVTEhxBPxad68ufgn0mq1onHjxiIhIUFcv35d+Pv7i6lTp5ZKt3//frFs2TKRkpIihBDizJkzom7dumLOnDlCCCFCQkJE69atxdWrV8W1a9eEv7+/CAkJEUIIcfbsWVGtWjVx/PhxkZubK0aNGiUAERsbK4QQYsqUKWLAgAGlyvX999+rv59//nlRu3btMq8hLCxM+Pv7CyGEKCoqEnFxcWL06NHC0tJS/Prrr0IIIRYvXiyaNGki0tPTRVZWlnjjjTcMyhAUFCRCQkJESUmJGDFihGjfvr3IyckRQggxaNAg0bNnT5GRkSEuXbok6tevL5YvXy6EECIyMlJotVqRn5+vlsfJyUmsWrVKCCHEoUOHhJOTkxg0aFCFn4kkPc50Op3Q6XSPuhiSJElPNeCoeAzqv1X5kT0Jj4GKvJHWaDRs3boVX19frKys6NKlC//3f//H5MmTAZg+fTotWrTgmWeeUVc8unWeevXqMWPGDDp06EDdunVLrXQ0YsQITp8+jUajoWfPngAUFhYSFxeHEILDhw9z/Phxg0mmd7q1wpCNjQ3t2rUjIyODI0eOqCsZDR069K69I7coisKyZcuoVasWr7zyCnl5efznP//B0tISLy8v2rRpw8CBA3n11VcBCAgIwMfHB2dnZxwdHQH4/PPPmTFjBtbW1syePbvc5TsflgcVmO6ll176W0NyKnP8381LkiRJkqR/NkU8IWuRt2jRQhw9evSR5b/5eCILdp0lSZ+Lq8aCSZ3r07NpRQNMP36OHDnCgAED1LkO//rXvwgODi5zeM/TrCLP3dPTkxUrVtChQweD7VFRUYSGhhIVFUWDBg2YPHmy2vi55ZNPPmH16tU8yr/te/H09CQqKuqBBamT7q5du3YA5cbikCRJkqqeoii/CCFaPOpyVCXZk/AAbD6eyNRvTpKoz0UAifpcpn5zks3HKx7/bfPxRPzn/0Dt4O34z/+hUsdWBT8/P86fP09OTg4XL15k6tSpsoFwhwfx3G8JCgoiPDy81PbVq1dXKH5AUVFRpfN8GB5UL8rDEB8fj5WVlTpBX/amSJIkSU8z2Uh4ABbsOktuYbHBtthPgwhZXDoOQFkeZGXz75o3bx6vvfbaQ8/3n6is555bWMyCXWdLpT1y5AiNGjXCzs5ODZh3uyFDhvDjjz8aTNA+ffo0v/76q7pkabt27VixYgUAq1atwt/fn3HjxuHg4EBoaCjFxcVMmDABR0dHateuzWeffYaiKGoD4s7j27Rpw8SJE7Gzs6N27dpERkaqed+e9sKFCwQEBODg4ICjoyODBg1SY1Lcr6ioKPWNONwcZnb+/HmDNKGhoQwePPhv5VMZHh4eZGVlYWxsDNyMAXGrgbZq1SqGDRv20MoiSZIkSY+abCQ8AEn63DK3X88qKLXtzsqREILxM9/nwhdvEP9hbxI+G8wfa4NJPfFDmZXNqjZt2jS1cngvw4YNq/BqOU+i8p57WdsjIiLYtWsXFy5c4Ny5c8yZM4d27dqpQ0Zq1apF+/btDeI/rF69mq5du6pzLe506NAhvLy8SElJISQkhOXLlxMZGUlMTAzHjh1TA8iV59ChQ9SvX5/U1FQmT57MiBEjylwFSgjB1KlTSUpK4syZMyQkJBhEsb506ZIcaiRJkiRJTxjZSHgAXDUWZW53sKp2z2PHjBlDwv7/Ydd+BLXGrKXWm1+haTuE3IvHyq2E3q/HdUjKP1V5z72s7aNHj8bd3R17e3tCQkJYt25dqTRBQUFqI6GkpISIiIi7DjVydXXlrbfewsTEBAsLCzZu3MjYsWOpVasWdnZ2BAcH37X8Wq2WkSNHYmxsTFBQEMnJyQbL6N7i7e1Nx44dMTMzw8nJifHjx1cqON29elEqauzYsbi7u2NjY0Pz5s3Zv38/AHl5eVhYWKixMObOnYuJiQkZGRkAvPPOO7z99tvAzVgcTZs2xcbGBnd391KNnfJ6XiRJkiTpaSMbCQ/ApM71sTA1NtimKNDMIu2ulaNz587x+eef02jQO1jUboqRqRmKkTHmtXxw7DZOrWymp6czYsQIXFxccHNzY/r06eq46ZKSEubMmYNWq6VGjRoMHTpUjWJ8q9KzcuVKPDw8CAgIACA8PBytVouDgwPvvvuuwbjxO4d49O3bF2dnZ2xtbWnbti2nTp2qmpv4D1TWc7cwNWZS5/ql0t6+MlR5ge569epFcnIyBw8eJCoqipycHLp161Zu/neuNpWUlGSw7W6rUQE4Ozur36tXrw5AVlZWqXQpKSn0798fNzc3bGxsGDx4cKWC092rF6Wi/Pz8iImJIS0tjYEDB9K3b1/y8vIwNzfHz89Pbbjs27cPrVbLTz/9pP7W6XQAWFpaEh4ejl6vZ/v27SxZsuSePS4ge80kSZKkp49sJDwAPZu68V4vX9w0FiiAm8YCu+rVOL53210rRz/88APu7u7MGtHjrpXNYcOGYWJiwvnz5zl+/DjfffedwdjyVatWsXfvXuLi4sjKymL06NEG59q3bx9nzpxh165dnD59mjfffJOIiAiSk5NJT0+/a/Til156idjYWK5evUqzZs0YNGiQuu9pH6dd1nN/r5dvmataJSQkqN9vD3R3u+rVq9OnTx/Cw8NZvXo1/fv3p1q18nuj7pxI7uLiwpUrV8rM8++YNm0aiqJw8uRJMjIyWLNmTaWC01WkFwWgWbNmaDQa9TN//nyD/YMHD8bBwQETExMmTJhAfn4+Z8/eHJKn0+nYt28fRUVF/Prrr4wZM4Z9+/aRl5fHkSNH1GCF7dq1w9fXFyMjI5555hkGDBhQqV4RSZIkSXpamDzqAjwpejZ1M6gcei41VitHACEhIbz11lvMmTNHTZOamoqzs7N63IJdZzk0NxCK8jAqKaLpa2dJSUlhx44d6PV6LCwssLS0ZNy4cSxbtoxRo0YRERHB+PHj8fLyAuC9996jcePGhIWFqfmEhoZiaWkJwP/+9z+6d++uxkqYPXs2n376abnXdfuSnKGhodjZ2ZGeno6tre3fvWVPhDufe3kWL17Myy+/TPXq1Zk7dy79+vUrM11QUBC9evWisLCQPXv2VKosgYGBfPLJJ3Tr1g1LS0vef//9Sh1fnszMTGxtbbG1tSUxMbFCkaJvV5FeFIBjx47h7e2t/g4NDTWYzLxw4UJWrlxJUlISiqKQkZGh9mjodDrGjx/PsWPH8PX1pWPHjowYMYKDBw/i7e2Ng4MDcHMeRnBwML/99hsFBQXk5+fTt2/fSl2PJEmSJD0NZE9CFbpX5cjBwYHk5GTgZmXzp+AAijJTycvUU1iQjxCCy5cvU1hYiIuLi/qGddSoUVy9ehW4OcREq9Ua5FNUVGQwtvz2ctw5JKV69epqBepOxcXFBAcHU6dOHWxsbNTJqZUZaiLdVJGAeQBt27bF1taWWrVq4efnV6k8Ro4cSadOnXjmmWdo2rQpXbt2xcTERF2t537NnDmTY8eOYWtrS7du3ejVq1eljq9IL8q97N+/nw8++ICNGzdy48YN9Ho9tra2ao/G888/z9mzZ9m0aRM6nY5GjRoRHx/Pjh071KFGcPM59OjRg4SEBNLT03n99dcr1SsiSZIkSU8L2ZNQhe5VOQoICGD06NEcPXqUFi3Kjsfh7u6OmZkZqampmJiUflyurq4Gy2bGx8djYmJCzZo11aEntw9LcXFxUYdoAOTm5nL9+vUy8167di1btmxh9+7deHp6kp6ejp2dnaxUVdKlS5cAmDp16j3TKopCXFxcmftuH8M/bNiwUkO9TExM+Oijj/joo4+Am0t4urq6qs//Xsff/lxvT+vj48Mvv/xikHbChAn3vJZbKtqLcjeZmZmYmJjg5OREUVER8+fPVycmw83GbvPmzVm8eDHbt28HbjYcli5dysqVKw3OY29vj7m5OYcPH2bt2rV06tSp0uWRJEmSpCed7EmoQosXL+bKlSukpaWVWTmqX78+o0aNon///nz//ffk5uZSXFzMzz//rKZxcXGhU6dOTJgwgYyMDEpKSrhw4YI6jnrAgAF89NFHXLx4kaysLKZNm0a/fv3KbFAA9OnTh23btvHzzz9TUFBAaGhouZX+zMxMzMzMcHBwICcnh2nTpj2gO/N0quqAebm5uezYsYOioiISExOZNWsW//d///dA87gfFe1FuZvOnTvTpUsX6tWrh1arxdzcvNTEbJ1OR2FhIS1btlR/Z2ZmqvMRAD7//HNmzJiBtbU1s2fPJjAw8O9dnCRJkiQ9qYQQT8SnefPm4nGi1WrFvHnzRMOGDYWtra0YOnSoyM7OLpWupKREfPLJJ6Jx48bC3NxcODs7i7Zt24oNGzaI4uJiIYQQer1evP7668LNzU3Y2NiIZ599Vqxbt04IIURxcbGYNWuWqFWrlnB0dBSDBg0SaWlpQgghLl68KABRWFhokGdYWJhwd3cX9vb2Yvbs2cLV1VVER0cLIYSYOXOmGDRokBBCiMzMTNGjRw9hZWUlPDw8xFdffSUAERsbW2X37Um16dgV0WB6pNBO+Vb9NJgeKTYdu/LA8sjOzhYtWrQQVlZWwsnJSQwbNkykp6c/sPNLjwedTid0Ot2jLoYkSdJTDTgqHoP6b1V+FPGEDB1p0aKFOHr06KMuxj9OVlYWGo2G2NhYateu/aiL88Tyn/8DiWXEvXDTWPBTcMAjKNHDtfl4Igt2nSVJn4urxoJJnetXaMK3VNqtYIyVXUJWkiRJenAURflFCFH2WPEnhBxu9BTatm0bOTk5ZGdnM3HiRHx9fWXE3CpWmejMT5rNxxOZ+s1JEvW5CCBRn8vUb04+8OFWkiRJkiQ9OLKRUMVuH4dubufMrKXrH9i5X3rpJb766qtKH7dlyxZcXV1xdXUlNjaW9evXl1pzX3qwKhOd+UmzYNdZcguLDbblFhazYNfZco6QJEmSJOlRk42EKnTnG9SiEsHK/RdLvUGNiopShxDAzRVuatSoQVFRkbqtsLCQGjVqGFTmIyMjCQoKqnS5VqxYgV6vJz09nT179lC/fukIwX/XrWjPt1/DvXh6eqorAT1pKhOd+UnzNPeiSJIkSdI/lWwkVKGy3qDmF5dU6A2qnZ0dkZGR6u/IyEjs7OweeBmlh6My0ZmfNE9zL4okSZIk/VPJRkIVKutNaUHyOY4sHIadnR3Dhw8nLy+vzGOHDBlCeHi4+js8PJyhQ4capGnXrh0rVqwAYNWqVbRp04aJEydiZ2dH7dq1DRoZ7dq145133sHf3x9ra2s6depkEBTt4MGDPP/882g0Gpo0aWIwKXLVqlV4eXlhbW1N7dq1iYiIAKCkpIQ5c+ag1WqpUaMGQ4cOJT09HUBddlKj0WBlZcWBAwe4cOECAQEBODg44OjoyKBBg9Dr9ZW5pf9otwLmXZzfjZ+CA56KBgI83b0okiRJkvRPJRsJVaisN6XZp6N45rUPuHDhAufOnWPOnDm0a9eu1EolPXv2JDo6Gr1ez40bN9i/fz+vvPLKXfM7dOgQ9evXJzU1lcmTJzNixAiDGAhr164lLCyMq1evUlBQwMKFCwFITEykW7duTJ8+nbS0NBYuXEjv3r25du0a2dnZjBkzhsjISDIzM/n555959tlngZuNh1WrVrF3717i4uLIyspi9OjRAERHRwOg1+vJysqidevWCCGYOnUqSUlJnDlzhoSEBEJDQ9XyXbp0SU6gfgI9zb0okiRJkvRPJRsJVaisN6j2fj2Y3u8F7O3tCQkJYd26dWUea25uTvfu3dmwYQMbNmygR48emJub3zU/rVbLyJEjMTY2JigoiOTkZFJSUtT9w4cPp169elhYWBAYGEhMTAwAa9asoWvXrnTt2hUjIyM6duxIixYt2LFjBwBGRkb89ttv5Obm4uLigo+PDwARERGMHz8eLy8vrKyseO+991i/fn258xC8vb3p2LEjZmZmODk5MX78eDUonPRke1p7USRJkiTpn0o2EqrQnW9QTYwUXuvip1aQtFotSUlJ5R4/dOhQwsPDyxxqVBZnZ2f1e/Xq1YGbcRDK239r3+XLl/nvf/+LRqNRPz/++CPJyclYWlqyYcMGli5diouLC926deP3338HICkpCa1Wq55Tq9VSVFRk0DC5XUpKCv3798fNzQ0bGxsGDx5sMORJkiRJkiRJejzIRkIVu/0NqrOtObWq5aj74uPjcXV1LffYF154Qe0NaNOmTZWV0d3dnSFDhqDX69VPdnY2wcHBAHTu3Jnvv/+e5ORkGjRowMiRIwFwdXXl8uXLBtdjYmJCzZo1y1xSddq0aSiKwsmTJ8nIyGDNmjU8KcH8JEmSJEmSniSykfCQLV68mCtXrpCWlsbcuXPp169fuWkVRWHbtm1s3bq1SuMYDB48mG3btrFr1y6Ki4vJy8sjKiqKK1eukJKSwpYtW8jOzsbMzAwrKyuMjG7+2QwYMICPPvqIixcvkpWVxbRp0+jXrx8mJiY4OTlhZGREXFycmk9mZiZWVlbY2tqSmJjIggULquyaJEmSJEmSpPsnGwkP2cCBA+nUqRNeXl7UqVOH6dOn3zW9j4+POgegqri7u7NlyxbmzZuHk5MT7u7uLFiwgJKSEkpKSli0aBGurq7Y29uzb98+lixZAsCrr77KkCFDaNu2LbVr18bc3Jz//Oc/wM3hTCEhIfj7+6PRaDh48CAzZ87k2LFj2Nra0q1bN3r16lWl1yVJkiRJkiTdH+VJGe7RokULcfTo0UddDEmSpCp1K/DinSuiSZIkSQ+Poii/CCFaPOpyVCXZk1CFPD092b17d6ntm48n4j//B2oHb8d//g+lIjA/qnI9DqKioqhVq5b628fHR1aGJEmSJEmSHjLZSHjINh9PZOo3J7nw6yGS1waTqM9l6jcnmfrRlzz77LPY2Njg6OhIQEAAFy9e/Nv5DRs27J5Dmu4lOTmZESNG4OLigrW1NQ0aNGDmzJlkZ2f/7fLdy6lTp9Q3p3/HqlWrGDZs2N8+jyRJkiRJ0tNANhIesgW7zpJbWGywLeNqAgumjuHDDz8kPT2dixcv8u9//xtjY+NyzvLwpKWl0bp1a3Jzczlw4ACZmZl8//336PV6Lly4UOnzlRdD4VF6nHtWJEmSJEmSHgXZSKhiR44coVGjRtjZ2TF8+HCupKaXSlOQEoeRbQ1efPFFFEXB2tqa3r174+HhAUB+fj5vv/02rq6uuLq68vbbb5Ofnw/cfEN+5/KoiqJw/vx5li1bRkREBB988AFWVlZ0795dTRMTE8MzzzyDra0t/fr1Iy8vr8zyL1q0CGtra9asWaNGQ3Z3d+eTTz7hmWeeAWDs2LG4u7tjY2ND8+bN2b9/v3p8aGgoffr0YfDgwdjY2LBq1aq7Xs+dbq/Ah4aGEhgYyNChQ7G2tsbHx4fb56HMnz+fOnXqYG1tTaNGjdi0adNdn829REVFGfRiCCFYsGABdevWxcLCAg8PD6ZOnVpu2R+1siJ5S5IkSZIkVYRsJFSxiIgIdu3axYULFzh37hwlx74GwNzjGZwHzgegmrM3RWlXGDduHHv37jUIgAYwd+5cDh48SExMDCdOnODw4cPMmTPnnnn/61//YtCgQUyePJmsrCy2bdum7tu4cSM7d+7k4sWL/Prrr6xatarMc+zevZtevXqpy56Wxc/Pj5iYGNLS0hg4cCB9+/Y1aHRs2bKFPn36oNfrGTRo0H1fD8DWrVvp378/er2eHj16MHr0aHVfnTp12L9/P+np6cycOZPBgweTnJwM3Bx2Vd41VtSYMWNYtmwZ4eHhZGZmEhkZyZ49ewgMDCz3mOLi4nL3SZIkSZIkPa5kI6GKjR49Gnd3d+zt7QkJCaEw9kcsTA2HEdk4ufHeiv+RmJhIYGAgjo6ODBs2TG0sREREMGPGDGrUqIGTkxMzZ85k9erVf6tcY8aMUZc17d69OzExMWWmu379Oi4uLnc91+DBg3FwcMDExIQJEyaQn5/P2bNn1f2tW7emZ8+eGBkZYWFh8beup02bNnTt2hVjY2OGDBnCiRMn1H19+/bF1dUVIyMj+vXrR926dTl8+HCFzntnj8+dPSuxsbF8/vnnRERE0Lp1a0xMTPDx8eHrr79m586d/PDDD8DNxsgbb7xB165dsbS0ZO/eveTn5zNx4kQ8PDyoWbMmr7/+Orm5ueq5P/jgA1xcXHB1dWXFihVqTxBAeno6Q4cOxcnJCa1Wy5w5cygpKQH+6kWaOHEidnZ21K5dm8jIyApdryRJkiRJ0t3IRkIVc3d3V79rtVrSU1N4r5cvbhoLFMBNY8F7vXyZEtSDjRs3cu3aNfbv3090dDRz584FICkpCa1Wa3CepKSkv1UuZ2dn9Xv16tVL9V7c4uDgoL6NL8/ChQtp2LAhtra2aDQa0tPTSU1NVffffg/g713PneXOy8tT5zmEh4fz7LPPotFo0Gg0/PbbbwbluJs7e3zmzJljMFxnz5491KpVi5YtWxoc5+7uTqtWrfj+++/VbWvXriUkJITMzEzatGlDcHAw586dIyYmhvPnz5OYmMjs2bMB2LlzJ4sWLWL37t2cP3++1PCgt956i/T0dOLi4ti3bx/h4eGEhYWp+w8dOkT9+vVJTU1l8uTJjBgxQo1ifedwKUmSJEmSpIqSjYQqlpCQoH6Pj4/H1dWVnk3d+Ck4gIvzu/FTcAA9m7oZHOPn50evXr347bffAHB1deXy5culzgNgaWlJTk6Ouu+PP/4wONffjdTcoUMHNm3apL69vtP+/fv54IMP2LhxIzdu3ECv12Nra8vt8TfuLMPdrud+Xb58mZEjR/LZZ59x/fp19Ho9jRs3ptc5ZPkAACAASURBVKJxQO7s8Vm3bp3B/tTU1HJ7VFxcXAwaI6+88gr+/v4YGRlhZmbGsmXL+Oijj7C3t8fa2ppp06axfv164Oawr+HDh+Pj40P16tUJDQ1Vz1NcXMz69et57733sLa2xtPTkwkTJhj0umi1WkaOHImxsTFBQUEkJyeTkpJS0dsmSZIkSZJUJtlIqGKLFy/mypUrpKWlMXfuXPr161cqzY8//sjy5cu5evUqAL///jtbt26lVatWAAwYMIA5c+Zw7do1UlNTmT17NoMHDwagSZMmnDp1ipiYGPLy8gwqmQA1a9YkLi7uvss/fvx4MjIyCAoKUiv2iYmJjB8/nl9//ZXMzExMTExwcnKiqKiI2bNnk5GRcddz3u167ld2djaKouDk5ARAWFiY2siqiDt7fO7s2XB0dCy3RyU5ORlHR8cyz3Xt2jVycnJo3ry52sPRpUsXrl27BtzsVbk9/e3fU1NTKSwsLNXrkpj4V1yNO3tWgHJ7hSRJkiRJkipKNhKq2MCBA+nUqRNeXl7UqVOnzJgFGo2GrVu34uvri5WVFV26dOH//u//mDx5MgDTp0+nRYsWPPPMM/j6+tKsWTP1PPXq1WPGjBl06NCBunXrllrpaMSIEZw+fRqNRkPPnj0N9lVk6U97e3t+/vlnTE1Nee6557C2tubFF1/E1tYWb29vOnfuTJcuXahXrx5arRZzc/NSw4vudLfruV+NGjViwoQJtG7dmpo1a3Ly5En8/f0rfHxZPT63CwgIICEhodQch4SEBA4ePMiLL76obru958TR0RELCwtOnTqFXq9Hr9eTnp6uVuRdXFy4cuVKmeVwdHTE1NS0VK+Lm5thz5MkSZIkSdIDJ4R4Ij7NmzcXUuVotVrx/fffl9q+d+9eodPp1N+AiI2Nfajlunjx4kPNr3HjxiIhIUFcv35d+Pv7i6lTp5ZK98Ybbwhvb29x4MABUVRUJH777Tfh5+cnunbtqqYJCgoSISEhBseNGTNG9O3bV6SkpAghhLhy5YrYuXOnEEKIHTt2CGdnZ3H69GmRnZ0thg4danC/Bw0aJHr27CkyMjLEpUuXRP369cXy5cuFEEKEhYUJf39/g7we9rOSHj6dTmfw71OSJEl6+ICj4jGo/1blR/YkPASbjyfiP/8Hagdvx3/+D2w+nnjvg6SHqiI9Pp999hmvvfYagwcPVnt82rVrx9dff33Xc7///vt4e3vTqlUrbGxs6NChg7r600svvcSYMWNo3769mgbAzMwMgP/85z9YWlri5eVFmzZtGDhwIK+++uoDvnpJkiRJkiRDiqjgxM7HXYsWLcTtgbUeF5uPJzL1m5MGUZYtTI15r5dvqQnLD5unpyejRo1i9erVJCcn07NnT5YsWcLBgwcJDQ1VV9pRFIXY2Fi8vb3Jz89nypQpbNy4EYDAwEDef/99zMzM0Ol0jBkzht69e/PTTz/Rpk0bvv32W7p168aePXuYMGECMTExXLhwgZEjR3LixAkURaFz584sXrwYjUajlisqKkoN3vY0OXPmDI0bNyY/Px8TE5NHXRzpMXRrxSoZKE+SJOnRURTlFyFEi0ddjqokexKq2IJdZw0aCAC5hcUs2HW2nCMernst/XmnuwVC0+l06nH79u3Dy8uL6Oho9bdOpwNuDnGbOnUqSUlJnDlzhoSEBIMJ15cuXXokDYRH1eOzadMm8vPzuXHjBlOmTKF79+6ygSBJkiRJ0iMlGwlVLEmfW6ntD9u9lv68090Coel0Ovbt2wdAdHQ0U6dOVX/f3kjw9vamY8eOmJmZ4eTkxPjx49V0j8qtHp9EfS4CSNTnMvWbkw+lofDFF19Qo0YN6tSpg7GxMUuWLKnyPCVJkiRJku5Gvq6sYq4aCxLLaBC4aiweQWlKu9fSn3e6WyC01q1bc+7cOVJSUoiJiWHr1q3MnDmT1NRUDh8+TNu2bQFISUlh7Nix7N+/n8zMTEpKSrCzs6uCq6u4u/X4VPWwsJ07d1bp+SVJkiRJkipL9iRUsUmd62NhamywzcLUmEmd6z+iEhm619Kfd7pbILTq1avTvHlzPvnkExo3bky1atV4/vnnWbRoEXXq1FFjCUybNg1FUTh58iQZGRmsWbOmwkHPqsrj3uMjSZIkSZL0MMlGQhXr2dSN93r54qaxQAHcNBaPxaTlWyoS7O129wqEptPp+Oyzz9ShRe3atTP4DZCZmYmVlRW2trYkJiayYMGCqrm4SiivZ+dx6fGRJEmSJEl6mGQj4SHo2dSNn4IDuDi/Gz8FBzw2DQSo2NKft7tXIDSdTkdmZqY6tOjO3wAzZ87k2LFj2Nra0q1bN3r16lU1F1cJj3uPj1R1KhJU8EmyYsUKdYUkSZIkSSqPXAJVkv60+XgiC3adJUmfi6vGgkmd6z9WDTqpanh6erJixQo6dOhQal9UVFSp5YAbN27MiRMnMDK6+Y5l+vTpXLlyhVWrVj3wsgkh0Ol0vPjii8ycORO42Tv3xx9/UFhYyMmTJ6levXqlzrlixQrWrFlT5gpmK1as4ODBg6xYseJBFF+SJOmJ9TQsgSonLkuycvynnk3dnsrrlionKSmJ9evXM3DgwCrPS1EUVqxYQcuWLenTpw8+Pj4UFBQQFxfHrl27Kt1AKCoqqqKSSpIkSU8aOdzoPjys4Qnt2rWr8jd6m48nMmZRBIfmBj70pT8l6XFx5MgRGjVqhJ2dHcOHDycvL6/ctJMnT2bmzJnlVrgPHjzI888/j0ajoUmTJuob+7179+Lr66um69ixI35+furvF154gc2bN5c6X7169QgJCWHEiBGUlJRw/vx5HB0dad++PQB6vZ7Bgwfj5OSEp6cn7733nroQwIoVK2jbti1jxozB3t5ejWlyixCCcePGodPpyMjIqNjNkiRJkp4KspHwAEVFRRmM9VUUBV9fX0pKStRt06dPZ9iwYQ+/cOVYsOssBcX3H+ztboHXJOmfoqygglD233evXr2wsbEpc3hRYmIi3bp1Y/r06aSlpbFw4UJ69+7NtWvXaNWqFbGxsaSmplJYWMivv/5KUlISmZmZ5ObmcvToUV544YUyyzd+/HiEEPTp04eMjAy8vLzUfW+++SY5OTnExcXxww8/sHLlSsLDw9X9P//8Mw0bNuTatWtMmTJF3V5cXMyIESM4e/YsO3fuxMbGhtdee00ONZIkSZIA2UiocreGJlS1+x1GIJf+lKTKBRVUFIV3332Xd999l4KCAoN9a9asoWvXrnTt2hUjIyM6duxIixYt2LFjBxYWFvj5+REdHc0vv/xCkyZN8Pf356effuLgwYPUrVsXBweHMvM0Njbmyy+/ZNOmTXh7e6sRuQsLC9m4cSPz58/H2toaLy8vxo0bpwY4BPDw8OCNN97A2NgYC4ubq3UVFBTQr18/srKy2LJli7pdkiRJkm6RjYT7VNHhCfc7NOGWCxcu0LJlS2xsbHjllVdIS0sD4NKlSyiKwsqVK/Hw8CAgIACAvn374uzsjK2tLW3btuXUqVPquXbs2EGjRo2wtrbGzc2NhQsXlrnEZ8bRraSEvcmVK1e4ceMGL7/8Mk5OTtjZ2fHyyy9z5cqV+7llkvTYqmxQwa5du1KrVi2++OILg+2XL1/mv//9LxqNRv38+OOPJCcnAzdX+4qKiiI6OhqdTke7du3Yt2+fQUTy8vj4+ABgaWmpbrt69SrFxcWlAhwmJv41XPD2a7vl7NmzbN++nZkzZ2JqanrXfCVJkqSnk2wk3Keyhic8yKEJt4SHh/Pll1+SnJyMiYkJY8aMMTjHvn37OHPmDLt27QLgpZdeIjY2lqtXr9KsWTMGDRqkph0xYgRffPEFmZmZ/PbbbwQEBDCpc32qGf+19Kf+p3XknNrDknXbqFWrFiUlJQwfPpzLly8THx+PhYUFo0ePVtPfOcRKkv6JKhtUEGDu3LnMmzePnJwcdZu7uztDhgxBr9ern+zsbIKDg4HSjQSdTlfhRkJZatSogbGxcakAh25uf03AVxSl1HG+vr4sW7aMLl26cP78+UrnK0mSJD0FhBBPxKd58+biYdFqtWLJkiXq7+3btwsvL69S6QARGxsrtm/fLjw8PER+fr4ICQkRQUFBQggh5s+fLwYPHmxwTKdOncSqVauEEELodDoxZcoUdd+pU6eEqampKCoqEhcvXhSAuHDhQrnlvHHjhgCEXq8XQgjh7u4uli5dKtLT0w3Svbtso6hm4yBsWrwirLWNxZp9p8s95/Hjx4VGoyl3vyT902i1WtG4cWORkJAgrl+/Lvz9/cXUqVPLTHvr3/QtHTp0EPb29uq/6fj4eFGzZk2xc+dOUVRUJHJzc8XevXtFQkKCEEKIrKwsUa1aNeHk5CTy8/OFEEK4uroKCwsLkZKScs+yAqJly5ZCp9Op2/r16yd69+4tMjMzRVxcnPD29hZhYWFCCCGWL19ukPbObStWrBAeHh4iLi6uAndKkiRJugU4Kh6D+m9VfmRPwn2qzPCE+x2aUFY+hYWFpKamlrm/uLiY4OBg6tSpg42NDZ6engBq+q+//podO3ag1WrR6XQcOHAAgDZ1nTApyqX4zG7WLZ7PoLYN1XPm5OQwatQotFotNjY2tG3bFr1eT/Edk50l6Z+sskEFb5kzZ446BBBu/nvcsmUL8+bNw8nJCXd3dxYsWKAuXmBpaUmzZs3w8fGhWrVqALRu3RqtVkuNGjXuq+yff/451apVw9PTE51OR1BQEEOHDq3QsSNGjCA4OJiAgADi4+PvK39JkiTpCfWoWykP6vMoexJ27Nhx154EIYTYu3evcHZ2FuPGjVPfOs6bN0+89tpr5eZzZ0/C6dOnS/UkFBYWqvvDw8NFgwYNRFxcnCgpKVF7Em5/8ymEEAUFBWLRokWiVq1aatnc3NzE3r17hZOTk/jxxx/VtLNnzxY6nU4kJycLIW72JNyZryRJD49OpyvVOyBJkiQ9XMieBKk8ixcv5sqVK6SlpTF37lz69et31/Tt2rWjcePGfPXVV+q2wYMHs23bNnbt2kVxcTF5eXlERUUZTAxes2YNp0+fJicnhxkzZtCnTx+Mb5tDcLvMzEzMzMxwcHAgJyeHadOmqfsKCgqIiIggPT0dU1NTbGxs1Iixt5cxIiKCXr16cfjwYfWcFhYWaDQa0tLSmDVrVqXvlST9E2w+noj//B+oHbwd//k/yFghkiRJ0lNNNhLu0/0MT6js0ASAIUOGMGzYMJydncnLy+PTTz8t9/xDhw5Fq9Xi5uZGo0aNaNWqlcH+1atX4+npiY2NDUuXLiUiIqLUOTp27MiXX35J9+7dOXbsGG+//Ta5ubk4OjrSqlUrunTpUpHbI0n/KJuPJzL1m5Mk6nNlUEFJkiRJApSbPSb/fC1atBBHjx591MWQJOkfyH/+DySWERvETWPBT8EBj6BE5bu1mpgMYihJkvToKIryixCixaMuR1WSPQmSJD31ZFBBSZIkSTJk8qgL8E+2+XgiC3adJUmfi6vGgkmd69Ozqdu9D5Qk6bHiqrEosyehrGCDkiRJkvQ0kD0J90mOYX44PD092b1796MuhvSEm9S5PhamhgsCWJgaM6lz/UdUIkmSJEl6tGQj4T54enoSsngtuYWGsQJyC4tZsOvsQy/PpUuXUBSFoqKih553RYSGhjJ48GDgZjRYKyurBxJn4c5oz4qiGESPXbhwIS4uLpw6depv51XVyoqKKz08PZu68V4vX9w0FijcnIvwXi9f2TMoSZIkPbVkI+E+Xc8qKHP7hV8PqRVXT09PFEXB19fXYMWi6dOnM2zYsPvKNz4+HiMjI3bt2nVfx99y+PBhunbtikajwd7enpYtWxIWFnbf53v99dd5991375nOw8ODrKyscpdxrYz58+fzxx9/lLlvzpw5fPzxx+zbtw8fH5+/nZf05OvZ1I2fggO4OL8bPwUHyAaCJEmS9FSTjYT75GBVrcztjlZmpbYlJSWxfv169ffly5fZuXMnAKNGjeKNN95Q9xUWFmJpaVnmth9//BEPDw88PDz+ViX7wIEDBAQEoNPpOH/+PNevX2fJkiVERkZW+lxCCEpKSli6dCnvvPNOhY4ZNmwYq1atqnAeR44coVGjRtjZ2TF8+HDy8vLumn769OmsWLGC6Oho6tWrB8CNGzd4+eWXcXJyws7OjpdfftkgHkW7du1455138Pf3x9ramk6dOqmRqvPy8hg8eDAODg5oNBr8/PxISUkBICwsjIYNG2JtbY2Xl5dBVO3U1FRefvlltSH2wgsvGDQWJUmSJEmSHldV2khQFKWLoihnFUU5ryhKcBn7zRRF2fDn/kOKonj+ud1TUZRcRVFi/vwsrcpy3o9mFmkkr3yDhI/7kbr9Y0RRARamxvT3cy+VdvLkycycObPM4UBt27Zl+/bt+Pj4oNFoaNmyJc7Ozuzfvx+42RsxduxYcnNzCQgIYMCAAcTHx9O9e3esrKz44IMP1HNFRETg4eGBo6Mjc+fOLbfskyZNIigoiClTpuDo6IiiKDRv3pyNGzcCFatQh4SE4O/vT/Xq1YmLiyMpKYkePXpgb2+Pt7c3y5cvLzPvS5cu8dVXX6nDje5WOQe4du0aM2bM4MqVKzRs2JCYmBjmzJkDgLOzM8HBhn9WwcHBbNiwgejoaLy8vNTtJSUlDB8+nMuXLxMfH4+FhQWjR482OHbt2rWEhYVx9epVCgoKWLhwIQBfffUV6enpJCQkcP36dZYuXYqFxc0JrTVq1ODbb78lIyODsLAwxo0bx7FjxwD48MMPqVWrFteuXSMlJYV58+aVO6zoSVmKWJIkSZKkJ0RVhXIGjIELgBdQDTgBNLojzZvA0j+/9wc2/PndE/itMvk1b978XhG0HxitVit8fHzE8h2Hhd87m4SZW0NRq/0gsenYlVLpAFGnTh1hbGwsnn/+eZGbmysGDx4satasKYQQIioqSgDiv//9rygoKBAvvfSSsLOzE1qtVly7dk1otVrh4uIi/P39RU5Ojrh48aIAxM6dO4UQQoSFhQl3d3cBCCsrK/Hll1+KmJgYUa1aNTFr1izRoEEDYWVlJRo2bCh++eUXkZ2dLRRFEU2aNBG2traiUaNGYsuWLWqZg4KCxKuvviqaNWsmrKysRPPmzUWXLl3EK6+8IkpKSsTbb78tTE1NhaIoom7duuL48eOioKBA1KhRQzRr1kzk5uaK5cuXCyMjIzFq1Cjh5OQkrKysRKtWrYQQQi3/ihUrhBBC6HQ64eXlJc6ePStycnKETqcTU6ZMUcvj4OAgPvroI5GXlyfGjh0rateuLby8vMp8LoCwtrYWo0ePvuczPH78uNBoNOpvnU4n3n33XfX34sWLRefOnYUQQqxcuVK0bt1anDhx4p7nfeWVV8THH38shBDinXfeET169BCxsbH3PE6SKkqn0wmdTveoiyFJkvRUA46KKqpDPy6fquxJaAmcF0LECSEKgPXAK3ekeQX46s/v/wNeVP4hMzhHjx7Nay/5cXh2T75ZtpBqlw+UO4Y5LCyMiIgIjh49yqxZs9BqtWrk4ujoaKpXr46xsTGmpqYoioKRkRHe3t5qb4KTkxOdOnVS317fkp2dzZgxY9S5BFFRUbRs2ZImTZrg7u7Oxx9/THh4OBkZGWzduhUHBweuXr2KEIL27dtz9epV/vOf/zBo0CDOnv1rwvXmzZtZsmQJN27coH79+pSUlLBv3z6+++47oqOjadmyJSEhIWzZsoWaNWvyxx9/cPXqVQICAjA3N8fb2xshBAcOHCAxMZEePXpw5MgRbty4oeYRFBSkfh8+fDj16tXDwsKCwMBAYmJi1H1WVlbUrVsXMzMzQkNDuXjxIomJ5a8gtX79ev73v/8xc+ZMg+05OTmMGjUKrVaLjY0Nbdu2Ra/XG0ygdnZ2Vr9Xr16drKws4GbU686dO9O/f39cXV2ZPHkyhYWFAERGRtKqVSvs7e3RaDTs2LFD7QmZNGkS3t7eamTu+fPnl1tuSZIkSZKkx0lVNhLcgITbfl/5c1uZaYQQRUA64PDnvtqKohxXFGWfoigvlJWBoij/UhTlqKIoR69du/ZgS38P7u5/DSvSarUkJSWVm9bFxYV+/fpRp06dUsNwkpKSqF27NtHR0ZSUlHDkyBFq166Nh4cH0dHRCCGIi4tDp9OVeW4jIyPOnTsHQJMmTdRJumlpaXTq1Ak/Pz8URcHb2xutVktsbCwAL7/8MtWqVSMgIICXX36ZdevWqefs3r07K1eupE6dOmzatIndu3ej1+sxMjIiMzOTnJwcatWqRcOGDXFxcSEpKQkzMzPMzP6aj2FiYoKjoyOmpqbUrVsXExMTg4bI7cqrnBcXF3Pjxg2GDRuGjY0Nnp6ewM0hPuWpV68eu3fv5vPPPzeolH/44YecPXuWQ4cOkZGRQXR0NFCxYT6mpqbMnDmT06dP8/PPP/Ptt98SHh5Ofn4+vXv3ZuLEiaSkpKDX6+natat6Tmtraz788EPi4uLYunUrixYtYs+ePffMT5IkSZIk6VF7XCcuJwMeQoimwHhgraIoNncmEkIsE0K0EEK0cHJyeqgFTEj4q/0THx+Pq6vrPY+ZNGkS169fJycnR93m6uqKhYUF0dHRnDx5Ei8vL5KSknjhhReIjo6msLCQ/Px8nnvuuVLns7S0ZMOGDURERADQo0cPfv/9dwDy8/Mp656kpaVhZWXFpk2b1G1ardbg7fzFixfVCvWOHTtwdHQEoH379owePZrY2FgmTpzIv/71LzIyMnB1dSU/P5/8/Hz1HObm5gYNKRMTE7XyX1Fr164lJycHe3t7Tp06xfHjxwHo2rXrXY/z8fFh9+7dLFiwgI8//hiAzMxMLCws0Gg0pKWlMWvWrAqXY+/evZw8eZLi4mJsbGwwNTXFyMiIgoIC9T6bmJgQGRnJd999px737bffcv78eYQQ2NraYmxsjJHRw/knJ+NLSJIkSZL0d1RljSURuH0Wb60/t5WZRlEUE8AWuC6EyBdCXAcQQvzCzbkN9aqwrJW2ePFirly5QlpaGnPnzqVfv373PMbZ2RkLCwu++uordVtgYCC//fYbMTExbN26FXNzc8zMzBg4cCAJCQnk5uZSv359zM3NDc4VFxcHQOfOnVmzZg0A9evXZ+TIkQCYmZlRVu+Kq6sr1apVIywsjAULFnD9+nXi4+MxNjamf//+ABQUFKgV6oyMDDIyMtTjx4wZQ/PmzZk1axbnzp1jwYIFuLu7U6NGDfbu3UteXh4XLlwgOztbjY1wvzIzM1EUhf79+9OxY0caNGgAwL///e97HtukSRN27drFrFmzWLp0KW+//Ta5ubk4OjrSqlUrdbhXRfzxxx/06dMHGxsbGjZsiE6nY8iQIVhbW/Ppp58SGBiInZ0da9eupUePHupxsbGxdOjQASsrK1q3bs2bb75J+/btK38jHqBbsSVuxau49VEUBUtLS/X3raFuT7rff/9d/buSJEmSJOkvJlV47iNAXUVRanOzMdAfGHhHmq1AEHAA6AP8IIQQiqI4AWlCiGJFUbyAukBcFZa10gYOHEinTp1ISkrilVdeYfr06eWmTU5Oxt7enrlz5xIYGGjQSKhfvz5r164lMDCQmTNn0qhRI7Zt24aZmRnPPfccO3bswNfXt9Q5582bx5QpU+jVqxeTJ08Gbo7fv/Wm2sXFhe+++45ffvmFZs2aceHCBUxNTXnuueewtbWlf//+7N69m9DQUHJycvD19WXixIn88MMPtGzZkhMnTuDo6IhGo8Hc3Jy8vDyOHDmCkZERJSUlmJmZYW5urubXtm1bYmJicHV1xdzcHBsbGzp06PC37vHQoUPZtWsXixYtwt7enuXLlxMUFFRqbsYtdw4datGihcE8iKioKIP9o0aNKnffsGHD1FgWAwYMYMCAAWXm+e9//7vcRsu4ceMYN25cmfsetVvxKm5RFIUTJ07g7e39CEslSZIkSdJjoypnRQNdgXPc7AkI+XPbbKDHn9/Ngf8C54HDgNef23sDp4AY4BjQ/V55PczVjSpKq9WKefPmiYYNGwpbW1sxdOhQkZ2dXWba/v37C0VRRGpqqrrt/fffN1jJSIi/VgcqLCwUSUlJom3btsLGxkbY2toKnU4nTp06paZdsmSJqFevnrC0tBQ+Pj7i2LFjQgghfvvtN/W4hg0bim+++UY9JigoSISEhKi/9+7dK9zc3IQQQuzevVv4+voKS0tL4eDgIAYOHCgyMzNLHXf7Mbffi++///6+7qMQ/9/enYdXVd37H38vQoCQhCRAGDIQINRYAmoQkZlAJQwCRlBQZFSpXA2IV1EoOLQNiqJc5WpbhwpFQNDLICCQVixD7Y8ZBNELApJADjInBElIIOv3R07OzckAgZCRz+t5eDxn7bXX/u7TXdjftfday9qlO47amv4NbcMh8bbja2sLzCR1rfKfZ3G35detWzf74YcfliiW0pD32vP397ejRo2y6enp9p///GehM+MABWZhunDhgn366adtSEiIbdiwoY2Li7MZGRnWWmtXr15tw8PD7bRp02z9+vVtUFCQnTdvnmvfIUOG2KefftrGxMRYHx8f27FjR3v48GHX9j179tju3btbf39/e+utt9qlS5e6ti1btsxGRERYHx8fGxISYt955x1rrbXHjh2zvXr1sn5+frZu3bq2e/furn2SkpLsgAEDbL169WyzZs3sn//8Z9e2S5cu2T/84Q+2WbNmruv27Nmz1lprf/jhBxsREVGCX7rsaXYjEZHyx00wu1G5B3Cj/lTEJEFujKU7jtpbp662HnUa2AZD4m3YCyvtrVNXuxKF/De+2dnZ9o033rAtWrSwtWrVsqGhoXbSpEmuG1xrq16SMHLkSDt79mzX99xpepOSkuzp06dtx44dr3hOhSUJY8eOtYMGDbJnz561KSkpNiYmxr7yyivW2pwkoXr16jY+Pt5mZmbaJUuWWB8fmAW3tQAAIABJREFUH1fSOGTIEBsYGGi3b99uMzMz7aBBg+zIkSOttdampqbaRo0a2Xnz5tlLly7ZLVu22ICAANfx69atazdv3myttfbUqVOu5HbChAl2/PjxNisry168eNGuX7/eWpuTBLRq1cpOnz7dXrx40e7bt8+GhobadevWWWutnT59uu3cubNNTk626enpduTIkXbUqFEl/MXLj5IEEZHydzMkCRV14HKlsGxnMp2mf02zSV/SafrXLNtZcGrO4tSRK5uRsI/0rMtuZelZl5mRUPhsSePHj+eDDz5g7ty5pKWlsXr1atauXcvgwYPLItwKIy4ujtDQUOrWrcuUKVPcZrC6mkuXLvHXv/6Vd955B39/f/z8/Jg0aZLbyuG1a9dm8uTJeHp6cv/992OM4cCBA67tgwcPpk2bNnh6ejJ06FDX1LZLly6lVatWPPLII3h4eHDXXXfRv39/Fi9eDICHhwd79+4lLS2NevXqERUVBeTMMuVwOEhKSqJGjRp07doVgH/9619kZGTwwgsvUKNGDW655RZGjx7tivUvf/kL06dPd70K9/LLL7No0aKcXhIREREplJKE67RsZzKTl+whOSUdCySnpDN5yR63JKA4deTqHCnprs+Zx/bj+Chnpevd818jIyPDre6PP/7In/70J+bPn0+HDh2oXr06kZGRLF68mDVr1vD1118XaD8tLY3u3bszfvz4AjeOV1t9GiAxMbHIFaOXL1/uWk07OjqaH374wbWtadOmzJgxg9tuuw1vb28ee+wxjh8/Tp8+ffD19eWee+5xG1Px4IMP0qhRI/z8/OjatSt79+694u92LdP05udwOMjKynLF7u/vT2xsLCdOnHDVCQwMdJutKe/0tVD01LaJiYls2LDB1a6/vz+LFy/m2LFjAHzxxRcsXryYJk2a0KNHD7Zu3QrAlClTCAoKonv37rRo0YKZM2e62jt8+LBbezNnzuTnn3/GWsuRI0fo27eva1tUVBTZ2dmcPn262L+HiIjIzUZJwnUqTu/2tfaAS+GC/P9voPIv36+jweA/EPTER5i0Y8THxxMdHe0aeLx27VpCQkJo166dWxuhoaG0b9+ef/zjH27lp0+f5je/+Q2dOnVi1qxZ5F/LLzs7m9GjR5OYmEhSUhJeXl7ExcW51VmwYAGzZ8/mxIkTZGZm8uabbwKwf/9+Hn74Yd5++21OnjxJ37596d+/P5mZma59Fy9ezD/+8Q/279/PihUr6NOnD6+++ionT54kOzubWbNmuer26dOHH3/8kRMnTtCmTRseeeQR17Y5c+a4Blrnup5penM1btyY6tWrc/DgQVJSUkhJSSE1NfWG3FiHhoYSExPjajclJYXz58+7pqvt0KEDK1eu5Pjx48TExDB0aM58B35+frzzzjskJiayePFi4uPj+eabbwgNDeXWW291ay8tLY2lS5dijCE4OJivv/7abXtGRoZral8REREpSEnCdcrbu11UeXHqyNVN7BWBl6cHAL5t+lG9TiA+dfx5+tnnC7xCc+rUKRo3blxoO40bN3br5Xc4HHTr1o0HH3yQ+Pj4QvepV68egwYNonbt2vj6+jJlyhTWr1/vVqeoFaMXLVrEvffeS8+ePfH09OS5554jPT2df//73659x40bR8OGDQkODqZLly7cfffdREVFUatWLe6//37X2hAAjz76KL6+vq7Vp7/99ltSU1OL/N2uZ5reXJ6enjz66KM8/fTTnDp1ytUjnz/Juh6xsbHs3LmTRYsWkZWVRWZmJps2bWL//v388ssvLFy4kHPnzuHp6Ymvr6/racXy5cs5dOgQ1rqvO9G5c2cA3n77bTIyMrh06RK7d+9mx44dAIwdO5ZJkya5kqYTJ06wYsWKEp+HiIhIVaYk4Trl7d0uqrw4deTqYqOCeW1ga6pXM1SvE0iwvxevDWzNQz3uLPAKTf369V2vreR37Ngxt97jL7/8kvT0dMaOHVvksS9cuMATTzxBWFgYderUoWvXrqSkpHD58v89ISrqtRqHw0FYWJhrW7Vq1QgNDXVbuK5hw4auz15eXgW+5119etKkSYSHh7utPp036ckvd5re5s2bEx4efsVpegvz9ttvExQURNu2bfHz86N3795uYw6uV0BAAAkJCcyePZvGjRsTFBTE1KlTycrKAuDjjz8mLCwMPz8/5s6dy9y5cwH44Ycf6N69O76+vnTt2pXnnnuODh064OnpyapVq/j3v/9NWFgYgYGB/Md//Ifrt3v++ee555576NGjB76+vnTs2NGVQIiIiEjhSnOdhCptYq8IJi/Z4/Y6kZenBxN7RVxTHSme2KhgGvnVYtJvGjF2bA8AVq/eXeAVmh49evDkk0+yZcsWt1eOjhw5wqZNm3jxxRddZWPGjOHs2bP07duXNWvW4O3tXeC4b731lmv16UaNGrFr1y6ioqKKNeg1KCiIPXv2uL7n9sYHBwdf8/kvWLCAL774gq+++oqmTZuSmppKQEBAkXEcPnwYgMmTJxer/cLa8fLy4o033uCNN94osK2whOHnn392fc47wLmw+pGRkaxZs6bQWPKuWp3XCy+8wAsvvFDottDQUD777LNCt3l4eFxxXxERESlITxKuU27vdrC/FwZcvduxUcHXVEeuzdVeobnlllsYO3YsjzzyCJs2beLy5cvs3buXQYMGcc899xRY4O3dd98lIiKC/v37k55e8DWwtLQ01+rTZ86c4fe//32xYx08eDBffvkla9euJSsri7feeouaNWvSsWPHaz7vtLQ0atasSb169bhw4QK/+93vrrkNERERkeJSklACsVHBfDOpBz9Nv5dvJvUo9Oa/OHWk+IrzCs27777L448/zrBhw/Dx8aF3795ER0e7ptjMyxjDBx98QEhICPfdd1+B2ZImTJhAeno69evXp3379vTu3bvYsUZERDBv3jzGjRtH/fr1WbFiBStWrKBGjRrXfN4jRowgLCyM4OBgWrZsSfv27Yu1n6bgFRERkethqspc4W3btrXbtm0r7zBEKozcKXjzv+6mp1mVW3R0NIBrRi8RESl7xpjt1tq25R1HadKYBKl0lu1MZkbCPhwp6QT5ezGxV4RuegtxpSl49XuJiIjIlShJkEolf+947gJ1gG5889EUvCIiInK9NCZBKhUtUFd8moJXRERErpeSBKlU1DtefHkXoculKXhFRESkOJQkSKWi3vHi0xS8IiIicr00JkEqFS1Qd21io4KVFIiIiMg1U5IglUruDa9mNxIREREpPUoSpNJR77iIiIhI6dKYBBERERERcaMkQURERERE3ChJEClnTZs25auvvirvMG6o6OhoPvroo1I9RlJSEj4+Ply+fPnqlUVEROSaKEkQqaDWrVtHdHS067sxBm9vb3x8fAgODuY///M/b+ob5CZNmnD+/Hk8PDyuWjf/bykiIiJXpiRBXMqiR3vUqFFMnToVgI0bNxIRoalLr8W3337L+fPnWbt2LQsWLODDDz8s75BERESkClKSIFdVWI/2gQMHStxuly5d2LdvX4nbuZrK0Iu8detWWrZsSUBAAKNHjyYjI+OK9W+99Va6dOnCd999B4DD4WDQoEEEBgbSrFkzZs2a5ar7yiuvMHjwYEaMGIGvry+RkZFs27bNtb1p06a8+eab3Hbbbfj5+TFkyBDX8U+dOkW/fv3w9/enbt26dOnShezsbGbMmMGgQYPcYho/fjxPP/20W9nFixfx9/d3xQlw8uRJvLy8OHHiBGfPnqVfv34EBgYSEBBAv379OHr0qKtudHQ0L774Ip06dcLX15eYmBhOnToFwOHDhzHGcOnSJQBmz57Nr3/9a3x9fWnevDnvv/9+sX9/ERERcackQaQCmD9/PgkJCRw8eJD9+/cTHx9PdHQ069atK7T+999/z8aNG4mKiiI7O5v+/ftz++23k5yczNq1a3n77bdJSEhw1V++fDkPPfQQKSkpDBgwgLi4OLf2PvvsM9asWcNPP/3E7t27mTNnDgBvvfUWISEhnDx5kuPHj/Pqq69ijGHYsGGsWbOGlJQUAC5dusTChQsZMWKEW7s1a9Zk4MCBfPrpp27H6tatGw0aNCA7O5vRo0eTmJhIUlISXl5eBWJbsGABs2fP5sSJE2RmZvLmm28W+ps0aNCAlStXcu7cOWbPns0zzzzDjh07AK74W4qIiEhBShLEzbX2aF+tl3rnzp20adMGX19ftx5qyOnhDwkJcX2fPn064eHh+Pr60rJlS5YuXeraNmfOHDp37sxzzz1HQEAAzZo1Y/Xq1a7tlb0XOS4ujtDQUOrWrcuUKVPcbqrzatOmDQEBAfTv35/HH3+c0aNHs3XrVk6ePMlLL71EjRo1aN68OWPGjGHhwoWu/Tp37kzfvn3x8PBg+PDhfPvtt27tjh8/nqCgIOrWrUv//v3ZtWsXAJ6enhw7dozExEQ8PT3p0qULxhgaN25M165d+fzzzwFYs2YN9evX58477ywQ89ChQ91iWbBgAUOHDgWgXr16DBo0iNq1a+Pr68uUKVNYv3692/6jR4/mlltuwcvLi8GDB7tiy+/ee+8lPDwcYwzdunUjJiaGjRs3Xu2nFxERkUIoSRA319qjDUX3UmdmZhIbG8vw4cM5c+YMDz74IIsXLy6ynfDwcDZu3Ehqaiovv/wyw4YN49ixY67tmzdvJiIiglOnTvH888/z2GOPYa0FKn8vcmhoqOtzWFgYDoej0Ho7duzg7NmzHDx4kPj4eKpVq0ZiYiIOhwN/f3/Xn1dffZXjx4+79mvUqJHrc+3atcnIyHC9plPY9vPnzwMwceJEWrRoQUxMDM2bN2f69OmueiNHjmTevHkAzJs3j+HDhxcac/fu3blw4QKbN2/m8OHD7Nq1i/vvvx+ACxcu8MQTTxAWFkadOnXo2rUrKSkpbgOyi4otv9WrV9O+fXvq1q2Lv78/q1atcr2aJCIiItdGSYK4KW6Pdl5F9VJv2rSJrKwsJkyYgKenJw888AB33XVXke08+OCDBAUFUa1aNYYMGcKvfvUrtmzZ4toeFhbGmDFj8PDwYOTIkRw7dsx1I1zZe5GPHDni+pyUlERQUFCx9w0NDaVZs2akpKS4/qSlpbFq1aoSx+Xr68tbb73FoUOHWL58OTNnzmTt2rUAxMbGsnv3br777jtWrlzJI488UmgbHh4eDB48mE8//ZRPP/2Ufv364evrC+S8zrRv3z42b97MuXPn2LBhA4Ar+SuuixcvMmjQIJ577jmOHz9OSkoKffv2veZ2REREJIeSBHFT3B7tvIrqpXY4HAQHB2OMcWuzKHPnzuWOO+5w9YZ/9913bj3B+Y8DuHqVK3sv8nvvvcfRo0c5c+YM06ZNY8iQIcXet127dvj6+vL666+Tnp7O5cuX+e6779i6dWuJ41q5ciUHDhzAWoufnx8eHh5Uq5bz10atWrV44IEHGDp0KO3ataNJkyZFtjN06FAWLVrE/PnzXa8aAaSlpeHl5YW/vz9nzpzh97///XXFmZmZycWLFwkMDKR69eqsXr2av//979fVloiIiChJkHxK0qOdX+PGjUlOTnbrzU1KSiq0bmJiImPGjOHdd9/l9OnTpKSk0KpVq2L1BFeFXuShQ4e6XukJDw93TRNbHB4eHqxcuZJdu3bRrFkz6tevz+OPP05qamqJ4/rxxx+555578PHxoUOHDjz55JN0797dtX3kyJHs2bOnyFeNct199914e3vjcDjo06ePq3zChAmkp6dTv3592rdvT+/eva8rTl9fX2bNmsXgwYMJCAhgwYIFDBgw4LraEhEREahe3gFIxfLee+/Rr18/ateufc092vl16NCB6tWrM2vWLJ588klWrFjBli1b3G4yc/3yyy8YYwgMDARyBiLnnTbzSorqRW7VqtV1x16WDh8+DMDkyZOvWO9KSU9QUFCRr4a98sorbt+bNm3q1lbu8Qur/8wzz/DMM88UedwmTZrg5eVVYDrUwsaAFDZtblBQUIG6TzzxRJHtjBo1ilGjRhV6Hk899RRPPfVUkbGKiIhI8elJgrgpSY92fjVq1GDJkiXMmTOHunXrsmjRIgYOHFho3ZYtW/Lss8/SoUMHGjZsyJ49e+jUqVOxjlMVepGX7Uym0/SvaTbpSzpN/5plO5PLO6Srys7OZubMmTz00EPUqVOnvMMRERGRG8hUplcyrqRt27Y279SbIpXFsp3JTF6yh/Ss/5vRx8vTg9cGtiY2KrgcIyvaL7/8QsOGDQkLC2PNmjVuY1mkdOUuDFjRZ+wSEanKjDHbrbVtyzuO0qTXjaSAZTuTmZGwD0dKOkH+XkzsFVFhb1arghkJ+9wSBID0rMvMSNhXYX93b2/vIqciFRERkcpPSYK4yd+rnZySzuQlewAq7A1rZedISb+mchEREZHSpjEJ4uZKvdpSOoL8va6pXArXtGlTvvrqqxvWXp8+ffjb3/52w9orqYoWj4iIVG1KEsSNerXL3sReEXh5eriVeXl6MLFXRDlFVLWsW7fO9R4/gDGGBg0auK04nZWVRYMGDdzW9Fi9ejUjR44sy1CvKG88P//8s2uWJxERkdKgJEHcqFe77MVGBfPawNYE+3thgGB/rwo9aLkqCAgIYPXq1a7vq1evJiAgoBwjKpq1luzs7PIOQ0REbjJKEsSNerXLR2xUMN9M6sFP0+/lm0k9lCBcp61bt9KyZUsCAgIYPXo0GRkZhdYbPnw4c+fOdX2fO3cuI0aMcKsTHR3NRx99BOSs8dCtWzf8/PyoX7++a/0Qay3PPPMMDRo0oE6dOrRu3dq1vkdqaiojRowgMDCQsLAw4uPjXTf7r7zyCsOGDXMd6/DhwxhjXE83oqOjmTJlCp06daJ27docOnTILR4REZHSpiRB3KhXWyqz+fPnk5CQwMGDB9m/fz/x8fFER0cXmC40NjaWDRs2kJKSwtmzZ9m4cSP33Xdfke2++OKLxMTEcPbsWY4ePcq4ceMA+Pvf/86GDRvYv38/qampfPbZZ9SrVw+AcePGkZqayqFDh1i/fj1z585l9uzZxT6XTz75hA8++IC0tDTCwsLctjVq1Ig5c+YUuy0REZFrpdmNpIDYqGAlBVIpxcXFudZsmDJlCuPGjSM+Pr5AvVq1atG/f38WLVqEtZYBAwZQq1atItv19PQkMTERh8NBSEgInTt3dpWnpaXxv//7v7Rr145f//rXAFy+fJmFCxeya9cufH198fX15dlnn+WTTz7hscceK9a5jBo1isjIyGv9CURERG4IPUkQkSoj76JuYWFhOByOIuuOGDGCuXPnFvqqUX5vvPEG1lratWtHZGQkH3/8MQA9evQgLi6Op556igYNGvDb3/6Wc+fOcerUKbKystyeAISFhZGcXPyVtLVAnYiIlCclCSJSZRw5csT1OSkpiaCgoCLrdunShWPHjnH8+HHXk4GiNGrUiA8//BCHw8H777/Pk08+yYEDBwAYP34827dv5/vvv2f//v3MmDGD+vXru54+5I0nODjnCZ23tzcXLlxwbfv5558LHDPvTEsiIiJlTUmCiFQZ7733HkePHuXMmTNMmzbNNcC4MMYYVqxYwfLly696Q/75559z9OhRIGdmJGMM1apVY+vWrWzevJmsrCy8vb2pVasW1apVw8PDg8GDBzNlyhTS0tJITExk5syZrsHKd9xxBxs2bCApKYnU1FRee+21G/cjiIiI3ABKEkSkyhg6dCgxMTE0b96c8PBwpk6desX6kZGRxXrvf+vWrdx99934+PgwYMAA3nnnHZo3b865c+cYM2YMAQEBhIWFUa9ePSZOnAjAf//3f+Pt7U3z5s3p3LkzQ4cO5dFHHwWgZ8+eDBkyhNtuu40777yTfv36lfzkRUREbiBjrS3vGG6Itm3b2m3btpV3GCIipSp3Ybj8MzaJiEjZMcZst9a2Le84SpNmNxKRKmXZzmRmJOzDkZJOkL8XE3tFaLYuERGRa6QkQUSqjGU7k5m8ZA/pWZcBSE5JZ/KSPQBKFERERK6BxiSISJUxI2GfK0HIlZ51mRkJ+8opIhERkcpJSYKIVBmOlPRrKhcREZHCKUkQkSojyN/rmspFRESkcEoSRKTKmNgrAi9PD7cyL08PJvaKKKeIREREKicNXBaRKiN3cLJmNxIRESkZJQkiUqXERgUrKRARESkhvW4kIiIiIpVC06ZN+eqrr8o7jHJljOlijCn1afuUJIiIiIhIpbdu3TrXqvQAxhhat25Ndna2q2zq1KmMGjWqVOMwxlhjzAljTPU8ZZ7OMlvS9q21G621JRpsZ4xpaow5fKU6ShJEREREpEpyOBwsXLiwPA59FuiT53sfZ1mloSRBRERERCqNrVu30rJlSwICAhg9ejQZGRlF1n3++ed5+eWXuXTpUqHbN23aRMeOHfH39+f2229n3bp1APzzn/+kdevWrno9e/bkrrvuyrtrhDEm9gphfgKMyPN9BDA3bwVjjJ8x5q/GmGPGmGRjTLwxxsO57c/GmMV56r5ujFlrckQbY47m2RZqjFlijDlpjDltjHnXWV7NGDPVGJPofIox1xjjd4WY3ShJEBEREZFKY/78+SQkJHDw4EH2799PfHw8ANHR0a6b/FwDBw6kTp06zJkzp0A7ycnJ3HvvvUydOpUzZ87w5ptvMmjQIE6ePEn79u358ccfOXXqFFlZWezevRuHw0FaWhrp6ekA3sDGK4S5DOhqjPE3xgQAXYAv8tWZA1wCWgBRQAzwuHPbs0BrY8woY0wX4DFgpLXW7XUlZ1KxEkgEmgLBQO6jk1HOP92B5oAP8C6AtfawtbbpFeJXkiAiIiIilUdcXByhoaHUrVuXKVOm8OmnnxZZ1xjDH//4R/74xz+SmZnptm3evHn07duXvn37Uq1aNXr27Enbtm1ZtWoVXl5e3HXXXWzYsIHt27dz++2306lTJ7755hs2bdoEkGGtPX2FMDOAFcAQ55/lzrLcuBoCfYEJ1tpfrLUngP8CHgKw1l4AhgMzgXnAOGvtUQpqBwQBE53tZFhr/+Xc9ggw01p7yFp7HpgMPJR3rMSVaApUEREREak0QkNDXZ/DwsJwOBxXrN+3b19CQkJ4//333coTExP5/PPPWbFihassKyuL7t27A9CtWzfWrVtHSEgI3bp1IyAggPXr11OzZk2AtGKEOhd4DTDAC/m2hQGewDFjTG5ZNeBI7hdr7WZjzCGgAfBZEccIBRKttYW9TxVEzhMG1ymTc+/fEEi+WvBKEkRERESk0jhyxHUfTVJSEkFBQVfdZ9q0aTz88MM8/PDDrrLQ0FCGDx/Ohx9+WOg+3bp149lnn6VJkyZMmjSJgIAAxowZk5sknC9GqBuBxoAF/gWE5z0N4CJQv4gbfIwxTwE1AQfwPDkJR35HgCbGmOqFtOMgJxnJ1YSc15uOFyN2vW4kIiIiIpXHe++9x9GjRzlz5gzTpk1jyJAhV90nOjqaVq1a8be//c1VNmzYMFasWEFCQgKXL18mIyODdevWcfRozls9HTt2ZN++fWzZsoV27doRGRlJYmIimzdvhmI8SXCOH+gPDMg/lsBaewz4O/CWMaaOc5BxuDGmG4Ax5hYgHhhGzmtHzxtj7ijkMFuAY8B0Y4y3MaaWMaaTc9unwDPGmGbGGB/gVWBRUUlJfkoSRERERKTSGDp0KDExMTRv3pzw8HCmTp1arP3i4+M5c+aM63toaChffPEFr776KoGBgYSGhjJjxgzXugre3t60adOGyMhIatSoAUCHDh0ICwuDnB75q7LW7rXW7i1i8wigBvA9OdOj/g/Q2DlmYB7wurX2W2vtj8DvgE+MMTXztX+ZnESkBZAEHCVnDATAx+TMsrQB+ImcMRHjihM3gMmX2FRabdu2tdu2bSvvMERESlXuQkH5Z/AQEZGyY4zZbq1tW95xlCaNSRARERGRSmXZzmRmJOzDkZJOkL8XE3tFEBsVXN5hVSlKEkRERESk0li2M5nJS/aQnnUZgOSUdCYv2QOgROEG0pgEEREREak0ZiTscyUIudKzLjMjYV85RVQ1KUkQERERkUrDkZJ+TeVyfUo1STDG9DbG7DPGHDDGTCpke01jzCLn9s3GmKZ5tk12lu8zxvQqzThFREREpHII8ve6pnK5PqWWJBhjPID3gD5AS+BhY0zLfNUeA85aa1uQsxT16859W5KzLHUk0Bv4k7M9EREREbmJTewVgZen+22hl6cHE3tFlFNEVVNpPkloBxyw1h6y1mYCC4H78tW5D8hd1eJ/gN+YnLWp7wMWWmsvWmt/Ag442xMRERGRm1hsVDCvDWxNsL8XBgj29+K1ga01aPkGK83ZjYLJWSo611Hg7qLqWGsvGWNSgXrO8k359i3wv7wx5rfAbwGaNGlywwIXERERkYorNipYSUEpq9QDl621H1hr21pr2wYGBpZ3OCIiIiIiVUJpJgnJQGie7yHOskLrOJeg9gNOF3NfEREREREpBaWZJGwFfmWMaWaMqUHOQOTl+eosB0Y6Pz8AfG2ttc7yh5yzHzUDfgVsKcVYRURERETEqdTGJDjHGMQBCYAH8LG1dq8x5g/ANmvtcuCvwCfGmAPAGXISCZz1PgO+By4BT1lrLxd6IBERERERuaFKc+Ay1tpVwKp8ZS/l+ZwBPFjEvtOAaaUZn4iIiIiIFFSpBy6LiIiIiMiNpyRBRERERETcKEkQERERERE3ShJERERERMSNkgQREREREXGjJEFERERERNwoSRARERERETdKEkRERERExI2SBBERERERcaMkQURERERE3BhrbXnHcEMYY04CieV0+PrAqXI6tlQNuobkRtB1JCWla0hK6ma5hsKstYHlHURpqjJJQnkyxmyz1rYt7zik8tI1JDeCriMpKV1DUlK6hqoOvW4kIiIiIiJulCSIiIiIiIgbJQk3xgflHYBUerqG5EbQdSQlpWtISkrXUBWhMQkqh/iMAAAFeElEQVQiIiIiIuJGTxJERERERMSNkgQREREREXGjJOEKjDG9jTH7jDEHjDGTCtle0xizyLl9szGmaZ5tk53l+4wxvcoybqlYrvc6MsbUM8b80xhz3hjzblnHLRVHCa6hnsaY7caYPc7/9ijr2KXiKMF11M4Ys8v551tjzP1lHbtUDCW5L3Jub+L8N+25sopZrp+ShCIYYzyA94A+QEvgYWNMy3zVHgPOWmtbAP8FvO7ctyXwEBAJ9Ab+5GxPbjIluY6ADOBFQH+Z3sRKeA2dAvpba1sDI4FPyiZqqWhKeB19B7S11t5Bzr9p7xtjqpdN5FJRlPAayjUTWF3ascqNoSShaO2AA9baQ9baTGAhcF++OvcBf3N+/h/gN8YY4yxfaK29aK39CTjgbE9uPtd9HVlrf7HW/oucZEFuXiW5hnZaax3O8r2AlzGmZplELRVNSa6jC9baS87yWoBmPLk5leS+CGNMLPATOX8XSSWgJKFowcCRPN+POssKreP8CzQVqFfMfeXmUJLrSARu3DU0CNhhrb1YSnFKxVai68gYc7cxZi+wBxibJ2mQm8d1X0PGGB/gBeD3ZRCn3CBKEkREqjhjTCQ5j/2fKO9YpHKy1m621kYCdwGTjTG1yjsmqVReAf7LWnu+vAOR4lOSULRkIDTP9xBnWaF1nO9n+gGni7mv3BxKch2JQAmvIWNMCLAUGGGtPVjq0UpFdUP+LrLW/gCcB1qVWqRSUZXkGrobeMMYcxiYAPzOGBNX2gFLyShJKNpW4FfGmGbGmBrkDERenq/OcnIGAwI8AHxtc1anWw485Bzl3wz4FbCljOKWiqUk15EIlOAaMsb4A18Ck6y135RZxFIRleQ6apY7UNkYEwbcChwum7ClArnua8ha28Va29Ra2xR4G3jVWqtZ+yo4zU5QBGvtJWeWmwB4AB9ba/caY/4AbLPWLgf+CnxijDkAnCHn/zA4630GfA9cAp6y1l4ulxORclWS6wjA2etSB6jhHPQVY639vqzPQ8pPCa+hOKAF8JIx5iVnWYy19kTZnoWUtxJeR52BScaYLCAbeNJae6rsz0LKU0n/PZPKx6jDUkRERERE8tLrRiIiIiIi4kZJgoiIiIiIuFGSICIiIiIibpQkiIiIiIiIGyUJIiIiIiLiRkmCiMhNzhgz1hgzopDypsaY75yfexpjthtj9jj/26PsIxURkbKidRJERKoYY4whZ4rr7OLUt9b+pRjVTgH9rbUOY0wrcuZKDy5BmCIiUoHpSYKISBXg7PX/wRjzJ2AHMNwY8/+MMTuMMZ8bY3yc9aYbY743xuw2xrzpLHvFGPOc8/OdxphvjTH/D3gqt31r7U5rrcP5dS/gZYypWaYnKSIiZUZJgohI1REBzAV6Ao8B91hr2wDbgP80xtQD7gcirbW3AfGFtDEbGGet7XCF4wwCdlhrL97Q6EVEpMJQkiAiUnUkWms3Ae2BlsA3xphdwEggDEgFMoC/GmMGAhfy7myM8Qf8rbUbnEWf5D+AMSYSeB14otTOQkREyp3GJIiIVB2/OP9rgH9Yax/OX8EY0w74DfAQEAcUewCyMSYEWAqMsNYeLHm4IiJSUelJgohI1bMJ6GSMaQFgjPE2xtziHJfgZ61dBUwA7si7k7U2BUgxxnR2Fj2Su835lOFLYJK19puyOAkRESk/epIgIlLFWGtPGmNGAZ/mGVw8FUgDvjDG1CLnacMzhew+GvjYGHOBnBmMcsUBLYCXjDEvOctirLUnSuMcRESkfBlrbXnHICIiIiIiFYheNxIRERERETdKEkRERERExI2SBBERERERcaMkQURERERE3ChJEBERERERN0oSRERERETEjZIEERERERFx8/8BUL2fGW9xoSkAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from statsmodels.graphics import utils\n",
"fig, ax = plt.subplots(figsize=(12,8))\n",
"ax.plot(resid2[idx], hat_matrix_diag, 'o')\n",
"ax = utils.annotate_axes(range(nobs), labels=rob_crime_model.model.data.row_labels[idx], \n",
" points=lzip(resid2[idx], hat_matrix_diag), offset_points=[(-5,5)]*nobs,\n",
" size=\"large\", ax=ax)\n",
"ax.set_xlabel(\"resid2\")\n",
"ax.set_ylabel(\"leverage\")\n",
"ylim = ax.get_ylim()\n",
"ax.vlines(rm, *ylim)\n",
"xlim = ax.get_xlim()\n",
"ax.hlines(hm, *xlim)\n",
"ax.margins(0,0)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
}

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/srv/reproducible-results/rbuild-debian/tmp.FTzzuDJkiI/dbd-tmp-gqMLHgR/diffoscope_1mz05yxs/tmp8s62h56u/856879e8-30a0-408a-adaa-8705858c15a5 vs.

Similarity: 0.0%
Differences: {
"replace": {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Robust Linear Models"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
" from pandas.core import datetools\n"
]
}
],
"source": [
"%matplotlib inline\n",
"\n",
"from __future__ import print_function\n",
"import numpy as np\n",
"import statsmodels.api as sm\n",
"import matplotlib.pyplot as plt\n",
"from statsmodels.sandbox.regression.predstd import wls_prediction_std"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Estimation\n",
"\n",
"Load data:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/datasets/utils.py:100: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n",
" exog = np.column_stack(data[field] for field in exog_name)\n"
]
}
],
"source": [
"data = sm.datasets.stackloss.load()\n",
"data.exog = sm.add_constant(data.exog)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Huber's T norm with the (default) median absolute deviation scaling"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[-41.02649835 0.82938433 0.92606597 -0.12784672]\n",
"[9.79189854 0.11100521 0.30293016 0.12864961]\n",
" Robust linear Model Regression Results \n",
"==============================================================================\n",
"Dep. Variable: y No. Observations: 21\n",
"Model: RLM Df Residuals: 17\n",
"Method: IRLS Df Model: 3\n",
"Norm: HuberT \n",
"Scale Est.: mad \n",
"Cov Type: H1 \n",
"Date: Sat, 10 Apr 2021 \n",
"Time: 01:00:06 \n",
"No. Iterations: 19 \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"var_0 -41.0265 9.792 -4.190 0.000 -60.218 -21.835\n",
"var_1 0.8294 0.111 7.472 0.000 0.612 1.047\n",
"var_2 0.9261 0.303 3.057 0.002 0.332 1.520\n",
"var_3 -0.1278 0.129 -0.994 0.320 -0.380 0.124\n",
"==============================================================================\n",
"\n",
"If the model instance has been used for another fit with different fit\n",
"parameters, then the fit options might not be the correct ones anymore .\n"
]
}
],
"source": [
"huber_t = sm.RLM(data.endog, data.exog, M=sm.robust.norms.HuberT())\n",
"hub_results = huber_t.fit()\n",
"print(hub_results.params)\n",
"print(hub_results.bse)\n",
"print(hub_results.summary(yname='y',\n",
" xname=['var_%d' % i for i in range(len(hub_results.params))]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Huber's T norm with 'H2' covariance matrix"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[-41.02649835 0.82938433 0.92606597 -0.12784672]\n",
"[9.08950419 0.11945975 0.32235497 0.11796313]\n"
]
}
],
"source": [
"hub_results2 = huber_t.fit(cov=\"H2\")\n",
"print(hub_results2.params)\n",
"print(hub_results2.bse)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Andrew's Wave norm with Huber's Proposal 2 scaling and 'H3' covariance matrix"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Parameters: [-40.8817957 0.79276138 1.04857556 -0.13360865]\n"
]
}
],
"source": [
"andrew_mod = sm.RLM(data.endog, data.exog, M=sm.robust.norms.AndrewWave())\n",
"andrew_results = andrew_mod.fit(scale_est=sm.robust.scale.HuberScale(), cov=\"H3\")\n",
"print('Parameters: ', andrew_results.params)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"See ``help(sm.RLM.fit)`` for more options and ``module sm.robust.scale`` for scale options\n",
"\n",
"## Comparing OLS and RLM\n",
"\n",
"Artificial data with outliers:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"nsample = 50\n",
"x1 = np.linspace(0, 20, nsample)\n",
"X = np.column_stack((x1, (x1-5)**2))\n",
"X = sm.add_constant(X)\n",
"sig = 0.3 # smaller error variance makes OLS<->RLM contrast bigger\n",
"beta = [5, 0.5, -0.0]\n",
"y_true2 = np.dot(X, beta)\n",
"y2 = y_true2 + sig*1. * np.random.normal(size=nsample)\n",
"y2[[39,41,43,45,48]] -= 5 # add some outliers (10% of nsample)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Example 1: quadratic function with linear truth\n",
"\n",
"Note that the quadratic term in OLS regression will capture outlier effects. "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 5.00287224 0.52570133 -0.01318501]\n",
"[0.47236661 0.07292703 0.00645292]\n",
"[ 4.67324691 4.93943868 5.20123727 5.45864269 5.71165493 5.960274\n",
" 6.20449989 6.44433262 6.67977216 6.91081854 7.13747174 7.35973176\n",
" 7.57759861 7.79107229 8.0001528 8.20484013 8.40513428 8.60103526\n",
" 8.79254307 8.97965771 9.16237917 9.34070745 9.51464257 9.68418451\n",
" 9.84933327 10.01008886 10.16645128 10.31842052 10.46599659 10.60917949\n",
" 10.74796921 10.88236575 11.01236913 11.13797933 11.25919635 11.37602021\n",
" 11.48845088 11.59648839 11.70013272 11.79938387 11.89424186 11.98470666\n",
" 12.0707783 12.15245676 12.22974205 12.30263416 12.3711331 12.43523886\n",
" 12.49495145 12.55027087]\n"
]
}
],
"source": [
"res = sm.OLS(y2, X).fit()\n",
"print(res.params)\n",
"print(res.bse)\n",
"print(res.predict())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Estimate RLM:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 4.96337277e+00 4.99223077e-01 -7.74375914e-04]\n",
"[0.11741341 0.01812705 0.00160396]\n"
]
}
],
"source": [
"resrlm = sm.RLM(y2, X).fit()\n",
"print(resrlm.params)\n",
"print(resrlm.bse)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Draw a plot to compare OLS estimates to the robust estimates:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0xec8faeec>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12,8))\n",
"ax = fig.add_subplot(111)\n",
"ax.plot(x1, y2, 'o',label=\"data\")\n",
"ax.plot(x1, y_true2, 'b-', label=\"True\")\n",
"prstd, iv_l, iv_u = wls_prediction_std(res)\n",
"ax.plot(x1, res.fittedvalues, 'r-', label=\"OLS\")\n",
"ax.plot(x1, iv_u, 'r--')\n",
"ax.plot(x1, iv_l, 'r--')\n",
"ax.plot(x1, resrlm.fittedvalues, 'g.-', label=\"RLM\")\n",
"ax.legend(loc=\"best\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Example 2: linear function with linear truth\n",
"\n",
"Fit a new OLS model using only the linear term and the constant:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[5.534309 0.3938512]\n",
"[0.40714448 0.03508121]\n"
]
}
],
"source": [
"X2 = X[:,[0,1]] \n",
"res2 = sm.OLS(y2, X2).fit()\n",
"print(res2.params)\n",
"print(res2.bse)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Estimate RLM:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[4.98234176 0.49329108]\n",
"[0.09357591 0.00806288]\n"
]
}
],
"source": [
"resrlm2 = sm.RLM(y2, X2).fit()\n",
"print(resrlm2.params)\n",
"print(resrlm2.bse)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Draw a plot to compare OLS estimates to the robust estimates:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"prstd, iv_l, iv_u = wls_prediction_std(res2)\n",
"\n",
"fig, ax = plt.subplots(figsize=(8,6))\n",
"ax.plot(x1, y2, 'o', label=\"data\")\n",
"ax.plot(x1, y_true2, 'b-', label=\"True\")\n",
"ax.plot(x1, res2.fittedvalues, 'r-', label=\"OLS\")\n",
"ax.plot(x1, iv_u, 'r--')\n",
"ax.plot(x1, iv_l, 'r--')\n",
"ax.plot(x1, resrlm2.fittedvalues, 'g.-', label=\"RLM\")\n",
"legend = ax.legend(loc=\"best\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
}

⊟

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/srv/reproducible-results/rbuild-debian/tmp.FTzzuDJkiI/dbd-tmp-gqMLHgR/diffoscope_1mz05yxs/tmp3c2lipvy/6ad5ef7f-c002-474c-9eea-dcb4f50fa071 vs.

Similarity: 0.0%
Differences: {
"replace": {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# M-Estimators for Robust Linear Modeling"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
" from pandas.core import datetools\n"
]
}
],
"source": [
"%matplotlib inline\n",
"\n",
"from __future__ import print_function\n",
"from statsmodels.compat import lmap\n",
"import numpy as np\n",
"from scipy import stats\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import statsmodels.api as sm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* An M-estimator minimizes the function \n",
"\n",
"$$Q(e_i, \\rho) = \\sum_i~\\rho \\left (\\frac{e_i}{s}\\right )$$\n",
"\n",
"where $\\rho$ is a symmetric function of the residuals \n",
"\n",
"* The effect of $\\rho$ is to reduce the influence of outliers\n",
"* $s$ is an estimate of scale. \n",
"* The robust estimates $\\hat{\\beta}$ are computed by the iteratively re-weighted least squares algorithm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* We have several choices available for the weighting functions to be used"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"norms = sm.robust.norms"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def plot_weights(support, weights_func, xlabels, xticks):\n",
" fig = plt.figure(figsize=(12,8))\n",
" ax = fig.add_subplot(111)\n",
" ax.plot(support, weights_func(support))\n",
" ax.set_xticks(xticks)\n",
" ax.set_xticklabels(xlabels, fontsize=16)\n",
" ax.set_ylim(-.1, 1.1)\n",
" return ax"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Andrew's Wave"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on function weights in module statsmodels.robust.norms:\n",
"\n",
"weights(self, z)\n",
" Andrew's wave weighting function for the IRLS algorithm\n",
" \n",
" The psi function scaled by z\n",
" \n",
" Parameters\n",
" ----------\n",
" z : array-like\n",
" 1d array\n",
" \n",
" Returns\n",
" -------\n",
" weights : array\n",
" weights(z) = sin(z/a)/(z/a) for \\|z\\| <= a*pi\n",
" \n",
" weights(z) = 0 for \\|z\\| > a*pi\n",
"\n"
]
}
],
"source": [
"help(norms.AndrewWave.weights)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"a = 1.339\n",
"support = np.linspace(-np.pi*a, np.pi*a, 100)\n",
"andrew = norms.AndrewWave(a=a)\n",
"plot_weights(support, andrew.weights, ['$-\\pi*a$', '0', '$\\pi*a$'], [-np.pi*a, 0, np.pi*a]);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Hampel's 17A"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on function weights in module statsmodels.robust.norms:\n",
"\n",
"weights(self, z)\n",
" Hampel weighting function for the IRLS algorithm\n",
" \n",
" The psi function scaled by z\n",
" \n",
" Parameters\n",
" ----------\n",
" z : array-like\n",
" 1d array\n",
" \n",
" Returns\n",
" -------\n",
" weights : array\n",
" weights(z) = 1 for \\|z\\| <= a\n",
" \n",
" weights(z) = a/\\|z\\| for a < \\|z\\| <= b\n",
" \n",
" weights(z) = a*(c - \\|z\\|)/(\\|z\\|*(c-b)) for b < \\|z\\| <= c\n",
" \n",
" weights(z) = 0 for \\|z\\| > c\n",
"\n"
]
}
],
"source": [
"help(norms.Hampel.weights)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"c = 8\n",
"support = np.linspace(-3*c, 3*c, 1000)\n",
"hampel = norms.Hampel(a=2., b=4., c=c)\n",
"plot_weights(support, hampel.weights, ['3*c', '0', '3*c'], [-3*c, 0, 3*c]);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Huber's t"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on function weights in module statsmodels.robust.norms:\n",
"\n",
"weights(self, z)\n",
" Huber's t weighting function for the IRLS algorithm\n",
" \n",
" The psi function scaled by z\n",
" \n",
" Parameters\n",
" ----------\n",
" z : array-like\n",
" 1d array\n",
" \n",
" Returns\n",
" -------\n",
" weights : array\n",
" weights(z) = 1 for \\|z\\| <= t\n",
" \n",
" weights(z) = t/\\|z\\| for \\|z\\| > t\n",
"\n"
]
}
],
"source": [
"help(norms.HuberT.weights)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"t = 1.345\n",
"support = np.linspace(-3*t, 3*t, 1000)\n",
"huber = norms.HuberT(t=t)\n",
"plot_weights(support, huber.weights, ['-3*t', '0', '3*t'], [-3*t, 0, 3*t]);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Least Squares"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on function weights in module statsmodels.robust.norms:\n",
"\n",
"weights(self, z)\n",
" The least squares estimator weighting function for the IRLS algorithm.\n",
" \n",
" The psi function scaled by the input z\n",
" \n",
" Parameters\n",
" ----------\n",
" z : array-like\n",
" 1d array\n",
" \n",
" Returns\n",
" -------\n",
" weights : array\n",
" weights(z) = np.ones(z.shape)\n",
"\n"
]
}
],
"source": [
"help(norms.LeastSquares.weights)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"support = np.linspace(-3, 3, 1000)\n",
"lst_sq = norms.LeastSquares()\n",
"plot_weights(support, lst_sq.weights, ['-3', '0', '3'], [-3, 0, 3]);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Ramsay's Ea"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on function weights in module statsmodels.robust.norms:\n",
"\n",
"weights(self, z)\n",
" Ramsay's Ea weighting function for the IRLS algorithm\n",
" \n",
" The psi function scaled by z\n",
" \n",
" Parameters\n",
" ----------\n",
" z : array-like\n",
" 1d array\n",
" \n",
" Returns\n",
" -------\n",
" weights : array\n",
" weights(z) = exp(-a*\\|z\\|)\n",
"\n"
]
}
],
"source": [
"help(norms.RamsayE.weights)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"a = .3\n",
"support = np.linspace(-3*a, 3*a, 1000)\n",
"ramsay = norms.RamsayE(a=a)\n",
"plot_weights(support, ramsay.weights, ['-3*a', '0', '3*a'], [-3*a, 0, 3*a]);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Trimmed Mean"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on function weights in module statsmodels.robust.norms:\n",
"\n",
"weights(self, z)\n",
" Least trimmed mean weighting function for the IRLS algorithm\n",
" \n",
" The psi function scaled by z\n",
" \n",
" Parameters\n",
" ----------\n",
" z : array-like\n",
" 1d array\n",
" \n",
" Returns\n",
" -------\n",
" weights : array\n",
" weights(z) = 1 for \\|z\\| <= c\n",
" \n",
" weights(z) = 0 for \\|z\\| > c\n",
"\n"
]
}
],
"source": [
"help(norms.TrimmedMean.weights)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"c = 2\n",
"support = np.linspace(-3*c, 3*c, 1000)\n",
"trimmed = norms.TrimmedMean(c=c)\n",
"plot_weights(support, trimmed.weights, ['-3*c', '0', '3*c'], [-3*c, 0, 3*c]);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Tukey's Biweight"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on function weights in module statsmodels.robust.norms:\n",
"\n",
"weights(self, z)\n",
" Tukey's biweight weighting function for the IRLS algorithm\n",
" \n",
" The psi function scaled by z\n",
" \n",
" Parameters\n",
" ----------\n",
" z : array-like\n",
" 1d array\n",
" \n",
" Returns\n",
" -------\n",
" weights : array\n",
" psi(z) = (1 - (z/c)**2)**2 for \\|z\\| <= R\n",
" \n",
" psi(z) = 0 for \\|z\\| > R\n",
"\n"
]
}
],
"source": [
"help(norms.TukeyBiweight.weights)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"c = 4.685\n",
"support = np.linspace(-3*c, 3*c, 1000)\n",
"tukey = norms.TukeyBiweight(c=c)\n",
"plot_weights(support, tukey.weights, ['-3*c', '0', '3*c'], [-3*c, 0, 3*c]);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Scale Estimators"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Robust estimates of the location"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"x = np.array([1, 2, 3, 4, 500])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* The mean is not a robust estimator of location"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"102.0"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x.mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* The median, on the other hand, is a robust estimator with a breakdown point of 50%"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3.0"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.median(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Analagously for the scale\n",
"* The standard deviation is not robust"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"199.00251254695254"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x.std()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Median Absolute Deviation\n",
"\n",
"$$ median_i |X_i - median_j(X_j)|) $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Standardized Median Absolute Deviation is a consistent estimator for $\\hat{\\sigma}$\n",
"\n",
"$$\\hat{\\sigma}=K \\cdot MAD$$\n",
"\n",
"where $K$ depends on the distribution. For the normal distribution for example,\n",
"\n",
"$$K = \\Phi^{-1}(.75)$$"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.6744897501960818"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stats.norm.ppf(.75)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 1 2 3 4 500]\n"
]
}
],
"source": [
"print(x)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/robust/scale.py:49: FutureWarning: stand_mad is deprecated and will be removed in 0.7.0. Use mad instead.\n",
" \"instead.\", FutureWarning)\n"
]
},
{
"data": {
"text/plain": [
"1.4826022185056018"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sm.robust.scale.stand_mad(x)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1.4142135623730951"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.array([1,2,3,4,5.]).std()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* The default for Robust Linear Models is MAD\n",
"* another popular choice is Huber's proposal 2"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"np.random.seed(12345)\n",
"fat_tails = stats.t(6).rvs(40)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib/python3/dist-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
" return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"kde = sm.nonparametric.KDEUnivariate(fat_tails)\n",
"kde.fit()\n",
"fig = plt.figure(figsize=(12,8))\n",
"ax = fig.add_subplot(111)\n",
"ax.plot(kde.support, kde.density);"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.06882310448108744 1.3471633229698654\n"
]
}
],
"source": [
"print(fat_tails.mean(), fat_tails.std())"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(0.06882310448108744, 1.3471633229698654)\n"
]
}
],
"source": [
"print(stats.norm.fit(fat_tails))"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(6, 0.039009187170278126, 1.0564230978488927)\n"
]
}
],
"source": [
"print(stats.t.fit(fat_tails, f0=6))"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.04048984333271796 1.1557140047569667\n"
]
}
],
"source": [
"huber = sm.robust.scale.Huber()\n",
"loc, scale = huber(fat_tails)\n",
"print(loc, scale)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/robust/scale.py:49: FutureWarning: stand_mad is deprecated and will be removed in 0.7.0. Use mad instead.\n",
" \"instead.\", FutureWarning)\n"
]
},
{
"data": {
"text/plain": [
"1.115335001165415"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sm.robust.stand_mad(fat_tails)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/robust/scale.py:49: FutureWarning: stand_mad is deprecated and will be removed in 0.7.0. Use mad instead.\n",
" \"instead.\", FutureWarning)\n"
]
},
{
"data": {
"text/plain": [
"1.0483916565928977"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sm.robust.stand_mad(fat_tails, c=stats.t(6).ppf(.75))"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1.115335001165415"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sm.robust.scale.mad(fat_tails)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Duncan's Occupational Prestige data - M-estimation for outliers"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from statsmodels.graphics.api import abline_plot\n",
"from statsmodels.formula.api import ols, rlm"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"prestige = sm.datasets.get_rdataset(\"Duncan\", \"car\", cache=True).data"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" type income education prestige\n",
"accountant prof 62 86 82\n",
"pilot prof 72 76 83\n",
"architect prof 75 92 90\n",
"author prof 55 90 76\n",
"chemist prof 64 86 90\n",
"minister prof 21 84 87\n",
"professor prof 64 93 93\n",
"dentist prof 80 100 90\n",
"reporter wc 67 87 52\n",
"engineer prof 72 86 88\n"
]
}
],
"source": [
"print(prestige.head(10))"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib/python3/dist-packages/ipykernel_launcher.py:4: DeprecationWarning: \n",
".ix is deprecated. Please use\n",
".loc for label based indexing or\n",
".iloc for positional indexing\n",
"\n",
"See the documentation here:\n",
"http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
" after removing the cwd from sys.path.\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x864 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12,12))\n",
"ax1 = fig.add_subplot(211, xlabel='Income', ylabel='Prestige')\n",
"ax1.scatter(prestige.income, prestige.prestige)\n",
"xy_outlier = prestige.ix['minister'][['income','prestige']]\n",
"ax1.annotate('Minister', xy_outlier, xy_outlier+1, fontsize=16)\n",
"ax2 = fig.add_subplot(212, xlabel='Education',\n",
" ylabel='Prestige')\n",
"ax2.scatter(prestige.education, prestige.prestige);"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: prestige R-squared: 0.828\n",
"Model: OLS Adj. R-squared: 0.820\n",
"Method: Least Squares F-statistic: 101.2\n",
"Date: Sat, 10 Apr 2021 Prob (F-statistic): 8.65e-17\n",
"Time: 01:00:11 Log-Likelihood: -178.98\n",
"No. Observations: 45 AIC: 364.0\n",
"Df Residuals: 42 BIC: 369.4\n",
"Df Model: 2 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"Intercept -6.0647 4.272 -1.420 0.163 -14.686 2.556\n",
"income 0.5987 0.120 5.003 0.000 0.357 0.840\n",
"education 0.5458 0.098 5.555 0.000 0.348 0.744\n",
"==============================================================================\n",
"Omnibus: 1.279 Durbin-Watson: 1.458\n",
"Prob(Omnibus): 0.528 Jarque-Bera (JB): 0.520\n",
"Skew: 0.155 Prob(JB): 0.771\n",
"Kurtosis: 3.426 Cond. No. 163.\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
]
}
],
"source": [
"ols_model = ols('prestige ~ income + education', prestige).fit()\n",
"print(ols_model.summary())"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"accountant 0.303900\n",
"pilot 0.340920\n",
"architect 0.072256\n",
"author 0.000711\n",
"chemist 0.826578\n",
"minister 3.134519\n",
"professor 0.768277\n",
"dentist -0.498082\n",
"reporter -2.397022\n",
"engineer 0.306225\n",
"undertaker -0.187339\n",
"lawyer -0.303082\n",
"physician 0.355687\n",
"welfare.worker -0.411406\n",
"teacher 0.050510\n",
"conductor -1.704032\n",
"contractor 2.043805\n",
"factory.owner 1.602429\n",
"store.manager 0.142425\n",
"banker 0.508388\n",
"bookkeeper -0.902388\n",
"mail.carrier -1.433249\n",
"insurance.agent -1.930919\n",
"store.clerk -1.760491\n",
"carpenter 1.068858\n",
"electrician 0.731949\n",
"RR.engineer 0.808922\n",
"machinist 1.887047\n",
"auto.repairman 0.522735\n",
"plumber -0.377954\n",
"gas.stn.attendant -0.666596\n",
"coal.miner 1.018527\n",
"streetcar.motorman -1.104485\n",
"taxi.driver 0.023322\n",
"truck.driver -0.129227\n",
"machine.operator 0.499922\n",
"barber 0.173805\n",
"bartender -0.902422\n",
"shoe.shiner -0.429357\n",
"cook 0.127207\n",
"soda.clerk -0.883095\n",
"watchman -0.513502\n",
"janitor -0.079890\n",
"policeman 0.078847\n",
"waiter -0.475972\n",
"Name: student_resid, dtype: float64\n"
]
}
],
"source": [
"infl = ols_model.get_influence()\n",
"student = infl.summary_frame()['student_resid']\n",
"print(student)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"minister 3.134519\n",
"reporter -2.397022\n",
"contractor 2.043805\n",
"Name: student_resid, dtype: float64\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib/python3/dist-packages/ipykernel_launcher.py:1: DeprecationWarning: \n",
".ix is deprecated. Please use\n",
".loc for label based indexing or\n",
".iloc for positional indexing\n",
"\n",
"See the documentation here:\n",
"http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
" \"\"\"Entry point for launching an IPython kernel.\n"
]
}
],
"source": [
"print(student.ix[np.abs(student) > 2])"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"dfb_Intercept 0.144937\n",
"dfb_income -1.220939\n",
"dfb_education 1.263019\n",
"cooks_d 0.566380\n",
"standard_resid 2.849416\n",
"hat_diag 0.173058\n",
"dffits_internal 1.303510\n",
"student_resid 3.134519\n",
"dffits 1.433935\n",
"Name: minister, dtype: float64\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib/python3/dist-packages/ipykernel_launcher.py:1: DeprecationWarning: \n",
".ix is deprecated. Please use\n",
".loc for label based indexing or\n",
".iloc for positional indexing\n",
"\n",
"See the documentation here:\n",
"http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
" \"\"\"Entry point for launching an IPython kernel.\n"
]
}
],
"source": [
"print(infl.summary_frame().ix['minister'])"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" student_resid unadj_p sidak(p)\n",
"minister 3.134519 0.003177 0.133421\n",
"reporter -2.397022 0.021170 0.618213\n",
"contractor 2.043805 0.047433 0.887721\n",
"insurance.agent -1.930919 0.060428 0.939485\n",
"machinist 1.887047 0.066248 0.954247\n",
"store.clerk -1.760491 0.085783 0.982331\n",
"conductor -1.704032 0.095944 0.989315\n",
"factory.owner 1.602429 0.116738 0.996250\n",
"mail.carrier -1.433249 0.159369 0.999595\n",
"streetcar.motorman -1.104485 0.275823 1.000000\n",
"carpenter 1.068858 0.291386 1.000000\n",
"coal.miner 1.018527 0.314400 1.000000\n",
"bartender -0.902422 0.372104 1.000000\n",
"bookkeeper -0.902388 0.372122 1.000000\n",
"soda.clerk -0.883095 0.382334 1.000000\n",
"chemist 0.826578 0.413261 1.000000\n",
"RR.engineer 0.808922 0.423229 1.000000\n",
"professor 0.768277 0.446725 1.000000\n",
"electrician 0.731949 0.468363 1.000000\n",
"gas.stn.attendant -0.666596 0.508764 1.000000\n",
"auto.repairman 0.522735 0.603972 1.000000\n",
"watchman -0.513502 0.610357 1.000000\n",
"banker 0.508388 0.613906 1.000000\n",
"machine.operator 0.499922 0.619802 1.000000\n",
"dentist -0.498082 0.621088 1.000000\n",
"waiter -0.475972 0.636621 1.000000\n",
"shoe.shiner -0.429357 0.669912 1.000000\n",
"welfare.worker -0.411406 0.682918 1.000000\n",
"plumber -0.377954 0.707414 1.000000\n",
"physician 0.355687 0.723898 1.000000\n",
"pilot 0.340920 0.734905 1.000000\n",
"engineer 0.306225 0.760983 1.000000\n",
"accountant 0.303900 0.762741 1.000000\n",
"lawyer -0.303082 0.763360 1.000000\n",
"undertaker -0.187339 0.852319 1.000000\n",
"barber 0.173805 0.862874 1.000000\n",
"store.manager 0.142425 0.887442 1.000000\n",
"truck.driver -0.129227 0.897810 1.000000\n",
"cook 0.127207 0.899399 1.000000\n",
"janitor -0.079890 0.936713 1.000000\n",
"policeman 0.078847 0.937538 1.000000\n",
"architect 0.072256 0.942750 1.000000\n",
"teacher 0.050510 0.959961 1.000000\n",
"taxi.driver 0.023322 0.981507 1.000000\n",
"author 0.000711 0.999436 1.000000\n"
]
}
],
"source": [
"sidak = ols_model.outlier_test('sidak')\n",
"sidak.sort_values('unadj_p', inplace=True)\n",
"print(sidak)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" student_resid unadj_p fdr_bh(p)\n",
"minister 3.134519 0.003177 0.142974\n",
"reporter -2.397022 0.021170 0.476332\n",
"contractor 2.043805 0.047433 0.596233\n",
"insurance.agent -1.930919 0.060428 0.596233\n",
"machinist 1.887047 0.066248 0.596233\n",
"store.clerk -1.760491 0.085783 0.616782\n",
"conductor -1.704032 0.095944 0.616782\n",
"factory.owner 1.602429 0.116738 0.656653\n",
"mail.carrier -1.433249 0.159369 0.796844\n",
"streetcar.motorman -1.104485 0.275823 0.999436\n",
"carpenter 1.068858 0.291386 0.999436\n",
"coal.miner 1.018527 0.314400 0.999436\n",
"bartender -0.902422 0.372104 0.999436\n",
"bookkeeper -0.902388 0.372122 0.999436\n",
"soda.clerk -0.883095 0.382334 0.999436\n",
"chemist 0.826578 0.413261 0.999436\n",
"RR.engineer 0.808922 0.423229 0.999436\n",
"professor 0.768277 0.446725 0.999436\n",
"electrician 0.731949 0.468363 0.999436\n",
"gas.stn.attendant -0.666596 0.508764 0.999436\n",
"auto.repairman 0.522735 0.603972 0.999436\n",
"watchman -0.513502 0.610357 0.999436\n",
"banker 0.508388 0.613906 0.999436\n",
"machine.operator 0.499922 0.619802 0.999436\n",
"dentist -0.498082 0.621088 0.999436\n",
"waiter -0.475972 0.636621 0.999436\n",
"shoe.shiner -0.429357 0.669912 0.999436\n",
"welfare.worker -0.411406 0.682918 0.999436\n",
"plumber -0.377954 0.707414 0.999436\n",
"physician 0.355687 0.723898 0.999436\n",
"pilot 0.340920 0.734905 0.999436\n",
"engineer 0.306225 0.760983 0.999436\n",
"accountant 0.303900 0.762741 0.999436\n",
"lawyer -0.303082 0.763360 0.999436\n",
"undertaker -0.187339 0.852319 0.999436\n",
"barber 0.173805 0.862874 0.999436\n",
"store.manager 0.142425 0.887442 0.999436\n",
"truck.driver -0.129227 0.897810 0.999436\n",
"cook 0.127207 0.899399 0.999436\n",
"janitor -0.079890 0.936713 0.999436\n",
"policeman 0.078847 0.937538 0.999436\n",
"architect 0.072256 0.942750 0.999436\n",
"teacher 0.050510 0.959961 0.999436\n",
"taxi.driver 0.023322 0.981507 0.999436\n",
"author 0.000711 0.999436 0.999436\n"
]
}
],
"source": [
"fdr = ols_model.outlier_test('fdr_bh')\n",
"fdr.sort_values('unadj_p', inplace=True)\n",
"print(fdr)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Robust linear Model Regression Results \n",
"==============================================================================\n",
"Dep. Variable: prestige No. Observations: 45\n",
"Model: RLM Df Residuals: 42\n",
"Method: IRLS Df Model: 2\n",
"Norm: HuberT \n",
"Scale Est.: mad \n",
"Cov Type: H1 \n",
"Date: Sat, 10 Apr 2021 \n",
"Time: 01:00:11 \n",
"No. Iterations: 18 \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"Intercept -7.1107 3.879 -1.833 0.067 -14.713 0.492\n",
"income 0.7015 0.109 6.456 0.000 0.489 0.914\n",
"education 0.4854 0.089 5.441 0.000 0.311 0.660\n",
"==============================================================================\n",
"\n",
"If the model instance has been used for another fit with different fit\n",
"parameters, then the fit options might not be the correct ones anymore .\n"
]
}
],
"source": [
"rlm_model = rlm('prestige ~ income + education', prestige).fit()\n",
"print(rlm_model.summary())"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"accountant 1.000000\n",
"pilot 1.000000\n",
"architect 1.000000\n",
"author 1.000000\n",
"chemist 1.000000\n",
"minister 0.344596\n",
"professor 1.000000\n",
"dentist 1.000000\n",
"reporter 0.441669\n",
"engineer 1.000000\n",
"undertaker 1.000000\n",
"lawyer 1.000000\n",
"physician 1.000000\n",
"welfare.worker 1.000000\n",
"teacher 1.000000\n",
"conductor 0.538445\n",
"contractor 0.552262\n",
"factory.owner 0.706169\n",
"store.manager 1.000000\n",
"banker 1.000000\n",
"bookkeeper 1.000000\n",
"mail.carrier 0.690764\n",
"insurance.agent 0.533499\n",
"store.clerk 0.618656\n",
"carpenter 0.935848\n",
"electrician 1.000000\n",
"RR.engineer 1.000000\n",
"machinist 0.570360\n",
"auto.repairman 1.000000\n",
"plumber 1.000000\n",
"gas.stn.attendant 1.000000\n",
"coal.miner 0.963821\n",
"streetcar.motorman 0.832870\n",
"taxi.driver 1.000000\n",
"truck.driver 1.000000\n",
"machine.operator 1.000000\n",
"barber 1.000000\n",
"bartender 1.000000\n",
"shoe.shiner 1.000000\n",
"cook 1.000000\n",
"soda.clerk 1.000000\n",
"watchman 1.000000\n",
"janitor 1.000000\n",
"policeman 1.000000\n",
"waiter 1.000000\n",
"dtype: float64\n"
]
}
],
"source": [
"print(rlm_model.weights)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Hertzprung Russell data for Star Cluster CYG 0B1 - Leverage Points"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Data is on the luminosity and temperature of 47 stars in the direction of Cygnus."
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dta = sm.datasets.get_rdataset(\"starsCYG\", \"robustbase\", cache=True).data"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib/python3/dist-packages/ipykernel_launcher.py:15: DeprecationWarning: \n",
".ix is deprecated. Please use\n",
".loc for label based indexing or\n",
".iloc for positional indexing\n",
"\n",
"See the documentation here:\n",
"http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
" from ipykernel import kernelapp as app\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from matplotlib.patches import Ellipse\n",
"fig = plt.figure(figsize=(12,8))\n",
"ax = fig.add_subplot(111, xlabel='log(Temp)', ylabel='log(Light)', title='Hertzsprung-Russell Diagram of Star Cluster CYG OB1')\n",
"ax.scatter(*dta.values.T)\n",
"# highlight outliers\n",
"e = Ellipse((3.5, 6), .2, 1, alpha=.25, color='r')\n",
"ax.add_patch(e);\n",
"ax.annotate('Red giants', xy=(3.6, 6), xytext=(3.8, 6),\n",
" arrowprops=dict(facecolor='black', shrink=0.05, width=2),\n",
" horizontalalignment='left', verticalalignment='bottom',\n",
" clip_on=True, # clip to the axes bounding box\n",
" fontsize=16,\n",
" )\n",
"# annotate these with their index\n",
"for i,row in dta.ix[dta['log.Te'] < 3.8].iterrows():\n",
" ax.annotate(i, row, row + .01, fontsize=14)\n",
"xlim, ylim = ax.get_xlim(), ax.get_ylim()"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import Image\n",
"import os.path\n",
"Image(filename='star_diagram.png' if os.path.exists('star_diagram.png') else '../examples/notebooks/star_diagram.png')"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y = dta['log.light']\n",
"X = sm.add_constant(dta['log.Te'], prepend=True)\n",
"ols_model = sm.OLS(y, X).fit()\n",
"abline_plot(model_results=ols_model, ax=ax)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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A69fD1q3RjkRERESkS+paCXVdHSxbBmlpkJoa7WhiQ2Ii9OnjPmRUVEQ7GhEREZEup2sl1OvWuRHqrKxoRxJb0tIgJcVN0lQ9tYiIiEiH6joJ9a5d8PHHqptuSXY2lJbCZ59FOxIRERGRLiXu+lA3q64Oli937fESYu8zwiOvvMITr77K5h07ADhm2DBuuOgizpw8GYAbn3qKF5csobikhJSkJI4bOZJff+97fGX06I4NpH9/eO89GDjQLfwiIiIiIu0We9nnofj4Y6iqgh49oh1Js4b068fdl13Ge/Pns+rxxzn52GOZfeONrPv8cwDyc3N55Gc/Y/3vf8+y3/6W4QMHctovf8nOsrKODSQpyZV/rF7tVlQUERERkXYzNs4Sq4KCAhuwUuKePfDPf7puFnHU1SPnrLOY9/3vc/lZZzXZt2//frK++lVeu/tuZk2Y0PEX37oVTjgB8vI6/r5FREREughjzGprbUGo4+J7hNpaN9qakRE3ybTH42HBokVUVlc3W9JRW1fH/FdfpVePHowfMSIyQfTpAytXQk1NZO5fREREpBuJ7xrqbdtgxw4YPDjakYS0ftMmJv/4xxyoraVnejqv3HYbYw4/vGH/q++8wwW33UZVTQ0D+/Th/+65hwE5OZEJJi0N9u6FDRtg7NjIXENERESkm4jfko/6evjXv8AYN0Id42rr6ijatYu9lZW8tHQpv3v1VZY88ACjhw8HYH91NdvLyijdu5ffvfoq/33vPd555BEG9ukTmYA8HigpgbPOiovnT0RERKSzdf2Sjy1b3ChrnCSDKcnJjBg8mOPz85n3gx8wfsQI7n/xxYb9PdLTGTF4MJOOPprf/+IXJCcl8eQ//hG5gBIT3W3DhshdQ0RERKQbiM+E2uOBNWtcm7w4VW8tNXV1h7y/Q+TkwCefQGVlZK8jIiIi0oXFZw11UZFrkxcnKyJeO38+Z06aRG7//lRUVfGn//6XJWvX8o9589i3fz+/WbCA/5k8mYF9+lCyZw+PLFzIlpISzp82LbKBJSa6VnoffwyFhZG9loiIiEgXFX8JtbWwdq0bXY0ByzaWsGBlMaWVNfTtmcoFhblMHdkv4JgdZWVcdOed7CgrI6tHD8Yefjj/uusuZk2YQNWBA3y4eTNP/etf7N63jz69elGYn8/SBx5g7BFHRP4B9OnjVk/Mz9diLyIiIiKHIP4mJY4ZY1ddf71b7S/Klm0sYf7SL6j1eBq2pSQmctmJw5sk1TFt924YMgQmTox2JCIiIiIxo+tOSqyujpna6QUriwOSaYBaj4cFK4ujFNEh6t0bNm1yZTQiIiIi0ibxl1DX17s+yjGgtLL5hVFa2h6zEhLcrago2pGIiIiIxJ34S6gTYifkvj1T27Q9pvXu7SYnBo24i4iIiEjrYic7DVcMLTF+QWEuKUHxpCQmckFhbpQiaoeUFFdOs3NntCMRERERiSvx1+UjhvgmHobq8hE3MjPhww9h0KBoRyIiIiISN5RQt9PUkf3iN4EOlpkJ27bBnj2QnR3taERERETiQvyVfEhkJSfDF19EOwoRERGRuKGEWgL5WuhpcqKIiIhIWJRQS6CkJKithbKyaEciIiIiEheUUEtTKSnqSS0iIiISJiXU0lRWlqujVtmHiIiISEhKqKWppCSoq4Pdu6MdiYiIiEjMU0ItzUtNhS+/jHYUIiIiIjFPCbU0LysLNm9W2YeIiIhICEqopXmJiXDwIOzdG+1IRERERGKaEmppmTFQWhrtKERERERimhJqaVnPnlBcHO0oRERERGKaEmppWUYGlJS4jh8iIiIi0iwl1NKyBO/bY8+e6MYhIiIiEsOUUEvrEhJg165oRyEiIiISs5RQS+syM1VHLSIiItIKJdTSurQ0KC+H2tpoRyIiIiISk5RQS+uMcbfKymhHIiIiIhKTlFBLaNYqoRYRERFpgRJqCS011bXPExEREZEmlFBLaOnp6vQhIiIi0gIl1BJaWhrs2wcHD0Y7EhEREZGYo4RaQjPG1VHv3x/tSERERERijhJqCY+1UFER7ShEREREYo4SaglPUpIr+xARERGRAEnRDqArmff887z85ptsKC4mNTmZSUcfzbwf/IDRw4c3HLOzrIxfzp/Pv1etYk9lJSeOHctDP/0pI4cMiWLkYUhJUUItIiIi0gyNUHegJWvXcsXZZ/P2ww+z6L77SEpMZMbVV1PmTUSttcy+8UY2btnCwl//mjXz5zN0wABmXHMN+6uroxx9CCkpsHdvtKMQERERiTlKqDvQ6/fcwyWnn87o4cMZc/jhPHf99ZTs3ctbH3wAwMYtW1j+0Uc8euWVTDjqKPLz8njsqquorqnhhUWLohx9CCkpqqEWERERaYYS6giqqKqivr6e3pmZANTU1QGQlpLScExCQgKpycksW78+KjGGLTHRtc2rrY12JCIiIiIxRQl1BP3soYcYP2IEk48+GoBReXnkDRjA9U8+Sdm+fdTW1XH3Cy+wpaSE7bt3RznaMBgDBw5EOwoRERGRmKKEOkJ+/sgjLPvgA/56660kJiYCkJyUxMu33srn27bR5+yzyTjtNBavWcPpEyeSkBAHL4W1SqjjzNNPP40xpuGWkpLCEUccwfXXX8+BDn4tp02bxrRp0zrs/oYNG8bFF1/cYfcXbO3atdxyyy2UlZVF7BoiItI9qMtHBFz1yCMsWLSIxfffz+GDBgXsOz4/n7VPPsneykpqDx6kX3Y2E3/0Iwry86MUbRspoY5LL774IkOGDKGiooJXXnmFefPmUVFRwUMPPRTt0Fr0yiuv0KtXr4jd/9q1a7n11lu56KKLyMnJidh1RESk61NC3cF+9tBD/HnxYhbffz+j8vJaPC6rZ0/ATVRc9emn/Pp73+usEA9dQgJUVUU7CjkE48ePZ8SIEQCceuqpbNy4kaeeeooHH3wwZr8dOfbYY6MdgoiISFhi8y9pjFq2sYQ5f3qPC+a/w5w/vceyjSUB+3/8wAP84bXX+NMNN9A7M5MdZWXsKCuj0q8l3otLlrB4zRo2bdvG35Yt49RrrmH2lCnMLCzs7IfTdomJUFMT7SikAxx33HFUVVVRWloasP2LL77gW9/6Fv369SM1NZXx48fzyiuvNDl/wYIFjBo1itTUVI455phmj2nJpk2bOOOMM8jIyKB///5cffXVzJ8/H2MMmzdvbjguuOSjpKSEyy+/nCOPPJKMjAxyc3O58MIL2bp1a8D933LLLRhj2LhxI2eeeSY9e/Zk6NCh3HbbbdTX1wOuFOaSSy4BYOTIkQ0lMb7rP/jggxx11FGkp6fTu3dvCgoK2vQYRUSke9EIdZiWbSxh/tIvqPV4ACitrGH+0i8AmDqyHwCP/u1vAJxy9dUB59783e9yizcx2L57Nz9/9FF2lpczsE8fvjNzJjd++9ud9CjaSQl1l7F582aysrLo06dPw7bi4mImTpxI//79uf/+++nXrx9//vOf+drXvsbChQs566yzAPjPf/7DhRdeyJlnnsm9995LSUkJP/vZz6irqyM/ROlSbW0tp556KjU1NTz22GP069ePJ598kpdeeilkzGVlZaSlpTFv3jz69evHtm3buPfee5kyZQqffPIJaWlpAcefc845XHLJJVx11VX8/e9/5+abbyY3N5dLLrmEM888kxtuuIHbb7+9oRwGYODAgTz//PNcffXV3HTTTZxwwglUV1ezbt061VqLiEiLlFCHacHK4oZk2qfW42HByuKGhNouXhzyfn76ta/x0699LSIxRpwS6rjl8Xg4ePBgQw31X//6Vx544IGGCbPgRnattbzxxhsNifasWbMoLi7mpptuakiob775ZkaNGsXf/va3hnKRUaNGMXny5JAJ9dNPP82mTZtYsWIFEyZMAOD0009n/PjxFBUVtXpufn4+Dz74YMBjmjJlCnl5efzrX//inHPOCTj+6quvbhiFnjFjBosWLeKFF17gkksuoV+/fhxxxBFAYDkMwDvvvMPYsWO56aabGradccYZrcYmIiLdm0o+wlRa2Xwi2dL2LikpSQl1nBo1ahTJycnk5ORw6aWXcvnllzNnzpyAY1577TXOOOMMsrKyOHjwYMNt1qxZvP/+++zbtw+Px8PKlSs577zzAmqvJ02axLBhw0LGsXz5cvLy8hqSaQBjDF8L80PmY489xrhx4+jZsydJSUnkeecpbNiwocmxZ555ZsDvo0ePDpm0AxQWFrJ27Vp+8pOf8J///IcqzRsQEZEQlFCHqW/P1DZt75I6aIS6srKSgwcPdkBAEq5XXnmFlStX8s9//pMZM2bw6KOP8uyzzwYcs2vXLp599lmSk5MDbnPnzgVg9+7dlJaWUldXx4ABA5pco7ltwbZv307//v0P6dyHHnqIK664ghkzZvDyyy/z7rvvsnz5coBmWwAGd+5ITU0Nq1Xgd77zHR577DFWrFjBrFmzyMnJ4dxzzw2o7xYREfGnhDpMFxTmkuL39ThASmIiFxTmRimiKGhnQl1aWsrVV19N3759+ctf/tKBgUkoo0ePpqCggNNPP51XX32VI488krlz57J///6GY/r06cN5553HypUrm70NGjSIvn37kpyczM6dO5tco7ltwQYOHMiuXbsO6dwFCxZwyimncO+99zJz5kwKCwubTc7byxjD5ZdfzrvvvktpaSnPPPMM7777Lt/4xjc6/FoiItI1KKEO09SR/bjsxOENI9J9e6Zy2YnDG+qnu4WEBLf0uLVtOs2XSA8dOpRHHnkEY0yHLyoi4UtNTeWee+5h165dPProow3bTzvtNNatW8cxxxxDQUFBk1tqaiqJiYkUFhby0ksvNXTMAFixYkVYI7iTJk2iqKiId999t2GbtZa//vWvIc+tqqoiOTk5YNsf/vCHMB5x81JT3b/lar8uPMF69+7NN77xDc4//3w++OCDQ76WiIh0bZqU2AZTR/brXgl0MGPcf+vr3Wh1CKWlpdx111089thjeDwearyj2z169IhklBKGs846i8LCQu69917mzJlDeno6t912GxMmTODEE09kzpw5DBs2jPLycj744AM2bdrEU089BcCtt97KzJkzmT17NpdffjklJSXcfPPNHHbYYSGve/HFF3P33Xdz7rnncscddzR0+SgvLwdotSf2aaedxt13382dd97JhAkTWLRoUVjdQVpy9NFHA/DII4/w3e9+l+TkZMaOHcucOXPIzMxk8uTJ9O/fn08//ZTnnnuOmTNnHvK1RESka4voCLUxJtsY85Ix5hNjzMfGmMlB+40x5rfGmM+MMeuMMcdFMh7pANaGHKEuLS3lmmuuYejQoTz88MNUVVU1JNMSO26//XZ27tzJ448/DkBeXh6rVq1i3LhxXH/99Zx66qn86Ec/4o033uDkk09uOG/GjBk8//zzbNiwgXPPPZd77rmHBx54IGSHD4CUlBT+/e9/M3bsWH74wx/y3e9+l9zcXH784x8DkJWV1eK5N910E5dffjn3338/55xzDuvWreP1118/5Mc/btw4brnlFv7+978zdepUCgsL2bZtG1OmTGH16tVcccUVnHrqqdxxxx1cdNFFPPPMM4d8LRER6dqMbePX9226c2OeAd601j5pjEkBMqy1e/z2nwH8BDgDmAg8aK2d2Np9FuTn21VPPBGxmCWEHTvgvPNcx48gpaWlzJs3j8cffzxgRDpYjx49uO6668Lu7CDN69+/f5dZMvurX/0qH3/8MZ9//nm0QxEREWlgjFltrS0IdVzESj6MMVnAicDFANbaWqA26LCzgWety+qXe0e0B1prt0cqLukAQR/CysrKuOOOO0Im0j41NTXcfffd3H333ZGMskvzeDyMHz+et956K9qhtNl9991Hz549GTlyJBUVFbz44ov84x//4LHHHot2aCIiIockkjXUw4ES4A/GmHHAauBn1tr9fscMBor9ft/i3RaQUBtjLgMuA8gLo72WRFhQQv2nP/2J++67L+zTfQuMSPv4d+iIJ6mpqdx///0UFRXh8XjIz8/nySef5NJLL412aCIiIockkgl1EnAc8BNr7QpjzIPAtcCNbb0ja+18YD64ko8OjVLaxtrGyYlec+bMobCwkLlz57J69eqQC2GkpqaSlpamyYkdzYSrAAAgAElEQVTt5L84Sjz58Y9/3FAzLSIi0hVEMqHeAmyx1q7w/v4SLqH2txXwb+Q8xLtNYpUxTRJqgIkTJ7J06VJWrFgRMrFOSkrivvvu43vf+16koxURERGJuIh1+bDW7gCKjTG+qf+nAB8FHfb/gO94u31MAvaqfjoONJNQ+/gS60WLFjF16lQyMjI6MTARERGRzhfphV1+AjxvjFkHjAfuNMb80BjzQ+/+fwKbgM+A3wFXRDge6SQTJ07kzTffZNGiRZxwwglKrEVERKTLiujCLtbatUBwq5HH/fZbQMWU8aK+3o1Oh7Goi09zpSC1tcHNXkRERETil5Yel/B5POBdrrmtfIn14sWLOfnkkzniiCM6ODgRERGR6FBC3QmWvv8+Z/3qVwz++tcx06fz9GuvBezfWVbGxXfdxaDzziPjtNM47Re/YOOWLVGKthXtSKh9JkyYwGuvvcZJJ53UQUGJiIiIRJcS6k5QWV3N6GHDeHDOHNKDElJrLbNvvJGNW7aw8Ne/Zs38+QwdMIAZ11zD/urqKEXcAo8HUlKiHYWIiIhITFFC3QnOmDSJO3/wA8476SQSgjpkbNyyheUffcSjV17JhKOOIj8vj8euuorqmhpeWLQoShG3oANGqEVERES6GiXUUVZTVwdAmt/Ib0JCAqnJySxbvz5aYTVPCbWIiIhIE0qoo2xUXh55AwZw/ZNPUrZvH7V1ddz9wgtsKSlh++7d0Q4vkMcDaWnRjkJEREQkpiihjrLkpCRevvVWPt+2jT5nn03GaaexeM0aTp84kYSEGHt5lFCLiIiINBHRPtQSnuPz81n75JPsrayk9uBB+mVnM/FHP6IgPz/0yZ3JWkhPj3YUIiIiIjElxoZAu7esnj3pl53Nxi1bWPXpp5w9ZUq0QwpkjBJqERERkSAaoe4EldXVfLZ1KwD11lK0cydrP/uMnMxM8gYM4MUlS+iblcXQAQNYv2kTP3v4YWZPmcLMwsIoRx7EWpV8iIiIiARRQt1OyzaWsGBlMaWVNfTtmcoFhblMHdkv4JhVGzYw/aqrGn6/+emnufnpp/nurFk8fe21bN+9m58/+ig7y8sZ2KcP35k5kxu//e3OfiihKaEWERERacJYa6MdQ5sU5OfbVU88Ee0wAJdMz1/6BbUeT8O2lMRELjtxeJOkOu7V1cH+/TB7drQjEREREekUxpjV1tqCUMephrodFqwsDkimAWo9HhasLI5SRBFUWwuZmdGOQkRERCTmKKFuh9LKmjZtj2u1tZCVFe0oRERERGKOEup26Nuz+VUDW9oe15RQi4iIiDRLCXU7XFCYS0piYsC2lMRELijMjVJEEWStSj5EREREmqEuH+3gm3gYqstHl9GzZ7QjEBEREYk5SqjbaerIfl03gfbxeCApCXr0iHYkIiIiIjFHJR8SWnU15OS4lRJFREREJIASagmtuhr6dfFReBEREZFDpIRaQvN4oE+faEchIiIiEpOUUEt4NCFRREREpFlKqKV19fWQkKAJiSIiIiItUEItrdu/HwYMgKB+2yIiIiLiKKGW1u3fD0OGRDsKERERkZilhFpaZ60mJIqIiIi0Qgm1tMzjgeRk6NUr2pGIiIiIxCwl1NKyykoYPNhNShQRERGRZilT6gRL33+fs371KwZ//euY6dN5+rXXAvZba7nl6acZdN55pM+axbQrr+TDL76IUrR+qqpcQi0iIiIiLVJC3Qkqq6sZPWwYD86ZQ3pqapP9v1mwgHv/8hce+slPWPn44/TPzubUuXOpqKqKQrRBeveOdgQiIiIiMU0JdSc4Y9Ik7vzBDzjvpJNIMCZgn7WWB156iWsvvJCvnXQSo4cP55nrrqOiqoo//ec/UYoYqKlxvae1oIuIiIhIq5RQR9kX27ezo6yMmQUFDdvSU1M5cexY3v7ww+gFtmcPjBwJQR8ARERERCSQEuoo21FWBsCAoNKKAb17N+yLCo8HBg2K3vVFRERE4oQSamnqwAHXKk/t8kRERERCUkIdZYfl5ACws7w8YPvO8vKGfZ1u714YMULlHiIiIiJhSIp2AN3d8IEDOSwnh/9btYrCUaMAOFBby5vr13PP5ZdHJyiVe4iIiHRbNyxczwsrivFYS6IxfHNiLrfPHhPtsGKaEupOUFldzWdbtwJQby1FO3ey9rPPyMnMJG/AAK487zzufP55RuXlcWRuLrc/9xw909O5cMaMzg+2uhqysiAzs/OvLSIiIlF1w8L1/HF5UcPvHmsbfldS3TJjrY12DG1SkJ9vVz3xRLTDaLBsYwkLVhZTWllD356pXFCYy9SR/QKOWbJ2LdOvuqrJud+dNYunr70Way23PvMMT/z975RXVDDxqKN45MorGT18eGc9jEY7dsBxx8GRR3b+tUVERCSqjrjun3iayQ0TjeHzeWdEIaLoMsasttYWhDpOI9TtsGxjCfOXfkGtxwNAaWUN85e6FQ79k+pp48djFy9u8X6MMdxy8cXccvHFEY03JO/jIDc3unGIiIhIVDSXTLe2XRxNSmyHBSuLG5Jpn1qPhwUri6MUUTvt2QOHHw7p6dGORERERKIgsYWGBC1tF0cJdTuUVta0aXvMq6lx3T1ERESkW/rmxOa/pW5puzjxl1AfPBjtCBr07Znapu0xrbIS+vSBoAVmREREpPu4ffYYLpqU1zAinWgMF03K04TEEOKvhtpad4uBrx4uKMwNqKEGSElM5ILCOPwUV1EBU6fGxPMqIiIi0XP77DFKoNso/hLq5GTYt8+1dosy38TDUF0+Yl5tLaSkwMCB0Y5EREREJO7EX0KdlubKE2IgoQaXVMddAh2srAzGj4ek+Hs7iIiIiERb/NVQJyfDgAFulFrar7bWJdKHHx7tSERERETiUvwl1ADjxrmaX2m/3bvd85mSEu1IREREROJSfCbU/frBkCGub7IcupoaSE2FYcOiHYmIiIhI3IrPhBpgzBjYv991/JBD46udTk6OdiQiIiIicSt+E+qcHBg6FMrLox1JfKquhowMyMuLdiQiIiIicS1+E2qA0aPhwAEIWv5bwlBWBsceq84eIiIiIu0U3wl1djYcfTSUlkY7kviydy/07+/q0EVERESkXeI7oQY46ihXA3zgQLQjiQ8ej6s9LyiAhPh/+UVERESiLf4zqtRUKCx07d80QTG00lI45hjo3TvakYiIiIh0CfGfUIMrXcjLc3XB0rL9+91Kk0cfHe1IRERERLqMrpFQGwPHH+/KGWprox1NbKqvdx1RJk9WmzwRERGJWQvXbGXKXYsYfu0/mHLXIhau2RrtkELqGgk1uBZwEyZASYlKP5qza5erN+/fP9qRiIiIiDRr4ZqtXPfyerbuqcYCW/dUc93L62M+qe46CTW4vtQjR7rkURqVl7u+3ePGRTsSERERkRbd8/oGqusC2yFX13m45/UNUYooPF0roTbG9VbOytKy5D4HDkBdHXzlK+o5LSIiIjFt257qNm2PFV0roQZXHzxliqul7u6t9Dwe19Vj6lTo2TPa0YiIiIi0alB2epu2x4qul1ADZGa6pHr37u69iuLOnTB+PAwaFO1IREREREKaOyuf9OTEgG3pyYnMnZUfpYjC0zUTaoDBg2HMGNixo3tOUty1yyXSapEnIiIicWL2sYOZd+4YBmenY4DB2enMO3cMs48dHO3QWtW1i2pHj4aqKvjiCzjsMFdj3R2Ulrpl2SdP1mqIIiIiEldmHzs45hPoYF07oU5IcKso1tdDUZFLqru6sjLXQvDEE90qkiIiEpcWrtnKPa9vYNueagZlpzN3Vn7cJRnxQM+zdISunVADJCa6/tQHD8L27TBgQLQjipw9e9ykzGnT3IqIIiISl3y9eH3tw3y9eAElex1Iz7N0lO5RD5CU5Mof+vXruj2q9+51JS3Tp7sRahERiVvx2os33uh5lo7SPRJqcCO3U6dC375upLorTVQsLW1MptUeT0Qk7sVrL954o+dZOkr3SagBUlJcbfERR8DWrfHfUs9a9+EgOxtOPRV69Yp2RCIi0gHitRdvvNHzLB2leyXU4Mo/CguhoMAlozU10Y7o0Hg87kPB8OGuZjpd//hFRLqKeO3FG2/0PLs68il3LWL4tf9gyl2LWLhma7RDiktdf1Jic4yBUaPcAjBvvulqjuNpdPfAAbdozXHHwVFHdZ92gCIi3YRvQpy6T0RWd3+eNSmz4xgbZ7XEBQUFdtWqVR13h+XlsHy565DRv7/rChKrrIWSEhfjpElu8RoRERGRQzDlrkVsbaZefHB2Om9de3IUIoo9xpjV1tqCUMd1zxFqf717w8yZsGEDvP++G63Oyop2VE1VV7se0yNGwLhxaosnIiIi7aJJmR2n+9VQNycx0S3RffrprhZ52zaorY12VI7HAzt3ulrvk0+GiROVTIuIiEi7aVJmx1FC7S87G045xU1arKiI7qTFgwddIl1a6uq9zzgDBg6MTiwiIiLS5WhSZseJaMmHMWYzUAF4gIPBNSjGmGnA34AvvJtettbeFsmYQkpMhJEjYdgwKC6GdevcBMDevTunk0ZtravrNsaNmo8YoQ4eIiIi0uG6+6TMjtQZNdTTrbWlrex/01r71U6Io22Sk+HwwyEvD7ZsgfXr3Yh1QoKrse7IsovaWti3D+rqXK/sceNcO7zU1I67hoiIiEiQ2ccOVgLdATQpMZSkJDdaPXSo6wSyYwds2tSYXKelucQ3NTW89nXWusT5wAF383jcuSNGuK4dOTmx3WlERERERAJEOqG2wL+NMRZ4wlo7v5ljJhtj3ge2AddYaz+McEyHxhhX9tG7t+v9XFEBu3a5Nnbl5e7ncFsQ9ujhEue+fV2rvuxsl5yLiIiISNyJdEI91Vq71RjTH/g/Y8wn1tqlfvvfA4ZaayuNMWcAC4GRwXdijLkMuAwgLy8vwiGHKTPT3Y44wv1eX+9a21VVuRIOa902Y1yynJTkWvKlp7ufRURERKRL6LSFXYwxtwCV1tr/beWYzUBBazXXHb6wi4iIiIhIM8Jd2CVidQbGmB7GmEzfz8BM4IOgYw4zxhUeG2MmeOPZHamYREREREQ6WiRrDwYAr3jz5STgT9ba14wxPwSw1j4OnAf8yBhzEKgGLrDxtha6iIiIiHRrEUuorbWbgHHNbH/c7+eHgYcjFYOIiIhIJC1cs1V9nEVt80RERKT7ak9CvHDNVq57eT3VdR4Atu6p5rqX1wMoqe5m1KtNREREuiVfQrx1TzWWxoR44ZqtYZ1/z+sbGpJpn+o6D/e8viEC0UosU0ItIiIi3VJ7E+Jte6rbtF26LiXUIiIi0i21NyEelJ3epu3SdSmhFhERkW6pvQnx3Fn5pCcnBmxLT05k7qz8dscm8UUJtYiIiHRL7U2IZx87mHnnjmFwdjoGGJydzrxzx7Q4IXHhmq1MuWsRw6/9B1PuWhR2rbbEPnX5EBERkW7Jl/i2p+3d7GMHh3W8OoJ0bUqoRUREpNsKNyFur9YmQCqhjn8q+RARERGJMHUE6dqUUIuIiIhEmDqCdG1KqEVEREQiTB1BujbVUIuIiIhEWEdMgJTYpYRaREREpBN01gRI6Xwq+RARERERaYe4G6HeuKuSy59bRV5OBrm+W+8MhvROJy2oNklEREREJNLiLqFOTjB8XrKfJRtKqDlYH7BvQK9UcntnkJeTwZCcDHJ7pzck3gN6pZGYYKIUtYiIiIh0VcZaG+0Y2qSgoMCuWrUKay0lFTUUl1dRVFZFcVk1xWXu5y3l1WzbW43/Q0tJTGBw73SG+CXZed7R7dycdLLSkzFGCbeIiIiIOMaY1dbaglDHxd0ItY8xhv690ujfK43jh+Y02V97sJ5te6qbJNzF5VX8c/12yqvqAo7PTEtqGN3OzUlXOYmIiIiIhCVuE+pQUpISGNa3B8P69mh2f8WBOorLqr0j2lUNo9uflVSyeMOukOUked6SktycDA7rlUaCyklEREREuqUum1CHkpmWzNGDkjl6UK8m++rrLaWVgeUk7r9VrPiijFfWbm22nCTXL8n2lZPk5WSQlZHciY9MREREImHhmq2d0ke6s64jIezfH/ah3Tahbk1CQnjlJEXeEhL/cpJ1W/awJ0Q5SeOkSZWTiIiIxIOFa7Zy3cvrqa7zALB1TzXXvbweoEOT3c66jjSjqgrefhuWLIHFi+Hdd8M+NW4nJcay4HIS3+h2cblLvJsrJ/GNaPuXk+T1yWBApspJREREom3KXYvYuqe6yfbB2em8de3JcXcdAaqrYflylzwvWeJ+rquDxEQoKIDp0zF33dW1JyXGsnDKSfxHt30J9/JNu9keopwkz2+ypMpJREREOse2ZpLc1rbH+nW6pZoalzT7RqCXL3fbEhLg+OPhyith+nSYOhUyM905d90V1l0roe5k/uUkBcNCl5MUlVWxpay61XIS//Z//pMmB2ernERERKQjDMpOb3bkeFB2elxep1uorXVlG74R6LffhgMHwBg49liYMwemTYMTToCsrHZdSgl1jAnVnWTfgTpXPlJWHVBOsnFXRYvdSRoT7ozGkW6Vk4iIiIRt7qz8gNpmgPTkRObOyo/L63RJdXWwcmXjCPRbb7myDmNg3Dj44Q/dCPSJJ0J2dodeWjXUXUh9vaWksqZhgmTR7mq/Ue4qtu870KScZEjv9CarSvoScJWTiIiINFKXjxhz8CCsXt04Ar1sWWNnjjFjXPI8bRqcdBLkNK0KCEe4C7sooe5Gag562LbnQEPP7eJyV07i+7m1cpK8Pi7pVjmJiIiIRMXBg7BmTeMI9JtvQmWl23fMMS55nj7dJdB9+3bIJbv8SonSdqlJiQzv24PhYZSTFPvVcLdUTnJYrzS3qqRfOYmvNaDKSURERKRdPB54//3GEeilS2HfPrdv1Cj49rddEj1tGvTvH8VAlVCLn15pyRwzKItjBjUtzPcvJ2lYyt2bcC/ftLvZxW6GNIxoNybdKicRERGRZtXXw/r1LoFevNgl0Hv2uH1HHgkXXNA4Aj1wYHRjDaKEWsKSkGAY0CuNAS10J2mtnKS57iS90pIa2//1aVxhMlflJCIiIt1DfT18+GFjCccbb0BZmdt3xBFw3nmNI9CDY7uGXAm1dIj2lJMs2rCL2pbKSfw6lKicREREJDo6ZKKktfDxx40lHEuWQGmp2zd8OJx9duNEwtzcDn4EkaWEWjpFW8tJfKPcyz/fzSv7QpeT+C94o3ISERGRjnPIy6FbCxs2NI5AL1kCu3a5fXl5cOaZjSPQw4ZF8BFEnhJqibpwy0kal3Cvahjtfr94D3urmy8nyfPru61yEhERkUNzz+sbAvpiA1TXebjn9Q2BCbW18NlngSPQ27e7fYMHw8yZjSPQw4e7/tBdhBJqiXnhl5MEjm5/urOC/34SWE5iDAzIDCwnaUi8VU4iIiLSRIvLoZdXweefB45Ab93qdg4c2Jg8T5/uaqK7UAIdTAm1xL1wykmKmkm432mlnMSXYAcm3BlkpaucREREuhf/5dCH7N3J5C/XMal4PVOKP4DfeEs4BgxoTJ6nTXNdObpwAh1MCbV0af7lJIXhlJM0lJRUszacchK/kpIhvdNJTVI5iYiIdCFFRfy2bj1f/usfFH65jty9OwEoy+hF9VdOgHPOcEn0qFHdKoEOppUSRVqxt9qVk2wpDxzdLiqrYkt5dbPlJHk5GQwJGt3Oy8mgf2aqyklERCS2bd3aWL6xeDFs2gRAbVY2bw8Zw5KBR/PZMYWc9+2ZzD4+vjpxHAotPS4SYcHlJP4L3hSXVbFj34HAcpKkBIZkN5aT5Pm1BFQ5iYh0Zx3Skk0OzfbtgTXQGze67dnZbgEVXwnHmDGQkBDFQKOjw5ceN8b0BgYB1cBma219iFNEurRwykm2lldTXF4dUE5SVFbVYjmJW+QmsJwkLyeDwSonEZEu6pBbssmh2bmzsQPH4sWurR1AVhaceCL88IcuiR47FhL1dydcrSbUxpgs4MfAN4EUoARIAwYYY5YDj1prF0c8SpE4lJqUyOH9enJ4v57N7vcvJ/Ef3d7QSncSXzmJ/+i2yklEJJ6F3ZItBtywcD0vrCjGYy2JxvDNibncPntMtMNqXUmJW4HQNwL90Udue2YmnHACfP/7bgT62GOVQLdDqBHql4BngROstXv8dxhjCoCLjDGHW2t/H6kARbqqrPRksgZnMXpw891JdlXUNJSPFPmtMvnO57t5Zc3WpuUkvdO9SbbKSUQkfrTYkq2F7dFyw8L1/HF5UcPvHmsbfo+ppHr3bpdA+0agP/jAbe/RwyXQ3/mOG4E+7jhIUm+KjtLqM2mtPbWVfasAFTOLREBCguGwrDQOywpdTlJUVsUWX9JdHrqcxI1yq5xERGKDf0u24O2x5IUVxS1uj2pCXV4OS5c2jkCvW+cWWMnIgClT4MIL3Qh0QQEka3AlUsL6aGKM+a+19pRQ20Skc4RbTuLfBrCozFtO8vEuaj2B5SSH9Uojt3dgOYkvAVc5iYhE0txZ+QE11ADpyYnMnZUfxaia8rTQxKGl7RGzdy+8+aZLoBcvhrVrXQKdluYS6NtucyPQhYWQktK5sXVjoWqo04AMoK93UqLvr2ovILYKm0SkQbjlJEW7GxPucMpJ8vwWvFE5iYh0BF+ddKx3+Ug0ptnkOTHSvZcrKlwC7SvheO89qK+H1FSYPBluucWNQE+c6LZJVIQaob4cuBLX3WM1jQn1PuDhCMYlIhHS0eUkWenJAatKDvEtfNM7XeUkIjEmVtvTzT52cEzE0ZpvTswNqKH2396hKivhrbcaR6BXrwaPx402T5oEN9zgRqAnTXKj0hITwupDbYz5ibX2oU6IJyT1oRaJrpbKSYrLq9hSVt1iOUnAcu4qJxHpdMHt6cCVVsw7d0zMJ7OxIiJdPqqqXALtG4FeuRIOHnQTBidObOwDPXmyq4sOQ6x+cIpHHb6wizHmK8Aw/Ea1rbXPHmqAh0oJtUjsaq6cxI1yu5aAzS52E1ROkpeTwZDeKicR6WhT7lrU7OS/wdnpvHXtyVGIqJuqroZ33mmcRLhiBdTVuZZ1hYUugZ4+Hb7yFdeZo430waljdejCLsaY54AjgLWA7xWyuJZ6IiJA+OUkbkS7OqCcZE1ROfsOHAw43ldO4psoqXISkUMXL+3pupwDB2D58sYR6OXLobbWrTpYUAA//7kbgZ46FXo2P9G8LeKpr3dXEm4DwgLgaBtv65SLSEwJ2Z2kqq6h93ax34I3n2yv4D8ftdydJGA59xyVk4g0J17a08W9mhp4993GEei333bbEhLc4ik//akbgZ46FXr16vDL64NTdISbUH8AHAZsj2AsItLNZWUkk5XReneSojK/xW68ddtvfVbKzormy0kaF7lpLCfJ65NBrzSVk0j3Ei/t6eJOba2re/aNQL/9tivrMAbGj4crrnAJ9AknQHZ2xMPRB6foCNU27++40o5M4CNjzLtAjW+/tfasyIYnIuL4l5NMGN60nORAnYete6q9o9vVDRMni8qqeO/L0OUkuQ2j2yonka4pXtrTxby6Otd5wzcCvWyZm1gIMHYsXHaZK+E48UTIafr/qkjTB6foaHVSojHmpNZOtta+0eERhaBJiSJyKPzLSYrKAvtvbylvoTtJ0Oh2rreGu19PlZOIdBsHD7rez74R6GXLXGs7gNGjXfI8fTqcdBL06RPNSBuoy0fH6fAuH7FCCbWIdLT6esvOigMNCXZjwu2S7h37DgQcH1xO4utQonISkS7A43GrD/pGoN98E/btc/uOOqqxjd1JJ0H//tGMVDpBR3f5qMCVfvjbC6wCrrbWbmp7iCIisSEhwTAwK52BWemhy0n8SkqKyqpY/WU5Fc2UkzS3qmReTgaDs9NJSUrorIcmIqHU18P77zeOQC9d6pb3BsjPh29+s3EE+rDDohqqxK5wJyXeB2wD/oRbLfEC3CTFDcBTwLRIBCciEgvSkhM5ol9PjgijO0lRQ4eSEN1Jgka3VU4i0knq6+GDDxpHoN94A8rL3b4RI+D8890I9LRpMGhQFAOVeBLuSokrrLUTg7Ytt9ZOMsa8b60dF7EIg6jkQ0TiiX85SVHACpMtl5Pk9k5vNuHOzVE5iUibWQsffdSYQC9ZArt3u32HH95YAz1tGgwZEr04JSZ1aMkHUG+MOR94yfv7eX774qsIW0SkEx1KOYlvlcmQ5SR+HUpUTiLxrEMn0VkLn3zSWMKxZAmUlLh9Q4fC//xP4wj00KEd8wCk2ws3of4W8CDwKC6BXg5cZIxJB+ZEKDYRkS4v3HKSwP7bLZeTDOyVxpCg0W1fhxKVk0gsCl4qe+ueaq57eT1AeEm1tbBxY+AI9I4dbt+QIXDaaY0j0MOHR+QxiKjLh4hInGq2nMSvJWBr5ST+C96onESiacpdi5pdiGRwdjpvXXty0xOshc8/DxyB3rbN7Rs0qDF5nj7dlXQYfYiUQ9chJR/GmF9Ya39jjHmIZko7rLU/bUeMIiLSDuGWkxSVVbEljHKS7IzkwCS7YZRb5SQSOWEtlf3FF43J8+LFsGWL2z5ggEucfUn0yJFKoGl/CY36WLddqJKPj73/bW5IOL6GtkVEuplIlZMEL+euchJpj+aWyh68dxenl34CF7/okugvv3Q7+vULHIHOz1cCHaS9JTTtLsHppg655MMY87/W2ms6OJ6QVPIhIhJ5vnISN6JdHVBOUlRWxc59NQHHp3oXuwksJ2kc7VY5ibRk4ZqtPPD0Io79fC2TitYzuWgdeXt3up19+gR24Tj6aCXQIbS5hKaDz+9qOrrLR3POBzo9oRYRkcjzLyeZ2Mz+4HKSIm8bwFDlJHk5GQzxLnijcpJubNu2hvKN2UuWMPuzzwDYk9aT9w8fx57LrmDst8+BY46BBL032iKsEpoInt9dtSeh1kdEEZFuKpxyEv8l3H3lJB9t38f/fbQzZDlJXp/GVSZVTtL/5gcAACAASURBVNIF7NjR2IFj8WL49FO3PSvLrUB4xRUwfTrZY8dykhLodmmuhMa3vTPO765CTUpsOsvFuwsl1CIi0oKsjGTGZGQxZkhWk32eesvOfQca+277jXK/ubGkxXIS34i2ykniwK5dbgVC30TCj71TsjIz4cQT4bLLXAnH+PGQmBjNSLucubPyA2qgAdKTE5k7K79Tzu+uQo1Qr8ZNPmwuea7t+HBERKSrS0wwDMpOZ1B2y+UkW8qr/VaUrGpoDbhqczkVNa2Xk/jXcKucpJOUlroE2jcC/eGHbnvPnnDCCXDxxa4O+thjIak9X45LKL6Jg4fapaO953dX6kMtIiJxw1rL3uq6hnpt/w4lW8qr2VJeRZ2n8e+ar5wk129021dOkpeTQb/MVIwmubVdWRksXdo4Ar1unduekQFTpzZOIjz+eEjuOt8gqJ1c99NRfaiHWWs3t7LfAIOttVvaHqKIiEjbGGPIzkghOyMlZDmJr267LeUkeTkZDPFrCZipchJnzx6XQPtGoN9/3y2wkp4OU6bA7be7JLqgAFJSoh1tRKidnLQm1Pcu9xhjEoC/4co/SoA0YAQwHTgFuBlQQi0iIlEXUE5yeJ8m+5srJ/F1KGmpnMRXQuJfTpKXk8GgrlxOsm8fvPlm4wj0mjVQXw+pqfCVr8Ctt7oR6AkT3LZu4J7XNwTUFQNU13m45/UNSqil9YTaWvt1Y8zRwLeA7wEDgWrcgi//AO6w1h5o5S5ERERiRlpyIiP692RE/6bdSfzLSYI7lHy0fR///mhHQDlJgoHD/MpJ8nwTJeOxnKSiAt56yyXQixfD6tUugU5JgcmT4cYb3Qj0xImQlhbtaKNC7eSkNSFnBlhrPwJ+1QmxiIiIRM2hlJP4RrlbKidxddvpAeUkvsQ7quUk+/e7BNpXwrFyJXg8rt554kT41a/cCPTkya6sQ9ROTloV1lRbY8y5zWzeC6y31u7q2JBERERiT9jlJMH9t8MoJ8kNGt3u8HKSqip4553GEo5334W6OtdxY8IE+OUv3Qj05MnQo0fHXbcLUTs5aU24vWsuBSYDi72/TwOWA0caY26z1j4XgdhERETiRlvLSXwdSloqJxmYlR64nLtfDXfIcpIDB1wC7RuBXrECamtdz+eCArj6ajcCPWWKa20Xg2Kto4bayUlrwmqbZ4z5O/B9a+1O7+8DgEeBHwBLrbWjIxqlH7XNExGRrsZXTuJLsv3LSYrKqthV0XI5SV5OBkN7JnJM8ScM/2AlOSvfJmnFcqipcct2H3ecG32ePt0l0L16RelRhi+4owa40eB5545RAiudqkPa5vkZ5kumvXYB+dbaMmNM3SFFKCIiIkBgOcmkMMpJtuzYQ9LqVRz23+Uc+dFqxm/5iLSDtdRj+HDA4aw5/qtsHlPI3uMn0W/IgMbR7dpEBh2sj/nuJOqoIfEm3IT6TWPMq8CL3t/PA5YaY3oAe1o6yRizGagAPMDB4Azf28f6QeAMoAq42Fr7XpsegYiISBeXRj0jPl/PCF8N9FtvubpowI4bR82Zl7P5uElsOPJYvvCkNI50l1WxdfOmFstJGpZzb0s5SSdQRw2JN+Em1D8GzgWmen9/BvirdfUi00OcO91aW9rCvtOBkd7bROAx739FRES6r4MHXes6Xw30smWuMwfAmDFw6aWuhOPEEzF9+pAGDPPegjUpJ/ErKXnj05Im5SRpyQlucZuAhNs3ebJzupOoo4bEm7ASamutNcYsA2oBC7xrO2bN8rOBZ733tdwYk22MGWit3d4B9y0iIhIfPB63eIpvBPrNN11vaICjj4aLL3aTCE86Cfr1a9Ndh1dOUtW4nPtu36TJ5ruT9M5IbpJk+0a3O6o7iTpqSLwJt23e+cA9wBLAAA8ZY+Zaa18KcaoF/m2MscAT1tr5/7+9O4+OqzzzPP57LMtYXkDesSULAzZmNTaWd1uWCBMnkIADDCELhAkN2UhIaETj0+nphHSfJK3TM/ljMpNhknMmJznZOnHcJJ2Ou6eRvBuQEVjGIDDGyJbBeJPBWNha3vnjreJWybJUolR1b1V9P+dwLN17VXrQ9fLTo+e+b6/zZZL2J7x/IHaMQA0AyF/d3X777ngHeuNGvzuhJF1+ufSZz/gO9MqV0pQpGS3Fr04yVjMnjz3rXHx1kvjyf4kb3rzQdkL/9sK5Vyep6GPDm1THSVhRA7km1ZGPv5a0IL7mtJlNkvT/JA0UqJc759rMbLKkfzezl5xzGwdbpJndL+l+SaqoqBjshwMAEK6eHqm5OehAb9ggtcceQZo1S7rzTt+Brq6Wpk4NsdBkiZvdzCkvPet8d4/Tm7HNbhLHSVoHGCepSNjw5lzjJKvnlUUuQEdtKT9ER6qBelivDVyOShrwZzrOubbYr2+Z2e8lLZSUGKjbJE1PeL88dqz36zwu6XHJL5uXYs0AAISjp0favTvYynvDBunYMX/u0kul227zHejqaqksdwNZ0TBTWWmJylIYJ2lNWAZw//EOPf3aMZ0cYJwksbs95JvdDFLvpfza2ju0Zm2zJBGqkXKg/rOZrZf0y9j7n5T0p/4+ILYCyDDn3Duxtz8s6bFelz0h6QEz+5X8w4gnmJ8GAOQc56QXXww60A0N0pHY8/gzZki33BJ0oAvoJ60DjZO0n+pM2OSmI2mcZP2uN9XVc/Y4STxgJ42TjB+lSWMyuzoJS/mhP6k+lFhrZrdJWhY79Lhz7vcDfNgUSb+P/eYeLukXzrk/m9kXY6/5I/lQfqOkPfLL5v2Xwf8vAACQZc5JL78cdKAbGqS3Yj/InT5duvHGoAM9Y0aIhUaXmWnc6BEaN3rgcZLWY6d0IKG7ne44yQfBUn7oT6odajnnfifpd4O4fq+ka/s4/qOEt538knwAAESXc9KePcFDhA0N0huxH6iWlUkf/rAPzzU10sUXSyGv45wPUh0neb+7ncI4ScX4USrvNU5SMd6PkxQX9T9OwlJ+6E+/gdrM3pFfqeOsU/J5OPr7lwIAMFjOSa+9FoTn+nqpLfaIz4UXBlt5V1dLM2cSoEMw2HGS1mOndOB4auMkiRvexMdJWMoP/ek3UDvnzv5dCgBAPtq3L7kD3drqj0+eHITnmhrpsssI0BGX6jhJfM3txHGShpcP63Af4yTTx43SxRNH6/Wj7+rdM90aP2qE/mLFxbrhyswua4jcYEOzP0v2VFZWusbGxrDLAADkuv37kzvQ+/b54xMnBuG5ulq64goCdIHpPU7S2muHyf7GSSp6bXiTyjgJosvMdjjnKge6LuUZagAAclpbW3IH+tVX/fHx4/0GKg895EP0lVdKw8IPQKx5HJ5UxkmCTW6CcZJdgxon8aE706uTIDsI1ACA/PTGG8ESdvX10iuv+OOlpT5Af/WrvgN9zTWRCNCJWPM4uhLHSa6dPvA4SeKGN/2Nk8SXASyPrVASD95jziOq5QLuEgAgPxw65DdQiXegX3rJHz//fKmqSvriF30Hes4cqago1FIHwprHuStxdZIlOnt1ko4zsc1u+tjwpq/VScaPHqHp40qSxkniK5QwThIdBGoAQG46ciS5A717tz8+dqy0YoX0+c/7AD1vXuQDdG+seZy/SkYUadaUsZo1ZeBxkvgMdyrjJMHsNuMkYSBQAwByw7FjyR3oZj8CodGjpeXLpbvv9iMc8+dLw3P7nzfWPC5MqYyTvHGiI2lXyXh3u74ltXGSxBluxkmGDl9JAEA0HT8ubdwYdKB37vTrQ5eU+AB9552+A11ZKRWntwte1LDmMQ9l9qVomKl83CiVjxs14DiJn+HuSGmcJHFXScZJPhiWzQMARMOJE9KmTUEHuqnJB+iRI6WlS4Nl7BYulEaMCLvajCvkQNn7oUzJf0Px3VuvKZivwVBzzun4qc7YA5LJO0zuP35Kbcc7BhwnqZjgw3whjZOkumwegRoAEI533vEBOt6BfvZZqadHOu88acmSYC3oRYv8MRSMZd97ss+Rl7LSEm159PoQKsp/Xd09evPt95JCduJ27r3HSUqKi5JGSPJ1nIR1qAEA0XLypLRlS9CBbmyUurv9uMbixdI3v+lD9OLFfqwDBYuHMrNveNGwYJzk0tTGSeIrlGzfe1Tvnklelab3OEnihjf5OE5CoAYAZMapU9LWrUGAfvppqavLPzC4aJH06KO+A71kiTRqVNjVIkJ4KDN6BlqdJD5O0noscf3tDjW3ndCfz7E6SXxeO3GcpGL8KE0cMyLnxkkI1ACAodHRIW3bFoxwPPWU1Nnpl6xbsECqrfUd6GXL/MocwDnwUGZuMTONHz1C48+xOkl8nKT12CkdiK1QEu9uP/nSYR052f84iX9gsiTS4yTRqwgAkBvee8+H5ngHets26cwZv+vg/PnSN77hO9DLlvm1oYEUxR88LNSHMvNN4jiJLj37fHycJNjkJlgW8JzjJAkhO3HDm6mlI0MZJ+GhRABAas6c8QE63oHets2H6mHD/OYp8VU4VqzwuxMCQJri4ySt72/hHoyTtB47pYPtfa9OEh8nCR6a/GDjJDyUCABIT2en9MwzQQd6yxY/1mEmXXut9KUv+RC9YoVUevaPeQEgXYnjJHNTGCdJnOE+1zhJfG47cZykYoLvco/+gOMkBGoAgNfV5VfeiHegN2/2DxZK0pw50v33+w50VZU0fnyYlQJ5o5DXGx8KA42TnDrTpQOxDW4GO05SMT71h6UJ1ABQqLq6/OYp8Q70pk1+aTtJuuoq6fOf9x3oqipp4sRQSwXyUe8NbNraO7RmbbMkEaqHyKgRw3XZlLG6rJ/VSVoTtnA/cNyPk+w84FcnSRWBGgAKRXe39NxzQQd60ybp7bf9uSuukO66ywfolSulyZNDLRUoBHXrW5JWMpGkjs5u1a1vIVBnQSrjJMXfTe21CNQAkK96eqSdO4MO9MaNUnu7P3fZZdKnPuVHOKqrpQsvDLFQoDCxgU20DR/EaiEEagDIFz090q5dQQd6wwbp+HF/buZM6fbbgw50Gd0vIGxsYJM/CNQAkKuck3bvDjrQGzZIR474cxdfLH3iE0EHevr0EAsF0Bc2sMkfBGoAyBXOSS0tPkDHQ/Thw/5cRYV0003BWtAXXRRmpQBSwAY2+YNADQBR5Zz0yitBeG5okN6MPXVeXi595CM+PNfUSDNm+PWhAeSU1fPKCNB5gEANAFHhnLR3b3IH+uBBf27qVOn664MO9KWXEqABICII1AAQptdeCx4ibGiQ9u/3x6dMCcJzTY00axYBGgAiikANANnU2hqE5/p66fXX/fFJk3x4XrPG/3r55QRoAMgRBGoAyKQDB5I70Hv3+uMTJvjl6x5+2Hegr7ySAA0AOYpADQBD6Y03kjvQe/b44+PG+QD94IO+A3311dKw1DcNAABEF4EaANJx6FByB7qlxR+/4AKpqkr68pd9B3rOHAI0AOQpAjUADMbhw8ESdvX10osv+uNjx/oAfd99vgM9d65UVBRioQCAbCFQA0B/jh71OxDGO9C7dvnjo0dLK1ZI99zjO9Dz5knD+SsVAAoRf/sDQKLjx32Ajnegd+70x0eNkpYvlz79aR+g58+XiotDLRUAEA0EagCF7cQJaePGoAP93HN+g5WRI6Vly6S/+zs/wrFggTRiRNjVAgAiiEANoLC8/ba0aVPQgW5qknp6pPPOk5Yulb71Ld+BXrjQHwMAYAAEagD57eRJafPmoAO9Y4fU3e27zYsXS3/zN74DvXix70oDADBIBGoA+eXdd6WtW32Arq+XnnnGB+jiYmnRIr8TYU2NtGSJVFISdrUAgDxAoAaQ2zo6ggDd0CA9/bTU2elX3FiwQPqrv/Id6KVL/cocAAAMMQI1gNzy3nvS9u1BB/qpp6QzZ/yaz/PnSw895DvQy5ZJY8aEXS0AoAAQqAFE2+nTPjTHHyLcts0fGzZMuu66YCvv5cul888Pu1oAQAEiUAOIljNn/NxzfIRj61Y/1mHmdx/8yld8B3r5cqm0NOxqAQAgUAMIWWen1NgYdKC3bJFOnfLnrr1W+sIXfAe6qkoaNy7MSgEA6BOBGkB2dXVJzz4bdKA3b/ZL20nS1VdL997rO9BVVdKECaGWCgBAKgjUADKru9tvnhLvQG/aJL3zjj935ZXS3Xf7AL1ypTRpUqilAgDwQRCoAQytnh7p+eeDDvTGjX57b0maPVv6zGeCAD1lSqilAgAwFAjUANLT0yM1Nwcd6I0bpePH/blZs6Q77vABurpamjo1zEoBAMgIAjWAwXFOeuGFoAO9YYN09Kg/d8kl0q23+vBcXS2Vl4dYKAAA2UGgBtA/56SXXgo2UtmwQTp82J+76CLp4x8POtAVFaGWCiCwrqlNdetbdLC9Q9NKS1S7arZWzysLu6xQ8TVBphCoASRzTnr55aAD3dAgHTrkz02fLn30oz4819RIM2aEVyeAc1rX1KY1a5vV0dktSWpr79Catc2SVLABkq8JMolADRQ656RXXw060A0N0htv+HPTpkk33KCmS+fqu6cu1DPDSjVt3CjVzp2t1TP4BwiIqrr1Le8Hx7iOzm7VrW8p2PDI1wSZRKAGCo1z0muvBeG5vl5qa/PnLrwwGN+oqZFmztS65w76rk4RXR0gVxxs7xjU8ULA1wSZRKAGCsHrryd3oFtb/fHJk4PwXF3tl7UzS/pQujpA7plWWqK2PoLitNKSEKqJBr4myCQCNZCP9u8Pus8NDb4jLUkTJ/rg/MgjPkRfccVZAbo3ujpA7qldNTtpXliSSoqLVLtqdohVhYuvCTKJQA3kg4MHk0c4Xn3VHx8/3m+g8o1v+CB91VXSsGGDemm6OkDuif/0iBUtAnxNkEnmnAu7hkGprKx0jY2NYZcBhOvNN5M70C+/7I+XlkpVVb77XFMjXXPNoAN0b72fjJd8V+e7t17DP0QAMoYl7hAFZrbDOVc50HV0qIFc8NZbwRJ29fV+XWhJOv98H6C/8AXfgb72WqmoaEg/NV0dANnGEnfINXSogSg6csRvoBLvQL/wgj8+Zoy0YkXQgZ47VxrO98UA8suy7z3Z56hZWWmJtjx6fQgVoVDRoQZyybFjPkDHO9DNvhOj0aOl5culu+7yHej58wnQAPIeD0Mj1/AvMxCG9nZp48agA/3883596JISadky6c47fQe6slIqLg67WgDIKh6GRq4hUAPZcOKEtGlT0IFuavIBeuRIaelS6dvf9gF64UJpxIiwqwWAULHEHXINgRrIhHfekTZvDjrQO3ZIPT0+LC9ZIv3t3/oRjkWLfKgGALyPh6GRa3goERgK774rbdkS7EbY2Ch1d/txjcWLg90IFy/2Yx0AgKxjKT4MFg8lApl06pS0dWvQgX76aamryz8wuHCh9OijPkQvXSqNGhV2tQBQ8FiKD5lEoAZS0dEhbd8edKCfekrq7PRrPldWSg8/7DvQS5f6pe0AAJFSt74laSZbkjo6u1W3voVAjbQRqIG+nD4dBOiGBv/26dN+18H584OtvJcvl8aODbtaAMAAWIoPmUSgBiTpzBk/thHvQG/bJr33nmQmzZsnPfCA70AvXy5dcEHY1QIABoml+JBJBGoUps5O6ZlngmXstmzxYx1mfvvuL33Jd6CrqqTS0rCrBQCkiaX4kEkEahSGri6/dF18hGPzZr8yhyRdc410332+A11VJY0fH2qpAIChx1J8yCQCNfJTV5ffPCXegd60STp50p+76irpnnt8gF65Upo4McxKAQBZsnpeGQEaGUGgRn7o7vbbd8c70Bs3Sm+/7c9dfrl0111BgJ48OdRSAQBAfiFQIzf19Eg7dwYd6I0bpfZ2f+6yy6Q77/QBurpauvDCMCsFAAB5jkCN3NDTI73wQtCB3rBBOnbMn7v0Uun224MOdBk/zgMAANlDoEY0OSe9+GKwjN2GDdKRI/7cxRdLt9wSdKCnTw+1VAAAUNgyHqjNrEhSo6Q259zHep27R1KdpLbYof/hnPtxpmtCBDkntbQEHeiGBumtt/y5igrpppt8eK6pkS66KMRCAQAAkmWjQ/2gpBclnX+O8792zj2QhToQJc5Je/YEHeiGBunNN/25sjLpwx8OOtAXX+zXhwYAIA3rmtpYNg8ZkdFAbWblkm6S9PeSHsrk50LEOSft3ZvcgW6L/WBi6lTp+uuDDvSllxKgAQBDal1TW9LGLm3tHVqztlmSCNVIW6Y71D+Q9Iiksf1cc5uZVUl6WdI3nHP7M1wTsmXfvuQO9P7YrZ0yJQjP1dV+VQ4CNAAgg+rWtyTtkihJHZ3dqlvfQqBG2jIWqM3sY5Lecs7tMLPqc1z2B0m/dM6dNrMvSPqppOv7eK37Jd0vSRUVFRmqGGlrbQ2Wsauvl15/3R+fNMkH50cf9SH68ssJ0ACArDrY3jGo48BgZLJDvUzSzWZ2o6SRks43s5875z4bv8A5dzTh+h9L+oe+Xsg597ikxyWpsrLSZa5kDEpbW3IHeu9ef3z8eB+gH37Y/3rVVQRoAECoppWWqK2P8DyttCSEapBvMhaonXNrJK2RpFiH+uHEMB07PtU590bs3ZvlH15EVL3xRnIHes8ef7y01K///LWv+Q701VdLw4aFWioAAIlqV81OmqGWpJLiItWumh1iVcgXWV+H2swek9TonHtC0tfM7GZJXZKOSbon2/WgH4cOBQ8Q1tf7Ze0k6YILpKoq6ctf9h3oOXOkoqIQCwUAoH/xOWlW+UAmmHO5NUFRWVnpGhsbwy4jPx0+7DdQiY9w7N7tj48dK61Y4bvPNTXS3LkEaAAAkPfMbIdzrnKg69gpsZAdPeoDdLwDvWuXPz56tA/Qn/uc70Bfd500nN8qAAAAfSElFZLjx6WNG4MO9M6dfn3oUaOkZcukT3/ad6Dnz5eKi8OuFgAAICcQqPPZiRM+QMc70M895wP0yJE+QD/2mA/QCxZII0aEXS0AAEBOIlDnk3fekTZtCjrQzz4r9fRI550nLVkifetbPkAvXOiPAQAAIG0E6lx28qS0eXPQgd6xQ+ru9t3mxYulb37TB+jFi31XGgAAAEOOQJ1LTp2StmwJOtDPPCN1dfkHBhctktas8Q8RLlni56IBAACQcQTqKOvokLZtCzZSefppqbPTL1m3YIFUW+s70EuX+pU5AADIE+ua2oZ8zehMvCYgEaij5b33pO3bgw709u3SmTN+18HKSumhh3wHevlyacyYsKsFACAj1jW1Je1q2NbeoTVrmyXpAwfgTLwmEEegDtPp077rHO9Ab9vmjw0bJs2bF2zlvXy5dP75YVcLAEBW1K1vSdoiXJI6OrtVt77lA4ffTLwmEEegzqYzZ/zcc7wDvXWrH+sw87sPfuUrvgO9YoVUWhp2tQAAhOJge8egjof1mkAcgTqTOjv9yhvxDvSWLf7BQkmaM0e6/37fgV6xQho/PtxaAQCIiGmlJWrrI+hOKy2J1GsCcQTqodTV5dd+ji9jt3mzX9pOkq6+Wrr3Xt+BXrlSmjAhzEoBAEOIh92GVu2q2UnzzpJUUlyk2lWzI/WaQByBOh3d3X73wfgIx6ZN0ttv+3NXXCHdfbfvQFdVSZMnh1oqACAzovqwWy6H/HidQ1l/Jl4TiDPnXNg1DEplZaVrbGwM55P39EjPPx90oDdu9Nt7S9Ls2b77XFPjf50yJZwaAQBZtex7T/Y5SlBWWqItj14fQkVnh3zJd2O/e+s1BEhgEMxsh3OucqDr6FD3p6dH2rUr6EBv2CAdP+7PzZwp3XGHD9ArV0rTpoVaKgAgHFF82I0VLYDsIlAnck7avTt4iHDDBunoUX/ukkukT3wi6ECXl4daKgAgGqL4sFsUQz6Qzwo7UDsnvfRS0IFuaJAOH/bnLrpI+vjHgwBdURFioQCAqIriw25RDPlAPiusQO2c9MorQQe6oUE6dMifKy+XPvIRH6BraqQZM8KsFACQI6L4sFsUQz6Qz/I7UDsnvfpqcgf64EF/bto06YYbggcJL7nEb7ACAMAgrZ5XFqnZ5CiGfCCf5Vegdk7aty+5A33ggD83ZUrQfa6ulmbNIkADADIm7GXrohbygXyW+4H69deDZezq66XWVn980qQgPNfU+GXtCNAAgCyI6trUADIj9wL1mTPSz34WdKBfe80fnzDBh+dHHvG/XnklARoAEAqWrQMKS+4F6uZmvwPhuHF+/eevf913oK+6Sho2LOzqAABg2TqgwOReoC4vl/7wB2nOHAI0ACCSWLYOKCy5l0inTJHmziVMAwAiq3bVbJUUFyUdY9k6IH/lXocaAICIY9k6oLAQqAEAyACWrQMKB3MTAAAAQBoI1AAAAEAaCNQAAABAGgjUAAAAQBoI1AAAAEAaWOUDAIA8tK6pjWX7gCwhUAMAkGfWNbVpzdpmdXR2S5La2ju0Zm2zJBGqgQxg5AMAgDxTt77l/TAd19HZrbr1LSFVBOQ3AjUAAHnmYHvHoI4DSA+BGgCAPDOttGRQxwGkh0ANAECeqV01WyXFRUnHSoqLVLtqdkgVAfmNhxIBAMgz8QcPWeUDyA461AAAAEAa6FADAJBnWDYPyC461AAA5BmWzQOyi0ANAECeYdk8ILsI1AAA5BmWzQOyi0ANAECeYdk8ILt4KBEAgDzDsnlAdhGoAQDIgHVNbaEG2tXzygjQvYR9T5C/CNQAAAwxlq2LHu4JMokZagAAhhjL1kUP9wSZRKAGAGCIsWxd9HBPkEkEagAAhhjL1kUP9wSZRKAGAGCIsWxd9HBPkEk8lAgAwBBj2bro4Z4gk8w5F3YNg1JZWekaGxvDLgMAAAB5zsx2OOcqB7qOkQ8AAAAgDQRqAAAAIA0EagAAACANBGoAAAAgDQRqAAAAIA0EagAAACANBGoAAAAgDQRqAAAAIA0EagAAACANBGoAAAAgDQRqAAAAIA0EagAAACANBGoAAAAgDcPDLgAA4K1ralPd+hYdbO/QtNIS1a6ardXzAefQOgAACg1JREFUysIuCwAwAAI1AETAuqY2rVnbrI7ObklSW3uH1qxtliRCNQBEHCMfABABdetb3g/TcR2d3apb3xJSRQCAVBGoASACDrZ3DOo4ACA6CNQAEAHTSksGdRwAEB0EagCIgNpVs1VSXJR0rKS4SLWrZodUEQAgVTyUCAAREH/wkFU+ACD3EKgBICJWzysjQANADsr4yIeZFZlZk5n9sY9z55nZr81sj5k9ZWYzMl0PAAAAMJSyMUP9oKQXz3HuXknHnXMzJf13Sd/PQj0AAADAkMlooDazckk3SfrxOS65RdJPY2//VtKHzMwyWRMAAAAwlDLdof6BpEck9ZzjfJmk/ZLknOuSdELShAzXBAAAAAyZjAVqM/uYpLecczuG4LXuN7NGM2s8fPjwEFQHAAAADI1MdqiXSbrZzPZJ+pWk683s572uaZM0XZLMbLikCyQd7f1CzrnHnXOVzrnKSZMmZbBkAAAAYHAyFqidc2ucc+XOuRmS7pT0pHPus70ue0LS52Jv3x67xmWqJgAAAGCoZX0dajN7TFKjc+4JST+R9DMz2yPpmHzwBgAAAHJGVgK1c65BUkPs7f+acPw9Sf85GzUAAAAAmZCNdagBAACAvEWgBgAAANKQ9RlqAAAKwbqmNtWtb9HB9g5NKy1R7arZWj2vLOyyAGQAgRoAgCG2rqlNa9Y2q6OzW5LU1t6hNWubJYlQDeQhRj4AABhidetb3g/TcR2d3apb3xJSRQAyiUANAMAQO9jeMajjAHIbgRoAgCE2rbRkUMcB5DYCNQAAQ6x21WyVFBclHSspLlLtqtkhVQQgk3goEQCAIRZ/8JBVPoDCQKAGACADVs8rI0ADBYKRDwAAACANBGoAAAAgDQRqAAAAIA0EagAAACANBGoAAAAgDQRqAAAAIA0EagAAACANBGoAAAAgDQRqAAAAIA0EagAAACANBGoAAAAgDQRqAAAAIA0EagAAACANBGoAAAAgDQRqAAAAIA0EagAAACAN5pwLu4ZBMbPDkl4Pu44CN1HSkbCLwIC4T7mB+xR93KPcwH3KDbl2ny5yzk0a6KKcC9QIn5k1Oucqw64D/eM+5QbuU/Rxj3ID9yk35Ot9YuQDAAAASAOBGgAAAEgDgRofxONhF4CUcJ9yA/cp+rhHuYH7lBvy8j4xQw0AAACkgQ41AAAAkAYCNc5iZiPN7Gkze97MXjCzb5/jujvMbHfsml9ku85Cl8p9MrMKM6s3syYz22lmN4ZRKyQzK4rdhz/2ce48M/u1me0xs6fMbEb2K4Q04H16KPZ33k4z+w8zuyiMGtH/fUq45jYzc2aWdytK5IqB7lM+5YjhYReASDot6Xrn3EkzK5a02cz+1Tm3PX6Bmc2StEbSMufccTObHFaxBWzA+yTpm5J+45z7X2Z2paQ/SZoRQq2QHpT0oqTz+zh3r6TjzrmZZnanpO9L+mQ2i8P7+rtPTZIqnXOnzOxLkv5B3Kew9HefZGZjY9c8lc2icJZz3qd8yxF0qHEW552MvVsc+6/3sP19kn7onDse+5i3slgilPJ9cgr+IrtA0sEslYcEZlYu6SZJPz7HJbdI+mns7d9K+pCZWTZqQ2Cg++Scq3fOnYq9u11SebZqQyCFP0+S9B35b0zfy0pROEsK9ymvcgSBGn2K/ZjmOUlvSfp351zv7/Ivk3SZmW0xs+1m9pHsV4kU7tO3JH3WzA7Id6e/muUS4f1A0iOSes5xvkzSfklyznVJOiFpQnZKQ4KB7lOieyX9a2bLwTn0e5/M7DpJ051z/5LVqtDbQH+e8ipHEKjRJ+dct3NurnwHZqGZXd3rkuGSZkmqlvQpSf/HzEqzWyVSuE+fkvR/nXPlkm6U9DMz4899FpnZxyS95ZzbEXYtOLfB3Ccz+6ykSkl1GS8MSQa6T7G/3/6bpL/MamFIkuKfp7zKEfzDin4559ol1Uvq/Z3jAUlPOOc6nXOvSXpZ/g8GQtDPfbpX0m9i12yTNFLSxOxWV/CWSbrZzPZJ+pWk683s572uaZM0XZLMbLj8eM7RbBaJlO6TzOwGSX8t6Wbn3OnslggNfJ/GSrpaUkPsmsWSnuDBxKxL5c9TXuUIAjXOYmaT4t8lmlmJpP8k6aVel62T/65SZjZR/kc3e7NYZsFL8T61SvpQ7Jor5AP14WzWWeicc2ucc+XOuRmS7pT0pHPus70ue0LS52Jv3x67hk0CsiiV+2Rm8yT9b/kwndPznrlqoPvknDvhnJvonJsRu2a7/P1qDKfiwpTi33t5lSMI1OjLVEn1ZrZT0jPys7l/NLPHzOzm2DXrJR01s93yndFa5xwdtexK5T79paT7zOx5Sb+UdA9BLRp63aefSJpgZnskPSTp0fAqQ6Je96lO0hhJ/2Rmz5nZEyGWhgS97hMiKp9zBDslAgAAAGmgQw0AAACkgUANAAAApIFADQAAAKSBQA0AAACkgUANAAAApIFADQARYGYn0/z435rZJWb2VGxJt1YzOxx7+zkzmzE0lfb5uf/DzC7I1OsDQNQND7sAAEB6zOwqSUXOub2SFsWO3SOp0jn3QBZK+IWkL0r6fhY+FwBEDh1qAIgQ8+rMbJeZNZvZJ2PHh5nZ/zSzF8zsj2b2JzO7PfZhn5H0zym89kfNbJuZPWtmvzaz0bHjB8zs781su5k9Y2bXmdm/mdmrZnZf7JobzKzezNaZ2W4z+6GZWeyl/1nSp4f+qwEAuYFADQDRcqukuZKulXSDpDozmxo7PkPSNZL+QtKShI9ZJmlHfy9qZpPld2D8kHPuOkk7JT2YcMk+59xi+a2afyLpE5KWSvpOwjWLJH09VsMVkm6RJOfcEUljzax08P+7AJD7GPkAgGhZLumXzrluSYfMbIOkBbHj/+Sc65H0ppnVJ3zMVEmHB3jdpZKulLQ11lgeIWlzwvn4NtrNkoY7596V9K6Z9ZjZmNi57c65fZJkZr+K1bQudu5wrI72Qf7/AkDOI1ADQO7rkDRygGtM0p+dc3ed4/zp2K89CW/H34//W+F6fUzi+yNjdQBAwWHkAwCiZZOkT5pZkZlNklQl6WlJWyTdFpulniKpOuFjXpQ0c4DX3SpppZldIklmNtrMZg2ytsVmVmFmRZLuUKzDHXt/oqTWQb4eAOQFAjUARMvv5eebn5f0pKRHnHNvSvqdpAOSdkn6kaSnJJ2Ifcy/KDlgn8U5d0jSvZJ+bWbPywfsywZZ21ZJ/yg/FtKiYExkgaTNsXEUACg45lzvn+ABAKLIzMY4506a2QT5rvUy59ybZlYiqT72fneGPvcNkh5wzq3u49wPJf3GObchE58bAKKOGWoAyB1/jK2kMULSd2KdaznnOszsbyWVKZyxiybCNIBCRocaAAAASAMz1AAAAEAaCNQAAABAGgjUAAAAQBoI1AAAAEAaCNQAAABAGgjUAAAAQBr+P1P3qEwKdYCOAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rlm_mod = sm.RLM(y, X, sm.robust.norms.TrimmedMean(.5)).fit()\n",
"abline_plot(model_results=rlm_mod, ax=ax, color='red')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Why? Because M-estimators are not robust to leverage points."
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"infl = ols_model.get_influence()"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib/python3/dist-packages/ipykernel_launcher.py:3: DeprecationWarning: \n",
".ix is deprecated. Please use\n",
".loc for label based indexing or\n",
".iloc for positional indexing\n",
"\n",
"See the documentation here:\n",
"http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
" This is separate from the ipykernel package so we can avoid doing imports until\n"
]
},
{
"data": {
"text/plain": [
"10 0.194103\n",
"19 0.194103\n",
"29 0.198344\n",
"33 0.194103\n",
"Name: hat_diag, dtype: float64"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"h_bar = 2*(ols_model.df_model + 1 )/ols_model.nobs\n",
"hat_diag = infl.summary_frame()['hat_diag']\n",
"hat_diag.ix[hat_diag > h_bar]"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" student_resid unadj_p sidak(p)\n",
"16 -2.049393 0.046415 0.892872\n",
"13 -2.035329 0.047868 0.900286\n",
"33 1.905847 0.063216 0.953543\n",
"18 -1.575505 0.122304 0.997826\n",
"1 1.522185 0.135118 0.998911\n",
"3 1.522185 0.135118 0.998911\n",
"21 -1.450418 0.154034 0.999615\n",
"17 -1.426675 0.160731 0.999735\n",
"29 1.388520 0.171969 0.999859\n",
"14 -1.374733 0.176175 0.999889\n",
"35 1.346543 0.185023 0.999933\n",
"34 -1.272159 0.209999 0.999985\n",
"28 -1.186946 0.241618 0.999998\n",
"20 -1.150621 0.256103 0.999999\n",
"44 1.134779 0.262612 0.999999\n",
"39 1.091886 0.280826 1.000000\n",
"19 1.064878 0.292740 1.000000\n",
"6 -1.026873 0.310093 1.000000\n",
"30 -1.009096 0.318446 1.000000\n",
"22 -0.979768 0.332557 1.000000\n",
"8 0.961218 0.341695 1.000000\n",
"5 0.913802 0.365801 1.000000\n",
"11 0.871997 0.387943 1.000000\n",
"12 0.856685 0.396261 1.000000\n",
"46 -0.833923 0.408829 1.000000\n",
"10 0.743920 0.460879 1.000000\n",
"42 0.727179 0.470968 1.000000\n",
"15 -0.689258 0.494280 1.000000\n",
"43 0.688272 0.494895 1.000000\n",
"7 0.655712 0.515424 1.000000\n",
"40 -0.646396 0.521381 1.000000\n",
"26 -0.640978 0.524862 1.000000\n",
"25 -0.545561 0.588123 1.000000\n",
"37 0.472819 0.638680 1.000000\n",
"32 0.472819 0.638680 1.000000\n",
"38 0.462187 0.646225 1.000000\n",
"0 0.430686 0.668799 1.000000\n",
"31 0.341726 0.734184 1.000000\n",
"36 0.318911 0.751303 1.000000\n",
"4 0.307890 0.759619 1.000000\n",
"9 0.235114 0.815211 1.000000\n",
"41 0.187732 0.851950 1.000000\n",
"2 -0.182093 0.856346 1.000000\n",
"23 -0.156014 0.876736 1.000000\n",
"27 -0.147406 0.883485 1.000000\n",
"24 0.065195 0.948314 1.000000\n",
"45 0.045675 0.963776 1.000000\n"
]
}
],
"source": [
"sidak2 = ols_model.outlier_test('sidak')\n",
"sidak2.sort_values('unadj_p', inplace=True)\n",
"print(sidak2)"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" student_resid unadj_p fdr_bh(p)\n",
"16 -2.049393 0.046415 0.764747\n",
"13 -2.035329 0.047868 0.764747\n",
"33 1.905847 0.063216 0.764747\n",
"18 -1.575505 0.122304 0.764747\n",
"1 1.522185 0.135118 0.764747\n",
"3 1.522185 0.135118 0.764747\n",
"21 -1.450418 0.154034 0.764747\n",
"17 -1.426675 0.160731 0.764747\n",
"29 1.388520 0.171969 0.764747\n",
"14 -1.374733 0.176175 0.764747\n",
"35 1.346543 0.185023 0.764747\n",
"34 -1.272159 0.209999 0.764747\n",
"28 -1.186946 0.241618 0.764747\n",
"20 -1.150621 0.256103 0.764747\n",
"44 1.134779 0.262612 0.764747\n",
"39 1.091886 0.280826 0.764747\n",
"19 1.064878 0.292740 0.764747\n",
"6 -1.026873 0.310093 0.764747\n",
"30 -1.009096 0.318446 0.764747\n",
"22 -0.979768 0.332557 0.764747\n",
"8 0.961218 0.341695 0.764747\n",
"5 0.913802 0.365801 0.768599\n",
"11 0.871997 0.387943 0.768599\n",
"12 0.856685 0.396261 0.768599\n",
"46 -0.833923 0.408829 0.768599\n",
"10 0.743920 0.460879 0.770890\n",
"42 0.727179 0.470968 0.770890\n",
"15 -0.689258 0.494280 0.770890\n",
"43 0.688272 0.494895 0.770890\n",
"7 0.655712 0.515424 0.770890\n",
"40 -0.646396 0.521381 0.770890\n",
"26 -0.640978 0.524862 0.770890\n",
"25 -0.545561 0.588123 0.837630\n",
"37 0.472819 0.638680 0.843682\n",
"32 0.472819 0.638680 0.843682\n",
"38 0.462187 0.646225 0.843682\n",
"0 0.430686 0.668799 0.849556\n",
"31 0.341726 0.734184 0.892552\n",
"36 0.318911 0.751303 0.892552\n",
"4 0.307890 0.759619 0.892552\n",
"9 0.235114 0.815211 0.922751\n",
"41 0.187732 0.851950 0.922751\n",
"2 -0.182093 0.856346 0.922751\n",
"23 -0.156014 0.876736 0.922751\n",
"27 -0.147406 0.883485 0.922751\n",
"24 0.065195 0.948314 0.963776\n",
"45 0.045675 0.963776 0.963776\n"
]
}
],
"source": [
"fdr2 = ols_model.outlier_test('fdr_bh')\n",
"fdr2.sort_values('unadj_p', inplace=True)\n",
"print(fdr2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Let's delete that line"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"del ax.lines[-1]"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"weights = np.ones(len(X))\n",
"weights[X[X['log.Te'] < 3.8].index.values - 1] = 0\n",
"wls_model = sm.WLS(y, X, weights=weights).fit()\n",
"abline_plot(model_results=wls_model, ax=ax, color='green')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* MM estimators are good for this type of problem, unfortunately, we don't yet have these yet. \n",
"* It's being worked on, but it gives a good excuse to look at the R cell magics in the notebook."
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"yy = y.values[:,None]\n",
"xx = X['log.Te'].values[:,None]"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "ModuleNotFoundError",
"evalue": "No module named 'rpy2'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-59-9555d23845d1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'load_ext rpy2.ipython'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'R library(robustbase)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Rpush yy xx'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'R mod <- lmrob(yy ~ xx);'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mmagic\u001b[0;34m(self, arg_s)\u001b[0m\n\u001b[1;32m 2158\u001b[0m \u001b[0mmagic_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marg_s\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpartition\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m' '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2159\u001b[0m \u001b[0mmagic_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmagic_name\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprefilter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mESC_MAGIC\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2160\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmagic_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2162\u001b[0m \u001b[0;31m#-------------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_line_magic\u001b[0;34m(self, magic_name, line)\u001b[0m\n\u001b[1;32m 2079\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'local_ns'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstack_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf_locals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2080\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2081\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2082\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2083\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<decorator-gen-63>\u001b[0m in \u001b[0;36mload_ext\u001b[0;34m(self, module_str)\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/magic.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m 186\u001b[0m \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 187\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 188\u001b[0;31m \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 189\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 190\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/magics/extension.py\u001b[0m in \u001b[0;36mload_ext\u001b[0;34m(self, module_str)\u001b[0m\n\u001b[1;32m 35\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mmodule_str\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mUsageError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Missing module name.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 37\u001b[0;31m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshell\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextension_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_extension\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule_str\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 38\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'already loaded'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/extensions.py\u001b[0m in \u001b[0;36mload_extension\u001b[0;34m(self, module_str)\u001b[0m\n\u001b[1;32m 81\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmodule_str\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodules\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 82\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mprepended_to_syspath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mipython_extension_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 83\u001b[0;31m \u001b[0m__import__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule_str\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 84\u001b[0m \u001b[0mmod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodules\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmodule_str\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 85\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_load_ipython_extension\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'rpy2'"
]
}
],
"source": [
"%load_ext rpy2.ipython\n",
"\n",
"%R library(robustbase)\n",
"%Rpush yy xx\n",
"%R mod <- lmrob(yy ~ xx);\n",
"%R params <- mod$coefficients;\n",
"%Rpull params"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"UsageError: Line magic function `%R` not found.\n"
]
}
],
"source": [
"%R print(mod)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'params' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-61-1b9f0c7a8c29>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'params' is not defined"
]
}
],
"source": [
"print(params)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'params' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-62-c69ec2593f52>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mabline_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mintercept\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslope\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'green'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'params' is not defined"
]
}
],
"source": [
"abline_plot(intercept=params[0], slope=params[1], ax=ax, color='green')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise: Breakdown points of M-estimator"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"np.random.seed(12345)\n",
"nobs = 200\n",
"beta_true = np.array([3, 1, 2.5, 3, -4])\n",
"X = np.random.uniform(-20,20, size=(nobs, len(beta_true)-1))\n",
"# stack a constant in front\n",
"X = sm.add_constant(X, prepend=True) # np.c_[np.ones(nobs), X]\n",
"mc_iter = 500\n",
"contaminate = .25 # percentage of response variables to contaminate"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"all_betas = []\n",
"for i in range(mc_iter):\n",
" y = np.dot(X, beta_true) + np.random.normal(size=200)\n",
" random_idx = np.random.randint(0, nobs, size=int(contaminate * nobs))\n",
" y[random_idx] = np.random.uniform(-750, 750)\n",
" beta_hat = sm.RLM(y, X).fit().params\n",
" all_betas.append(beta_hat)"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"all_betas = np.asarray(all_betas)\n",
"se_loss = lambda x : np.linalg.norm(x, ord=2)**2\n",
"se_beta = lmap(se_loss, all_betas - beta_true)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Squared error loss"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.4450294873068621"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.array(se_beta).mean()"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 2.99711706, 0.99898147, 2.49909344, 2.99712918, -3.99626521])"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"all_betas.mean(0)"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 3. , 1. , 2.5, 3. , -4. ])"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"beta_true"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3.2360913286762665e-05"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"se_loss(all_betas.mean(0) - beta_true)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
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/srv/reproducible-results/rbuild-debian/tmp.FTzzuDJkiI/dbd-tmp-gqMLHgR/diffoscope_1mz05yxs/tmpwfcodg_j/8cf658c9-6a37-4ae4-94c0-9b49d501e830 vs.

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"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# State space modeling: Local Linear Trends"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook describes how to extend the Statsmodels statespace classes to create and estimate a custom model. Here we develop a local linear trend model.\n",
"\n",
"The Local Linear Trend model has the form (see Durbin and Koopman 2012, Chapter 3.2 for all notation and details):\n",
"\n",
"$$\n",
"\\begin{align}\n",
"y_t & = \\mu_t + \\varepsilon_t \\qquad & \\varepsilon_t \\sim\n",
" N(0, \\sigma_\\varepsilon^2) \\\\\n",
"\\mu_{t+1} & = \\mu_t + \\nu_t + \\xi_t & \\xi_t \\sim N(0, \\sigma_\\xi^2) \\\\\n",
"\\nu_{t+1} & = \\nu_t + \\zeta_t & \\zeta_t \\sim N(0, \\sigma_\\zeta^2)\n",
"\\end{align}\n",
"$$\n",
"\n",
"It is easy to see that this can be cast into state space form as:\n",
"\n",
"$$\n",
"\\begin{align}\n",
"y_t & = \\begin{pmatrix} 1 & 0 \\end{pmatrix} \\begin{pmatrix} \\mu_t \\\\ \\nu_t \\end{pmatrix} + \\varepsilon_t \\\\\n",
"\\begin{pmatrix} \\mu_{t+1} \\\\ \\nu_{t+1} \\end{pmatrix} & = \\begin{bmatrix} 1 & 1 \\\\ 0 & 1 \\end{bmatrix} \\begin{pmatrix} \\mu_t \\\\ \\nu_t \\end{pmatrix} + \\begin{pmatrix} \\xi_t \\\\ \\zeta_t \\end{pmatrix}\n",
"\\end{align}\n",
"$$\n",
"\n",
"Notice that much of the state space representation is composed of known values; in fact the only parts in which parameters to be estimated appear are in the variance / covariance matrices:\n",
"\n",
"$$\n",
"\\begin{align}\n",
"H_t & = \\begin{bmatrix} \\sigma_\\varepsilon^2 \\end{bmatrix} \\\\\n",
"Q_t & = \\begin{bmatrix} \\sigma_\\xi^2 & 0 \\\\ 0 & \\sigma_\\zeta^2 \\end{bmatrix}\n",
"\\end{align}\n",
"$$"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
" from pandas.core import datetools\n"
]
}
],
"source": [
"%matplotlib inline\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"from scipy.stats import norm\n",
"import statsmodels.api as sm\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To take advantage of the existing infrastructure, including Kalman filtering and maximum likelihood estimation, we create a new class which extends from `statsmodels.tsa.statespace.MLEModel`. There are a number of things that must be specified:\n",
"\n",
"1. **k_states**, **k_posdef**: These two parameters must be provided to the base classes in initialization. The inform the statespace model about the size of, respectively, the state vector, above $\\begin{pmatrix} \\mu_t & \\nu_t \\end{pmatrix}'$, and the state error vector, above $\\begin{pmatrix} \\xi_t & \\zeta_t \\end{pmatrix}'$. Note that the dimension of the endogenous vector does not have to be specified, since it can be inferred from the `endog` array.\n",
"2. **update**: The method `update`, with argument `params`, must be specified (it is used when `fit()` is called to calculate the MLE). It takes the parameters and fills them into the appropriate state space matrices. For example, below, the `params` vector contains variance parameters $\\begin{pmatrix} \\sigma_\\varepsilon^2 & \\sigma_\\xi^2 & \\sigma_\\zeta^2\\end{pmatrix}$, and the `update` method must place them in the observation and state covariance matrices. More generally, the parameter vector might be mapped into many different places in all of the statespace matrices.\n",
"3. **statespace matrices**: by default, all state space matrices (`obs_intercept, design, obs_cov, state_intercept, transition, selection, state_cov`) are set to zeros. Values that are fixed (like the ones in the design and transition matrices here) can be set in initialization, whereas values that vary with the parameters should be set in the `update` method. Note that it is easy to forget to set the selection matrix, which is often just the identity matrix (as it is here), but not setting it will lead to a very different model (one where there is not a stochastic component to the transition equation).\n",
"4. **start params**: start parameters must be set, even if it is just a vector of zeros, although often good start parameters can be found from the data. Maximum likelihood estimation by gradient methods (as employed here) can be sensitive to the starting parameters, so it is important to select good ones if possible. Here it does not matter too much (although as variances, they should't be set zero).\n",
"5. **initialization**: in addition to defined state space matrices, all state space models must be initialized with the mean and variance for the initial distribution of the state vector. If the distribution is known, `initialize_known(initial_state, initial_state_cov)` can be called, or if the model is stationary (e.g. an ARMA model), `initialize_stationary` can be used. Otherwise, `initialize_approximate_diffuse` is a reasonable generic initialization (exact diffuse initialization is not yet available). Since the local linear trend model is not stationary (it is composed of random walks) and since the distribution is not generally known, we use `initialize_approximate_diffuse` below.\n",
"\n",
"The above are the minimum necessary for a successful model. There are also a number of things that do not have to be set, but which may be helpful or important for some applications:\n",
"\n",
"1. **transform / untransform**: when `fit` is called, the optimizer in the background will use gradient methods to select the parameters that maximize the likelihood function. By default it uses unbounded optimization, which means that it may select any parameter value. In many cases, that is not the desired behavior; variances, for example, cannot be negative. To get around this, the `transform` method takes the unconstrained vector of parameters provided by the optimizer and returns a constrained vector of parameters used in likelihood evaluation. `untransform` provides the reverse operation.\n",
"2. **param_names**: this internal method can be used to set names for the estimated parameters so that e.g. the summary provides meaningful names. If not present, parameters are named `param0`, `param1`, etc."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\"\"\"\n",
"Univariate Local Linear Trend Model\n",
"\"\"\"\n",
"class LocalLinearTrend(sm.tsa.statespace.MLEModel):\n",
" def __init__(self, endog):\n",
" # Model order\n",
" k_states = k_posdef = 2\n",
"\n",
" # Initialize the statespace\n",
" super(LocalLinearTrend, self).__init__(\n",
" endog, k_states=k_states, k_posdef=k_posdef,\n",
" initialization='approximate_diffuse',\n",
" loglikelihood_burn=k_states\n",
" )\n",
"\n",
" # Initialize the matrices\n",
" self.ssm['design'] = np.array([1, 0])\n",
" self.ssm['transition'] = np.array([[1, 1],\n",
" [0, 1]])\n",
" self.ssm['selection'] = np.eye(k_states)\n",
"\n",
" # Cache some indices\n",
" self._state_cov_idx = ('state_cov',) + np.diag_indices(k_posdef)\n",
"\n",
" @property\n",
" def param_names(self):\n",
" return ['sigma2.measurement', 'sigma2.level', 'sigma2.trend']\n",
"\n",
" @property\n",
" def start_params(self):\n",
" return [np.std(self.endog)]*3\n",
"\n",
" def transform_params(self, unconstrained):\n",
" return unconstrained**2\n",
"\n",
" def untransform_params(self, constrained):\n",
" return constrained**0.5\n",
"\n",
" def update(self, params, *args, **kwargs):\n",
" params = super(LocalLinearTrend, self).update(params, *args, **kwargs)\n",
" \n",
" # Observation covariance\n",
" self.ssm['obs_cov',0,0] = params[0]\n",
"\n",
" # State covariance\n",
" self.ssm[self._state_cov_idx] = params[1:]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using this simple model, we can estimate the parameters from a local linear trend model. The following example is from Commandeur and Koopman (2007), section 3.4., modeling motor vehicle fatalities in Finland."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "ProxyError",
"evalue": "HTTPConnectionPool(host='127.0.0.1', port=9): Max retries exceeded with url: http://staff.feweb.vu.nl/koopman/projects/ckbook/OxCodeAll.zip (Caused by ProxyError('Cannot connect to proxy.', NewConnectionError('<urllib3.connection.HTTPConnection object at 0xec9067cc>: Failed to establish a new connection: [Errno 111] Connection refused')))",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mConnectionRefusedError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 158\u001b[0m conn = connection.create_connection(\n\u001b[0;32m--> 159\u001b[0;31m (self._dns_host, self.port), self.timeout, **extra_kw)\n\u001b[0m\u001b[1;32m 160\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/util/connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m 79\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0merr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 80\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 81\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/util/connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource_address\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m \u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msa\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 71\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msock\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mConnectionRefusedError\u001b[0m: [Errno 111] Connection refused",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mNewConnectionError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m 599\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 600\u001b[0;31m chunked=chunked)\n\u001b[0m\u001b[1;32m 601\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m 353\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 354\u001b[0;31m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mhttplib_request_kw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 355\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1243\u001b[0m \u001b[0;34m\"\"\"Send a complete request to the server.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1244\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1245\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36m_send_request\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1289\u001b[0m \u001b[0mbody\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_encode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'body'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1290\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendheaders\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1291\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36mendheaders\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m 1238\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mCannotSendHeader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1239\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage_body\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1240\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36m_send_output\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m 1025\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_buffer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1026\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1027\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 965\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_open\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 966\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 967\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connection.py\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 180\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 181\u001b[0;31m \u001b[0mconn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_new_conn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 182\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prepare_conn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 167\u001b[0m raise NewConnectionError(\n\u001b[0;32m--> 168\u001b[0;31m self, \"Failed to establish a new connection: %s\" % e)\n\u001b[0m\u001b[1;32m 169\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNewConnectionError\u001b[0m: <urllib3.connection.HTTPConnection object at 0xec9067cc>: Failed to establish a new connection: [Errno 111] Connection refused",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mMaxRetryError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/usr/lib/python3/dist-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m 448\u001b[0m \u001b[0mretries\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_retries\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 449\u001b[0;31m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 450\u001b[0m )\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m 637\u001b[0m retries = retries.increment(method, url, error=e, _pool=self,\n\u001b[0;32m--> 638\u001b[0;31m _stacktrace=sys.exc_info()[2])\n\u001b[0m\u001b[1;32m 639\u001b[0m \u001b[0mretries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/util/retry.py\u001b[0m in \u001b[0;36mincrement\u001b[0;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[1;32m 397\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnew_retry\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_exhausted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 398\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mMaxRetryError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_pool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merror\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mResponseError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcause\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 399\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mMaxRetryError\u001b[0m: HTTPConnectionPool(host='127.0.0.1', port=9): Max retries exceeded with url: http://staff.feweb.vu.nl/koopman/projects/ckbook/OxCodeAll.zip (Caused by ProxyError('Cannot connect to proxy.', NewConnectionError('<urllib3.connection.HTTPConnection object at 0xec9067cc>: Failed to establish a new connection: [Errno 111] Connection refused')))",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mProxyError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-3-adacc4910a58>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m# Download the dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mck\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'http://staff.feweb.vu.nl/koopman/projects/ckbook/OxCodeAll.zip'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0mzipped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mZipFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mck\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m df = pd.read_table(\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/requests/api.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(url, params, **kwargs)\u001b[0m\n\u001b[1;32m 73\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'allow_redirects'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 75\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'get'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 76\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 77\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/requests/api.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(method, url, **kwargs)\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[0;31m# cases, and look like a memory leak in others.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 59\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0msessions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 60\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 61\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/requests/sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m 531\u001b[0m }\n\u001b[1;32m 532\u001b[0m \u001b[0msend_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 533\u001b[0;31m \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 534\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 535\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/requests/sessions.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m 644\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 645\u001b[0m \u001b[0;31m# Send the request\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 646\u001b[0;31m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 647\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 648\u001b[0m \u001b[0;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m 508\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 509\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreason\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_ProxyError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 510\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mProxyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 511\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 512\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreason\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_SSLError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mProxyError\u001b[0m: HTTPConnectionPool(host='127.0.0.1', port=9): Max retries exceeded with url: http://staff.feweb.vu.nl/koopman/projects/ckbook/OxCodeAll.zip (Caused by ProxyError('Cannot connect to proxy.', NewConnectionError('<urllib3.connection.HTTPConnection object at 0xec9067cc>: Failed to establish a new connection: [Errno 111] Connection refused')))"
]
}
],
"source": [
"import requests\n",
"from io import BytesIO\n",
"from zipfile import ZipFile\n",
" \n",
"# Download the dataset\n",
"ck = requests.get('http://staff.feweb.vu.nl/koopman/projects/ckbook/OxCodeAll.zip').content\n",
"zipped = ZipFile(BytesIO(ck))\n",
"df = pd.read_table(\n",
" BytesIO(zipped.read('OxCodeIntroStateSpaceBook/Chapter_2/NorwayFinland.txt')),\n",
" skiprows=1, header=None, sep='\\s+', engine='python',\n",
" names=['date','nf', 'ff']\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since we defined the local linear trend model as extending from `MLEModel`, the `fit()` method is immediately available, just as in other Statsmodels maximum likelihood classes. Similarly, the returned results class supports many of the same post-estimation results, like the `summary` method.\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'df' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-4-1cb9a80a34dd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Load Dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdate_range\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'%d-01-01'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'%d-01-01'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'AS'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m# Log transform\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'lff'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ff'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
]
}
],
"source": [
"# Load Dataset\n",
"df.index = pd.date_range(start='%d-01-01' % df.date[0], end='%d-01-01' % df.iloc[-1, 0], freq='AS')\n",
"\n",
"# Log transform\n",
"df['lff'] = np.log(df['ff'])\n",
"\n",
"# Setup the model\n",
"mod = LocalLinearTrend(df['lff'])\n",
"\n",
"# Fit it using MLE (recall that we are fitting the three variance parameters)\n",
"res = mod.fit()\n",
"print(res.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we can do post-estimation prediction and forecasting. Notice that the end period can be specified as a date."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'res' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-5-575a2795f6fa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Perform prediction and forecasting\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mpredict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_prediction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mforecast\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_forecast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'2014'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'res' is not defined"
]
}
],
"source": [
"# Perform prediction and forecasting\n",
"predict = res.get_prediction()\n",
"forecast = res.get_forecast('2014')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'df' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-6-acfd2ead849d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m# Plot the results\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'lff'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstyle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'k.'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Observations'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredicted_mean\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'One-step-ahead Prediction'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mpredict_ci\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconf_int\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.05\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
]
},
{
"data": {
"image/png": 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V1zL7T8zO+TV1H03y4iTp7s9ndo3dTUlOJXkqybsmPa79BQAYxzvIAwAMJLYAAAYSWwAAA4ktAICBxBYAwEBiCwBgILEFADDQ/wE8pKTmGm5RBwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 720x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(10,4))\n",
"\n",
"# Plot the results\n",
"df['lff'].plot(ax=ax, style='k.', label='Observations')\n",
"predict.predicted_mean.plot(ax=ax, label='One-step-ahead Prediction')\n",
"predict_ci = predict.conf_int(alpha=0.05)\n",
"predict_index = np.arange(len(predict_ci))\n",
"ax.fill_between(predict_index[2:], predict_ci.iloc[2:, 0], predict_ci.iloc[2:, 1], alpha=0.1)\n",
"\n",
"forecast.predicted_mean.plot(ax=ax, style='r', label='Forecast')\n",
"forecast_ci = forecast.conf_int()\n",
"forecast_index = np.arange(len(predict_ci), len(predict_ci) + len(forecast_ci))\n",
"ax.fill_between(forecast_index, forecast_ci.iloc[:, 0], forecast_ci.iloc[:, 1], alpha=0.1)\n",
"\n",
"# Cleanup the image\n",
"ax.set_ylim((4, 8));\n",
"legend = ax.legend(loc='lower left');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### References\n",
"\n",
" Commandeur, Jacques J. F., and Siem Jan Koopman. 2007.\n",
" An Introduction to State Space Time Series Analysis.\n",
" Oxford\u202f; New York: Oxford University Press.\n",
"\n",
" Durbin, James, and Siem Jan Koopman. 2012.\n",
" Time Series Analysis by State Space Methods: Second Edition.\n",
" Oxford University Press."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
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/srv/reproducible-results/rbuild-debian/tmp.FTzzuDJkiI/dbd-tmp-gqMLHgR/diffoscope_1mz05yxs/tmpmlfsqci3/fa9f9fe8-0125-4e7b-9c3d-587ecfc18649 vs.

Similarity: 0.0%
Differences: {
"replace": {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# SARIMAX: Model selection, missing data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The example mirrors Durbin and Koopman (2012), Chapter 8.4 in application of Box-Jenkins methodology to fit ARMA models. The novel feature is the ability of the model to work on datasets with missing values."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
" from pandas.core import datetools\n"
]
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from scipy.stats import norm\n",
"import statsmodels.api as sm\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "ProxyError",
"evalue": "HTTPConnectionPool(host='127.0.0.1', port=9): Max retries exceeded with url: http://www.ssfpack.com/files/DK-data.zip (Caused by ProxyError('Cannot connect to proxy.', NewConnectionError('<urllib3.connection.HTTPConnection object at 0xec82c50c>: Failed to establish a new connection: [Errno 111] Connection refused')))",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mConnectionRefusedError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 158\u001b[0m conn = connection.create_connection(\n\u001b[0;32m--> 159\u001b[0;31m (self._dns_host, self.port), self.timeout, **extra_kw)\n\u001b[0m\u001b[1;32m 160\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/util/connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m 79\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0merr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 80\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 81\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/util/connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource_address\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m \u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msa\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 71\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msock\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mConnectionRefusedError\u001b[0m: [Errno 111] Connection refused",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mNewConnectionError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m 599\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 600\u001b[0;31m chunked=chunked)\n\u001b[0m\u001b[1;32m 601\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m 353\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 354\u001b[0;31m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mhttplib_request_kw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 355\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1243\u001b[0m \u001b[0;34m\"\"\"Send a complete request to the server.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1244\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1245\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36m_send_request\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1289\u001b[0m \u001b[0mbody\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_encode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'body'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1290\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendheaders\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1291\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36mendheaders\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m 1238\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mCannotSendHeader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1239\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage_body\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1240\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36m_send_output\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m 1025\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_buffer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1026\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1027\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 965\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_open\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 966\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 967\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connection.py\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 180\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 181\u001b[0;31m \u001b[0mconn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_new_conn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 182\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prepare_conn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 167\u001b[0m raise NewConnectionError(\n\u001b[0;32m--> 168\u001b[0;31m self, \"Failed to establish a new connection: %s\" % e)\n\u001b[0m\u001b[1;32m 169\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNewConnectionError\u001b[0m: <urllib3.connection.HTTPConnection object at 0xec82c50c>: Failed to establish a new connection: [Errno 111] Connection refused",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mMaxRetryError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/usr/lib/python3/dist-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m 448\u001b[0m \u001b[0mretries\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_retries\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 449\u001b[0;31m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 450\u001b[0m )\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m 637\u001b[0m retries = retries.increment(method, url, error=e, _pool=self,\n\u001b[0;32m--> 638\u001b[0;31m _stacktrace=sys.exc_info()[2])\n\u001b[0m\u001b[1;32m 639\u001b[0m \u001b[0mretries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/util/retry.py\u001b[0m in \u001b[0;36mincrement\u001b[0;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[1;32m 397\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnew_retry\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_exhausted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 398\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mMaxRetryError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_pool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merror\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mResponseError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcause\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 399\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mMaxRetryError\u001b[0m: HTTPConnectionPool(host='127.0.0.1', port=9): Max retries exceeded with url: http://www.ssfpack.com/files/DK-data.zip (Caused by ProxyError('Cannot connect to proxy.', NewConnectionError('<urllib3.connection.HTTPConnection object at 0xec82c50c>: Failed to establish a new connection: [Errno 111] Connection refused')))",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mProxyError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-3-074aec8a1161>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m# Download the dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mdk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'http://www.ssfpack.com/files/DK-data.zip'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mzipped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mZipFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/requests/api.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(url, params, **kwargs)\u001b[0m\n\u001b[1;32m 73\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'allow_redirects'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 75\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'get'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 76\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 77\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/requests/api.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(method, url, **kwargs)\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[0;31m# cases, and look like a memory leak in others.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 59\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0msessions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 60\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 61\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/requests/sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m 531\u001b[0m }\n\u001b[1;32m 532\u001b[0m \u001b[0msend_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 533\u001b[0;31m \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 534\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 535\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/requests/sessions.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m 644\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 645\u001b[0m \u001b[0;31m# Send the request\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 646\u001b[0;31m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 647\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 648\u001b[0m \u001b[0;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m 508\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 509\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreason\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_ProxyError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 510\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mProxyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 511\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 512\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreason\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_SSLError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mProxyError\u001b[0m: HTTPConnectionPool(host='127.0.0.1', port=9): Max retries exceeded with url: http://www.ssfpack.com/files/DK-data.zip (Caused by ProxyError('Cannot connect to proxy.', NewConnectionError('<urllib3.connection.HTTPConnection object at 0xec82c50c>: Failed to establish a new connection: [Errno 111] Connection refused')))"
]
}
],
"source": [
"import requests\n",
"from io import BytesIO\n",
"from zipfile import ZipFile\n",
"\n",
"# Download the dataset\n",
"dk = requests.get('http://www.ssfpack.com/files/DK-data.zip').content\n",
"f = BytesIO(dk)\n",
"zipped = ZipFile(f)\n",
"df = pd.read_table(\n",
" BytesIO(zipped.read('internet.dat')),\n",
" skiprows=1, header=None, sep='\\s+', engine='python',\n",
" names=['internet','dinternet']\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Model Selection\n",
"\n",
"As in Durbin and Koopman, we force a number of the values to be missing."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'df' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-4-70c0b0b5593e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Get the basic series\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdta_full\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdinternet\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mdta_miss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdta_full\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m# Remove datapoints\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
]
}
],
"source": [
"# Get the basic series\n",
"dta_full = df.dinternet[1:].values\n",
"dta_miss = dta_full.copy()\n",
"\n",
"# Remove datapoints\n",
"missing = np.r_[6,16,26,36,46,56,66,72,73,74,75,76,86,96]-1\n",
"dta_miss[missing] = np.nan"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then we can consider model selection using the Akaike information criteria (AIC), but running the model for each variant and selecting the model with the lowest AIC value.\n",
"\n",
"There are a couple of things to note here:\n",
"\n",
"- When running such a large batch of models, particularly when the autoregressive and moving average orders become large, there is the possibility of poor maximum likelihood convergence. Below we ignore the warnings since this example is illustrative.\n",
"- We use the option `enforce_invertibility=False`, which allows the moving average polynomial to be non-invertible, so that more of the models are estimable.\n",
"- Several of the models do not produce good results, and their AIC value is set to NaN. This is not surprising, as Durbin and Koopman note numerical problems with the high order models."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'dta_full' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-5-735b63dc22a3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;31m# Estimate the model with no missing datapoints\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mmod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtsa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatespace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSARIMAX\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdta_full\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menforce_invertibility\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 16\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'dta_full' is not defined"
]
}
],
"source": [
"import warnings\n",
"\n",
"aic_full = pd.DataFrame(np.zeros((6,6), dtype=float))\n",
"aic_miss = pd.DataFrame(np.zeros((6,6), dtype=float))\n",
"\n",
"warnings.simplefilter('ignore')\n",
"\n",
"# Iterate over all ARMA(p,q) models with p,q in [0,6]\n",
"for p in range(6):\n",
" for q in range(6):\n",
" if p == 0 and q == 0:\n",
" continue\n",
" \n",
" # Estimate the model with no missing datapoints\n",
" mod = sm.tsa.statespace.SARIMAX(dta_full, order=(p,0,q), enforce_invertibility=False)\n",
" try:\n",
" res = mod.fit()\n",
" aic_full.iloc[p,q] = res.aic\n",
" except:\n",
" aic_full.iloc[p,q] = np.nan\n",
" \n",
" # Estimate the model with missing datapoints\n",
" mod = sm.tsa.statespace.SARIMAX(dta_miss, order=(p,0,q), enforce_invertibility=False)\n",
" try:\n",
" res = mod.fit()\n",
" aic_miss.iloc[p,q] = res.aic\n",
" except:\n",
" aic_miss.iloc[p,q] = np.nan"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For the models estimated over the full (non-missing) dataset, the AIC chooses ARMA(1,1) or ARMA(3,0). Durbin and Koopman suggest the ARMA(1,1) specification is better due to parsimony.\n",
"\n",
"$$\n",
"\\text{Replication of:}\\\\\n",
"\\textbf{Table 8.1} ~~ \\text{AIC for different ARMA models.}\\\\\n",
"\\newcommand{\\r}[1]{{\\color{red}{#1}}}\n",
"\\begin{array}{lrrrrrr}\n",
"\\hline\n",
"q & 0 & 1 & 2 & 3 & 4 & 5 \\\\\n",
"\\hline\n",
"p & {} & {} & {} & {} & {} & {} \\\\\n",
"0 & 0.00 & 549.81 & 519.87 & 520.27 & 519.38 & 518.86 \\\\\n",
"1 & 529.24 & \\r{514.30} & 516.25 & 514.58 & 515.10 & 516.28 \\\\\n",
"2 & 522.18 & 516.29 & 517.16 & 515.77 & 513.24 & 514.73 \\\\\n",
"3 & \\r{511.99} & 513.94 & 515.92 & 512.06 & 513.72 & 514.50 \\\\\n",
"4 & 513.93 & 512.89 & nan & nan & 514.81 & 516.08 \\\\\n",
"5 & 515.86 & 517.64 & nan & nan & nan & nan \\\\\n",
"\\hline\n",
"\\end{array}\n",
"$$\n",
"\n",
"For the models estimated over missing dataset, the AIC chooses ARMA(1,1)\n",
"\n",
"$$\n",
"\\text{Replication of:}\\\\\n",
"\\textbf{Table 8.2} ~~ \\text{AIC for different ARMA models with missing observations.}\\\\\n",
"\\begin{array}{lrrrrrr}\n",
"\\hline\n",
"q & 0 & 1 & 2 & 3 & 4 & 5 \\\\\n",
"\\hline\n",
"p & {} & {} & {} & {} & {} & {} \\\\\n",
"0 & 0.00 & 488.93 & 464.01 & 463.86 & 462.63 & 463.62 \\\\\n",
"1 & 468.01 & \\r{457.54} & 459.35 & 458.66 & 459.15 & 461.01 \\\\\n",
"2 & 469.68 & nan & 460.48 & 459.43 & 459.23 & 460.47 \\\\\n",
"3 & 467.10 & 458.44 & 459.64 & 456.66 & 459.54 & 460.05 \\\\\n",
"4 & 469.00 & 459.52 & nan & 463.04 & 459.35 & 460.96 \\\\\n",
"5 & 471.32 & 461.26 & nan & nan & 461.00 & 462.97 \\\\\n",
"\\hline\n",
"\\end{array}\n",
"$$\n",
"\n",
"**Note**: the AIC values are calculated differently than in Durbin and Koopman, but show overall similar trends."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Postestimation\n",
"\n",
"Using the ARMA(1,1) specification selected above, we perform in-sample prediction and out-of-sample forecasting."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'dta_miss' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-6-dd0a0a728f6a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Statespace\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtsa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatespace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSARIMAX\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdta_miss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'dta_miss' is not defined"
]
}
],
"source": [
"# Statespace\n",
"mod = sm.tsa.statespace.SARIMAX(dta_miss, order=(1,0,1))\n",
"res = mod.fit()\n",
"print(res.summary())"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'res' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-7-c5a0278f6f27>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# In-sample one-step-ahead predictions, and out-of-sample forecasts\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mnforecast\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m20\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mpredict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_prediction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnobs\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mnforecast\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0midx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredicted_mean\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mpredict_ci\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconf_int\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'res' is not defined"
]
}
],
"source": [
"# In-sample one-step-ahead predictions, and out-of-sample forecasts\n",
"nforecast = 20\n",
"predict = res.get_prediction(end=mod.nobs + nforecast)\n",
"idx = np.arange(len(predict.predicted_mean))\n",
"predict_ci = predict.conf_int(alpha=0.5)\n",
"\n",
"# Graph\n",
"fig, ax = plt.subplots(figsize=(12,6))\n",
"ax.xaxis.grid()\n",
"ax.plot(dta_miss, 'k.')\n",
"\n",
"# Plot\n",
"ax.plot(idx[:-nforecast], predict.predicted_mean[:-nforecast], 'gray')\n",
"ax.plot(idx[-nforecast:], predict.predicted_mean[-nforecast:], 'k--', linestyle='--', linewidth=2)\n",
"ax.fill_between(idx, predict_ci.iloc[:, 0], predict_ci.iloc[:, 1], alpha=0.15)\n",
"\n",
"ax.set(title='Figure 8.9 - Internet series');"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
}

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/srv/reproducible-results/rbuild-debian/tmp.FTzzuDJkiI/dbd-tmp-gqMLHgR/diffoscope_1mz05yxs/tmpt1u183yq/7cd6d7ce-4df0-4ad6-8920-6e10e8e91e61 vs.

Similarity: 0.0%
Differences: {
"replace": {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# SARIMAX: Introduction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook replicates examples from the Stata ARIMA time series estimation and postestimation documentation.\n",
"\n",
"First, we replicate the four estimation examples http://www.stata.com/manuals13/tsarima.pdf:\n",
"\n",
"1. ARIMA(1,1,1) model on the U.S. Wholesale Price Index (WPI) dataset.\n",
"2. Variation of example 1 which adds an MA(4) term to the ARIMA(1,1,1) specification to allow for an additive seasonal effect.\n",
"3. ARIMA(2,1,0) x (1,1,0,12) model of monthly airline data. This example allows a multiplicative seasonal effect.\n",
"4. ARMA(1,1) model with exogenous regressors; describes consumption as an autoregressive process on which also the money supply is assumed to be an explanatory variable.\n",
"\n",
"Second, we demonstrate postestimation capabilitites to replicate http://www.stata.com/manuals13/tsarimapostestimation.pdf. The model from example 4 is used to demonstrate:\n",
"\n",
"1. One-step-ahead in-sample prediction\n",
"2. n-step-ahead out-of-sample forecasting\n",
"3. n-step-ahead in-sample dynamic prediction"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
" from pandas.core import datetools\n"
]
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from scipy.stats import norm\n",
"import statsmodels.api as sm\n",
"import matplotlib.pyplot as plt\n",
"from datetime import datetime\n",
"import requests\n",
"from io import BytesIO"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ARIMA Example 1: Arima\n",
"\n",
"As can be seen in the graphs from Example 2, the Wholesale price index (WPI) is growing over time (i.e. is not stationary). Therefore an ARMA model is not a good specification. In this first example, we consider a model where the original time series is assumed to be integrated of order 1, so that the difference is assumed to be stationary, and fit a model with one autoregressive lag and one moving average lag, as well as an intercept term.\n",
"\n",
"The postulated data process is then:\n",
"\n",
"$$\n",
"\\Delta y_t = c + \\phi_1 \\Delta y_{t-1} + \\theta_1 \\epsilon_{t-1} + \\epsilon_{t}\n",
"$$\n",
"\n",
"where $c$ is the intercept of the ARMA model, $\\Delta$ is the first-difference operator, and we assume $\\epsilon_{t} \\sim N(0, \\sigma^2)$. This can be rewritten to emphasize lag polynomials as (this will be useful in example 2, below):\n",
"\n",
"$$\n",
"(1 - \\phi_1 L ) \\Delta y_t = c + (1 + \\theta_1 L) \\epsilon_{t}\n",
"$$\n",
"\n",
"where $L$ is the lag operator.\n",
"\n",
"Notice that one difference between the Stata output and the output below is that Stata estimates the following model:\n",
"\n",
"$$\n",
"(\\Delta y_t - \\beta_0) = \\phi_1 ( \\Delta y_{t-1} - \\beta_0) + \\theta_1 \\epsilon_{t-1} + \\epsilon_{t}\n",
"$$\n",
"\n",
"where $\\beta_0$ is the mean of the process $y_t$. This model is equivalent to the one estimated in the Statsmodels SARIMAX class, but the interpretation is different. To see the equivalence, note that:\n",
"\n",
"$$\n",
"(\\Delta y_t - \\beta_0) = \\phi_1 ( \\Delta y_{t-1} - \\beta_0) + \\theta_1 \\epsilon_{t-1} + \\epsilon_{t} \\\\\n",
"\\Delta y_t = (1 - \\phi_1) \\beta_0 + \\phi_1 \\Delta y_{t-1} + \\theta_1 \\epsilon_{t-1} + \\epsilon_{t}\n",
"$$\n",
"\n",
"so that $c = (1 - \\phi_1) \\beta_0$."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "ProxyError",
"evalue": "HTTPConnectionPool(host='127.0.0.1', port=9): Max retries exceeded with url: http://www.stata-press.com/data/r12/wpi1.dta (Caused by ProxyError('Cannot connect to proxy.', NewConnectionError('<urllib3.connection.HTTPConnection object at 0xec8f3a8c>: Failed to establish a new connection: [Errno 111] Connection refused')))",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mConnectionRefusedError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 158\u001b[0m conn = connection.create_connection(\n\u001b[0;32m--> 159\u001b[0;31m (self._dns_host, self.port), self.timeout, **extra_kw)\n\u001b[0m\u001b[1;32m 160\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/util/connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m 79\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0merr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 80\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 81\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/util/connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource_address\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m \u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msa\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 71\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msock\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mConnectionRefusedError\u001b[0m: [Errno 111] Connection refused",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mNewConnectionError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m 599\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 600\u001b[0;31m chunked=chunked)\n\u001b[0m\u001b[1;32m 601\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m 353\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 354\u001b[0;31m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mhttplib_request_kw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 355\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1243\u001b[0m \u001b[0;34m\"\"\"Send a complete request to the server.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1244\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1245\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36m_send_request\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1289\u001b[0m \u001b[0mbody\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_encode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'body'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1290\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendheaders\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1291\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36mendheaders\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m 1238\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mCannotSendHeader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1239\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage_body\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1240\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36m_send_output\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m 1025\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_buffer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1026\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1027\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 965\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_open\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 966\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 967\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connection.py\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 180\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 181\u001b[0;31m \u001b[0mconn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_new_conn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 182\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prepare_conn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 167\u001b[0m raise NewConnectionError(\n\u001b[0;32m--> 168\u001b[0;31m self, \"Failed to establish a new connection: %s\" % e)\n\u001b[0m\u001b[1;32m 169\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNewConnectionError\u001b[0m: <urllib3.connection.HTTPConnection object at 0xec8f3a8c>: Failed to establish a new connection: [Errno 111] Connection refused",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mMaxRetryError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/usr/lib/python3/dist-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m 448\u001b[0m \u001b[0mretries\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_retries\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 449\u001b[0;31m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 450\u001b[0m )\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m 637\u001b[0m retries = retries.increment(method, url, error=e, _pool=self,\n\u001b[0;32m--> 638\u001b[0;31m _stacktrace=sys.exc_info()[2])\n\u001b[0m\u001b[1;32m 639\u001b[0m \u001b[0mretries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/util/retry.py\u001b[0m in \u001b[0;36mincrement\u001b[0;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[1;32m 397\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnew_retry\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_exhausted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 398\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mMaxRetryError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_pool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merror\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mResponseError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcause\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 399\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mMaxRetryError\u001b[0m: HTTPConnectionPool(host='127.0.0.1', port=9): Max retries exceeded with url: http://www.stata-press.com/data/r12/wpi1.dta (Caused by ProxyError('Cannot connect to proxy.', NewConnectionError('<urllib3.connection.HTTPConnection object at 0xec8f3a8c>: Failed to establish a new connection: [Errno 111] Connection refused')))",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mProxyError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-3-d7a18dd7d756>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mwpi1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'http://www.stata-press.com/data/r12/wpi1.dta'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_stata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwpi1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/requests/api.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(url, params, **kwargs)\u001b[0m\n\u001b[1;32m 73\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'allow_redirects'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 75\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'get'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 76\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 77\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/requests/api.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(method, url, **kwargs)\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[0;31m# cases, and look like a memory leak in others.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 59\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0msessions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 60\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 61\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/requests/sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m 531\u001b[0m }\n\u001b[1;32m 532\u001b[0m \u001b[0msend_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 533\u001b[0;31m \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 534\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 535\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/requests/sessions.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m 644\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 645\u001b[0m \u001b[0;31m# Send the request\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 646\u001b[0;31m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 647\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 648\u001b[0m \u001b[0;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m 508\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 509\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreason\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_ProxyError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 510\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mProxyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 511\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 512\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreason\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_SSLError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mProxyError\u001b[0m: HTTPConnectionPool(host='127.0.0.1', port=9): Max retries exceeded with url: http://www.stata-press.com/data/r12/wpi1.dta (Caused by ProxyError('Cannot connect to proxy.', NewConnectionError('<urllib3.connection.HTTPConnection object at 0xec8f3a8c>: Failed to establish a new connection: [Errno 111] Connection refused')))"
]
}
],
"source": [
"# Dataset\n",
"wpi1 = requests.get('http://www.stata-press.com/data/r12/wpi1.dta').content\n",
"data = pd.read_stata(BytesIO(wpi1))\n",
"data.index = data.t\n",
"\n",
"# Fit the model\n",
"mod = sm.tsa.statespace.SARIMAX(data['wpi'], trend='c', order=(1,1,1))\n",
"res = mod.fit()\n",
"print(res.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Thus the maximum likelihood estimates imply that for the process above, we have:\n",
"\n",
"$$\n",
"\\Delta y_t = 0.1050 + 0.8740 \\Delta y_{t-1} - 0.4206 \\epsilon_{t-1} + \\epsilon_{t}\n",
"$$\n",
"\n",
"where $\\epsilon_{t} \\sim N(0, 0.5226)$. Finally, recall that $c = (1 - \\phi_1) \\beta_0$, and here $c = 0.1050$ and $\\phi_1 = 0.8740$. To compare with the output from Stata, we could calculate the mean:\n",
"\n",
"$$\\beta_0 = \\frac{c}{1 - \\phi_1} = \\frac{0.1050}{1 - 0.8740} = 0.83$$\n",
"\n",
"**Note**: these values are slightly different from the values in the Stata documentation because the optimizer in Statsmodels has found parameters here that yield a higher likelihood. Nonetheless, they are very close."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ARIMA Example 2: Arima with additive seasonal effects\n",
"\n",
"This model is an extension of that from example 1. Here the data is assumed to follow the process:\n",
"\n",
"$$\n",
"\\Delta y_t = c + \\phi_1 \\Delta y_{t-1} + \\theta_1 \\epsilon_{t-1} + \\theta_4 \\epsilon_{t-4} + \\epsilon_{t}\n",
"$$\n",
"\n",
"The new part of this model is that there is allowed to be a annual seasonal effect (it is annual even though the periodicity is 4 because the dataset is quarterly). The second difference is that this model uses the log of the data rather than the level.\n",
"\n",
"Before estimating the dataset, graphs showing:\n",
"\n",
"1. The time series (in logs)\n",
"2. The first difference of the time series (in logs)\n",
"3. The autocorrelation function\n",
"4. The partial autocorrelation function.\n",
"\n",
"From the first two graphs, we note that the original time series does not appear to be stationary, whereas the first-difference does. This supports either estimating an ARMA model on the first-difference of the data, or estimating an ARIMA model with 1 order of integration (recall that we are taking the latter approach). The last two graphs support the use of an ARMA(1,1,1) model."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'wpi1' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-4-4c25c1c8c40d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_stata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwpi1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ln_wpi'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'wpi'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'D.ln_wpi'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ln_wpi'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdiff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'wpi1' is not defined"
]
}
],
"source": [
"# Dataset\n",
"data = pd.read_stata(BytesIO(wpi1))\n",
"data.index = data.t\n",
"data['ln_wpi'] = np.log(data['wpi'])\n",
"data['D.ln_wpi'] = data['ln_wpi'].diff()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'data' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-5-3bd451372e07>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m# Levels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mpl_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'wpi'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'-'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'US Wholesale Price Index'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Graph data\n",
"fig, axes = plt.subplots(1, 2, figsize=(15,4))\n",
"\n",
"# Levels\n",
"axes[0].plot(data.index._mpl_repr(), data['wpi'], '-')\n",
"axes[0].set(title='US Wholesale Price Index')\n",
"\n",
"# Log difference\n",
"axes[1].plot(data.index._mpl_repr(), data['D.ln_wpi'], '-')\n",
"axes[1].hlines(0, data.index[0], data.index[-1], 'r')\n",
"axes[1].set(title='US Wholesale Price Index - difference of logs');"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'data' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-6-3a0723368be2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraphics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtsa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_acf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'D.ln_wpi'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlags\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m40\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraphics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtsa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_pacf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'D.ln_wpi'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlags\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m40\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Graph data\n",
"fig, axes = plt.subplots(1, 2, figsize=(15,4))\n",
"\n",
"fig = sm.graphics.tsa.plot_acf(data.ix[1:, 'D.ln_wpi'], lags=40, ax=axes[0])\n",
"fig = sm.graphics.tsa.plot_pacf(data.ix[1:, 'D.ln_wpi'], lags=40, ax=axes[1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To understand how to specify this model in Statsmodels, first recall that from example 1 we used the following code to specify the ARIMA(1,1,1) model:\n",
"\n",
"```python\n",
"mod = sm.tsa.statespace.SARIMAX(data['wpi'], trend='c', order=(1,1,1))\n",
"```\n",
"\n",
"The `order` argument is a tuple of the form `(AR specification, Integration order, MA specification)`. The integration order must be an integer (for example, here we assumed one order of integration, so it was specified as 1. In a pure ARMA model where the underlying data is already stationary, it would be 0).\n",
"\n",
"For the AR specification and MA specification components, there are two possiblities. The first is to specify the **maximum degree** of the corresponding lag polynomial, in which case the component is an integer. For example, if we wanted to specify an ARIMA(1,1,4) process, we would use:\n",
"\n",
"```python\n",
"mod = sm.tsa.statespace.SARIMAX(data['wpi'], trend='c', order=(1,1,4))\n",
"```\n",
"\n",
"and the corresponding data process would be:\n",
"\n",
"$$\n",
"y_t = c + \\phi_1 y_{t-1} + \\theta_1 \\epsilon_{t-1} + \\theta_2 \\epsilon_{t-2} + \\theta_3 \\epsilon_{t-3} + \\theta_4 \\epsilon_{t-4} + \\epsilon_{t}\n",
"$$\n",
"\n",
"or\n",
"\n",
"$$\n",
"(1 - \\phi_1 L)\\Delta y_t = c + (1 + \\theta_1 L + \\theta_2 L^2 + \\theta_3 L^3 + \\theta_4 L^4) \\epsilon_{t}\n",
"$$\n",
"\n",
"When the specification parameter is given as a maximum degree of the lag polynomial, it implies that all polynomial terms up to that degree are included. Notice that this is *not* the model we want to use, because it would include terms for $\\epsilon_{t-2}$ and $\\epsilon_{t-3}$, which we don't want here.\n",
"\n",
"What we want is a polynomial that has terms for the 1st and 4th degrees, but leaves out the 2nd and 3rd terms. To do that, we need to provide a tuple for the specifiation parameter, where the tuple describes **the lag polynomial itself**. In particular, here we would want to use:\n",
"\n",
"```python\n",
"ar = 1 # this is the maximum degree specification\n",
"ma = (1,0,0,1) # this is the lag polynomial specification\n",
"mod = sm.tsa.statespace.SARIMAX(data['wpi'], trend='c', order=(ar,1,ma)))\n",
"```\n",
"\n",
"This gives the following form for the process of the data:\n",
"\n",
"$$\n",
"\\Delta y_t = c + \\phi_1 \\Delta y_{t-1} + \\theta_1 \\epsilon_{t-1} + \\theta_4 \\epsilon_{t-4} + \\epsilon_{t} \\\\\n",
"(1 - \\phi_1 L)\\Delta y_t = c + (1 + \\theta_1 L + \\theta_4 L^4) \\epsilon_{t}\n",
"$$\n",
"\n",
"which is what we want."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'data' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-7-2f5408f59767>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Fit the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtsa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatespace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSARIMAX\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ln_wpi'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'c'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
]
}
],
"source": [
"# Fit the model\n",
"mod = sm.tsa.statespace.SARIMAX(data['ln_wpi'], trend='c', order=(1,1,1))\n",
"res = mod.fit()\n",
"print(res.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ARIMA Example 3: Airline Model\n",
"\n",
"In the previous example, we included a seasonal effect in an *additive* way, meaning that we added a term allowing the process to depend on the 4th MA lag. It may be instead that we want to model a seasonal effect in a multiplicative way. We often write the model then as an ARIMA $(p,d,q) \\times (P,D,Q)_s$, where the lowercast letters indicate the specification for the non-seasonal component, and the uppercase letters indicate the specification for the seasonal component; $s$ is the periodicity of the seasons (e.g. it is often 4 for quarterly data or 12 for monthly data). The data process can be written generically as:\n",
"\n",
"$$\n",
"\\phi_p (L) \\tilde \\phi_P (L^s) \\Delta^d \\Delta_s^D y_t = A(t) + \\theta_q (L) \\tilde \\theta_Q (L^s) \\epsilon_t\n",
"$$\n",
"\n",
"where:\n",
"\n",
"- $\\phi_p (L)$ is the non-seasonal autoregressive lag polynomial\n",
"- $\\tilde \\phi_P (L^s)$ is the seasonal autoregressive lag polynomial\n",
"- $\\Delta^d \\Delta_s^D y_t$ is the time series, differenced $d$ times, and seasonally differenced $D$ times.\n",
"- $A(t)$ is the trend polynomial (including the intercept)\n",
"- $\\theta_q (L)$ is the non-seasonal moving average lag polynomial\n",
"- $\\tilde \\theta_Q (L^s)$ is the seasonal moving average lag polynomial\n",
"\n",
"sometimes we rewrite this as:\n",
"\n",
"$$\n",
"\\phi_p (L) \\tilde \\phi_P (L^s) y_t^* = A(t) + \\theta_q (L) \\tilde \\theta_Q (L^s) \\epsilon_t\n",
"$$\n",
"\n",
"where $y_t^* = \\Delta^d \\Delta_s^D y_t$. This emphasizes that just as in the simple case, after we take differences (here both non-seasonal and seasonal) to make the data stationary, the resulting model is just an ARMA model.\n",
"\n",
"As an example, consider the airline model ARIMA $(2,1,0) \\times (1,1,0)_{12}$, with an intercept. The data process can be written in the form above as:\n",
"\n",
"$$\n",
"(1 - \\phi_1 L - \\phi_2 L^2) (1 - \\tilde \\phi_1 L^{12}) \\Delta \\Delta_{12} y_t = c + \\epsilon_t\n",
"$$\n",
"\n",
"Here, we have:\n",
"\n",
"- $\\phi_p (L) = (1 - \\phi_1 L - \\phi_2 L^2)$\n",
"- $\\tilde \\phi_P (L^s) = (1 - \\phi_1 L^12)$\n",
"- $d = 1, D = 1, s=12$ indicating that $y_t^*$ is derived from $y_t$ by taking first-differences and then taking 12-th differences.\n",
"- $A(t) = c$ is the *constant* trend polynomial (i.e. just an intercept)\n",
"- $\\theta_q (L) = \\tilde \\theta_Q (L^s) = 1$ (i.e. there is no moving average effect)\n",
"\n",
"It may still be confusing to see the two lag polynomials in front of the time-series variable, but notice that we can multiply the lag polynomials together to get the following model:\n",
"\n",
"$$\n",
"(1 - \\phi_1 L - \\phi_2 L^2 - \\tilde \\phi_1 L^{12} + \\phi_1 \\tilde \\phi_1 L^{13} + \\phi_2 \\tilde \\phi_1 L^{14} ) y_t^* = c + \\epsilon_t\n",
"$$\n",
"\n",
"which can be rewritten as:\n",
"\n",
"$$\n",
"y_t^* = c + \\phi_1 y_{t-1}^* + \\phi_2 y_{t-2}^* + \\tilde \\phi_1 y_{t-12}^* - \\phi_1 \\tilde \\phi_1 y_{t-13}^* - \\phi_2 \\tilde \\phi_1 y_{t-14}^* + \\epsilon_t\n",
"$$\n",
"\n",
"This is similar to the additively seasonal model from example 2, but the coefficients in front of the autoregressive lags are actually combinations of the underlying seasonal and non-seasonal parameters.\n",
"\n",
"Specifying the model in Statsmodels is done simply by adding the `seasonal_order` argument, which accepts a tuple of the form `(Seasonal AR specification, Seasonal Integration order, Seasonal MA, Seasonal periodicity)`. The seasonal AR and MA specifications, as before, can be expressed as a maximum polynomial degree or as the lag polynomial itself. Seasonal periodicity is an integer.\n",
"\n",
"For the airline model ARIMA $(2,1,0) \\times (1,1,0)_{12}$ with an intercept, the command is:\n",
"\n",
"```python\n",
"mod = sm.tsa.statespace.SARIMAX(data['lnair'], order=(2,1,0), seasonal_order=(1,1,0,12))\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "ProxyError",
"evalue": "HTTPConnectionPool(host='127.0.0.1', port=9): Max retries exceeded with url: http://www.stata-press.com/data/r12/air2.dta (Caused by ProxyError('Cannot connect to proxy.', NewConnectionError('<urllib3.connection.HTTPConnection object at 0xec7da90c>: Failed to establish a new connection: [Errno 111] Connection refused')))",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mConnectionRefusedError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 158\u001b[0m conn = connection.create_connection(\n\u001b[0;32m--> 159\u001b[0;31m (self._dns_host, self.port), self.timeout, **extra_kw)\n\u001b[0m\u001b[1;32m 160\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/util/connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m 79\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0merr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 80\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 81\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/util/connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource_address\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m \u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msa\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 71\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msock\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mConnectionRefusedError\u001b[0m: [Errno 111] Connection refused",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mNewConnectionError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m 599\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 600\u001b[0;31m chunked=chunked)\n\u001b[0m\u001b[1;32m 601\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m 353\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 354\u001b[0;31m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mhttplib_request_kw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 355\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1243\u001b[0m \u001b[0;34m\"\"\"Send a complete request to the server.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1244\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1245\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36m_send_request\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1289\u001b[0m \u001b[0mbody\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_encode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'body'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1290\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendheaders\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1291\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36mendheaders\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m 1238\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mCannotSendHeader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1239\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage_body\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1240\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36m_send_output\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m 1025\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_buffer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1026\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1027\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 965\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_open\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 966\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 967\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connection.py\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 180\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 181\u001b[0;31m \u001b[0mconn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_new_conn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 182\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prepare_conn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 167\u001b[0m raise NewConnectionError(\n\u001b[0;32m--> 168\u001b[0;31m self, \"Failed to establish a new connection: %s\" % e)\n\u001b[0m\u001b[1;32m 169\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNewConnectionError\u001b[0m: <urllib3.connection.HTTPConnection object at 0xec7da90c>: Failed to establish a new connection: [Errno 111] Connection refused",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mMaxRetryError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/usr/lib/python3/dist-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m 448\u001b[0m \u001b[0mretries\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_retries\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 449\u001b[0;31m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 450\u001b[0m )\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m 637\u001b[0m retries = retries.increment(method, url, error=e, _pool=self,\n\u001b[0;32m--> 638\u001b[0;31m _stacktrace=sys.exc_info()[2])\n\u001b[0m\u001b[1;32m 639\u001b[0m \u001b[0mretries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/util/retry.py\u001b[0m in \u001b[0;36mincrement\u001b[0;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[1;32m 397\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnew_retry\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_exhausted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 398\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mMaxRetryError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_pool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merror\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mResponseError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcause\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 399\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mMaxRetryError\u001b[0m: HTTPConnectionPool(host='127.0.0.1', port=9): Max retries exceeded with url: http://www.stata-press.com/data/r12/air2.dta (Caused by ProxyError('Cannot connect to proxy.', NewConnectionError('<urllib3.connection.HTTPConnection object at 0xec7da90c>: Failed to establish a new connection: [Errno 111] Connection refused')))",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mProxyError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-8-ed689d52402c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mair2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'http://www.stata-press.com/data/r12/air2.dta'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_stata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mair2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdate_range\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mperiods\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'MS'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'lnair'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'air'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/requests/api.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(url, params, **kwargs)\u001b[0m\n\u001b[1;32m 73\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'allow_redirects'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 75\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'get'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 76\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 77\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/requests/api.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(method, url, **kwargs)\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[0;31m# cases, and look like a memory leak in others.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 59\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0msessions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 60\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 61\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/requests/sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m 531\u001b[0m }\n\u001b[1;32m 532\u001b[0m \u001b[0msend_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 533\u001b[0;31m \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 534\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 535\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/requests/sessions.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m 644\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 645\u001b[0m \u001b[0;31m# Send the request\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 646\u001b[0;31m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 647\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 648\u001b[0m \u001b[0;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m 508\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 509\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreason\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_ProxyError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 510\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mProxyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 511\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 512\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreason\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_SSLError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mProxyError\u001b[0m: HTTPConnectionPool(host='127.0.0.1', port=9): Max retries exceeded with url: http://www.stata-press.com/data/r12/air2.dta (Caused by ProxyError('Cannot connect to proxy.', NewConnectionError('<urllib3.connection.HTTPConnection object at 0xec7da90c>: Failed to establish a new connection: [Errno 111] Connection refused')))"
]
}
],
"source": [
"# Dataset\n",
"air2 = requests.get('http://www.stata-press.com/data/r12/air2.dta').content\n",
"data = pd.read_stata(BytesIO(air2))\n",
"data.index = pd.date_range(start=datetime(data.time[0], 1, 1), periods=len(data), freq='MS')\n",
"data['lnair'] = np.log(data['air'])\n",
"\n",
"# Fit the model\n",
"mod = sm.tsa.statespace.SARIMAX(data['lnair'], order=(2,1,0), seasonal_order=(1,1,0,12), simple_differencing=True)\n",
"res = mod.fit()\n",
"print(res.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice that here we used an additional argument `simple_differencing=True`. This controls how the order of integration is handled in ARIMA models. If `simple_differencing=True`, then the time series provided as `endog` is literatlly differenced and an ARMA model is fit to the resulting new time series. This implies that a number of initial periods are lost to the differencing process, however it may be necessary either to compare results to other packages (e.g. Stata's `arima` always uses simple differencing) or if the seasonal periodicity is large.\n",
"\n",
"The default is `simple_differencing=False`, in which case the integration component is implemented as part of the state space formulation, and all of the original data can be used in estimation."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ARIMA Example 4: ARMAX (Friedman)\n",
"\n",
"This model demonstrates the use of explanatory variables (the X part of ARMAX). When exogenous regressors are included, the SARIMAX module uses the concept of \"regression with SARIMA errors\" (see http://robjhyndman.com/hyndsight/arimax/ for details of regression with ARIMA errors versus alternative specifications), so that the model is specified as:\n",
"\n",
"$$\n",
"y_t = \\beta_t x_t + u_t \\\\\n",
" \\phi_p (L) \\tilde \\phi_P (L^s) \\Delta^d \\Delta_s^D u_t = A(t) +\n",
" \\theta_q (L) \\tilde \\theta_Q (L^s) \\epsilon_t\n",
"$$\n",
"\n",
"Notice that the first equation is just a linear regression, and the second equation just describes the process followed by the error component as SARIMA (as was described in example 3). One reason for this specification is that the estimated parameters have their natural interpretations.\n",
"\n",
"This specification nests many simpler specifications. For example, regression with AR(2) errors is:\n",
"\n",
"$$\n",
"y_t = \\beta_t x_t + u_t \\\\\n",
"(1 - \\phi_1 L - \\phi_2 L^2) u_t = A(t) + \\epsilon_t\n",
"$$\n",
"\n",
"The model considered in this example is regression with ARMA(1,1) errors. The process is then written:\n",
"\n",
"$$\n",
"\\text{consump}_t = \\beta_0 + \\beta_1 \\text{m2}_t + u_t \\\\\n",
"(1 - \\phi_1 L) u_t = (1 - \\theta_1 L) \\epsilon_t\n",
"$$\n",
"\n",
"Notice that $\\beta_0$ is, as described in example 1 above, *not* the same thing as an intercept specified by `trend='c'`. Whereas in the examples above we estimated the intercept of the model via the trend polynomial, here, we demonstrate how to estimate $\\beta_0$ itself by adding a constant to the exogenous dataset. In the output, the $beta_0$ is called `const`, whereas above the intercept $c$ was called `intercept` in the output."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "ProxyError",
"evalue": "HTTPConnectionPool(host='127.0.0.1', port=9): Max retries exceeded with url: http://www.stata-press.com/data/r12/friedman2.dta (Caused by ProxyError('Cannot connect to proxy.', NewConnectionError('<urllib3.connection.HTTPConnection object at 0xec76bd0c>: Failed to establish a new connection: [Errno 111] Connection refused')))",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mConnectionRefusedError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 158\u001b[0m conn = connection.create_connection(\n\u001b[0;32m--> 159\u001b[0;31m (self._dns_host, self.port), self.timeout, **extra_kw)\n\u001b[0m\u001b[1;32m 160\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/util/connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m 79\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0merr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 80\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 81\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/util/connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource_address\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m \u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msa\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 71\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msock\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mConnectionRefusedError\u001b[0m: [Errno 111] Connection refused",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mNewConnectionError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m 599\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 600\u001b[0;31m chunked=chunked)\n\u001b[0m\u001b[1;32m 601\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m 353\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 354\u001b[0;31m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mhttplib_request_kw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 355\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1243\u001b[0m \u001b[0;34m\"\"\"Send a complete request to the server.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1244\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1245\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36m_send_request\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1289\u001b[0m \u001b[0mbody\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_encode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'body'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1290\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendheaders\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1291\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36mendheaders\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m 1238\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mCannotSendHeader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1239\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage_body\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1240\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36m_send_output\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m 1025\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_buffer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1026\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1027\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 965\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_open\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 966\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 967\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connection.py\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 180\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 181\u001b[0;31m \u001b[0mconn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_new_conn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 182\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prepare_conn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 167\u001b[0m raise NewConnectionError(\n\u001b[0;32m--> 168\u001b[0;31m self, \"Failed to establish a new connection: %s\" % e)\n\u001b[0m\u001b[1;32m 169\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNewConnectionError\u001b[0m: <urllib3.connection.HTTPConnection object at 0xec76bd0c>: Failed to establish a new connection: [Errno 111] Connection refused",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mMaxRetryError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/usr/lib/python3/dist-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m 448\u001b[0m \u001b[0mretries\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_retries\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 449\u001b[0;31m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 450\u001b[0m )\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m 637\u001b[0m retries = retries.increment(method, url, error=e, _pool=self,\n\u001b[0;32m--> 638\u001b[0;31m _stacktrace=sys.exc_info()[2])\n\u001b[0m\u001b[1;32m 639\u001b[0m \u001b[0mretries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/util/retry.py\u001b[0m in \u001b[0;36mincrement\u001b[0;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[1;32m 397\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnew_retry\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_exhausted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 398\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mMaxRetryError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_pool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merror\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mResponseError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcause\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 399\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mMaxRetryError\u001b[0m: HTTPConnectionPool(host='127.0.0.1', port=9): Max retries exceeded with url: http://www.stata-press.com/data/r12/friedman2.dta (Caused by ProxyError('Cannot connect to proxy.', NewConnectionError('<urllib3.connection.HTTPConnection object at 0xec76bd0c>: Failed to establish a new connection: [Errno 111] Connection refused')))",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mProxyError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-9-1caba5d05731>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfriedman2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'http://www.stata-press.com/data/r12/friedman2.dta'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_stata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfriedman2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/requests/api.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(url, params, **kwargs)\u001b[0m\n\u001b[1;32m 73\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'allow_redirects'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 75\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'get'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 76\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 77\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/requests/api.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(method, url, **kwargs)\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[0;31m# cases, and look like a memory leak in others.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 59\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0msessions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 60\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 61\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/requests/sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m 531\u001b[0m }\n\u001b[1;32m 532\u001b[0m \u001b[0msend_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 533\u001b[0;31m \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 534\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 535\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/requests/sessions.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m 644\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 645\u001b[0m \u001b[0;31m# Send the request\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 646\u001b[0;31m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 647\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 648\u001b[0m \u001b[0;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3/dist-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m 508\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 509\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreason\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_ProxyError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 510\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mProxyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 511\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 512\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreason\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_SSLError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mProxyError\u001b[0m: HTTPConnectionPool(host='127.0.0.1', port=9): Max retries exceeded with url: http://www.stata-press.com/data/r12/friedman2.dta (Caused by ProxyError('Cannot connect to proxy.', NewConnectionError('<urllib3.connection.HTTPConnection object at 0xec76bd0c>: Failed to establish a new connection: [Errno 111] Connection refused')))"
]
}
],
"source": [
"# Dataset\n",
"friedman2 = requests.get('http://www.stata-press.com/data/r12/friedman2.dta').content\n",
"data = pd.read_stata(BytesIO(friedman2))\n",
"data.index = data.time\n",
"\n",
"# Variables\n",
"endog = data.ix['1959':'1981', 'consump']\n",
"exog = sm.add_constant(data.ix['1959':'1981', 'm2'])\n",
"\n",
"# Fit the model\n",
"mod = sm.tsa.statespace.SARIMAX(endog, exog, order=(1,0,1))\n",
"res = mod.fit()\n",
"print(res.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ARIMA Postestimation: Example 1 - Dynamic Forecasting\n",
"\n",
"Here we describe some of the post-estimation capabilities of Statsmodels' SARIMAX.\n",
"\n",
"First, using the model from example, we estimate the parameters using data that *excludes the last few observations* (this is a little artificial as an example, but it allows considering performance of out-of-sample forecasting and facilitates comparison to Stata's documentation)."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'friedman2' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-10-2387f4ef1496>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mraw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_stata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfriedman2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mraw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mraw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mraw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m'1981'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'friedman2' is not defined"
]
}
],
"source": [
"# Dataset\n",
"raw = pd.read_stata(BytesIO(friedman2))\n",
"raw.index = raw.time\n",
"data = raw.ix[:'1981']\n",
"\n",
"# Variables\n",
"endog = data.ix['1959':, 'consump']\n",
"exog = sm.add_constant(data.ix['1959':, 'm2'])\n",
"nobs = endog.shape[0]\n",
"\n",
"# Fit the model\n",
"mod = sm.tsa.statespace.SARIMAX(endog.ix[:'1978-01-01'], exog=exog.ix[:'1978-01-01'], order=(1,0,1))\n",
"fit_res = mod.fit()\n",
"print(fit_res.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we want to get results for the full dataset but using the estimated parameters (on a subset of the data)."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'endog' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-11-9b773553794d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtsa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatespace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSARIMAX\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mendog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexog\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfit_res\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'endog' is not defined"
]
}
],
"source": [
"mod = sm.tsa.statespace.SARIMAX(endog, exog=exog, order=(1,0,1))\n",
"res = mod.filter(fit_res.params)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `predict` command is first applied here to get in-sample predictions. We use the `full_results=True` argument to allow us to calculate confidence intervals (the default output of `predict` is just the predicted values).\n",
"\n",
"With no other arguments, `predict` returns the one-step-ahead in-sample predictions for the entire sample."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'res' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-12-cff6581c7519>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# In-sample one-step-ahead predictions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mpredict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_prediction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mpredict_ci\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconf_int\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'res' is not defined"
]
}
],
"source": [
"# In-sample one-step-ahead predictions\n",
"predict = res.get_prediction()\n",
"predict_ci = predict.conf_int()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also get *dynamic predictions*. One-step-ahead prediction uses the true values of the endogenous values at each step to predict the next in-sample value. Dynamic predictions use one-step-ahead prediction up to some point in the dataset (specified by the `dynamic` argument); after that, the previous *predicted* endogenous values are used in place of the true endogenous values for each new predicted element.\n",
"\n",
"The `dynamic` argument is specified to be an *offset* relative to the `start` argument. If `start` is not specified, it is assumed to be `0`.\n",
"\n",
"Here we perform dynamic prediction starting in the first quarter of 1978."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'res' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-13-24c8c7a5e61f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Dynamic predictions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mpredict_dy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_prediction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdynamic\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'1978-01-01'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mpredict_dy_ci\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpredict_dy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconf_int\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'res' is not defined"
]
}
],
"source": [
"# Dynamic predictions\n",
"predict_dy = res.get_prediction(dynamic='1978-01-01')\n",
"predict_dy_ci = predict_dy.conf_int()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can graph the one-step-ahead and dynamic predictions (and the corresponding confidence intervals) to see their relative performance. Notice that up to the point where dynamic prediction begins (1978:Q1), the two are the same."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'data' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-14-e8669824a91c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;31m# Plot data points\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'1977-07-01'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'consump'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstyle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'o'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Observed'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;31m# Plot predictions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 648x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Graph\n",
"fig, ax = plt.subplots(figsize=(9,4))\n",
"npre = 4\n",
"ax.set(title='Personal consumption', xlabel='Date', ylabel='Billions of dollars')\n",
"\n",
"# Plot data points\n",
"data.ix['1977-07-01':, 'consump'].plot(ax=ax, style='o', label='Observed')\n",
"\n",
"# Plot predictions\n",
"predict.predicted_mean.ix['1977-07-01':].plot(ax=ax, style='r--', label='One-step-ahead forecast')\n",
"ci = predict_ci.ix['1977-07-01':]\n",
"ax.fill_between(ci.index, ci.ix[:,0], ci.ix[:,1], color='r', alpha=0.1)\n",
"predict_dy.predicted_mean.ix['1977-07-01':].plot(ax=ax, style='g', label='Dynamic forecast (1978)')\n",
"ci = predict_dy_ci.ix['1977-07-01':]\n",
"ax.fill_between(ci.index, ci.ix[:,0], ci.ix[:,1], color='g', alpha=0.1)\n",
"\n",
"legend = ax.legend(loc='lower right')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, graph the prediction *error*. It is obvious that, as one would suspect, one-step-ahead prediction is considerably better."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'predict' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-15-aa5ccb6e7d4d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;31m# In-sample one-step-ahead predictions and 95% confidence intervals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mpredict_error\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredicted_mean\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mendog\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 10\u001b[0m \u001b[0mpredict_error\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'1977-10-01'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'One-step-ahead forecast'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mci\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpredict_ci\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'1977-10-01'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'predict' is not defined"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 648x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Prediction error\n",
"\n",
"# Graph\n",
"fig, ax = plt.subplots(figsize=(9,4))\n",
"npre = 4\n",
"ax.set(title='Forecast error', xlabel='Date', ylabel='Forecast - Actual')\n",
"\n",
"# In-sample one-step-ahead predictions and 95% confidence intervals\n",
"predict_error = predict.predicted_mean - endog\n",
"predict_error.ix['1977-10-01':].plot(ax=ax, label='One-step-ahead forecast')\n",
"ci = predict_ci.ix['1977-10-01':].copy()\n",
"ci.iloc[:,0] -= endog.loc['1977-10-01':]\n",
"ci.iloc[:,1] -= endog.loc['1977-10-01':]\n",
"ax.fill_between(ci.index, ci.ix[:,0], ci.ix[:,1], alpha=0.1)\n",
"\n",
"# Dynamic predictions and 95% confidence intervals\n",
"predict_dy_error = predict_dy.predicted_mean - endog\n",
"predict_dy_error.ix['1977-10-01':].plot(ax=ax, style='r', label='Dynamic forecast (1978)')\n",
"ci = predict_dy_ci.ix['1977-10-01':].copy()\n",
"ci.iloc[:,0] -= endog.loc['1977-10-01':]\n",
"ci.iloc[:,1] -= endog.loc['1977-10-01':]\n",
"ax.fill_between(ci.index, ci.ix[:,0], ci.ix[:,1], color='r', alpha=0.1)\n",
"\n",
"legend = ax.legend(loc='lower left');\n",
"legend.get_frame().set_facecolor('w')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
}

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/srv/reproducible-results/rbuild-debian/tmp.FTzzuDJkiI/dbd-tmp-gqMLHgR/diffoscope_1mz05yxs/tmpandzkvja/9a2f7f5e-1499-484c-ad76-ff4f6149e579 vs.

Similarity: 0.0%
Differences: {
"replace": {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Autoregressive Moving Average (ARMA): Sunspots data"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
" from pandas.core import datetools\n"
]
}
],
"source": [
"%matplotlib inline\n",
"\n",
"from __future__ import print_function\n",
"import numpy as np\n",
"from scipy import stats\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import statsmodels.api as sm"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from statsmodels.graphics.api import qqplot"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sunpots Data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"::\n",
"\n",
" Number of Observations - 309 (Annual 1700 - 2008)\n",
" Number of Variables - 1\n",
" Variable name definitions::\n",
"\n",
" SUNACTIVITY - Number of sunspots for each year\n",
"\n",
" The data file contains a 'YEAR' variable that is not returned by load.\n",
"\n"
]
}
],
"source": [
"print(sm.datasets.sunspots.NOTE)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dta = sm.datasets.sunspots.load_pandas().data"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dta.index = pd.Index(sm.tsa.datetools.dates_from_range('1700', '2008'))\n",
"del dta[\"YEAR\"]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"dta.plot(figsize=(12,8));"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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aHarPAQbrpncV846KiMuAFZn51y2uRZI0DY3czOqN1pLmmraO/hERPcBvAe+ewrobImJzRGzet29f64uTpDmukeEiHV1G0lzT6lC9G1hRN31uMa/mNOAHgE0R8SjwCmBjRKwd+0aZeXNmrs3MtUuWLGlhyZKkmpO9r6Nbx2+XpJPV6tE/7gcuiogLqIbpa4E31xZm5rPAWbXpiNgE/Iqjf0hS93N0GUlzSUtbqjNzBLgRuBt4GPhcZm6NiI9GxBta+dmSJEnSTGn5ONWZeRdw15h5H5pg3XWtrkeSJElqNp+oKEmSpKbLTCpZ950s5tevc/z8LLatvQZYPH/eDFV98gzVkiSp65QryaZte9m65wCXLF/cdU8czvpkOe1tjwXP5FgwPRpK66bHrkdCJbP4Gh14KwmVSnW7seskxbxK9Tu16br3oP49GB2eGxEBr3jJmc15sxYyVEuSpK5SriTX33IfWwaHGC6VWdDfy8vOfRGffuu/JGJ02CtXRge/Wlg8FhxHt6jWwmNteeaxkFqpm0ddaK05FmqPzR1vuWYnQ7UkSV2mFgKrgbEaHMuZZKUuNDI6FNaHyfFC4tjL7dXPGfO5jJ4x9jL+pDUz8Qr1Lay12rP++9EW1+ryBx59hgd2PsPhkQoAh0plvvHYELf/w6Ncdv7pkxcitYihWpJmuW6/TN7JjgbRMX1Bx14qr7WIViqjL6mXc/Ql93Jt3SIkl2vzi+lK5VjL61z26P7nKRWBuqY0UuHR/c8bqtU2hmpJmsXGu0y+ZsVARzyIpVwExHIlGalUqFRgpFI5GiZHyqMv09e3Wta3sNb37xy9LjBJ6+iJ1F/ar7+sP3aeZt7KM0+lv6/naEs1QH9fDyvPPLWNVWmuM1RL0iy2adtetgwOcahUBqqXybcMDrFp296GHshSC8LV79UW1JGjATkpl6vLh0tlEvj27mep5LF1ai2w0slYs2KAC5cuYutjT0FvH6fM6+PCpYtYs2JgSttXKsmWwSEe3f88K888lTUrBujx6o0aZKiWpBaqXcYf25cVxvRHrX0f5wanUeFznCA6UV/VTNj86DMMF4G6ZrhU5r7vPc3qsxcfd8NWdbruhq4KPF8agYQHH3vmaHieaiA+Uq62JD73wsjUNpCmoKcneP/Vq/m5X3w35UXLuPEdG6YcjCuV5GNffJjtew9SGqnQ39fDhUsX8f6rVxus1RBDtaQ5rb7v6th+q+W6ZZUKxevK0cBZa20dN5B2SL/XRaf0jXuZ/EXz57Fz/6EpvUe5XP1BDh+pnGDN5rElUSfS0xP0798O+7dz2fnvmfJ2WwaH2L734NHficMjFbbvPciWwSH7Y6shhmpJJ22kXO3/Ot4IAPWtsWNbYUeNXzqqtbaulXYKrbi1VtbaCAj1r+uHzjruZrGcO10QGr1M3g62JKqVvMlRrWKolgRUg0ypXGGkkoyUKxwp+sSOlJMjxfwj5er0SKW6fLYH0tmgkcvk7WJLolrJmxzVKoZqaQ6pVJIXRsocKpUZLlW/Hzw8QiWT+773dLvLU4uc7GXydrElce5oRzefbrx6o+5gqJZmocxk+Mix4Dx8pPr9hSPl41qXK53Q8VeqY0vi3NCubj7dePVG3cFQLXW5F46UGSlXb6h75MnnjoZou2aoW9mSODe0s5tPt129UXcwVEtdIjM5VCrzfGmEQ4eL76VqoD5Uqg5X9tTBUpurlBpnS+LcYDcfzTaGaqkDjZQrPF8qc6g0wvOHq9+HS+WOGKJNmgm2JM5+dvPRbNPyUB0RVwG/A/QCn87Mj49Z/svA24ERYB/w7zNzZ6vrktptpFyhVK5QGqlwpFwd+/g7Tz7HwcMjMzoesCS1g918NNu0NFRHRC9wE/AaYBdwf0RszMyH6lb7BrA2Mw9FxH8A/gvwU62sS2qlzORwEZRLI6OD8+GR2uvqGMk1tSfe7bf7hqQ5wm4+mm1a3VJ9ObA9M3cARMSdwDXA0VCdmV+pW/9e4C0trkmzxEhtTOViXOXq9+oYyvWPgz72MJJq4B314JFief3joGvb1D/EZOzDTfK47ZKDh0fIhHt3ODSdJE2F3Xw0m7Q6VJ8DDNZN7wKumGT99cAXW1qROlK5eLBIqVzhSNGSe6RcoVw59qCR2jrlIkh32ugWDk0nSdLc1TE3KkbEW4C1wI9MsHwDsAHgvPPOm8HKdLIy82g4PjKq/3Ae7RpxpHx8VwhJkqRu0+pQvRtYUTd9bjFvlIh4NfAB4Ecy8/B4b5SZNwM3A6xdu9YEdgLlSnJ4pExppNot4mRlQiWrobeSSaUC5ay9ro6NXKkcW6ecSWZSLuZ1WmuyJElSK7Q6VN8PXBQRF1AN09cCb65fISIuBf4AuCoz97a4nlkhM4+2+h4eOXbzWy1EHx6pMFI2zU5XOx6X283cX5IkHdPSUJ2ZIxFxI3A31SH1bs3MrRHxUWBzZm4E/iuwCPiziAB4LDPf0Mq6ukm5kpRGKpQz2brn2aMB2hbg5mrX43K7lftLkqTRWt6nOjPvAu4aM+9Dda9f3eoaulG5kjxx4AWeeHaYF45Uh1s7MDzS5qpmr3Y+Lrcbub8kSRqtp90FaLSRcoVdzxziG489w2P7D1EasUl6Jkz2uFwdz/0laS6rVJIHdz7D/3pwFw/ufMbRnwR00Ogfc91IucLjz77AEwdesD90G/i43Olxf0maq+z+ponYUt1mR8oVBp8+xDcGh9j1zLCBuk1qj8tlpARZ4ZTij6SPyx2f+0vSXFXf/S0Z3f1Nc5uhuk2OlCs8tv8Q33jMMN0Jao/LXfTQX7Dge3/Pf/zRi2x1mIT7S9JcZfc3TcRQPcNKIxV27n+ebzw2xO6hYR960kFqj8tdsPNrXHb+6QbEE3B/SZqLat3f6tn9TWCf6hmTCd976nn2HngBc7Sk6XBMcKlz1Lq/bX3sKejt45R5fXZ/E2ConjGlcoUnnn2h3WVI6jLeFCV1llr3t5/7xXdTXrSMG9+xwRNdAXb/kKSO5k1R6nRzcXg5u79pPLZUS1IHm+ymKB+0o3bzSop0jC3VktTBvClKncwrKdIxhmpJ6mCOCa5O5vBy0jGGaknqYI4JPn1zsY9vu3glRTrGPtWzmMNwSbND7aYo9m/nsvPf0+5yOpp9fGeWw8tJxxiqZyn/Y5E0F9X38YXRfXy9sbP5HF5OOsbuH7OUN49Imovs4zvzHF5OqjJUz1L+xyJpLrKPr6R2aXmojoirImJbRGyPiPeOs/yUiPhssfy+iFjZ6prmAv9jkTQXOVqKpHZpaaiOiF7gJuBq4GLguoi4eMxq64FnMvNC4JPAb7ayprnC/1gkzUWOliKpXVrdUn05sD0zd2RmCbgTuGbMOtcAtxevPw9cGRH+9WuQ/7FImqvs4yupHSKzdeN3RsRPAldl5tuL6euBKzLzxrp1vl2ss6uY/m6xzlMTve8Z56/O17z/1pbVPZ4t39wCwJqXrzmpbTPhpd9/yUl99iMPfRuAiy7+gRnddi5q175u9N+pW+vW1M3FY8Rjs3t0479zO3+nNHWZycHDZUhYeEovAwvmMZNtr597x796IDPXTmXdrgnVEbEB2ACw6OyX/uBrP3xHy+puhcMjFQ6PlNtdxrR06x8r/9B1h249Rjw254ZuPEYMid1jLh4jJ7NtZvLY08McOnwECHp6gkWn9PH9Lz5txoJ1J4XqVwIfycx/W0y/DyAz/3PdOncX63w9IvqAJ4AlOUlha9euzc2bN7es7lYYfPoQu54ZbncZ0/ILb34DADf96cYZ3bbdn62Z0a3HiMfm3NCNx0g7f6c0PXPxGDmZbR/c+Qyf+vIjR8edB1jY38vvXncpV65eNu0aTkZETDlUt7pP9f3ARRFxQUT0A9cCY/fmRuCG4vVPAl+eLFBLkiRp9htveODhUpmH9hxoU0WTa+kTFTNzJCJuBO4GeoFbM3NrRHwU2JyZG4FbgDsiYjvwNNXgLUmSpDmsNjxwfUv1gv5eLl6+uI1VTazljynPzLuAu8bM+1Dd6xeA/7vVdUiSJLVbpZKUzryQ8qJlPLjzGR/rPona8MDb9x6kNFJhQX8va1YMsG7V0naXNq6Wh2pJkiRVA/XHvvgwBy9+I/T28akvP8KFSxc55O0EasMDb9k1RKWSXLx8MetWLaW3Q/eVoVqSJGkGbBkcYvveg9DXD1RHBtu+9yBbBoe47PzT21xdZ+rpCX7w/NN5xUvObHcpJ9Tyx5RLkiRp/BvvSiMVHt3/fJsqUjMZqiVJkmZA7ca7ev19Paw889Q2VaRmMlRLkiTNgNqNd6f09RDAKX09XLh0EWtWDLS7NDWBfaolSZJmwNEb7waHeHT/86w881RH/5hFDNWSJEkzpKcnuOz8070xcRay+4ekGVcbp3X4/Ffx4M5nqFR8iKrUTv5OSo2zpVrSjHKcVqmz+DspNYct1TNk2eL5LB+Y37EDlkszZdQ4rdEzapzW2cyWQHWqufo7KTWboXqG9Pf1cP6Zp3LpeQOcM7DAcK05ay6O01rfEjh8wQ/zqS8/wse++LDBWh1hLv5OSq1gqJ5h83p7OO/MhVx63gDnnr6Avl7DteaWuThOqy2B6mRz8XdSagVDdZvM6+1hxRkLuXSF4bqZvMTe+ebiOK22BKqTzcXfSakVvFGxzfqKcL18YAGPPzvME8++wJGyQfBkeLNNd5iL47TWWgIP1wVrWwLVKebi76TUCobqDtHbE5x7+kLOftECnjzwAo8/O0xpxHA9HaMuscOoS+yOB9pZ5to4rbWWwO17D1IaqdBvS2BHq13xKi9axoM7n5kTAXOu/U5KrWCo7jC9PcHygQW8ePF8nnzuBfYMGa6narJL7P5HoXayJbB7eMVLar65cqLaslAdEWcAnwVWAo8Cb8rMZ8asswb4fWAxUAZ+IzM/26qauklPT3D2ixaw7LT57Dt4mIOHRzh8pEKpXOHwkTJ2FT6el9jVyWwJ7A5e8ZKaay6dqLaypfq9wD2Z+fGIeG8x/Z4x6xwC3pqZj0TEcuCBiLg7M70lvtDTEyxbPJ9lY+aXRo4F7Or3St338pxs3fYSu6RGecVLaq65dKLaylB9DbCueH07sIkxoTozv1P3ek9E7AWWAIbqE+jv66G/r4dFp4z/T1ipJIdHKpRGKpTz+ICd48wba0F/LwAvXXIqlYRyJpVKUsmkXHyvJJQr1eks1ilXkoggmdlg7yV2SY3yipfUXHPpRLWVoXpZZj5evH4CjmtsHSUiLgf6ge+2sKY5o6cnWNDfezQYn4x5vdURF5cunj/tbU+bXz201q48nSPlCkdGklK5Un1dfJVGsm66OQHcS+ySGuEVL6m55tKJakOhOiL+DnjxOIs+UD+RmRkRE6amiDgbuAO4ITMrE6yzAdgAcN555510zZpZ83p7quG8f/L1KpX60J3M7+8lE5YPzOdIudr6faRcoVxJRioVRsppv3JJTecVL6m55tKJakOhOjNfPdGyiHgyIs7OzMeL0Lx3gvUWA38NfCAz753ks24GbgZYu3atcWqW6ekJ5vf0Mn9etWW9v2glP3+SM9n6gD1SLl5XkpFKjnroSyYkWXyvzat2TjnWC+bY8tq8o9uMmZ7sfWvhfwq9ayR1KK94qVN14ygac+lEtZXdPzYCNwAfL77/5dgVIqIf+HPgjzLz8y2sRbNQb0/Q29PLBN3K2yYzOVKutryXRmpdXSpH+7gfKVdvKh3xIT+SpCnq5lE05sqJaivjyMeBz0XEemAn8CaAiFgLvCMz317M+9fAmRHxtmK7t2XmlhbWJbVURNDfF/T39cApE69Xa9Wuhe1SucKhwyM8XyrzwpGyrd2SpKPm0iga3aploToz9wNXjjN/M/D24vUfA3/cqhqkTlZraa91ealXriSHSiMcKpV5/vCx7/Yjl6S5aS6NotGtOuzCuSSoBu7T5s/jtPnzjs7LTIaPlHn+cJlDpZGj35s1cookqXPNpVE0upWhWuoSEcHC/j4W9vdR36/k8EiZQ4fLHDpSZrho3R4u+dRNSZpN5tIoGt3KUK1xlSvJoYGXUDp1Gfc8/CTrVi2lt8NvhJirTunr5ZS+Xuov/mVWH/5zqFRtzX7hSNmwLUldbC6NotGtDNU6TrmSXH/LfexS28ALAAAgAElEQVS76PVkTx/v/Mw3WLNigDvWX2Gw7hIRwfx51f7aZ5x6bJDwzOSFIxWGj1TD9nCpGrYdjUTqbt041Jqmb66MotGtDNU6zqZte9kyOET2VsPYoVKZLYNDbNq2lytXT/pgTHW4iGNP2qwP23BsKMCRSvUBPCPl6rjfR4rAfWx+cqQYH7xss7fUdt081Jq6hyduJ2ao1nG27jnAcKk8at5wqcxDew4Yqmexo0MB0jPlbcqVY8F67MNx4NgDco69rs2vbXNsvWMb1b8c/RCf+lUW9veRJC9dciqVhEpWa8na60wyq0/eLFeSSlaXlStJT0+QWb0h1BMDdTuHWlOreeI2NYZqHeeS5YtZ0N/LobpgvaC/l4uXL25jVepE1WEB2/MHta83gGDp4vnT3nZR8cSgyy84gyzCeDmTSgXKmZTL1elaGK+dPJRGKlSWXsSRU5fxnSef4wfPP50Ijgb5WoCvhflakJdayaHW1GqeuE2NoVrHWbdqKWtWDLBlcIjhUpkF/b2sWTHAulVL212aBDT3RtqIoK83TvjHsHavwbPffw3Z08dv/s0/T+leg/qAXR+yc5wW+aPLxmw/dt7Y7UcvGG9WdebYOsb7fuxkoK7uSvX7SCUpF11/bODvHA61pqlopPuGJ25TY6jWcXp7gjvWX8GmbXt5aM8BLl6+2NE/1DHadSPtyd5rEBH0BvQyu35/KpVjrfkjlWrr/kilcrTVv9bnfqRyrKW/Ol2pBnSDedM41JpOpNHuG564TY2hWuPq7QmuXL3MPtTqOO26kdZ7DUbr6Ql6CMZ5IOiUZY4O3WMDeLVLTh5tQa+1nkNtenQrO4xpfWeSFv2pVznqCsKxKw057j0B7biZy6HWdCKNdt/wxG1qDNWSukq7wq33GjRfRDCvt7Fg3klGyhXeeus/8vwlbyR7+rhp03Zedu6L+MO3riUiqkG/6Ldf3w2n/kba/r7qjcJLTuunXDl2A+7o78f313eoNU2m0e4bnrhNjaFaUldpV7j1XgOdyP/+zr7jrqJ8a9ez/OP3np7yCd/84gzjwqWnnXDd+i44Y2+Ure8jXxuZZ2wf+voW/WMhfeIRd+qNd0/AWCe6SjB2ee1qQ23UoNrVidoVgole68Sa0X3DE7cTM1RL6irtCrfea6ATmemrKM3ogtPtxp5A1K4AHL3htnL8vKOvKznuiUXWTVcqx05KJjsJGe8EZNS8uokJ7zFu4QmC3TdmhqFaUldpZ7j1XgNNxi5CMy8iiICeWXYj8InUTiaSMS349fcA1E0n8Cdvv4K/f+Qp/vmJ51i1bBGvfOlZR4cEHW+UorH3MowdKvToSQjHr5dQ3A/Rvn3UDoZqdZxmDpem2amRcOvxpVZp9CqKx6amqnYyUUxNebvXv3w5r395S0qaUH0Yr04fP4xo/evJHhjW6aJbCq23du3a3Lx5c7vL0CTWrVsHwKZNm6a1XW24tK9/53Gyp4+Fp8ybkeHSNDd4fKnVypU8qasoHptSZ4qIBzJz7VTWnfrziKUZMGq4tOgZNVya1CiPL7Va7SrKO6+8iCtXL5tyIPbYlLpfy0J1RJwREV+KiEeK7xPeLhoRiyNiV0T8XqvqUXeY7EYfqVEeX+pUHptS92tlS/V7gXsy8yLgnmJ6Ir8OfLWFtahL1G70qeeNPmoWjy91Ko9Nqfu1MlRfA9xevL4deON4K0XEDwLLgL9tYS3qErUbfRb2Vx/qvNCxgNVEHl/qVB6bUvdr2Y2KETGUmQPF6wCeqU3XrdMDfBl4C/BqYG1m3jjB+20ANgCcd955P7hz586W1K3mONkbFeHkb/SRpsLjS53KY1PqPNO5UbGhIfUi4u+AF4+z6AP1E5mZETFeev954K7M3BUx+R+OzLwZuBmqo3+cXMWaCY0OC+VYwGoljy91Ko9Nqbs1FKoz89UTLYuIJyPi7Mx8PCLOBsa7hfmVwA9HxM8Di4D+iDiYmZP1v1YHqw0Lte+i15M9fbzzM99wWChJkjTrtbJP9UbghuL1DcBfjl0hM386M8/LzJXArwB/ZKDubg4LJUmS5qJWhuqPA6+JiEeo9pf+OEBErI2IT7fwc9VGDgslSZLmopY9pjwz9wNXjjN/M/D2cebfBtzWqno0M2rDQh2qC9YOCyVJkmY7n6iopnJYKEmSNBe1rKVac1NvT3DH+iscFkqSJM0phmo1ncNCSZKkucbuH5IkSVKDDNWSJElSgwzVkiRJUoMM1ZIkSVKDIjPbXcO0RcQ+YGcbPvos4Kk2fG63cn9Nj/tr+txn0+P+mh731/S4v6bH/TU97dpf52fmkqms2JWhul0iYnNmrm13Hd3C/TU97q/pc59Nj/tretxf0+P+mh731/R0w/6y+4ckSZLUIEO1JEmS1CBD9fTc3O4Cuoz7a3rcX9PnPpse99f0uL+mx/01Pe6v6en4/WWfakmSJKlBtlRLkiRJDTJUT1FEXBUR2yJie0S8t931dLqIeDQi/ikitkTE5nbX02ki4taI2BsR366bd0ZEfCkiHim+n97OGjvJBPvrIxGxuzjGtkTEa9tZYyeJiBUR8ZWIeCgitkbELxbzPcbGMcn+8hgbR0TMj4h/jIhvFvvr14r5F0TEfcX/k5+NiP5219oJJtlft0XE9+qOrzXtrrWTRERvRHwjIv6qmO7448tQPQUR0QvcBFwNXAxcFxEXt7eqrvBvMnNNpw+B0ya3AVeNmfde4J7MvAi4p5hW1W0cv78APlkcY2sy864ZrqmTjQDvzsyLgVcAv1D8zfIYG99E+ws8xsZzGPjRzHw5sAa4KiJeAfwm1f11IfAMsL6NNXaSifYXwP9Td3xtaV+JHekXgYfrpjv++DJUT83lwPbM3JGZJeBO4Jo216QulplfBZ4eM/sa4Pbi9e3AG2e0qA42wf7SBDLz8cx8sHj9HNX/mM7BY2xck+wvjSOrDhaT84qvBH4U+Hwx3+OrMMn+0gQi4lzgx4FPF9NBFxxfhuqpOQcYrJvehX9wTySBv42IByJiQ7uL6RLLMvPx4vUTwLJ2FtMlboyIbxXdQ+zKMI6IWAlcCtyHx9gJjdlf4DE2ruLS/BZgL/Al4LvAUGaOFKv4/2SdsfsrM2vH128Ux9cnI+KUNpbYaX4b+FWgUkyfSRccX4ZqtcoPZeZlVLvM/EJE/Ot2F9RNsjosjy0Zk/t94KVUL6c+DnyiveV0nohYBPxP4Jcy80D9Mo+x442zvzzGJpCZ5cxcA5xL9Wru97e5pI42dn9FxA8A76O63/4lcAbwnjaW2DEi4nXA3sx8oN21TJehemp2Ayvqps8t5mkCmbm7+L4X+HOqf3Q1uScj4myA4vveNtfT0TLzyeI/qgrwh3iMjRIR86gGxD/JzP9VzPYYm8B4+8tj7MQycwj4CvBKYCAi+opF/j85jrr9dVXR7Sgz8zDwP/D4qnkV8IaIeJRqd9sfBX6HLji+DNVTcz9wUXHnaT9wLbCxzTV1rIg4NSJOq70Gfgz49uRbieoxdUPx+gbgL9tYS8erhcPC/4XH2FFF/8NbgIcz87fqFnmMjWOi/eUxNr6IWBIRA8XrBcBrqPZD/wrwk8VqHl+FCfbXP9ed4AbV/sEeX0Bmvi8zz83MlVTz1pcz86fpguPLh79MUTGU0m8DvcCtmfkbbS6pY0XES6i2TgP0AX/q/hotIj4DrAPOAp4EPgz8BfA54DxgJ/CmzPTmPCbcX+uoXpZP4FHg5+r6C89pEfFDwN8D/8SxPonvp9pP2GNsjEn213V4jB0nIl5G9UaxXqqNc5/LzI8Wf/vvpNqV4RvAW4pW2Dltkv31ZWAJEMAW4B11NzQKiIh1wK9k5uu64fgyVEuSJEkNsvuHJEmS1CBDtSRJktQgQ7UkSZLUIEO1JEmS1CBDtSRJktQgQ7UkSZLUIEO1JEmS1CBDtSQ1ICLeHxGfnuK6t0XEf2p1TZ0uIt4WEf+nge2/GBE3nHhNSZo5hmpJs1pEPBoRwxFxMCKeLILtopN8r3URsat+XmZ+LDPf3pxqj35GRsR7prndRyLij5tVR6cY7+fKzKsz8/Z21SRJ4zFUS5oLXp+Zi4DLgLXAB6f7BhHR1/SqxncD8DTw1hn6vJMWVT0nmidJc4F/+CTNGZm5G/gi8AMAEfEzEfFwRDwXETsi4udq69ZapSPiPRHxBPCZYtvlRav3wYhYPrYlNSL+LCKeiIhnI+KrEXHJVOuLiFOBnwR+AbgoItaOrWfM+o9GxKsj4irg/cBPFXV9s1i+PCI2RsTTEbE9In62btveouvKd4uf/4GIWFEs+1cRcX/xM9wfEf+qbrtNEfEbEfE14BDwkgnmvSgibomIxyNid0T8p4joneDn/p2IGIyIA0UdP1zMn+jn2hQRby9e90TEByNiZ0TsjYg/iogXFctWFq3+N0TEYxHxVER8YKr/HpI0HYZqSXNGERpfC3yjmLUXeB2wGPgZ4JMRcVndJi8GzgDOp9pyfDWwJzMXFV97xvmYLwIXAUuBB4E/mUaJPwEcBP4MuJtqq/UJZebfAB8DPlvU9fJi0Z3ALmA51bD+sYj40WLZLwPXUd0fi4F/DxyKiDOAvwY+BZwJ/Bbw1xFxZt1HXg9sAE4Ddk4w7zZgBLgQuBT4MWCibjL3A2uo7us/Bf4sIuZP8nPVe1vx9W+AlwCLgN8bs84PAauAK4EPRcTqCeqQpJNmqJY0F/xFRAwB/wf431SDGpn515n53az638DfAj9ct10F+HBmHs7M4al8UGbempnPZeZh4CPAy2stp1NwA9UAWaYaLq+NiHlT3HaU4gTiVcB7MvOFzNwCfJpj3UreDnwwM7cVP/83M3M/8OPAI5l5R2aOZOZngH8GXl/39rdl5tZi+ZGx86iG49cCv5SZz2fmXuCTwLXj1ZqZf5yZ+4v3+wRwCtUQPBU/DfxWZu7IzIPA+6jut/ruOr+WmcOZ+U3gm8B44VySGmKoljQXvDEzBzLz/Mz8+VpAjoirI+LeonvEENUgeFbddvsy84WpfkjRpeLjRZeKA8CjxaKzJtmstu0Kqq2ttZbtvwTmUw25J2M58HRmPlc3bydwTvF6BfDdCbbbOWZe/XYAg+NsVz/vfGAe8HhEDBX79g+ott4fJyJ+peiG82yx7ouYwj6boN6dQB+wrG7eE3WvD1FtzZakpjJUS5qTIuIU4H8C/x+wLDMHgLuAqFstx2w2dnqsNwPXAK+mGgxX1j5uCiVdT/Vv8heKPtw7qIbqWheQ54GFdfX3AksmqW0PcEZEnFY37zxgd/F6EHjpOHXsoRqK69VvN95njZ03CBwGzipOZgYyc3FmHte/vOg//avAm4DTi3+HZzm2z060z8fWex7VbidPnmA7SWoqQ7WkuaqfajeDfcBIRFxNtd/vZJ4EzpykO8dpVMPkfqoB+GPTqOcG4Neo9i2uff074LVFf+bvAPMj4seLLiEfLOqvr21lbeSNzBwE/gH4zxExPyJeBqwHajdVfhr49Yi4qBix42XF59wFfF9EvDki+iLip4CLgb+a6g+SmY9T7UrziYhYXNxM+NKI+JFxVj+NagjeB/RFxIeo9vEe9+cax2eAd0XEBVEdKrHWB3tkqvVKUjMYqiXNSUW3iP8IfA54hmor88YTbPPPVEPcjqJbw/Ixq/wR1e4Hu4GHgHunUktEvIJqa+tNmflE3ddGYDtwXWY+C/w81TC8m2rLdf1oIH9WfN8fEQ8Wr6+j2lq+B/hzqv3D/65Y9lvFz/63wAHgFmBB0a/6dcC7qZ4c/Crwusx8aio/S523Uj1xeYjq/v08cPY4690N/A3Vk4adwAuM7koy3s9V71bgDuCrwPeK7d85zVolqWGReaIra5IkSZImY0u1JEmS1CBDtSRJktQgQ7UkSZLUIEO1JEmS1CBDtSRJktSgvhOv0nnOOuusXLlyZbvLkCRJ0iz2wAMPPJWZS068ZpeG6pUrV7J58+Z2lyFJkqRZLCJ2TnVdu39IkiRJDTJUS5IkSQ0yVEuSJEkNakqojohbI2JvRHx7guUREZ+KiO0R8a2IuKxu2Q0R8UjxdUMz6pEkSZJmUrNaqm8Drppk+dXARcXXBuD3ASLiDODDwBXA5cCHI+L0JtXUNOVKcs/DT/Kpex7hnoefpFzJdpckSZKkDtKU0T8y86sRsXKSVa4B/igzE7g3IgYi4mxgHfClzHwaICK+RDWcf6YZdTVDuZJcf8t9bBkcYrhUZkF/L2tWDHDH+ivo7Yl2lydJkqQOMFN9qs8BBuumdxXzJprfMTZt28uWwSEOlcokcKhUZsvgEJu27W13aZIkSeoQXXOjYkRsiIjNEbF53759M/a5W/ccYLhUHjVvuFTmoT0HZqwGSZIkdbaZCtW7gRV10+cW8yaaf5zMvDkz12bm2iVLpvRgm6a4ZPliFvT3jpq3oL+Xi5cvnrEaJEmS1NlmKlRvBN5ajALyCuDZzHwcuBv4sYg4vbhB8ceKeR1j3aqlrFkxQJRLkBUWFn2q161a2u7SJEmS1CGacqNiRHyG6k2HZ0XELqojeswDyMz/DtwFvBbYDhwCfqZY9nRE/Dpwf/FWH63dtNgpenuCO9ZfwSt/Yj2lU5fyiQ++i3WrlnqToiRJko5q1ugf151geQK/MMGyW4Fbm1FHq/T2BAuHdrBwaAdXrl7W7nIkSZLUYbrmRkVJkiSpUxmqJUmSpAYZqiVJkqQGGaolSZKkBhmqJUmSpAYZqiVJkqQGGaolSZKkBhmqJUmSpAYZqiVJkqQGGaolSZKkBhmqJUmSpAb1tbuA2a5cSTZt28vWPQe4ZPli1q1aSm9PtLssSZIkNZGhuoXKleT6W+5jy+AQw6UyC/p7WbNigDvWX2GwliRJmkXs/tFCm7btZcvgEIdKZRI4VCqzZXCITdv2trs0SZIkNZGhuoW27jnAcKk8at5wqcxDew60qSJJkiS1gqG6hS5ZvpgF/b2j5i3o7+Xi5YvbVJEkSZJawVDdQutWLWXNigGiXIKssLDoU71u1dJ2lyZJkqQmMlS3UG9PcMf6K1jyyBcY2PU1fve6S71JUZIkaRZqSqiOiKsiYltEbI+I946z/JMRsaX4+k5EDNUtK9ct29iMejpJb0+wcGgHA7vv5crVywzUkiRJs1DDQ+pFRC9wE/AaYBdwf0RszMyHautk5rvq1n8ncGndWwxn5ppG65AkSZLapRkt1ZcD2zNzR2aWgDuBayZZ/zrgM034XEmSJKkjNCNUnwMM1k3vKuYdJyLOBy4Avlw3e35EbI6IeyPijU2oR5IkSZpRM/1ExWuBz2dm/eDN52fm7oh4CfDliPinzPzu2A0jYgOwAeC8886bmWolSZKkKWhGS/VuYEXd9LnFvPFcy5iuH5m5u/i+A9jE6P7W9evdnJlrM3PtkiVLGq1ZkiRJappmhOr7gYsi4oKI6KcanI8bxSMivh84Hfh63bzTI+KU4vVZwKuAh8ZuK0mSJHWyhrt/ZOZIRNwI3A30Ardm5taI+CiwOTNrAfta4M7MzLrNVwN/EBEVqgH/4/WjhkiSJEndoCl9qjPzLuCuMfM+NGb6I+Ns9w/Av2hGDZIkSVK7+ERFSZIkqUGGakmSJKlBhmpJkiSpQYZqSZIkqUGGakmSJKlBhmpJkiSpQYZqSZIkqUGGakmSJKlBhmpJkiSpQYZqSZIkqUGGakmSJKlBhmpJkiSpQYZqSZIkqUGGakmSJKlBhmpJkiSpQYZqSZIkqUGGakmSJKlBhmpJkiSpQU0J1RFxVURsi4jtEfHecZa/LSL2RcSW4uvtdctuiIhHiq8bmlGPJEmSNJP6Gn2DiOgFbgJeA+wC7o+IjZn50JhVP5uZN47Z9gzgw8BaIIEHim2fabQuSZIkaaY0o6X6cmB7Zu7IzBJwJ3DNFLf9t8CXMvPpIkh/CbiqCTVJkiRJM6YZofocYLBuelcxb6x/FxHfiojPR8SKaW4rSZIkdayZulHxC8DKzHwZ1dbo26f7BhGxISI2R8Tmffv2Nb1ASZIk6WQ1I1TvBlbUTZ9bzDsqM/dn5uFi8tPAD05127r3uDkz12bm2iVLljShbEmSJKk5mhGq7wcuiogLIqIfuBbYWL9CRJxdN/kG4OHi9d3Aj0XE6RFxOvBjxTxJkiSpazQ8+kdmjkTEjVTDcC9wa2ZujYiPApszcyPwHyPiDcAI8DTwtmLbpyPi16kGc4CPZubTjdYkSZIkzaSGQzVAZt4F3DVm3ofqXr8PeN8E294K3NqMOiRJkqR28ImKkiRJUoMM1ZIkSVKDDNWSJElSgwzVkiRJUoMM1ZIkSVKDDNWSJElSgwzVkiRJUoMM1ZIkSVKDDNWSJElSgwzVkiRJUoMM1ZIkSVKDDNWSJElSgwzVkiRJUoMM1ZIkSVKDDNWSJElSgwzVkiRJUoMM1ZIkSVKDDNWSJElSg5oSqiPiqojYFhHbI+K94yz/5Yh4KCK+FRH3RMT5dcvKEbGl+NrYjHokSZKkmdTX6BtERC9wE/AaYBdwf0RszMyH6lb7BrA2Mw9FxH8A/gvwU8Wy4cxc02gdUjuVK8mmbXvZuucAlyxfzLpVS+ntiXaXJUmSZkjDoRq4HNiemTsAIuJO4BrgaKjOzK/UrX8v8JYmfK7UEcqV5Ppb7mPL4BDDpTIL+ntZs2KAO9ZfYbCWJGmOaEb3j3OAwbrpXcW8iawHvlg3PT8iNkfEvRHxxok2iogNxXqb9+3b11jFUhNt2raXLYNDHCqVSeBQqcyWwSE2bdvb7tIkSdIMmdEbFSPiLcBa4L/WzT4/M9cCbwZ+OyJeOt62mXlzZq7NzLVLliyZgWo115QryT0PP8mn7nmEex5+knIlp7Td1j0HGC6VR80bLpV5aM+BVpQpSZI6UDO6f+wGVtRNn1vMGyUiXg18APiRzDxcm5+Zu4vvOyJiE3Ap8N0m1KU56GT7NjfSheOS5YtZ0N/LobpgvaC/l4uXL27455EkSd2hGaH6fuCiiLiAapi+lmqr81ERcSnwB8BVmbm3bv7pwKHMPBwRZwGvonoTozRtjQTj+i4cMLoLx5Wrl0267bpVS1mzYoCvf+dxsqePhafMY82KAdatWtq0n02SJHW2hrt/ZOYIcCNwN/Aw8LnM3BoRH42INxSr/VdgEfBnY4bOWw1sjohvAl8BPj5m1BBpyhrp29xIF47enuCO9Vew5JEvMLDra/zudZd6k6IkSXNMM1qqycy7gLvGzPtQ3etXT7DdPwD/ohk1SJMF4xO1NjfahaO3J1g4tIOFQztO+FmSJGn28YmK6jgne8NgLRjXm2owrnXhiHIJssLCouuIXTgkSdJUNKWlWmqWRvpFN9K3udaF45U/sZ7SqUv5xAff5QNcJEnSlNlSrY7SSL/oRvs217pwDOy+lytXLzNQS5KkKTNUq6M0OuazwViSJLWDoVodpZF+0ZIkSe1iqFZH8YZBSZLUjQzV6iiO+SxJkrqRo3+o4zjmsyRJ6jaGaknShMqVZNO2vWzdc4BLli92qElJmoCheoYcGD7C17+7v91ldI0Dw0cATmqfdeO2UieqVJKPffFhtu89SGmkQn9fDxcuXcT7r15Nj8G6o1QqyZbBIR7d/zwrzzyVNSsG/DfSrPLKl57Z7hJOyFAtSRrXlsEhtu89yOGRCgCHRyps33uQLYNDXHb+6W2ubvY52WDsyY/UGQzVkqRxPbr/eUpFoK4pjVR4dP/zhuomayQYe/IjdQZH/5A04yqV5MGdz/C/HtzFgzufoVLJdpekcaw881T6+0b/N9Hf18PKM09tU0WzV30wTkYH4xOZ7ORHqvHvbuvZUq1x2T9PreKl6u6xZsUAFy5dxNbHnoLePk6Z18eFSxexZsVAu0ubdRq5KlA7+Tlct70nP6rn392ZYajWcfzlUyt5qXr62nWS29MTvP/q1fzcL76b8qJl3PiODZ5gt0gjwdiTH51Iu//uzpWGOkO1jtPuXz7NbvbTnZ52n+T29AT9+7fD/u1cdv57Wv55c1UjwdiTH51IO//uNvo3rBbI73/06Y4f1tNQreMYetRKXqqeHk9yZ147WtUaDcbtPPmZK62Q3aydf3cb+Rs2NpD//+3dfZBdZX3A8e8vN2wIhjSEl5BXBEmRoHVlUhGxNhJQ2qpYxyq2tXEGJzqD1k6rAjJjrSMt/lGxzDidoaKk+AaDL2RaHV8S0jpTRAJEgdAYVCyQQBCIkUlIYPPrH/es3ix7d/feZ7Pn3uT7mcnsOeeeZ8+T333Os7/73OecM3OgweDiOT37pGWTaj2PSY8OJr+q7owfcqdWnd8M9OO3AnV/k9KP6vgQUme/W9KHjUzId+8bYtNDO9mwZUdPPnF5Uu7+EREXRMSWiHggIi4b5fUZEXFj9frtEfHCltcur7ZviYjXT0Z9VGb45OO5fZD7mVF1khM9+bzC+PDQ7fs8PCI3a/M3mPnz7/PX5y71D/AYJuMOHJ6TE1dyF47D0eEar27PqeEPIdes38rNdz7MNeu38o/fuv+gn5N19rslfdhoCfmefUNs3rZrUus4WYpHqiOiAXwGOB94GLgjItZm5uaW3S4GnsrMUyPiIuCTwNsjYhlwEXAGsAD4XkT8bmYOldZL3Sv5GtJRi86VjlqUlK/rYRP9OCJXl9IRpjrPyX6cFuA3A52pe67uVPd9w2X78Z7idfW7JX3YaN+czxxosGzB7INZ5a5FZtmno4g4G/hYZr6+Wr8cIDP/qWWfb1f73BYR04FHgeOBy1r3bd1vrGPOPen0PP8jnyuqd6c2/WgTAIMvG+yq7NBQsnTZSya7WgfV1s33AnRU718/8xyP7NxDa7OKgIVzZnL0kRP/DNfNsfuxbGbyf0/uYc+zQ2Q2YzXziAZL5s4kYvwOvqR8SdnJeJ9LYn24yUx+8sDPoDHAggXzmTWjMaH2AfW9V6Vtuy6T1Yd1q67+q1t1xauuvg/K/s+P/3ovv3x6317eU18AABBtSURBVPO2Hz9rgOOOnjHusUvV1e9224cNv1e79z4LBNOmBbNmTOfFJx49Zf3ITe991Z2ZuXwi+05Gi18IPNSy/jBwVrt9MvO5iPgVcGy1/Qcjyi4c7SARsRpYDTBr/osmodqd6SaZbi2765lnuy5fVyfbTZlnqk6qVSbsfXaoow625ITvp7JP7x36TccOzVjteXaIp/dOLF4l5UvKTsb7XNqp99OHp9KyEcFpS7vr9+p6r0rbNtQT61kzGsw8ovG8hGvWjMZBP3a3ZSajLHRX77riVVffB2Xn1JFHNIjgeQn5jCN6v32VHLvbPiwiWDJ3Jk/vHYCEo2Y0mDPziJ79YN43Fypm5rXAtQDLly/PG99zds016sxtP32i67KXfOlyAD764bVTWrYbd/3iKa5Zv/WAr2pmTJ/Gu151sl+djuJrdz3MzXc+fODGhLNPOZa3nLnooJYvKdsL73Nd50U/nY9Q33tV2rahvliXTlup432eDN3Wu3QqxXu+fR1Ds+bxhpe+bsJl6+r7oOycmozby3UTr8lQ57EBzn7RsVN2rFY3vXfi+05GUv0IsLhlfVG1bbR9Hq6mf/wO8MQEy6qPDM+dGtlheGeH0ZXeaaWk/GQ8bML3uffV9V71812Epk0LzjzpGAcCJqjbeA0nmE8vezM0pnPN+q0TTjDr6vug7Jwavmap5FqWbuJVqs5j95PJSKrvAJZGxMk0E+KLgD8fsc9aYBVwG/BWYH1mZkSsBb4UEZ+ieaHiUuCHk1An1aSkwzgclSY8JeXr+sOgqVXXe1XnB6/9+5N9x57K0Kx53PWLp6Z8NK+uY/eb4Yv2mD4AdHbRXl19H5SfU91+CCmJV6k6j91PipPqao70+4BvAw3gc5l5X0R8HNiYmWuB64AbIuIB4EmaiTfVfjcBm4HngEu880f/c5Rn4iajc+62fF1/GDT16niv6krmHc3rHyV3Dqmz7xv+HVN9TtV5pxXvijMxkzKnOjO/CXxzxLaPtiw/A/xZm7JXAldORj2kflTaOZeU79fEuGQ00JHEqVNH+3I0r3+UTsM43Pq+OqdU9fN0rqk0KQ9/kaSp0joauOfkP+jo4QklZSej3vuOPZU9J53jA1gmoNt4jTWidrDVeex+NDwNY8b0aQR0/KCxw02d8fK9mpi+ufuHJEHZaGBdI4lOC+hMXRewlXI0rzNem9GZOuPlezUxjlRL6islo4F1jSQekMzHtMPmUc7dKomXo3n9ZXgaxlvOXMSZJx1jkjaOOuPlezU+R6ol9ZU6b6XVLS/y6UxdF7CV6ufRPK81kMqZVEvqK3XeSqtbTgvoTJ0XsJXqxwvgnJ4kTQ6Takl9pe5baXXDh+V0xnhNLe9aIk0Ok+opUvJ4zdkzj+j6d5SUlXrZOUuPq6Vst+fU2he9mg1bdrB52y6WLZjNitNOoOEoYFvGa+rc8eCTo0632Z/p3w6pAybVh7Ch/cnuOaew7wXzWHf/Y/5RkgqVnFONacHK0+ex8vR5B7mWhwbjNXXOWDCbmQMNdu/77bPXZg40WLZgdo21kvqPd/84RA3tT9553e08vvSN7Fz0Kt7/5bt553W3M+S9caWueE7pULXitBMYXDyHowYaBHDUQIPBxXNYcdoJdVdN6iuOVB+iNmzZwaaHdpKN5hy53fuG2PTQTjZs2eHIj9QFzykdqhrTghsuPsvpNlIhR6oPUfdt28Welq/yAPbsG2Lztl011Ujqb55TOpQNT7d5/8qlrDx9ngm11AWT6kPU8By5Vs6Rk7rnOSVJGotJ9SHKOXLS5PKckiSNxTnVhyjnyEmTy3NKkjQWk+pDmLekkiaX55QkqR2nf0iSJEmFTKolSZKkQkVJdUTMjYjvRsTW6ucxo+wzGBG3RcR9EfHjiHh7y2vXR8TPI2JT9W+wpD6SJElSHUpHqi8D1mXmUmBdtT7SbuCvMvMM4ALg0xExp+X1D2XmYPVvU2F9JEmSpClXmlRfCKypltcAbx65Q2b+JDO3VsvbgB3A8YXHlSRJknpGaVI9LzO3V8uPAmNeEh8RrwAGgJ+2bL6ymhZydUTMKKyPJEmSNOXGvaVeRHwPOHGUl65oXcnMjIgc4/fMB24AVmXm/mrz5TST8QHgWuBS4ONtyq8GVgMsWbJkvGpLkiRJU2bcpDozz2v3WkQ8FhHzM3N7lTTvaLPfbOA/gSsy8wctv3t4lHtvRHwe+OAY9biWZuLN8uXL2ybvkiRJ0lQrnf6xFlhVLa8Cbhm5Q0QMAF8H/j0zbx7x2vzqZ9Ccj31vYX0kSZKkKVeaVF8FnB8RW4HzqnUiYnlEfLba523Aa4B3jXLrvC9GxD3APcBxwCcK6yNJkiRNuaLHlGfmE8DKUbZvBN5dLX8B+EKb8ueWHF+SJEnqBT5RUZIkSSpkUi1JkiQVMqmWJEmSCplUS5IkSYVMqiVJkqRCJtWSJElSIZNqSZIkqZBJtSRJklTIpFqSJEkqZFItSZIkFTKpliRJkgqZVEuSJEmFTKolSZKkQibVkiRJUiGTakmSJKmQSbUkSZJUyKRakiRJKmRSLUmSJBUqSqojYm5EfDcitlY/j2mz31BEbKr+rW3ZfnJE3B4RD0TEjRExUFIfSZIkqQ6lI9WXAesycymwrlofzZ7MHKz+vall+yeBqzPzVOAp4OLC+kiSJElTrjSpvhBYUy2vAd480YIREcC5wM3dlJckSZJ6RWlSPS8zt1fLjwLz2ux3ZERsjIgfRMRw4nwssDMzn6vWHwYWFtZHkiRJmnLTx9shIr4HnDjKS1e0rmRmRkS2+TUnZeYjEXEKsD4i7gF+1UlFI2I1sBpgyZIlnRSVJEmSDqpxk+rMPK/daxHxWETMz8ztETEf2NHmdzxS/fxZRGwAXg58FZgTEdOr0epFwCNj1ONa4FqA5cuXt0veJUmSpClXOv1jLbCqWl4F3DJyh4g4JiJmVMvHAecAmzMzgVuBt45VXpIkSep1pUn1VcD5EbEVOK9aJyKWR8Rnq31OBzZGxI9oJtFXZebm6rVLgb+NiAdozrG+rrA+kiRJ0pQbd/rHWDLzCWDlKNs3Au+ulv8HeGmb8j8DXlFSB0mSJKluPlFRkiRJKmRSLUmSJBUyqZYkSZIKmVRLkiRJhUyqJUmSpEIm1ZIkSVIhk2pJkiSpkEm1JEmSVMikWpIkSSpkUi1JkiQVMqmWJEmSCplU97ih/cnuOaewc+HZrLv/MYb2Z91VkiRJ0gjT666A2hvan7zzutt5fOkbyWnTef+X72Zw8RxuuPgsGtOi7upJkiSp4kh1D9uwZQebHtpJNgYgprF73xCbHtrJhi076q6aJEmSWphU97D7tu1iz76hA7bt2TfE5m27aqqRJEmSRmNS3cPOWDCbmQONA7bNHGiwbMHsmmokSZKk0ZhU97AVp53A4OI5HDXQIICjBhoMLp7DitNOqLtqkiRJauGFij2sMS244eKz2LBlB5u37WLZgtmsOO0EL1KUJEnqMUVJdUTMBW4EXgg8CLwtM58asc9rgatbNr0YuCgzvxER1wN/CPyqeu1dmbmppE6Hmsa0YOXp81h5+ry6qyJJkqQ2Sqd/XAasy8ylwLpq/QCZeWtmDmbmIHAusBv4TssuHxp+3YRakiRJ/ag0qb4QWFMtrwHePM7+bwW+lZm7C48rSZIk9YzSpHpeZm6vlh8FxpujcBHw5RHbroyIH0fE1RExo7A+kiRJ0pQbd051RHwPOHGUl65oXcnMjIi2z9COiPnAS4Fvt2y+nGYyPgBcC1wKfLxN+dXAaoAlS5aMV21JkiRpykRm2zx4/MIRW4AVmbm9Spo3ZOZpbfb9AHBGZq5u8/oK4IOZ+YYJHPdx4BddV7x7xwG/rOG4/cp4dcZ4dc6YdcZ4dcZ4dcZ4dcZ4daaueJ2UmcdPZMfSW+qtBVYBV1U/bxlj33fQHJn+jYiYXyXkQXM+9r0TOehE/3OTLSI2ZubyOo7dj4xXZ4xX54xZZ4xXZ4xXZ4xXZ4xXZ/ohXqVzqq8Czo+IrcB51ToRsTwiPju8U0S8EFgM/NeI8l+MiHuAe2h+AvlEYX0kSZKkKVc0Up2ZTwArR9m+EXh3y/qDwMJR9ju35PiSJElSL/Ax5Z25tu4K9Bnj1Rnj1Tlj1hnj1Rnj1Rnj1Rnj1Zmej1fRhYqSJEmSHKmWJEmSiplUT1BEXBARWyLigYh43uPYdaCIeDAi7omITRGxse769JqI+FxE7IiIe1u2zY2I70bE1urnMXXWsZe0idfHIuKRqo1tiog/rrOOvSQiFkfErRGxOSLuq25pahtrY4x42cZGERFHRsQPI+JHVbz+odp+ckTcXv2dvDEiBuquay8YI17XR8TPW9rXYN117SUR0YiIuyPiP6r1nm9fJtUTEBEN4DPAHwHLgHdExLJ6a9UXXpuZg71+C5yaXA9cMGLbZcC6zFwKrKvW1XQ9z48XwNVVGxvMzG9OcZ162XPA32XmMuCVwCVVn2UbG127eIFtbDR7gXMz82XAIHBBRLwS+CTNeJ0KPAVcXGMde0m7eAF8qKV9baqvij3pA8D9Les9375MqifmFcADmfmzzNwHfAW4sOY6qY9l5n8DT47YfCGwplpeQ/Pe7aJtvNRGZm7PzLuq5V/T/MO0ENvYqMaIl0aRTU9Xq0dU/xI4F7i52m77qowRL7UREYuAPwE+W60HfdC+TKonZiHwUMv6w9jhjieB70TEndUj5jW+eZm5vVp+FJhXZ2X6xPsi4sfV9BCnMoyiek7Ay4HbsY2Na0S8wDY2quqr+U3ADuC7wE+BnZn5XLWLfydbjIxXZg63ryur9nV1RMyosYq95tPAh4H91fqx9EH7MqnWwfLqzDyT5pSZSyLiNXVXqJ9k87Y8jmSM7V+BF9H8OnU78M/1Vqf3RMQs4KvA32TmrtbXbGPPN0q8bGNtZOZQZg4Ci2h+m/vimqvU00bGKyJeQvMp0y8Gfh+YC1xaYxV7RkS8AdiRmXfWXZdOmVRPzCM0nwg5bFG1TW1k5iPVzx3A12l2uhrbYxExH6D6uaPm+vS0zHys+kO1H/g3bGMHiIgjaCaIX8zMr1WbbWNtjBYv29j4MnMncCtwNjAnIoYfKuffyVG0xOuCatpRZuZe4PPYvoadA7wpIh6kOd32XOBf6IP2ZVI9MXcAS6srTweAi4C1NdepZ0XECyLi6OFl4HXAvWOXEs02tapaXgXcUmNdet5wclj5U2xjv1HNP7wOuD8zP9Xykm1sFO3iZRsbXUQcHxFzquWZwPk056HfCry12s32VWkTr/9t+YAbNOcH276AzLw8Mxdl5gtp5lvrM/Mv6IP25cNfJqi6ldKngQbwucy8suYq9ayIOIXm6DTAdOBLxutAEfFlYAVwHPAY8PfAN4CbgCXAL4C3ZaYX59E2Xitofi2fwIPAe1rmCx/WIuLVwPeBe/jtnMSP0JwnbBsbYYx4vQPb2PNExO/RvFCsQXNw7qbM/HjV93+F5lSGu4G/rEZhD2tjxGs9cDwQwCbgvS0XNAqIiBXABzPzDf3QvkyqJUmSpEJO/5AkSZIKmVRLkiRJhUyqJUmSpEIm1ZIkSVIhk2pJkiSpkEm1JEmSVMikWpIkSSpkUi1JkiQV+n+rI288cHZdPAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 864x576 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12,8))\n",
"ax1 = fig.add_subplot(211)\n",
"fig = sm.graphics.tsa.plot_acf(dta.values.squeeze(), lags=40, ax=ax1)\n",
"ax2 = fig.add_subplot(212)\n",
"fig = sm.graphics.tsa.plot_pacf(dta, lags=40, ax=ax2)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"const 49.659417\n",
"ar.L1.SUNACTIVITY 1.390656\n",
"ar.L2.SUNACTIVITY -0.688571\n",
"dtype: float64\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib/python3/dist-packages/scipy/signal/signaltools.py:1341: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
" out_full[ind] += zi\n",
"/usr/lib/python3/dist-packages/scipy/signal/signaltools.py:1344: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
" out = out_full[ind]\n",
"/usr/lib/python3/dist-packages/scipy/signal/signaltools.py:1350: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
" zf = out_full[ind]\n",
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:646: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
" if issubdtype(paramsdtype, float):\n",
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:650: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n",
" elif issubdtype(paramsdtype, complex):\n"
]
}
],
"source": [
"arma_mod20 = sm.tsa.ARMA(dta, (2,0)).fit()\n",
"print(arma_mod20.params)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib/python3/dist-packages/scipy/signal/signaltools.py:1341: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
" out_full[ind] += zi\n",
"/usr/lib/python3/dist-packages/scipy/signal/signaltools.py:1344: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
" out = out_full[ind]\n",
"/usr/lib/python3/dist-packages/scipy/signal/signaltools.py:1350: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
" zf = out_full[ind]\n",
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:646: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
" if issubdtype(paramsdtype, float):\n",
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:650: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n",
" elif issubdtype(paramsdtype, complex):\n"
]
}
],
"source": [
"arma_mod30 = sm.tsa.ARMA(dta, (3,0)).fit()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2622.6363380637476 2637.5697031713385 2628.6067259089937\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:646: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
" if issubdtype(paramsdtype, float):\n"
]
}
],
"source": [
"print(arma_mod20.aic, arma_mod20.bic, arma_mod20.hqic)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"const 49.749875\n",
"ar.L1.SUNACTIVITY 1.300810\n",
"ar.L2.SUNACTIVITY -0.508093\n",
"ar.L3.SUNACTIVITY -0.129649\n",
"dtype: float64\n"
]
}
],
"source": [
"print(arma_mod30.params)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2619.4036286970572 2638.070335081546 2626.8666135036146\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:646: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
" if issubdtype(paramsdtype, float):\n"
]
}
],
"source": [
"print(arma_mod30.aic, arma_mod30.bic, arma_mod30.hqic)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Does our model obey the theory?"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:577: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
" if issubdtype(paramsdtype, float):\n"
]
},
{
"data": {
"text/plain": [
"1.9564812118219916"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sm.stats.durbin_watson(arma_mod30.resid.values)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12,8))\n",
"ax = fig.add_subplot(111)\n",
"ax = arma_mod30.resid.plot(ax=ax);"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"resid = arma_mod30.resid"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"NormaltestResult(statistic=49.845007349151125, pvalue=1.5007010241367565e-11)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stats.normaltest(resid)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib/python3/dist-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
" return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12,8))\n",
"ax = fig.add_subplot(111)\n",
"fig = qqplot(resid, line='q', ax=ax, fit=True)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x576 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12,8))\n",
"ax1 = fig.add_subplot(211)\n",
"fig = sm.graphics.tsa.plot_acf(resid.values.squeeze(), lags=40, ax=ax1)\n",
"ax2 = fig.add_subplot(212)\n",
"fig = sm.graphics.tsa.plot_pacf(resid, lags=40, ax=ax2)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" AC Q Prob(>Q)\n",
"lag \n",
"1.0 0.009179 0.026286 8.712050e-01\n",
"2.0 0.041793 0.573043 7.508711e-01\n",
"3.0 -0.001335 0.573602 9.024481e-01\n",
"4.0 0.136089 6.408929 1.706198e-01\n",
"5.0 0.092468 9.111841 1.046855e-01\n",
"6.0 0.091948 11.793258 6.674312e-02\n",
"7.0 0.068748 13.297217 6.518947e-02\n",
"8.0 -0.015020 13.369245 9.976082e-02\n",
"9.0 0.187592 24.641924 3.393892e-03\n",
"10.0 0.213718 39.322001 2.229468e-05\n",
"11.0 0.201082 52.361138 2.344948e-07\n",
"12.0 0.117182 56.804190 8.574255e-08\n",
"13.0 -0.014055 56.868326 1.893902e-07\n",
"14.0 0.015398 56.945566 3.997656e-07\n",
"15.0 -0.024967 57.149320 7.741466e-07\n",
"16.0 0.080916 59.296773 6.872154e-07\n",
"17.0 0.041138 59.853743 1.110942e-06\n",
"18.0 -0.052021 60.747433 1.548429e-06\n",
"19.0 0.062496 62.041696 1.831640e-06\n",
"20.0 -0.010302 62.076984 3.381236e-06\n",
"21.0 0.074453 63.926657 3.193581e-06\n",
"22.0 0.124955 69.154771 8.978352e-07\n",
"23.0 0.093162 72.071034 5.799783e-07\n",
"24.0 -0.082152 74.346687 4.713015e-07\n",
"25.0 0.015695 74.430043 8.289039e-07\n",
"26.0 -0.025037 74.642901 1.367284e-06\n",
"27.0 -0.125861 80.041144 3.722569e-07\n",
"28.0 0.053225 81.009979 4.716281e-07\n",
"29.0 -0.038693 81.523805 6.916635e-07\n",
"30.0 -0.016904 81.622224 1.151661e-06\n",
"31.0 -0.019296 81.750937 1.868766e-06\n",
"32.0 0.104990 85.575061 8.927967e-07\n",
"33.0 0.040086 86.134561 1.247510e-06\n",
"34.0 0.008829 86.161804 2.047828e-06\n",
"35.0 0.014588 86.236442 3.263812e-06\n",
"36.0 -0.119329 91.248890 1.084456e-06\n",
"37.0 -0.036665 91.723858 1.521926e-06\n",
"38.0 -0.046193 92.480506 1.938739e-06\n",
"39.0 -0.017768 92.592874 2.990686e-06\n",
"40.0 -0.006220 92.606697 4.696995e-06\n"
]
}
],
"source": [
"r,q,p = sm.tsa.acf(resid.values.squeeze(), qstat=True)\n",
"data = np.c_[range(1,41), r[1:], q, p]\n",
"table = pd.DataFrame(data, columns=['lag', \"AC\", \"Q\", \"Prob(>Q)\"])\n",
"print(table.set_index('lag'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* This indicates a lack of fit."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* In-sample dynamic prediction. How good does our model do?"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1990-12-31 167.047395\n",
"1991-12-31 140.992948\n",
"1992-12-31 94.859029\n",
"1993-12-31 46.860797\n",
"1994-12-31 11.242479\n",
"1995-12-31 -4.721387\n",
"1996-12-31 -1.166985\n",
"1997-12-31 16.185639\n",
"1998-12-31 39.021846\n",
"1999-12-31 59.449841\n",
"2000-12-31 72.170107\n",
"2001-12-31 75.376737\n",
"2002-12-31 70.436397\n",
"2003-12-31 60.731512\n",
"2004-12-31 50.201715\n",
"2005-12-31 42.075945\n",
"2006-12-31 38.114211\n",
"2007-12-31 38.454575\n",
"2008-12-31 41.963756\n",
"2009-12-31 46.869232\n",
"2010-12-31 51.423208\n",
"2011-12-31 54.399663\n",
"2012-12-31 55.321631\n",
"Freq: A-DEC, dtype: float64\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:577: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
" if issubdtype(paramsdtype, float):\n"
]
}
],
"source": [
"predict_sunspots = arma_mod30.predict('1990', '2012', dynamic=True)\n",
"print(predict_sunspots)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib/python3/dist-packages/ipykernel_launcher.py:2: DeprecationWarning: \n",
".ix is deprecated. Please use\n",
".loc for label based indexing or\n",
".iloc for positional indexing\n",
"\n",
"See the documentation here:\n",
"http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
" \n",
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:577: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
" if issubdtype(paramsdtype, float):\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(12, 8))\n",
"ax = dta.ix['1950':].plot(ax=ax)\n",
"fig = arma_mod30.plot_predict('1990', '2012', dynamic=True, ax=ax, plot_insample=False)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def mean_forecast_err(y, yhat):\n",
" return y.sub(yhat).mean()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5.637023332548461"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mean_forecast_err(dta.SUNACTIVITY, predict_sunspots)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise: Can you obtain a better fit for the Sunspots model? (Hint: sm.tsa.AR has a method select_order)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Simulated ARMA(4,1): Model Identification is Difficult"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from statsmodels.tsa.arima_process import arma_generate_sample, ArmaProcess"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"np.random.seed(1234)\n",
"# include zero-th lag\n",
"arparams = np.array([1, .75, -.65, -.55, .9])\n",
"maparams = np.array([1, .65])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's make sure this model is estimable."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"arma_t = ArmaProcess(arparams, maparams)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arma_t.isinvertible"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arma_t.isstationary"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* What does this mean?"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12,8))\n",
"ax = fig.add_subplot(111)\n",
"ax.plot(arma_t.generate_sample(nsample=50));"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arparams = np.array([1, .35, -.15, .55, .1])\n",
"maparams = np.array([1, .65])\n",
"arma_t = ArmaProcess(arparams, maparams)\n",
"arma_t.isstationary"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"arma_rvs = arma_t.generate_sample(nsample=500, burnin=250, scale=2.5)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x576 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12,8))\n",
"ax1 = fig.add_subplot(211)\n",
"fig = sm.graphics.tsa.plot_acf(arma_rvs, lags=40, ax=ax1)\n",
"ax2 = fig.add_subplot(212)\n",
"fig = sm.graphics.tsa.plot_pacf(arma_rvs, lags=40, ax=ax2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* For mixed ARMA processes the Autocorrelation function is a mixture of exponentials and damped sine waves after (q-p) lags. \n",
"* The partial autocorrelation function is a mixture of exponentials and dampened sine waves after (p-q) lags."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" AC Q Prob(>Q)\n",
"lag \n",
"1.0 0.254921 32.687661 1.082221e-08\n",
"2.0 -0.172416 47.670720 4.450765e-11\n",
"3.0 -0.420945 137.159375 1.548479e-29\n",
"4.0 -0.046875 138.271282 6.617765e-29\n",
"5.0 0.103240 143.675884 2.958755e-29\n",
"6.0 0.214864 167.132980 1.823736e-33\n",
"7.0 -0.000889 167.133382 1.009215e-32\n",
"8.0 -0.045418 168.185732 3.094866e-32\n",
"9.0 -0.061445 170.115783 5.837269e-32\n",
"10.0 0.034623 170.729837 1.958754e-31\n",
"11.0 0.006351 170.750538 8.267127e-31\n",
"12.0 -0.012882 170.835890 3.220261e-30\n",
"13.0 -0.053959 172.336529 6.181249e-30\n",
"14.0 -0.016606 172.478946 2.160233e-29\n",
"15.0 0.051742 173.864468 4.089582e-29\n",
"16.0 0.078917 177.094262 3.217964e-29\n",
"17.0 -0.001834 177.096010 1.093177e-28\n",
"18.0 -0.101604 182.471918 3.103851e-29\n",
"19.0 -0.057342 184.187752 4.624108e-29\n",
"20.0 0.026975 184.568266 1.235682e-28\n",
"21.0 0.062359 186.605943 1.530272e-28\n",
"22.0 -0.009400 186.652345 4.548235e-28\n",
"23.0 -0.068037 189.088164 4.562053e-28\n",
"24.0 -0.035566 189.755181 9.901184e-28\n",
"25.0 0.095679 194.592602 3.354319e-28\n",
"26.0 0.065650 196.874856 3.487654e-28\n",
"27.0 -0.018404 197.054592 9.008831e-28\n",
"28.0 -0.079244 200.393988 5.773764e-28\n",
"29.0 0.008499 200.432481 1.541400e-27\n",
"30.0 0.053372 201.953754 2.133212e-27\n",
"31.0 0.074816 204.949374 1.550175e-27\n",
"32.0 -0.071187 207.667222 1.262299e-27\n",
"33.0 -0.088145 211.843134 5.480866e-28\n",
"34.0 -0.025283 212.187428 1.215239e-27\n",
"35.0 0.125690 220.714877 8.231681e-29\n",
"36.0 0.142724 231.734095 1.923101e-30\n",
"37.0 0.095768 236.706137 5.937838e-31\n",
"38.0 -0.084744 240.607781 2.890913e-31\n",
"39.0 -0.150126 252.878958 3.963042e-33\n",
"40.0 -0.083767 256.707716 1.996192e-33\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:646: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
" if issubdtype(paramsdtype, float):\n",
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:650: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n",
" elif issubdtype(paramsdtype, complex):\n",
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:577: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
" if issubdtype(paramsdtype, float):\n"
]
}
],
"source": [
"arma11 = sm.tsa.ARMA(arma_rvs, (1,1)).fit()\n",
"resid = arma11.resid\n",
"r,q,p = sm.tsa.acf(resid, qstat=True)\n",
"data = np.c_[range(1,41), r[1:], q, p]\n",
"table = pd.DataFrame(data, columns=['lag', \"AC\", \"Q\", \"Prob(>Q)\"])\n",
"print(table.set_index('lag'))"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:646: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
" if issubdtype(paramsdtype, float):\n",
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:650: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n",
" elif issubdtype(paramsdtype, complex):\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" AC Q Prob(>Q)\n",
"lag \n",
"1.0 -0.007888 0.031301 0.859570\n",
"2.0 0.004132 0.039906 0.980245\n",
"3.0 0.018103 0.205416 0.976710\n",
"4.0 -0.006760 0.228540 0.993948\n",
"5.0 0.018120 0.395025 0.995466\n",
"6.0 0.050688 1.700449 0.945087\n",
"7.0 0.010252 1.753956 0.972196\n",
"8.0 -0.011206 1.818018 0.986092\n",
"9.0 0.020292 2.028518 0.991009\n",
"10.0 0.001029 2.029061 0.996113\n",
"11.0 -0.014035 2.130169 0.997984\n",
"12.0 -0.023858 2.422926 0.998427\n",
"13.0 -0.002108 2.425216 0.999339\n",
"14.0 -0.018783 2.607429 0.999590\n",
"15.0 0.011316 2.673698 0.999805\n",
"16.0 0.042159 3.595419 0.999443\n",
"17.0 0.007943 3.628205 0.999734\n",
"18.0 -0.074311 6.503854 0.993686\n",
"19.0 -0.023379 6.789066 0.995256\n",
"20.0 0.002398 6.792073 0.997313\n",
"21.0 0.000487 6.792197 0.998516\n",
"22.0 0.017953 6.961434 0.999024\n",
"23.0 -0.038576 7.744465 0.998744\n",
"24.0 -0.029816 8.213248 0.998859\n",
"25.0 0.077850 11.415822 0.990675\n",
"26.0 0.040408 12.280447 0.989479\n",
"27.0 -0.018612 12.464274 0.992262\n",
"28.0 -0.014764 12.580185 0.994586\n",
"29.0 0.017650 12.746188 0.996111\n",
"30.0 -0.005486 12.762261 0.997504\n",
"31.0 0.058256 14.578542 0.994614\n",
"32.0 -0.040840 15.473081 0.993887\n",
"33.0 -0.019493 15.677306 0.995393\n",
"34.0 0.037269 16.425463 0.995214\n",
"35.0 0.086212 20.437446 0.976296\n",
"36.0 0.041271 21.358844 0.974774\n",
"37.0 0.078704 24.716875 0.938948\n",
"38.0 -0.029729 25.197053 0.944895\n",
"39.0 -0.078397 28.543385 0.891179\n",
"40.0 -0.014466 28.657576 0.909268\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:577: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
" if issubdtype(paramsdtype, float):\n"
]
}
],
"source": [
"arma41 = sm.tsa.ARMA(arma_rvs, (4,1)).fit()\n",
"resid = arma41.resid\n",
"r,q,p = sm.tsa.acf(resid, qstat=True)\n",
"data = np.c_[range(1,41), r[1:], q, p]\n",
"table = pd.DataFrame(data, columns=['lag', \"AC\", \"Q\", \"Prob(>Q)\"])\n",
"print(table.set_index('lag'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise: How good of in-sample prediction can you do for another series, say, CPI"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"macrodta = sm.datasets.macrodata.load_pandas().data\n",
"macrodta.index = pd.Index(sm.tsa.datetools.dates_from_range('1959Q1', '2009Q3'))\n",
"cpi = macrodta[\"cpi\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Hint: "
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12,8))\n",
"ax = fig.add_subplot(111)\n",
"ax = cpi.plot(ax=ax);\n",
"ax.legend();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"P-value of the unit-root test, resoundly rejects the null of no unit-root."
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.9904328188337422\n"
]
}
],
"source": [
"print(sm.tsa.adfuller(cpi)[1])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
}

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/srv/reproducible-results/rbuild-debian/tmp.FTzzuDJkiI/dbd-tmp-gqMLHgR/diffoscope_1mz05yxs/tmpjvhg40jt/a4cb38f6-4c5a-4649-8f25-0a60bc62c83e vs.

Similarity: 0.0%
Differences: {
"replace": {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Autoregressive Moving Average (ARMA): Artificial data"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
" from pandas.core import datetools\n"
]
}
],
"source": [
"%matplotlib inline\n",
"\n",
"from __future__ import print_function\n",
"import numpy as np\n",
"import statsmodels.api as sm\n",
"import pandas as pd\n",
"from statsmodels.tsa.arima_process import arma_generate_sample\n",
"np.random.seed(12345)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generate some data from an ARMA process:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"arparams = np.array([.75, -.25])\n",
"maparams = np.array([.65, .35])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The conventions of the arma_generate function require that we specify a 1 for the zero-lag of the AR and MA parameters and that the AR parameters be negated."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"arparams = np.r_[1, -arparams]\n",
"maparams = np.r_[1, maparams]\n",
"nobs = 250\n",
"y = arma_generate_sample(arparams, maparams, nobs)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Now, optionally, we can add some dates information. For this example, we'll use a pandas time series."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:646: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
" if issubdtype(paramsdtype, float):\n",
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:650: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n",
" elif issubdtype(paramsdtype, complex):\n"
]
}
],
"source": [
"dates = sm.tsa.datetools.dates_from_range('1980m1', length=nobs)\n",
"y = pd.Series(y, index=dates)\n",
"arma_mod = sm.tsa.ARMA(y, order=(2,2))\n",
"arma_res = arma_mod.fit(trend='nc', disp=-1)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" ARMA Model Results \n",
"==============================================================================\n",
"Dep. Variable: y No. Observations: 250\n",
"Model: ARMA(2, 2) Log Likelihood -353.445\n",
"Method: css-mle S.D. of innovations 0.990\n",
"Date: Sat, 10 Apr 2021 AIC 716.891\n",
"Time: 01:00:05 BIC 734.498\n",
"Sample: 01-31-1980 HQIC 723.977\n",
" - 10-31-2000 \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"ar.L1.y 0.7904 0.134 5.878 0.000 0.527 1.054\n",
"ar.L2.y -0.2314 0.113 -2.044 0.042 -0.453 -0.009\n",
"ma.L1.y 0.7007 0.127 5.525 0.000 0.452 0.949\n",
"ma.L2.y 0.4061 0.095 4.291 0.000 0.221 0.592\n",
" Roots \n",
"=============================================================================\n",
" Real Imaginary Modulus Frequency\n",
"-----------------------------------------------------------------------------\n",
"AR.1 1.7079 -1.1851j 2.0788 -0.0965\n",
"AR.2 1.7079 +1.1851j 2.0788 0.0965\n",
"MA.1 -0.8628 -1.3108j 1.5693 -0.3427\n",
"MA.2 -0.8628 +1.3108j 1.5693 0.3427\n",
"-----------------------------------------------------------------------------\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:646: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
" if issubdtype(paramsdtype, float):\n",
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:650: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n",
" elif issubdtype(paramsdtype, complex):\n"
]
}
],
"source": [
"print(arma_res.summary())"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2000-06-30 0.173211\n",
"2000-07-31 -0.048325\n",
"2000-08-31 -0.415804\n",
"2000-09-30 0.338725\n",
"2000-10-31 0.360838\n",
"dtype: float64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y.tail()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:577: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
" if issubdtype(paramsdtype, float):\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"fig, ax = plt.subplots(figsize=(10,8))\n",
"fig = arma_res.plot_predict(start='1999m6', end='2001m5', ax=ax)\n",
"legend = ax.legend(loc='upper left')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
}

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/srv/reproducible-results/rbuild-debian/tmp.FTzzuDJkiI/dbd-tmp-gqMLHgR/diffoscope_1mz05yxs/tmpzo7uzbom/6113ff51-6510-4a67-a369-52b064a1a18f vs.

Similarity: 0.0%
Differences: {
"replace": {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Dates in timeseries models"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
" from pandas.core import datetools\n"
]
}
],
"source": [
"from __future__ import print_function\n",
"import statsmodels.api as sm\n",
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Getting started"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data = sm.datasets.sunspots.load()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Right now an annual date series must be datetimes at the end of the year."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from datetime import datetime\n",
"dates = sm.tsa.datetools.dates_from_range('1700', length=len(data.endog))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Using Pandas\n",
"\n",
"Make a pandas TimeSeries or DataFrame"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"endog = pd.Series(data.endog, index=dates)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Instantiate the model"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ar_model = sm.tsa.AR(endog, freq='A')\n",
"pandas_ar_res = ar_model.fit(maxlag=9, method='mle', disp=-1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Out-of-sample prediction"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2005-12-31 20.003301\n",
"2006-12-31 24.704004\n",
"2007-12-31 20.026137\n",
"2008-12-31 23.473656\n",
"2009-12-31 30.858572\n",
"2010-12-31 61.335454\n",
"2011-12-31 87.024691\n",
"2012-12-31 91.321263\n",
"2013-12-31 79.921649\n",
"2014-12-31 60.799552\n",
"2015-12-31 40.374909\n",
"Freq: A-DEC, dtype: float64\n"
]
}
],
"source": [
"pred = pandas_ar_res.predict(start='2005', end='2015')\n",
"print(pred)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Using explicit dates"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[20.00330148 24.70400422 20.02613667 23.47365624 30.85857155 61.33545351\n",
" 87.02469145 91.32126315 79.92164942 60.79955229 40.37490941]\n"
]
}
],
"source": [
"ar_model = sm.tsa.AR(data.endog, dates=dates, freq='A')\n",
"ar_res = ar_model.fit(maxlag=9, method='mle', disp=-1)\n",
"pred = ar_res.predict(start='2005', end='2015')\n",
"print(pred)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This just returns a regular array, but since the model has date information attached, you can get the prediction dates in a roundabout way."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"DatetimeIndex(['2005-12-31', '2006-12-31', '2007-12-31', '2008-12-31',\n",
" '2009-12-31', '2010-12-31', '2011-12-31', '2012-12-31',\n",
" '2013-12-31', '2014-12-31', '2015-12-31'],\n",
" dtype='datetime64[ns]', freq='A-DEC')\n"
]
}
],
"source": [
"print(ar_res.data.predict_dates)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note: This attribute only exists if predict has been called. It holds the dates associated with the last call to predict."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
}

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/srv/reproducible-results/rbuild-debian/tmp.FTzzuDJkiI/dbd-tmp-gqMLHgR/diffoscope_1mz05yxs/tmppc6bl1w2/6020cf52-904a-4b7b-b376-c95d37d91d43 vs.

Similarity: 0.0%
Differences: {
"replace": {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Weighted Least Squares"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
" from pandas.core import datetools\n"
]
}
],
"source": [
"%matplotlib inline\n",
"\n",
"from __future__ import print_function\n",
"import numpy as np\n",
"from scipy import stats\n",
"import statsmodels.api as sm\n",
"import matplotlib.pyplot as plt\n",
"from statsmodels.sandbox.regression.predstd import wls_prediction_std\n",
"from statsmodels.iolib.table import (SimpleTable, default_txt_fmt)\n",
"np.random.seed(1024)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## WLS Estimation\n",
"\n",
"### Artificial data: Heteroscedasticity 2 groups \n",
"\n",
"Model assumptions:\n",
"\n",
" * Misspecification: true model is quadratic, estimate only linear\n",
" * Independent noise/error term\n",
" * Two groups for error variance, low and high variance groups"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"nsample = 50\n",
"x = np.linspace(0, 20, nsample)\n",
"X = np.column_stack((x, (x - 5)**2))\n",
"X = sm.add_constant(X)\n",
"beta = [5., 0.5, -0.01]\n",
"sig = 0.5\n",
"w = np.ones(nsample)\n",
"w[nsample * 6//10:] = 3\n",
"y_true = np.dot(X, beta)\n",
"e = np.random.normal(size=nsample)\n",
"y = y_true + sig * w * e \n",
"X = X[:,[0,1]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### WLS knowing the true variance ratio of heteroscedasticity"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" WLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: y R-squared: 0.910\n",
"Model: WLS Adj. R-squared: 0.909\n",
"Method: Least Squares F-statistic: 487.9\n",
"Date: Sat, 10 Apr 2021 Prob (F-statistic): 8.52e-27\n",
"Time: 01:00:05 Log-Likelihood: -57.048\n",
"No. Observations: 50 AIC: 118.1\n",
"Df Residuals: 48 BIC: 121.9\n",
"Df Model: 1 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const 5.2726 0.185 28.488 0.000 4.900 5.645\n",
"x1 0.4379 0.020 22.088 0.000 0.398 0.478\n",
"==============================================================================\n",
"Omnibus: 5.040 Durbin-Watson: 2.242\n",
"Prob(Omnibus): 0.080 Jarque-Bera (JB): 6.431\n",
"Skew: 0.024 Prob(JB): 0.0401\n",
"Kurtosis: 4.756 Cond. No. 17.0\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
]
}
],
"source": [
"mod_wls = sm.WLS(y, X, weights=1./w)\n",
"res_wls = mod_wls.fit()\n",
"print(res_wls.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## OLS vs. WLS\n",
"\n",
"Estimate an OLS model for comparison: "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[5.24256099 0.43486879]\n",
"[5.27260714 0.43794441]\n"
]
}
],
"source": [
"res_ols = sm.OLS(y, X).fit()\n",
"print(res_ols.params)\n",
"print(res_wls.params)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compare the WLS standard errors to heteroscedasticity corrected OLS standard errors:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"=====================\n",
" x1 const \n",
"---------------------\n",
"WLS 0.1851 0.0198\n",
"OLS 0.2707 0.0233\n",
"OLS_HC0 0.194 0.0281\n",
"OLS_HC1 0.198 0.0287\n",
"OLS_HC3 0.2003 0.029 \n",
"OLS_HC3 0.207 0.03 \n",
"---------------------\n"
]
}
],
"source": [
"se = np.vstack([[res_wls.bse], [res_ols.bse], [res_ols.HC0_se], \n",
" [res_ols.HC1_se], [res_ols.HC2_se], [res_ols.HC3_se]])\n",
"se = np.round(se,4)\n",
"colnames = ['x1', 'const']\n",
"rownames = ['WLS', 'OLS', 'OLS_HC0', 'OLS_HC1', 'OLS_HC3', 'OLS_HC3']\n",
"tabl = SimpleTable(se, colnames, rownames, txt_fmt=default_txt_fmt)\n",
"print(tabl)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calculate OLS prediction interval:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"covb = res_ols.cov_params()\n",
"prediction_var = res_ols.mse_resid + (X * np.dot(covb,X.T).T).sum(1)\n",
"prediction_std = np.sqrt(prediction_var)\n",
"tppf = stats.t.ppf(0.975, res_ols.df_resid)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"prstd_ols, iv_l_ols, iv_u_ols = wls_prediction_std(res_ols)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Draw a plot to compare predicted values in WLS and OLS:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"prstd, iv_l, iv_u = wls_prediction_std(res_wls)\n",
"\n",
"fig, ax = plt.subplots(figsize=(8,6))\n",
"ax.plot(x, y, 'o', label=\"Data\")\n",
"ax.plot(x, y_true, 'b-', label=\"True\")\n",
"# OLS\n",
"ax.plot(x, res_ols.fittedvalues, 'r--')\n",
"ax.plot(x, iv_u_ols, 'r--', label=\"OLS\")\n",
"ax.plot(x, iv_l_ols, 'r--')\n",
"# WLS\n",
"ax.plot(x, res_wls.fittedvalues, 'g--.')\n",
"ax.plot(x, iv_u, 'g--', label=\"WLS\")\n",
"ax.plot(x, iv_l, 'g--')\n",
"ax.legend(loc=\"best\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Feasible Weighted Least Squares (2-stage FWLS)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" WLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: y R-squared: 0.914\n",
"Model: WLS Adj. R-squared: 0.912\n",
"Method: Least Squares F-statistic: 507.1\n",
"Date: Sat, 10 Apr 2021 Prob (F-statistic): 3.65e-27\n",
"Time: 01:00:06 Log-Likelihood: -55.777\n",
"No. Observations: 50 AIC: 115.6\n",
"Df Residuals: 48 BIC: 119.4\n",
"Df Model: 1 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const 5.2710 0.177 29.828 0.000 4.916 5.626\n",
"x1 0.4390 0.019 22.520 0.000 0.400 0.478\n",
"==============================================================================\n",
"Omnibus: 4.076 Durbin-Watson: 2.251\n",
"Prob(Omnibus): 0.130 Jarque-Bera (JB): 4.336\n",
"Skew: 0.003 Prob(JB): 0.114\n",
"Kurtosis: 4.443 Cond. No. 16.5\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
]
}
],
"source": [
"resid1 = res_ols.resid[w==1.]\n",
"var1 = resid1.var(ddof=int(res_ols.df_model)+1)\n",
"resid2 = res_ols.resid[w!=1.]\n",
"var2 = resid2.var(ddof=int(res_ols.df_model)+1)\n",
"w_est = w.copy()\n",
"w_est[w!=1.] = np.sqrt(var2) / np.sqrt(var1)\n",
"res_fwls = sm.WLS(y, X, 1./w_est).fit()\n",
"print(res_fwls.summary())"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
}


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3	-rw-r--r--···0········0········0·1171~~6264~~·2019-03-02·14:59:35.000000·data.tar.xz	3	-rw-r--r--···0········0········0·11714856·2019-03-02·14:59:35.000000·data.tar.xz


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1	Package:·python-statsmodels-doc	1	Package:·python-statsmodels-doc
2	Source:·statsmodels	2	Source:·statsmodels
3	Version:·0.8.0-9	3	Version:·0.8.0-9
4	Architecture:·all	4	Architecture:·all
5	Maintainer:·Debian·Science·Maintainers·<debian-science-maintainers@lists.alioth.debian.org>	5	Maintainer:·Debian·Science·Maintainers·<debian-science-maintainers@lists.alioth.debian.org>
6	Installed-Size:·71301	6	Installed-Size:·71300
7	Depends:·libjs-requirejs,·libjs-sphinxdoc·(>=·1.0),·libjs-mathjax	7	Depends:·libjs-requirejs,·libjs-sphinxdoc·(>=·1.0),·libjs-mathjax
8	Suggests:·python-statsmodels,·python3-doc,·python-numpy-doc,·python-patsy-doc,·python-pandas-doc,·python-scipy-doc	8	Suggests:·python-statsmodels,·python3-doc,·python-numpy-doc,·python-patsy-doc,·python-pandas-doc,·python-scipy-doc
9	Breaks:·python-scikits-statsmodels-doc,·python-scikits.statsmodels-doc	9	Breaks:·python-scikits-statsmodels-doc,·python-scikits.statsmodels-doc
10	Replaces:·python-scikits-statsmodels-doc,·python-scikits.statsmodels-doc	10	Replaces:·python-scikits-statsmodels-doc,·python-scikits.statsmodels-doc
11	Section:·doc	11	Section:·doc
12	Priority:·optional	12	Priority:·optional
13	Homepage:·http://statsmodels.sourceforge.net/	13	Homepage:·http://statsmodels.sourceforge.net/


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00008500:·3d22·6~~f22~~·~~3e3d~~·~~3c2f~~·~~7370~~·~~616~~e·3e3c·7370··="o">=</~~span><~~sp		00008500:·6e20·636c·6173·733d·226f·223e·3d3c·2f73··n·class="o">=</s
00008510:·616e·~~206~~3·6c61·~~7373~~·~~3d22~~·6270·223e·~~5472··~~an·class=~~"bp">Tr~~		00008510:·7061·6e3e·3c73·7061·6e20·636c·6173·733d··pan><span·class=
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000085f0:·~~3c7~~3~~·7~~0~~61·~~6e20·636c·6173·~~733d~~·226e·~~6922~~··<span·cla~~ss="ni"~~		000085f0:·616e·3e20·696e·203c·7370·616e·2063·6c61··an>·in·<span·cla
00008600:·3~~e67~~·6~~574~~·~~5f72~~·6461·7461·7365·74~~3c·2f73··~~>get_rdataset~~</s~~		00008600:·7373·3d22·6e69·223e·6765·745f·7264·6174··ss="ni">get_rdat
00008610:·~~7061~~·6~~e3e~~·3c73·7~~061~~·6~~e20~~·~~636~~c·6173·~~733d~~··~~pan>~~<span·~~class=~~		00008610:·6173·6574·3c2f·7370·616e·3e3c·7370·616e··aset</span><span
00008620:·226e·7~~422~~·3e28·6~~461~~·~~7461~~·6e61·6~~d65~~·2c~~20··~~"nt">(dat~~aname,·~~		00008620:·2063·6c61·7373·3d22·6e74·223e·2864·6174···class="nt">(dat
00008630:·7061~~·63~~6b·6167·652c·2063·6163·6865·2~~93c~~··package,·cache~~)<~~		00008630:·616e·616d·652c·2070·6163·6b61·6765·2c20··aname,·package,·
00008640:·~~2f73~~·7061·6~~e3e~~·~~0a3c~~·7370·616e·~~2063~~·6c61··~~/span>.<~~span~~·cla~~		00008640:·6361·6368·6529·3c2f·7370·616e·3e0a·3c73··cache)</span>.<s
00008650:·7~~373~~·3d22·6~~720~~·672d·5768·697~~4·6~~573·~~7061··~~ss="g·g-W~~hitespa~~		00008650:·7061·6e20·636c·6173·733d·2267·2067·2d57··pan·class="g·g-W
00008660:·6365·~~223e~~·~~202~~0·~~2020~~·3c2f·~~737~~0·~~616e~~·3e3c··ce">····~~</span>~~<		00008660:·6869·7465·7370·6163·6522·3e20·2020·203c··hitespace">····<
00008670:·7370·616e·~~2063~~·6c61·7~~373~~·3d22·6d69·223e··span·~~class="mi">~~		00008670:·2f73·7061·6e3e·3c73·7061·6e20·636c·6173··/span><span·clas
00008680:·3238·~~383c~~·~~2f73~~·~~7061~~·6e3e·2020·~~202~~0·~~2020~~··288</span~~>······~~		00008680:·733d·226d·6922·3e32·3838·3c2f·7370·616e··s="mi">288</span
00008690:·2020·2020·2020·2020·2020·2020·2020·2020··················		00008690:·3e20·2020·2020·2020·2020·2020·2020·2020··>···············
000086a0:·~~3c73~~·7061·6e20·~~636~~c·6173·~~733d~~·2273·~~3222~~··<span·cla~~ss="s2"~~		000086a0:·2020·2020·2020·203c·7370·616e·2063·6c61·········<span·cla
000086b0:·~~3e26~~·~~7175~~·~~6f74~~·~~3b6d~~·6173·7465·7~~22f~~·646f··>"ma~~ster/do~~		000086b0:·7373·3d22·7332·223e·2671·756f·743b·6d61··ss="s2">"ma
000086c0:·632f·2671·756f·~~743b~~·3c2f·7370·6~~16e~~·3e3c··c/"~~</span>~~<		000086c0:·7374·6572·2f64·6f63·2f26·7175·6f74·3b3c··ster/doc/"<
000086d0:·7370·616e·~~206~~3·6c61·~~7373~~·~~3d22~~·6~~f22~~·~~3e2b··~~span·class~~="o">+~~		000086d0:·2f73·7061·6e3e·3c73·7061·6e20·636c·6173··/span><span·clas
000086e0:·~~3c2f~~·~~7370~~·~~616~~e·3e3c·7370·616e·~~2063~~·6c61··~~</span>~~<span·~~cla~~		000086e0:·733d·226f·223e·2b3c·2f73·7061·6e3e·3c73··s="o">+</span><s
000086f0:·7~~373~~·3d22·6~~e22~~·3e70·6163·~~6b61~~·6765·~~3c2f~~··ss="n">p~~ack~~a~~ge</~~		000086f0:·7061·6e20·636c·6173·733d·226e·223e·7061··pan·class="n">pa
00008700:·7370·616e·3e3c·7370·616e·~~2063~~·6c61·7~~373~~··span><span·~~class~~		00008700:·636b·6167·653c·2f73·7061·6e3e·3c73·7061··ckage</span><spa
00008710:·3d22·6~~f22~~·~~3e2b~~·~~3c2f~~·~~7370~~·~~616~~e·3e3c·7370··="o">+</~~span><~~sp		00008710:·6e20·636c·6173·733d·226f·223e·2b3c·2f73··n·class="o">+</s
00008720:·616e·~~206~~3·6c61·~~7373~~·~~3d22~~·~~7332~~·223e·~~2671··~~an·class=~~"s2">&q~~		00008720:·7061·6e3e·3c73·7061·6e20·636c·6173·733d··pan><span·class=
00008730:·~~756f·~~743b·~~2f7~~2·73~~74·2~~f26·7175·6f74·3b3c··~~uot;/r~~s~~t/~~"<		00008730:·2273·3222·3e26·7175·6f74·3b2f·7273·742f··"s2">"/rst/
00008740:·2f73·7061·6e3e·3c73·7~~061~~·6~~e20~~·~~636~~c·6173··~~/span>~~<span·~~clas~~		00008740:·2671·756f·743b·3c2f·7370·616e·3e3c·7370··"</span><sp
00008750:·~~733d~~·2~~270~~·~~223e~~·~~293c~~·~~2f73~~·7061·6e3e·0a3c~~··~~s="p">)</~~span>.<~~		00008750:·616e·2063·6c61·7373·3d22·7022·3e29·3c2f··an·class="p">)</
00008760:·7370·616e·~~2063~~·6c61·7~~373~~·3d22·6~~720~~·6~~72d~~··span~~·class="g·g-~~		00008760:·7370·616e·3e0a·3c73·7061·6e20·636c·6173··span>.<span·clas
00008770:·5768·697~~4·6~~573·7061·6365·~~223e~~·~~2020~~·~~2020~~··Whitespac~~e">····~~		00008770:·733d·2267·2067·2d57·6869·7465·7370·6163··s="g·g-Whitespac
00008780:·3c2f·~~737~~0·~~616e~~·3e3c·7370·616e·~~206~~3·~~6c61~~··</span><s~~pan·cla~~		00008780:·6522·3e20·2020·203c·2f73·7061·6e3e·3c73··e">····</span><s
00008790:·7~~373~~·3d22·6d69·223e·3238·393c·~~2f73~~·~~7061~~··ss="mi">2~~89</spa~~		00008790:·7061·6e20·636c·6173·733d·226d·6922·3e32··pan·class="mi">2
000087a0:·6e3e·2020·~~202~~0·~~203c~~·~~737~~0·~~616e~~·2063·6c61··n>·····<s~~pan·cla~~		000087a0:·3839·3c2f·7370·616e·3e20·2020·2020·3c73··89</span>·····<s
000087b0:·7~~373~~·3d22·6~~e22~~·~~3e6~~3·~~6163~~·6865·~~3c2f~~·7370··ss="n">ca~~che</sp~~		000087b0:·7061·6e20·636c·6173·733d·226e·223e·6361··pan·class="n">ca
000087c0:·616e·~~3e20~~·3c73·7061·6e20·~~636~~c·6173·~~733d~~··~~an>·<~~span~~·class=~~		000087c0:·6368·653c·2f73·7061·6e3e·203c·7370·616e··che</span>·<span
000087d0:·226f·~~223e~~·3d3c·~~2f73~~·~~7061~~·6e3e·~~203c~~·7370··"~~o">=</span~~>·<sp		000087d0:·2063·6c61·7373·3d22·6f22·3e3d·3c2f·7370···class="o">=</sp
000087e0:·616e·2063·6c61·~~7373~~·~~3d22~~·6~~e22~~·3~~e5f~~·~~6765··~~an·class=~~"n">_ge~~		000087e0:·616e·3e20·3c73·7061·6e20·636c·6173·733d··an>·<span·class=
000087f0:·~~745f~~·~~6361~~·6368·653c·2f73·7061·6~~e3e~~·3c73··~~t_cach~~e~~</span>~~<s		000087f0:·226e·223e·5f67·6574·5f63·6163·6865·3c2f··"n">_get_cache</
00008800:·7061·6e20·636c·6173·~~733d~~·~~2270~~·223e·~~283c··~~pan·class~~="p">(<~~		00008800:·7370·616e·3e3c·7370·616e·2063·6c61·7373··span><span·class
00008810:·~~2f73~~·7061·6e3e·3c73·7~~061~~·6~~e20~~·~~636~~c·6173··~~/s~~p~~an>~~<span·~~clas~~		00008810:·3d22·7022·3e28·3c2f·7370·616e·3e3c·7370··="p">(</span><sp
00008820:·~~733d~~·226e·~~223e~~·6361·~~6368~~·6~~53c~~·~~2f7~~3·7061··s="n">cac~~he</spa~~		00008820:·616e·2063·6c61·7373·3d22·6e22·3e63·6163··an·class="n">cac
00008830:·6~~e3e~~·3c73·7~~061~~·6~~e20~~·~~636~~c·6173·~~733d~~·2~~270~~··n><span·~~class="p~~		00008830:·6865·3c2f·7370·616e·3e3c·7370·616e·2063··he</span><span·c
00008840:·~~223e~~·~~293c~~·~~2f73~~·7061·6e3e·~~0a3c~~·7370·616e··~~">)</s~~pan>.<span		00008840:·6c61·7373·3d22·7022·3e29·3c2f·7370·616e··lass="p">)</span
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00001a60:·6574·686f·643a·2020·2020·2020·2020·2020··ethod:··········		00001a60:·6574·686f·643a·2020·2020·2020·2020·2020··ethod:··········
00001a70:·2020·2020·2020·2020·2020·2020·2020·2020··················		00001a70:·2020·2020·2020·2020·2020·2020·2020·2020··················
00001a80:·204d·4c45·2020·2044·6620·4d6f·6465·6c3a···MLE···Df·Model:		00001a80:·204d·4c45·2020·2044·6620·4d6f·6465·6c3a···MLE···Df·Model:
00001a90:·2020·2020·2020·2020·2020·2020·2020·2020··················		00001a90:·2020·2020·2020·2020·2020·2020·2020·2020··················
00001aa0:·2020·2020·2020·2020·2020·2020·333c·2f73··············3</s		00001aa0:·2020·2020·2020·2020·2020·2020·333c·2f73··············3</s
00001ab0:·7061·6e3e·0a3c·7370·616e·2063·6c61·7373··pan>.<span·class		00001ab0:·7061·6e3e·0a3c·7370·616e·2063·6c61·7373··pan>.<span·class
00001ac0:·3d22·676f·223e·4461·7465·3a20·2020·2020··="go">Date:·····		00001ac0:·3d22·676f·223e·4461·7465·3a20·2020·2020··="go">Date:·····
00001ad0:·2020·2020·2020·2020·2020·2046·~~7269~~·2c20·············~~Fri~~,·		00001ad0:·2020·2020·2020·2020·2020·2053·6174·2c20·············Sat,·
00001ae0:·3036·204d·6172·2032·3032·3020·2020·5073··06·Mar·2020···Ps		00001ae0:·3130·2041·7072·2032·3032·3120·2020·5073··10·Apr·2021···Ps
00001af0:·6575·646f·2052·2d73·7175·2e3a·2020·2020··eudo·R-squ.:····		00001af0:·6575·646f·2052·2d73·7175·2e3a·2020·2020··eudo·R-squ.:····
00001b00:·2020·2020·2020·2020·2020·2020·2020·302e················0.		00001b00:·2020·2020·2020·2020·2020·2020·2020·302e················0.
00001b10:·3337·3430·3c2f·7370·616e·3e0a·3c73·7061··3740</span>.<spa		00001b10:·3337·3430·3c2f·7370·616e·3e0a·3c73·7061··3740</span>.<spa
00001b20:·6e20·636c·6173·733d·2267·6f22·3e54·696d··n·class="go">Tim		00001b20:·6e20·636c·6173·733d·2267·6f22·3e54·696d··n·class="go">Tim
00001b30:·653a·2020·2020·2020·2020·2020·2020·2020··e:··············		00001b30:·653a·2020·2020·2020·2020·2020·2020·2020··e:··············
00001b40:·2020·2020·2020·2020·2020·3135·3a34·323a············15:42:		00001b40:·2020·2020·2020·2020·2020·3031·3a30·303a············01:00:
00001b50:·3235·2020·204c·6f67·2d4c·696b·656c·6968··25···Log-Likelih		00001b50:·3433·2020·204c·6f67·2d4c·696b·656c·6968··43···Log-Likelih
00001b60:·6f6f·643a·2020·2020·2020·2020·2020·2020··ood:············		00001b60:·6f6f·643a·2020·2020·2020·2020·2020·2020··ood:············
00001b70:·2020·2020·2d31·322e·3839·303c·2f73·7061······-12.890</spa		00001b70:·2020·2020·2d31·322e·3839·303c·2f73·7061······-12.890</spa
00001b80:·6e3e·0a3c·7370·616e·2063·6c61·7373·3d22··n>.<span·class="		00001b80:·6e3e·0a3c·7370·616e·2063·6c61·7373·3d22··n>.<span·class="
00001b90:·676f·223e·636f·6e76·6572·6765·643a·2020··go">converged:··		00001b90:·676f·223e·636f·6e76·6572·6765·643a·2020··go">converged:··
00001ba0:·2020·2020·2020·2020·2020·2020·2020·2020··················		00001ba0:·2020·2020·2020·2020·2020·2020·2020·2020··················
00001bb0:·2020·2020·2054·7275·6520·2020·4c4c·2d4e·······True···LL-N		00001bb0:·2020·2020·2054·7275·6520·2020·4c4c·2d4e·······True···LL-N
00001bc0:·756c·6c3a·2020·2020·2020·2020·2020·2020··ull:············		00001bc0:·756c·6c3a·2020·2020·2020·2020·2020·2020··ull:············


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00001c70:·616e·3e3c·7370·616e·2063·6c61·7373·3d22··an><span·class="		00001c70:·616e·3e3c·7370·616e·2063·6c61·7373·3d22··an><span·class="
00001c80:·6e22·3e63·695f·6d65·616e·3c2f·7370·616e··n">ci_mean</span		00001c80:·6e22·3e63·695f·6d65·616e·3c2f·7370·616e··n">ci_mean</span
00001c90:·3e3c·7370·616e·2063·6c61·7373·3d22·7022··><span·class="p"		00001c90:·3e3c·7370·616e·2063·6c61·7373·3d22·7022··><span·class="p"
00001ca0:·3e28·293c·2f73·7061·6e3e·0a3c·7370·616e··>()</span>.<span		00001ca0:·3e28·293c·2f73·7061·6e3e·0a3c·7370·616e··>()</span>.<span
00001cb0:·2063·6c61·7373·3d22·6768·223e·4f75·745b···class="gh">Out[		00001cb0:·2063·6c61·7373·3d22·6768·223e·4f75·745b···class="gh">Out[
00001cc0:·355d·3a20·3c2f·7370·616e·3e3c·7370·616e··5]:·</span><span		00001cc0:·355d·3a20·3c2f·7370·616e·3e3c·7370·616e··5]:·</span><span
00001cd0:·2063·6c61·7373·3d22·676f·223e·282d·302e···class="go">(-0.		00001cd0:·2063·6c61·7373·3d22·676f·223e·282d·302e···class="go">(-0.
00001ce0:·3036·3831·3938·3736·3934·3638·3333·3639··06~~819~~8~~76946833~~69		00001ce0:·3236·3730·3838·3035·3039·3330·3935·3639··2670880509309569
00001cf0:·372c·2030·2e35·3634·3232·3930·3837·3630··7,·0.~~5642~~2~~908760~~		00001cf0:·342c·2030·2e32·3930·3832·3234·3131·3038··4,·0.29082241108
00001d00:·3336·3333·3729·3c2f·7370·616e·3e0a·0a3c··~~36337~~)</span>..<		00001d00:·3835·3638·293c·2f73·7061·6e3e·0a0a·3c73··8568)</span>..<s
00001d10:·7370·616e·2063·6c61·7373·3d22·676f·223e··span·class="go">		00001d10:·7061·6e20·636c·6173·733d·2267·6f22·3e23··pan·class="go">#
00001d20:·2320·7465·7374·2076·6172·6961·6e63·6520··#·test·variance·		00001d20:·2074·6573·7420·7661·7269·616e·6365·2069···test·variance·i
00001d30:·6973·2031·3c2f·7370·616e·3e0a·3c73·7061··is·1</span>.<spa		00001d30:·7320·313c·2f73·7061·6e3e·0a3c·7370·616e··s·1</span>.<span
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00002380:·6162·6c65·2e3c·2f74·643e·0a3c·2f74·723e··able.</td>.</tr>		00002380:·6961·626c·652e·3c2f·7464·3e0a·3c2f·7472··iable.</td>.</tr
00002390:·0a3c·7472·2063·6c61·7373·3d22·726f·772d··.<tr·class="row-		00002390:·3e0a·3c74·7220·636c·6173·733d·2272·6f77··>.<tr·class="row
000023a0:·6f64·6422·3e3c·7464·3e3c·6120·636c·6173··odd"><td><a·clas		000023a0:·2d6f·6464·223e·3c74·643e·3c61·2063·6c61··-odd"><td><a·cla
000023b0:·733d·2272·6566·6572·656e·6365·2069·6e74··s="reference·int		000023b0:·7373·3d22·7265·6665·7265·6e63·6520·696e··ss="reference·in
000023c0:·6572·6e61·6c22·2068·7265·663d·2267·656e··ernal"·href="gen		000023c0:·7465·726e·616c·2220·6872·6566·3d22·6765··ternal"·href="ge
000023d0:·6572·6174·6564·2f73·7461·7473·6d6f·6465··erated/statsmode		000023d0:·6e65·7261·7465·642f·7374·6174·736d·6f64··nerated/statsmod
000023e0:·6c73·2e65·6d70·6c69·6b65·2e64·6573·6372··ls.emplike.descr		000023e0:·656c·732e·656d·706c·696b·652e·6465·7363··els.emplike.desc
000023f0:·6970·7469·7665·2e44·6573·6353·7461·744d··iptive.DescStatM		000023f0:·7269·7074·6976·652e·4465·7363·5374·6174··riptive.DescStat
00002400:·562e·6874·6d6c·2373·7461·7473·6d6f·6465··V.html#statsmode		00002400:·4d56·2e68·746d·6c23·7374·6174·736d·6f64··MV.html#statsmod
00002410:·6c73·2e65·6d70·6c69·6b65·2e64·6573·6372··ls.emplike.descr		00002410:·656c·732e·656d·706c·696b·652e·6465·7363··els.emplike.desc
00002420:·6970·7469·7665·2e44·6573·6353·7461·744d··iptive.DescStatM		00002420:·7269·7074·6976·652e·4465·7363·5374·6174··riptive.DescStat
00002430:·5622·2074·6974·6c65·3d22·7374·6174·736d··V"·title="statsm		00002430:·4d56·2220·7469·746c·653d·2273·7461·7473··MV"·title="stats
00002440:·6f64·656c·732e·656d·706c·696b·652e·6465··odels.emplike.de		00002440:·6d6f·6465·6c73·2e65·6d70·6c69·6b65·2e64··models.emplike.d
00002450:·7363·7269·7074·6976·652e·4465·7363·5374··scriptive.DescSt		00002450:·6573·6372·6970·7469·7665·2e44·6573·6353··escriptive.DescS
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00002830:·643a·2020·2020·2020·2020·2020·2020·2020··d:··············		00002830:·643a·2020·2020·2020·2020·2020·2020·2020··d:··············
00002840:·2020·204c·6561·7374·2053·7175·6172·6573·····Least·Squares		00002840:·2020·204c·6561·7374·2053·7175·6172·6573·····Least·Squares
00002850:·2020·2046·2d73·7461·7469·7374·6963·3a20·····F-statistic:·		00002850:·2020·2046·2d73·7461·7469·7374·6963·3a20·····F-statistic:·
00002860:·2020·2020·2020·2020·2020·2020·2020·2020··················		00002860:·2020·2020·2020·2020·2020·2020·2020·2020··················
00002870:·2020·2020·362e·3633·363c·2f73·7061·6e3e······6.636</span>		00002870:·2020·2020·362e·3633·363c·2f73·7061·6e3e······6.636</span>
00002880:·0a3c·7370·616e·2063·6c61·7373·3d22·676f··.<span·class="go		00002880:·0a3c·7370·616e·2063·6c61·7373·3d22·676f··.<span·class="go
00002890:·223e·4461·7465·3a20·2020·2020·2020·2020··">Date:·········		00002890:·223e·4461·7465·3a20·2020·2020·2020·2020··">Date:·········
000028a0:·2020·2020·2020·2046·~~7269~~·2c20·3036·204d·········~~Fri~~,·06·M		000028a0:·2020·2020·2020·2053·6174·2c20·3130·2041·········Sat,·10·A
000028b0:·6172·2032·3032·3020·2020·5072·6f62·2028··ar·2020···Prob·(		000028b0:·7072·2032·3032·3120·2020·5072·6f62·2028··pr·2021···Prob·(
000028c0:·462d·7374·6174·6973·7469·6329·3a20·2020··F-statistic):···		000028c0:·462d·7374·6174·6973·7469·6329·3a20·2020··F-statistic):···
000028d0:·2020·2020·2020·2020·312e·3037·652d·3035··········1.07e-05		000028d0:·2020·2020·2020·2020·312e·3037·652d·3035··········1.07e-05
000028e0:·3c2f·7370·616e·3e0a·3c73·7061·6e20·636c··</span>.<span·cl		000028e0:·3c2f·7370·616e·3e0a·3c73·7061·6e20·636c··</span>.<span·cl
000028f0:·6173·733d·2267·6f22·3e54·696d·653a·2020··ass="go">Time:··		000028f0:·6173·733d·2267·6f22·3e54·696d·653a·2020··ass="go">Time:··
00002900:·2020·2020·2020·2020·2020·2020·2020·2020··················		00002900:·2020·2020·2020·2020·2020·2020·2020·2020··················
00002910:·2020·2020·2020·3135·3a34·323a·3239·2020········15:42:29··		00002910:·2020·2020·2020·3031·3a30·303a·3434·2020········01:00:44··
00002920:·204c·6f67·2d4c·696b·656c·6968·6f6f·643a···Log-Likelihood:		00002920:·204c·6f67·2d4c·696b·656c·6968·6f6f·643a···Log-Likelihood:
00002930:·2020·2020·2020·2020·2020·2020·2020·2020··················		00002930:·2020·2020·2020·2020·2020·2020·2020·2020··················
00002940:·2d33·3735·2e33·303c·2f73·7061·6e3e·0a3c··-375.30</span>.<		00002940:·2d33·3735·2e33·303c·2f73·7061·6e3e·0a3c··-375.30</span>.<
00002950:·7370·616e·2063·6c61·7373·3d22·676f·223e··span·class="go">		00002950:·7370·616e·2063·6c61·7373·3d22·676f·223e··span·class="go">
00002960:·4e6f·2e20·4f62·7365·7276·6174·696f·6e73··No.·Observations		00002960:·4e6f·2e20·4f62·7365·7276·6174·696f·6e73··No.·Observations
00002970:·3a20·2020·2020·2020·2020·2020·2020·2020··:···············		00002970:·3a20·2020·2020·2020·2020·2020·2020·2020··:···············
00002980:·2020·2038·3520·2020·4149·433a·2020·2020·····85···AIC:····		00002980:·2020·2038·3520·2020·4149·433a·2020·2020·····85···AIC:····
Offset 1942, 22 lines modified		Offset 1942, 22 lines modified
00007950:·2020·2020·2020·2020·2020·2020·2020·2020··················		00007950:·2020·2020·2020·2020·2020·2020·2020·2020··················
00007960:·4c65·6173·7420·5371·7561·7265·7320·2020··Least·Squares···		00007960:·4c65·6173·7420·5371·7561·7265·7320·2020··Least·Squares···
00007970:·462d·7374·6174·6973·7469·633a·2020·2020··F-statistic:····		00007970:·462d·7374·6174·6973·7469·633a·2020·2020··F-statistic:····
00007980:·2020·2020·2020·2020·2020·2020·2020·2020··················		00007980:·2020·2020·2020·2020·2020·2020·2020·2020··················
00007990:·2031·322e·3036·3c2f·7370·616e·3e0a·3c73···12.06</span>.<s		00007990:·2031·322e·3036·3c2f·7370·616e·3e0a·3c73···12.06</span>.<s
000079a0:·7061·6e20·636c·6173·733d·2267·6f22·3e44··pan·class="go">D		000079a0:·7061·6e20·636c·6173·733d·2267·6f22·3e44··pan·class="go">D
000079b0:·6174·653a·2020·2020·2020·2020·2020·2020··ate:············		000079b0:·6174·653a·2020·2020·2020·2020·2020·2020··ate:············
000079c0:·2020·2020·~~4672~~·692c·2030·3620·4~~d61~~·7220······~~Fri~~,·06·Mar·		000079c0:·2020·2020·5361·742c·2031·3020·4170·7220······Sat,·10·Apr·
000079d0:·3230·3230·2020·2050·726f·6220·2846·2d73··2020···Prob·(F-s		000079d0:·3230·3231·2020·2050·726f·6220·2846·2d73··2021···Prob·(F-s
000079e0:·7461·7469·7374·6963·293a·2020·2020·2020··tatistic):······		000079e0:·7461·7469·7374·6963·293a·2020·2020·2020··tatistic):······
000079f0:·2020·2020·2031·2e33·3265·2d30·363c·2f73·······1.32e-06</s		000079f0:·2020·2020·2031·2e33·3265·2d30·363c·2f73·······1.32e-06</s
00007a00:·7061·6e3e·0a3c·7370·616e·2063·6c61·7373··pan>.<span·class		00007a00:·7061·6e3e·0a3c·7370·616e·2063·6c61·7373··pan>.<span·class
00007a10:·3d22·676f·223e·5469·6d65·3a20·2020·2020··="go">Time:·····		00007a10:·3d22·676f·223e·5469·6d65·3a20·2020·2020··="go">Time:·····
00007a20:·2020·2020·2020·2020·2020·2020·2020·2020··················		00007a20:·2020·2020·2020·2020·2020·2020·2020·2020··················
00007a30:·2020·2031·353a·3432·3a32·3920·2020·4c6f·····15:42:29···Lo		00007a30:·2020·2030·313a·3030·3a34·3420·2020·4c6f·····01:00:44···Lo
00007a40:·672d·4c69·6b65·6c69·686f·6f64·3a20·2020··g-Likelihood:···		00007a40:·672d·4c69·6b65·6c69·686f·6f64·3a20·2020··g-Likelihood:···
00007a50:·2020·2020·2020·2020·2020·2020·202d·3337···············-37		00007a50:·2020·2020·2020·2020·2020·2020·202d·3337···············-37
00007a60:·372e·3133·3c2f·7370·616e·3e0a·3c73·7061··7.13</span>.<spa		00007a60:·372e·3133·3c2f·7370·616e·3e0a·3c73·7061··7.13</span>.<spa
00007a70:·6e20·636c·6173·733d·2267·6f22·3e4e·6f2e··n·class="go">No.		00007a70:·6e20·636c·6173·733d·2267·6f22·3e4e·6f2e··n·class="go">No.
00007a80:·204f·6273·6572·7661·7469·6f6e·733a·2020···Observations:··		00007a80:·204f·6273·6572·7661·7469·6f6e·733a·2020···Observations:··
00007a90:·2020·2020·2020·2020·2020·2020·2020·2020··················		00007a90:·2020·2020·2020·2020·2020·2020·2020·2020··················
00007aa0:·3835·2020·2041·4943·3a20·2020·2020·2020··85···AIC:·······		00007aa0:·3835·2020·2041·4943·3a20·2020·2020·2020··85···AIC:·······


Offset 13432, 16 lines modified		Offset 13432, 16 lines modified

13432	<div·class="output_subarea·output_stream·output_stdout·output_text">	13432	<div·class="output_subarea·output_stream·output_stdout·output_text">
13433	<pre>····························WLS·Regression·Results····························	13433	<pre>····························WLS·Regression·Results····························
13434	==============================================================================	13434	==============================================================================
13435	Dep.·Variable:······················y···R-squared:·······················0.400	13435	Dep.·Variable:······················y···R-squared:·······················0.400
13436	Model:····························WLS···Adj.·R-squared:··················0.367	13436	Model:····························WLS···Adj.·R-squared:··················0.367
13437	Method:·················Least·Squares···F-statistic:·····················193.5	13437	Method:·················Least·Squares···F-statistic:·····················193.5
13438	Date:················~~Fri~~,·06·Mar·2020···Prob·(F-statistic):···········4.52e-11	13438	Date:················Sat,·10·Apr·2021···Prob·(F-statistic):···········4.52e-11
13439	Time:························15:40:10···Log-Likelihood:················-119.06	13439	Time:························01:00:04···Log-Likelihood:················-119.06
13440	No.·Observations:··················20···AIC:·····························242.1	13440	No.·Observations:··················20···AIC:·····························242.1
13441	Df·Residuals:······················18···BIC:·····························244.1	13441	Df·Residuals:······················18···BIC:·····························244.1
13442	Df·Model:···························1·········································	13442	Df·Model:···························1·········································
13443	Covariance·Type:··········fixed·scale·········································	13443	Covariance·Type:··········fixed·scale·········································
13444	==============================================================================	13444	==============================================================================
13445	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]	13445	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]
13446	------------------------------------------------------------------------------	13446	------------------------------------------------------------------------------


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13527	<div·class="output_subarea·output_stream·output_stdout·output_text">	13527	<div·class="output_subarea·output_stream·output_stdout·output_text">
13528	<pre>···························Logit·Regression·Results···························	13528	<pre>···························Logit·Regression·Results···························
13529	==============================================================================	13529	==============================================================================
13530	Dep.·Variable:·················affair···No.·Observations:·················6366	13530	Dep.·Variable:·················affair···No.·Observations:·················6366
13531	Model:··························Logit···Df·Residuals:·····················6357	13531	Model:··························Logit···Df·Residuals:·····················6357
13532	Method:···························MLE···Df·Model:····························8	13532	Method:···························MLE···Df·Model:····························8
13533	Date:················~~Fri~~,·06·Mar·2020···Pseudo·R-squ.:··················0.1327	13533	Date:················Sat,·10·Apr·2021···Pseudo·R-squ.:··················0.1327
13534	Time:························15:40:10···Log-Likelihood:················-3471.5	13534	Time:························01:00:11···Log-Likelihood:················-3471.5
13535	converged:·······················True···LL-Null:·······················-4002.5	13535	converged:·······················True···LL-Null:·······················-4002.5
13536	········································LLR·p-value:················5.807e-224	13536	········································LLR·p-value:················5.807e-224
13537	===================================================================================	13537	===================================================================================
13538	······················coef····std·err··········z······P>\|z\|······[0.025······0.975]	13538	······················coef····std·err··········z······P>\|z\|······[0.025······0.975]
13539	-----------------------------------------------------------------------------------	13539	-----------------------------------------------------------------------------------
13540	Intercept···········3.7257······0.299·····12.470······0.000·······3.140·······4.311	13540	Intercept···········3.7257······0.299·····12.470······0.000·······3.140·······4.311
13541	occupation··········0.1602······0.034······4.717······0.000·······0.094·······0.227	13541	occupation··········0.1602······0.034······4.717······0.000·······0.094·······0.227
Offset 14422, 16 lines modified		Offset 14422, 16 lines modified
14422	<pre>··················Generalized·Linear·Model·Regression·Results···················	14422	<pre>··················Generalized·Linear·Model·Regression·Results···················
14423	================================================================================	14423	================================================================================
14424	Dep.·Variable:·····['NABOVE',·'NBELOW']···No.·Observations:··················303	14424	Dep.·Variable:·····['NABOVE',·'NBELOW']···No.·Observations:··················303
14425	Model:······························GLM···Df·Residuals:······················282	14425	Model:······························GLM···Df·Residuals:······················282
14426	Model·Family:··················Binomial···Df·Model:···························20	14426	Model·Family:··················Binomial···Df·Model:···························20
14427	Link·Function:····················logit···Scale:·····························1.0	14427	Link·Function:····················logit···Scale:·····························1.0
14428	Method:····························IRLS···Log-Likelihood:················-2998.6	14428	Method:····························IRLS···Log-Likelihood:················-2998.6
14429	Date:··················~~Fri~~,·06·Mar·2020···Deviance:·······················4078.8	14429	Date:··················Sat,·10·Apr·2021···Deviance:·······················4078.8
14430	Time:··························15:40:22···Pearson·chi2:·····················9.60	14430	Time:··························01:00:13···Pearson·chi2:·····················9.60
14431	No.·Iterations:·······················5·········································	14431	No.·Iterations:·······················5·········································
14432	============================================================================================	14432	============================================================================================
14433	·······························coef····std·err··········z······P>\|z\|······[0.025······0.975]	14433	·······························coef····std·err··········z······P>\|z\|······[0.025······0.975]
14434	--------------------------------------------------------------------------------------------	14434	--------------------------------------------------------------------------------------------
14435	Intercept····················2.9589······1.547······1.913······0.056······-0.073·······5.990	14435	Intercept····················2.9589······1.547······1.913······0.056······-0.073·······5.990
14436	LOWINC······················-0.0168······0.000····-38.749······0.000······-0.018······-0.016	14436	LOWINC······················-0.0168······0.000····-38.749······0.000······-0.018······-0.016
14437	PERASIAN·····················0.0099······0.001·····16.505······0.000·······0.009·······0.011	14437	PERASIAN·····················0.0099······0.001·····16.505······0.000·······0.009·······0.011


Offset 13487, 16 lines modified		Offset 13487, 16 lines modified

13487	<div·class="output_subarea·output_stream·output_stdout·output_text">	13487	<div·class="output_subarea·output_stream·output_stdout·output_text">
13488	<pre>···························Logit·Regression·Results···························	13488	<pre>···························Logit·Regression·Results···························
13489	==============================================================================	13489	==============================================================================
13490	Dep.·Variable:······················y···No.·Observations:···················32	13490	Dep.·Variable:······················y···No.·Observations:···················32
13491	Model:··························Logit···Df·Residuals:·······················28	13491	Model:··························Logit···Df·Residuals:·······················28
13492	Method:···························MLE···Df·Model:····························3	13492	Method:···························MLE···Df·Model:····························3
13493	Date:················~~Fri~~,·06·Mar·2020···Pseudo·R-squ.:··················0.3740	13493	Date:················Sat,·10·Apr·2021···Pseudo·R-squ.:··················0.3740
13494	Time:························15:39:46···Log-Likelihood:················-12.890	13494	Time:························01:00:04···Log-Likelihood:················-12.890
13495	converged:·······················True···LL-Null:·······················-20.592	13495	converged:·······················True···LL-Null:·······················-20.592
13496	········································LLR·p-value:··················0.001502	13496	········································LLR·p-value:··················0.001502
13497	==============================================================================	13497	==============================================================================
13498	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]	13498	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]
13499	------------------------------------------------------------------------------	13499	------------------------------------------------------------------------------
13500	x1·············2.8261······1.263······2.238······0.025·······0.351·······5.301	13500	x1·············2.8261······1.263······2.238······0.025·······0.351·······5.301
13501	x2·············0.0952······0.142······0.672······0.501······-0.182·······0.373	13501	x2·············0.0952······0.142······0.672······0.501······-0.182·······0.373
Offset 13797, 16 lines modified		Offset 13797, 16 lines modified
13797	·········Current·function·value:·3.091609	13797	·········Current·function·value:·3.091609
13798	·········Iterations·12	13798	·········Iterations·12
13799	··························Poisson·Regression·Results··························	13799	··························Poisson·Regression·Results··························
13800	==============================================================================	13800	==============================================================================
13801	Dep.·Variable:······················y···No.·Observations:················20190	13801	Dep.·Variable:······················y···No.·Observations:················20190
13802	Model:························Poisson···Df·Residuals:····················20180	13802	Model:························Poisson···Df·Residuals:····················20180
13803	Method:···························MLE···Df·Model:····························9	13803	Method:···························MLE···Df·Model:····························9
13804	Date:················~~Fri~~,·06·Mar·2020···Pseudo·R-squ.:·················0.06343	13804	Date:················Sat,·10·Apr·2021···Pseudo·R-squ.:·················0.06343
13805	Time:························15:39:50···Log-Likelihood:················-62420.	13805	Time:························01:00:05···Log-Likelihood:················-62420.
13806	converged:·······················True···LL-Null:·······················-66647.	13806	converged:·······················True···LL-Null:·······················-66647.
13807	········································LLR·p-value:·····················0.000	13807	········································LLR·p-value:·····················0.000
13808	==============================================================================	13808	==============================================================================
13809	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]	13809	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]
13810	------------------------------------------------------------------------------	13810	------------------------------------------------------------------------------
13811	x1············-0.0525······0.003····-18.216······0.000······-0.058······-0.047	13811	x1············-0.0525······0.003····-18.216······0.000······-0.058······-0.047
13812	x2············-0.2471······0.011····-23.272······0.000······-0.268······-0.226	13812	x2············-0.2471······0.011····-23.272······0.000······-0.268······-0.226
Offset 13872, 16 lines modified		Offset 13872, 16 lines modified

13872	<div·class="output_subarea·output_stream·output_stdout·output_text">	13872	<div·class="output_subarea·output_stream·output_stdout·output_text">
13873	<pre>·····················NegativeBinomial·Regression·Results······················	13873	<pre>·····················NegativeBinomial·Regression·Results······················
13874	==============================================================================	13874	==============================================================================
13875	Dep.·Variable:······················y···No.·Observations:················20190	13875	Dep.·Variable:······················y···No.·Observations:················20190
13876	Model:···············NegativeBinomial···Df·Residuals:····················20180	13876	Model:···············NegativeBinomial···Df·Residuals:····················20180
13877	Method:···························MLE···Df·Model:····························9	13877	Method:···························MLE···Df·Model:····························9
13878	Date:················~~Fri~~,·06·Mar·2020···Pseudo·R-squ.:·················0.01845	13878	Date:················Sat,·10·Apr·2021···Pseudo·R-squ.:·················0.01845
13879	Time:························15:39:54···Log-Likelihood:················-43384.	13879	Time:························01:00:07···Log-Likelihood:················-43384.
13880	converged:······················False···LL-Null:·······················-44199.	13880	converged:······················False···LL-Null:·······················-44199.
13881	········································LLR·p-value:·····················0.000	13881	········································LLR·p-value:·····················0.000
13882	==============================================================================	13882	==============================================================================
13883	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]	13883	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]
13884	------------------------------------------------------------------------------	13884	------------------------------------------------------------------------------
13885	x1············-0.0580······0.006·····-9.517······0.000······-0.070······-0.046	13885	x1············-0.0580······0.006·····-9.517······0.000······-0.070······-0.046
13886	x2············-0.2678······0.023····-11.802······0.000······-0.312······-0.223	13886	x2············-0.2678······0.023····-11.802······0.000······-0.312······-0.223
Offset 13940, 16 lines modified		Offset 13940, 16 lines modified
13940	·········Function·evaluations:·106	13940	·········Function·evaluations:·106
13941	·········Gradient·evaluations:·106	13941	·········Gradient·evaluations:·106
13942	··························MNLogit·Regression·Results··························	13942	··························MNLogit·Regression·Results··························
13943	==============================================================================	13943	==============================================================================
13944	Dep.·Variable:······················y···No.·Observations:··················944	13944	Dep.·Variable:······················y···No.·Observations:··················944
13945	Model:························MNLogit···Df·Residuals:······················908	13945	Model:························MNLogit···Df·Residuals:······················908
13946	Method:···························MLE···Df·Model:···························30	13946	Method:···························MLE···Df·Model:···························30
13947	Date:················~~Fri~~,·06·Mar·2020···Pseudo·R-squ.:··················0.1648	13947	Date:················Sat,·10·Apr·2021···Pseudo·R-squ.:··················0.1648
13948	Time:························15:39:54···Log-Likelihood:················-1461.9	13948	Time:························01:00:07···Log-Likelihood:················-1461.9
13949	converged:······················False···LL-Null:·······················-1750.3	13949	converged:······················False···LL-Null:·······················-1750.3
13950	········································LLR·p-value:················1.827e-102	13950	········································LLR·p-value:················1.827e-102
13951	==============================================================================	13951	==============================================================================
13952	·······y=1·······coef····std·err··········z······P>\|z\|······[0.025······0.975]	13952	·······y=1·······coef····std·err··········z······P>\|z\|······[0.025······0.975]
13953	------------------------------------------------------------------------------	13953	------------------------------------------------------------------------------
13954	x1············-0.0116······0.034·····-0.338······0.735······-0.079·······0.056	13954	x1············-0.0116······0.034·····-0.338······0.735······-0.079·······0.056
13955	x2·············0.2973······0.094······3.175······0.001·······0.114·······0.481	13955	x2·············0.2973······0.094······3.175······0.001·······0.114·······0.481


Offset 13540, 16 lines modified		Offset 13540, 16 lines modified

13540	<div·class="output_subarea·output_stream·output_stdout·output_text">	13540	<div·class="output_subarea·output_stream·output_stdout·output_text">
13541	<pre>····························OLS·Regression·Results····························	13541	<pre>····························OLS·Regression·Results····························
13542	==============================================================================	13542	==============================================================================
13543	Dep.·Variable:················Lottery···R-squared:·······················0.338	13543	Dep.·Variable:················Lottery···R-squared:·······················0.338
13544	Model:····························OLS···Adj.·R-squared:··················0.287	13544	Model:····························OLS···Adj.·R-squared:··················0.287
13545	Method:·················Least·Squares···F-statistic:·····················6.636	13545	Method:·················Least·Squares···F-statistic:·····················6.636
13546	Date:················~~Fri~~,·06·Mar·2020···Prob·(F-statistic):···········1.07e-05	13546	Date:················Sat,·10·Apr·2021···Prob·(F-statistic):···········1.07e-05
13547	Time:························15:40:34···Log-Likelihood:················-375.30	13547	Time:························01:00:05···Log-Likelihood:················-375.30
13548	No.·Observations:··················85···AIC:·····························764.6	13548	No.·Observations:··················85···AIC:·····························764.6
13549	Df·Residuals:······················78···BIC:·····························781.7	13549	Df·Residuals:······················78···BIC:·····························781.7
13550	Df·Model:···························6·········································	13550	Df·Model:···························6·········································
13551	Covariance·Type:············nonrobust·········································	13551	Covariance·Type:············nonrobust·········································
13552	===============================================================================	13552	===============================================================================
13553	··················coef····std·err··········t······P>\|t\|······[0.025······0.975]	13553	··················coef····std·err··········t······P>\|t\|······[0.025······0.975]
13554	-------------------------------------------------------------------------------	13554	-------------------------------------------------------------------------------
Offset 13963, 16 lines modified		Offset 13963, 16 lines modified

13963	<div·class="output_subarea·output_stream·output_stdout·output_text">	13963	<div·class="output_subarea·output_stream·output_stdout·output_text">
13964	<pre>····························OLS·Regression·Results····························	13964	<pre>····························OLS·Regression·Results····························
13965	==============================================================================	13965	==============================================================================
13966	Dep.·Variable:················Lottery···R-squared:·······················0.309	13966	Dep.·Variable:················Lottery···R-squared:·······················0.309
13967	Model:····························OLS···Adj.·R-squared:··················0.283	13967	Model:····························OLS···Adj.·R-squared:··················0.283
13968	Method:·················Least·Squares···F-statistic:·····················12.06	13968	Method:·················Least·Squares···F-statistic:·····················12.06
13969	Date:················~~Fri~~,·06·Mar·2020···Prob·(F-statistic):···········1.32e-06	13969	Date:················Sat,·10·Apr·2021···Prob·(F-statistic):···········1.32e-06
13970	Time:························15:40:36···Log-Likelihood:················-377.13	13970	Time:························01:00:06···Log-Likelihood:················-377.13
13971	No.·Observations:··················85···AIC:·····························762.3	13971	No.·Observations:··················85···AIC:·····························762.3
13972	Df·Residuals:······················81···BIC:·····························772.0	13972	Df·Residuals:······················81···BIC:·····························772.0
13973	Df·Model:···························3·········································	13973	Df·Model:···························3·········································
13974	Covariance·Type:············nonrobust·········································	13974	Covariance·Type:············nonrobust·········································
13975	===================================================================================	13975	===================================================================================
13976	······················coef····std·err··········t······P>\|t\|······[0.025······0.975]	13976	······················coef····std·err··········t······P>\|t\|······[0.025······0.975]
13977	-----------------------------------------------------------------------------------	13977	-----------------------------------------------------------------------------------


Offset 13406, 16 lines modified		Offset 13406, 16 lines modified
13406	·········Iterations:·292	13406	·········Iterations:·292
13407	·········Function·evaluations:·494	13407	·········Function·evaluations:·494
13408	·······························MyProbit·Results·······························	13408	·······························MyProbit·Results·······························
13409	==============================================================================	13409	==============================================================================
13410	Dep.·Variable:··················GRADE···Log-Likelihood:················-12.819	13410	Dep.·Variable:··················GRADE···Log-Likelihood:················-12.819
13411	Model:·······················MyProbit···AIC:·····························33.64	13411	Model:·······················MyProbit···AIC:·····························33.64
13412	Method:············Maximum·Likelihood···BIC:·····························39.50	13412	Method:············Maximum·Likelihood···BIC:·····························39.50
13413	Date:················~~Fri~~,·06·Mar·2020·········································	13413	Date:················Sat,·10·Apr·2021·········································
13414	Time:························15:39:57·········································	13414	Time:························01:00:12·········································
13415	No.·Observations:··················32·········································	13415	No.·Observations:··················32·········································
13416	Df·Residuals:······················28·········································	13416	Df·Residuals:······················28·········································
13417	Df·Model:···························3·········································	13417	Df·Model:···························3·········································
13418	==============================================================================	13418	==============================================================================
13419	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]	13419	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]
13420	------------------------------------------------------------------------------	13420	------------------------------------------------------------------------------
13421	const·········-7.4523······2.542·····-2.931······0.003·····-12.435······-2.469	13421	const·········-7.4523······2.542·····-2.931······0.003·····-12.435······-2.469
Offset 13970, 16 lines modified		Offset 13970, 16 lines modified

13970	<div·class="output_subarea·output_stream·output_stdout·output_text">	13970	<div·class="output_subarea·output_stream·output_stdout·output_text">
13971	<pre>·································NBin·Results·································	13971	<pre>·································NBin·Results·································
13972	==============================================================================	13972	==============================================================================
13973	Dep.·Variable:····················los···Log-Likelihood:················-4797.5	13973	Dep.·Variable:····················los···Log-Likelihood:················-4797.5
13974	Model:···························NBin···AIC:·····························9605.	13974	Model:···························NBin···AIC:·····························9605.
13975	Method:············Maximum·Likelihood···BIC:·····························9632.	13975	Method:············Maximum·Likelihood···BIC:·····························9632.
13976	Date:················~~Fri~~,·06·Mar·2020·········································	13976	Date:················Sat,·10·Apr·2021·········································
13977	Time:························15:40:05·········································	13977	Time:························01:00:13·········································
13978	No.·Observations:················1495·········································	13978	No.·Observations:················1495·········································
13979	Df·Residuals:····················1490·········································	13979	Df·Residuals:····················1490·········································
13980	Df·Model:···························4·········································	13980	Df·Model:···························4·········································
13981	==============================================================================	13981	==============================================================================
13982	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]	13982	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]
13983	------------------------------------------------------------------------------	13983	------------------------------------------------------------------------------
13984	type2··········0.2213······0.051······4.373······0.000·······0.122·······0.320	13984	type2··········0.2213······0.051······4.373······0.000·······0.122·······0.320
Offset 14036, 16 lines modified		Offset 14036, 16 lines modified

14036	<div·class="output_subarea·output_stream·output_stdout·output_text">	14036	<div·class="output_subarea·output_stream·output_stdout·output_text">
14037	<pre>·····················NegativeBinomial·Regression·Results······················	14037	<pre>·····················NegativeBinomial·Regression·Results······················
14038	==============================================================================	14038	==============================================================================
14039	Dep.·Variable:····················los···No.·Observations:·················1495	14039	Dep.·Variable:····················los···No.·Observations:·················1495
14040	Model:···············NegativeBinomial···Df·Residuals:·····················1490	14040	Model:···············NegativeBinomial···Df·Residuals:·····················1490
14041	Method:···························MLE···Df·Model:····························4	14041	Method:···························MLE···Df·Model:····························4
14042	Date:················~~Fri~~,·06·Mar·2020···Pseudo·R-squ.:·················0.01215	14042	Date:················Sat,·10·Apr·2021···Pseudo·R-squ.:·················0.01215
14043	Time:························15:40:05···Log-Likelihood:················-4797.5	14043	Time:························01:00:14···Log-Likelihood:················-4797.5
14044	converged:·······················True···LL-Null:·······················-4856.5	14044	converged:·······················True···LL-Null:·······················-4856.5
14045	········································LLR·p-value:·················1.404e-24	14045	········································LLR·p-value:·················1.404e-24
14046	==============================================================================	14046	==============================================================================
14047	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]	14047	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]
14048	------------------------------------------------------------------------------	14048	------------------------------------------------------------------------------
14049	type2··········0.2212······0.051······4.373······0.000·······0.122·······0.320	14049	type2··········0.2212······0.051······4.373······0.000·······0.122·······0.320
14050	type3··········0.7062······0.076······9.276······0.000·······0.557·······0.855	14050	type3··········0.7062······0.076······9.276······0.000·······0.557·······0.855


Offset 13493, 16 lines modified		Offset 13493, 16 lines modified
13493	<pre>·················Generalized·Linear·Model·Regression·Results··················	13493	<pre>·················Generalized·Linear·Model·Regression·Results··················
13494	==============================================================================	13494	==============================================================================
13495	Dep.·Variable:···········['y1',·'y2']···No.·Observations:··················303	13495	Dep.·Variable:···········['y1',·'y2']···No.·Observations:··················303
13496	Model:····························GLM···Df·Residuals:······················282	13496	Model:····························GLM···Df·Residuals:······················282
13497	Model·Family:················Binomial···Df·Model:···························20	13497	Model·Family:················Binomial···Df·Model:···························20
13498	Link·Function:··················logit···Scale:·····························1.0	13498	Link·Function:··················logit···Scale:·····························1.0
13499	Method:··························IRLS···Log-Likelihood:················-2998.6	13499	Method:··························IRLS···Log-Likelihood:················-2998.6
13500	Date:················~~Fri~~,·06·Mar·2020···Deviance:·······················4078.8	13500	Date:················Sat,·10·Apr·2021···Deviance:·······················4078.8
13501	Time:························15:40:32···Pearson·chi2:·····················9.60	13501	Time:························01:00:06···Pearson·chi2:·····················9.60
13502	No.·Iterations:·····················5·········································	13502	No.·Iterations:·····················5·········································
13503	==============================================================================	13503	==============================================================================
13504	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]	13504	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]
13505	------------------------------------------------------------------------------	13505	------------------------------------------------------------------------------
13506	x1············-0.0168······0.000····-38.749······0.000······-0.018······-0.016	13506	x1············-0.0168······0.000····-38.749······0.000······-0.018······-0.016
13507	x2·············0.0099······0.001·····16.505······0.000·······0.009·······0.011	13507	x2·············0.0099······0.001·····16.505······0.000·······0.009·······0.011
13508	x3············-0.0187······0.001····-25.182······0.000······-0.020······-0.017	13508	x3············-0.0187······0.001····-25.182······0.000······-0.020······-0.017
Offset 14081, 16 lines modified		Offset 14081, 16 lines modified
14081	<pre>···················Generalized·Linear·Model·Regression·Results···················	14081	<pre>···················Generalized·Linear·Model·Regression·Results···················
14082	=================================================================================	14082	=================================================================================
14083	Dep.·Variable:······················y···No.·Observations:······················32	14083	Dep.·Variable:······················y···No.·Observations:······················32
14084	Model:····························GLM···Df·Residuals:··························24	14084	Model:····························GLM···Df·Residuals:··························24
14085	Model·Family:···················Gamma···Df·Model:·······························7	14085	Model·Family:···················Gamma···Df·Model:·······························7
14086	Link·Function:··········inverse_power···Scale:··············0.0035842831734937248	14086	Link·Function:··········inverse_power···Scale:··············0.0035842831734937248
14087	Method:··························IRLS···Log-Likelihood:···················-83.017	14087	Method:··························IRLS···Log-Likelihood:···················-83.017
14088	Date:················~~Fri~~,·06·Mar·2020···Deviance:························0.087389	14088	Date:················Sat,·10·Apr·2021···Deviance:························0.087389
14089	Time:························15:40:34···Pearson·chi2:······················0.0860	14089	Time:························01:00:07···Pearson·chi2:······················0.0860
14090	No.·Iterations:·····················4············································	14090	No.·Iterations:·····················4············································
14091	==============================================================================	14091	==============================================================================
14092	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]	14092	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]
14093	------------------------------------------------------------------------------	14093	------------------------------------------------------------------------------
14094	x1··········4.962e-05···1.62e-05······3.060······0.002····1.78e-05····8.14e-05	14094	x1··········4.962e-05···1.62e-05······3.060······0.002····1.78e-05····8.14e-05
14095	x2·············0.0020······0.001······3.824······0.000·······0.001·······0.003	14095	x2·············0.0020······0.001······3.824······0.000·······0.001·······0.003
14096	x3·········-7.181e-05···2.71e-05·····-2.648······0.008······-0.000···-1.87e-05	14096	x3·········-7.181e-05···2.71e-05·····-2.648······0.008······-0.000···-1.87e-05
Offset 14179, 16 lines modified		Offset 14179, 16 lines modified
14179	<pre>···················Generalized·Linear·Model·Regression·Results····················	14179	<pre>···················Generalized·Linear·Model·Regression·Results····················
14180	==================================================================================	14180	==================================================================================
14181	Dep.·Variable:······················y···No.·Observations:······················100	14181	Dep.·Variable:······················y···No.·Observations:······················100
14182	Model:····························GLM···Df·Residuals:···························97	14182	Model:····························GLM···Df·Residuals:···························97
14183	Model·Family:················Gaussian···Df·Model:································2	14183	Model·Family:················Gaussian···Df·Model:································2
14184	Link·Function:····················log···Scale:··············1.0531142558807228e-07	14184	Link·Function:····················log···Scale:··············1.0531142558807228e-07
14185	Method:··························IRLS···Log-Likelihood:·····················662.92	14185	Method:··························IRLS···Log-Likelihood:·····················662.92
14186	Date:················~~Fri~~,·06·Mar·2020···Deviance:·······················1.0215e-05	14186	Date:················Sat,·10·Apr·2021···Deviance:·······················1.0215e-05
14187	Time:························15:40:35···Pearson·chi2:·····················1.02e-05	14187	Time:························01:00:07···Pearson·chi2:·····················1.02e-05
14188	No.·Iterations:·····················5·············································	14188	No.·Iterations:·····················5·············································
14189	==============================================================================	14189	==============================================================================
14190	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]	14190	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]
14191	------------------------------------------------------------------------------	14191	------------------------------------------------------------------------------
14192	x1············-0.0300····5.6e-06··-5361.333······0.000······-0.030······-0.030	14192	x1············-0.0300····5.6e-06··-5361.333······0.000······-0.030······-0.030
14193	x2·········-9.939e-05···1.05e-07···-951.097······0.000···-9.96e-05···-9.92e-05	14193	x2·········-9.939e-05···1.05e-07···-951.097······0.000···-9.96e-05···-9.92e-05
14194	const··········1.0003···5.39e-05···1.86e+04······0.000·······1.000·······1.000	14194	const··········1.0003···5.39e-05···1.86e+04······0.000·······1.000·······1.000


Offset 13303, 18 lines modified		Offset 13303, 18 lines modified
13303	<tr>	13303	<tr>
13304	··<th>Link·Function:</th>········<td>logit</td>······<th>··Scale:·············</th>····<td>1.0</td>··	13304	··<th>Link·Function:</th>········<td>logit</td>······<th>··Scale:·············</th>····<td>1.0</td>··
13305	</tr>	13305	</tr>
13306	<tr>	13306	<tr>
13307	··<th>Method:</th>···············<td>IRLS</td>·······<th>··Log-Likelihood:····</th>·<td>·-189.70</td>	13307	··<th>Method:</th>···············<td>IRLS</td>·······<th>··Log-Likelihood:····</th>·<td>·-189.70</td>
13308	</tr>	13308	</tr>
13309	<tr>	13309	<tr>
13310	··<th>Date:</th>···········<td>~~Fri~~,·06·Mar·2020</td>·<th>··Deviance:··········</th>·<td>··380.66</td>	13310	··<th>Date:</th>···········<td>Sat,·10·Apr·2021</td>·<th>··Deviance:··········</th>·<td>··380.66</td>
13311	</tr>	13311	</tr>
13312	<tr>	13312	<tr>
13313	··<th>Time:</th>···············<td>15:39:44</td>·····<th>··Pearson·chi2:······</th>··<td>··8.48</td>·	13313	··<th>Time:</th>···············<td>01:00:10</td>·····<th>··Pearson·chi2:······</th>··<td>··8.48</td>·
13314	</tr>	13314	</tr>
13315	<tr>	13315	<tr>
13316	··<th>No.·Iterations:</th>·········<td>5</td>········<th>·····················</th>·····<td>·</td>···	13316	··<th>No.·Iterations:</th>·········<td>5</td>········<th>·····················</th>·····<td>·</td>···
13317	</tr>	13317	</tr>
13318	</table>	13318	</table>
13319	<table·class="simpletable">	13319	<table·class="simpletable">
13320	<tr>	13320	<tr>
Offset 13442, 18 lines modified		Offset 13442, 18 lines modified
13442	<tr>	13442	<tr>
13443	··<th>Link·Function:</th>········<td>logit</td>······<th>··Scale:·············</th>····<td>1.0</td>··	13443	··<th>Link·Function:</th>········<td>logit</td>······<th>··Scale:·············</th>····<td>1.0</td>··
13444	</tr>	13444	</tr>
13445	<tr>	13445	<tr>
13446	··<th>Method:</th>···············<td>IRLS</td>·······<th>··Log-Likelihood:····</th>·<td>·-189.70</td>	13446	··<th>Method:</th>···············<td>IRLS</td>·······<th>··Log-Likelihood:····</th>·<td>·-189.70</td>
13447	</tr>	13447	</tr>
13448	<tr>	13448	<tr>
13449	··<th>Date:</th>···········<td>~~Fri~~,·06·Mar·2020</td>·<th>··Deviance:··········</th>·<td>··380.66</td>	13449	··<th>Date:</th>···········<td>Sat,·10·Apr·2021</td>·<th>··Deviance:··········</th>·<td>··380.66</td>
13450	</tr>	13450	</tr>
13451	<tr>	13451	<tr>
13452	··<th>Time:</th>···············<td>15:39:45</td>·····<th>··Pearson·chi2:······</th>··<td>··8.48</td>·	13452	··<th>Time:</th>···············<td>01:00:10</td>·····<th>··Pearson·chi2:······</th>··<td>··8.48</td>·
13453	</tr>	13453	</tr>
13454	<tr>	13454	<tr>
13455	··<th>No.·Iterations:</th>·········<td>5</td>········<th>·····················</th>·····<td>·</td>···	13455	··<th>No.·Iterations:</th>·········<td>5</td>········<th>·····················</th>·····<td>·</td>···
13456	</tr>	13456	</tr>
13457	</table>	13457	</table>
13458	<table·class="simpletable">	13458	<table·class="simpletable">
13459	<tr>	13459	<tr>


Offset 3386, 16 lines modified		Offset 3386, 16 lines modified
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3390	-rw-r--r--···0·root·········(0)·root·········(0)······203·2019-02-03·14:12:13.000000·./usr/share/doc/python-statsmodels-doc/html/_static/up.png	3390	-rw-r--r--···0·root·········(0)·root·········(0)······203·2019-02-03·14:12:13.000000·./usr/share/doc/python-statsmodels-doc/html/_static/up.png
3391	-rw-r--r--···0·root·········(0)·root·········(0)·····8863·2019-03-02·14:59:35.000000·./usr/share/doc/python-statsmodels-doc/html/about.html	3391	-rw-r--r--···0·root·········(0)·root·········(0)·····8863·2019-03-02·14:59:35.000000·./usr/share/doc/python-statsmodels-doc/html/about.html
3392	-rw-r--r--···0·root·········(0)·root·········(0)····10118·2019-03-02·14:59:35.000000·./usr/share/doc/python-statsmodels-doc/html/anova.html	3392	-rw-r--r--···0·root·········(0)·root·········(0)····10118·2019-03-02·14:59:35.000000·./usr/share/doc/python-statsmodels-doc/html/anova.html
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00003890:·286d·6~~f73~~·~~7420~~·~~7265~~·6365·6e74·2063·~~616~~c··(most·r~~ecent·cal~~		00003890:·7261·6365·6261·636b·2028·6d6f·7374·2072··raceback·(most·r
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13513	<pre>···························GLSAR·Regression·Results···························	13513	<pre>···························GLSAR·Regression·Results···························
13514	==============================================================================	13514	==============================================================================
13515	Dep.·Variable:······················y···R-squared:·······················0.996	13515	Dep.·Variable:······················y···R-squared:·······················0.996
13516	Model:··························GLSAR···Adj.·R-squared:··················0.992	13516	Model:··························GLSAR···Adj.·R-squared:··················0.992
13517	Method:·················Least·Squares···F-statistic:·····················295.2	13517	Method:·················Least·Squares···F-statistic:·····················295.2
13518	Date:················~~Fri~~,·06·Mar·2020···Prob·(F-statistic):···········6.09e-09	13518	Date:················Sat,·10·Apr·2021···Prob·(F-statistic):···········6.09e-09
13519	Time:························15:39:50···Log-Likelihood:················-102.04	13519	Time:························01:00:05···Log-Likelihood:················-102.04
13520	No.·Observations:··················15···AIC:·····························218.1	13520	No.·Observations:··················15···AIC:·····························218.1
13521	Df·Residuals:·······················8···BIC:·····························223.0	13521	Df·Residuals:·······················8···BIC:·····························223.0
13522	Df·Model:···························6·········································	13522	Df·Model:···························6·········································
13523	Covariance·Type:············nonrobust·········································	13523	Covariance·Type:············nonrobust·········································
13524	==============================================================================	13524	==============================================================================
13525	·················coef····std·err··········t······P>\|t\|······[0.025······0.975]	13525	·················coef····std·err··········t······P>\|t\|······[0.025······0.975]
13526	------------------------------------------------------------------------------	13526	------------------------------------------------------------------------------


Offset 14892, 25 lines modified		Offset 14892, 14 lines modified


14892	<div·class="output_area">	14892	<div·class="output_area">

14893	····<div·class="prompt"></div>	14893	····<div·class="prompt"></div>


14894	<div·class="output_subarea·output_stream·output_stdout·output_text">
14895	<pre>The·history·saving·thread·hit·an·unexpected·error·(OperationalError('database·is·locked')).History·will·not·be·written·to·the·database.
14896	</pre>
14897	</div>
14898	</div>

14899	<div·class="output_area">

14900	····<div·class="prompt"></div>


14901	<div·class="output_subarea·output_text·output_error">	14894	<div·class="output_subarea·output_text·output_error">
14902	<pre>	14895	<pre>
14903	<span·class="ansi-red-fg">---------------------------------------------------------------------------</span>	14896	<span·class="ansi-red-fg">---------------------------------------------------------------------------</span>
14904	<span·class="ansi-red-fg">NameError</span>·································Traceback·(most·recent·call·last)	14897	<span·class="ansi-red-fg">NameError</span>·································Traceback·(most·recent·call·last)
14905	<span·class="ansi-green-fg"><ipython-input-27-0cc0b240e06d></span>·in·<span·class="ansi-cyan-fg"><module></span><span·class="ansi-blue-fg">()</span>	14898	<span·class="ansi-green-fg"><ipython-input-27-0cc0b240e06d></span>·in·<span·class="ansi-cyan-fg"><module></span><span·class="ansi-blue-fg">()</span>
14906	<span·class="ansi-green-fg">---->·1</span><span·class="ansi-red-fg">·</span>min_lm4·<span·class="ansi-blue-fg">=</span>·ols<span·class="ansi-blue-fg">(</span><span·class="ansi-blue-fg">'JPERF·~·TEST·*·ETHN'</span><span·class="ansi-blue-fg">,</span>·data·<span·class="ansi-blue-fg">=</span>·jobtest_table<span·class="ansi-blue-fg">)</span><span·class="ansi-blue-fg">.</span>fit<span·class="ansi-blue-fg">(</span><span·class="ansi-blue-fg">)</span>	14899	<span·class="ansi-green-fg">---->·1</span><span·class="ansi-red-fg">·</span>min_lm4·<span·class="ansi-blue-fg">=</span>·ols<span·class="ansi-blue-fg">(</span><span·class="ansi-blue-fg">'JPERF·~·TEST·*·ETHN'</span><span·class="ansi-blue-fg">,</span>·data·<span·class="ansi-blue-fg">=</span>·jobtest_table<span·class="ansi-blue-fg">)</span><span·class="ansi-blue-fg">.</span>fit<span·class="ansi-blue-fg">(</span><span·class="ansi-blue-fg">)</span>
14907	<span·class="ansi-green-intense-fg·ansi-bold">······2</span>·print<span·class="ansi-blue-fg">(</span>min_lm4<span·class="ansi-blue-fg">.</span>summary<span·class="ansi-blue-fg">(</span><span·class="ansi-blue-fg">)</span><span·class="ansi-blue-fg">)</span>	14900	<span·class="ansi-green-intense-fg·ansi-bold">······2</span>·print<span·class="ansi-blue-fg">(</span>min_lm4<span·class="ansi-blue-fg">.</span>summary<span·class="ansi-blue-fg">(</span><span·class="ansi-blue-fg">)</span><span·class="ansi-blue-fg">)</span>


Offset 13340, 18 lines modified		Offset 13340, 18 lines modified
13340	<tr>	13340	<tr>
13341	··<th>Dep.·Variable:</th>···········<td>y</td>········<th>··No.·Observations:··</th>····<td>226</td>··	13341	··<th>Dep.·Variable:</th>···········<td>y</td>········<th>··No.·Observations:··</th>····<td>226</td>··
13342	</tr>	13342	</tr>
13343	<tr>	13343	<tr>
13344	··<th>Model:</th>···········<td>MarkovRegression</td>·<th>··Log·Likelihood·····</th>·<td>-508.636</td>	13344	··<th>Model:</th>···········<td>MarkovRegression</td>·<th>··Log·Likelihood·····</th>·<td>-508.636</td>
13345	</tr>	13345	</tr>
13346	<tr>	13346	<tr>
13347	··<th>Date:</th>············<td>~~Fri~~,·06·Mar·2020</td>·<th>··AIC················</th>·<td>1027.272</td>	13347	··<th>Date:</th>············<td>Sat,·10·Apr·2021</td>·<th>··AIC················</th>·<td>1027.272</td>
13348	</tr>	13348	</tr>
13349	<tr>	13349	<tr>
13350	··<th>Time:</th>················<td>15:40:38</td>·····<th>··BIC················</th>·<td>1044.375</td>	13350	··<th>Time:</th>················<td>01:00:10</td>·····<th>··BIC················</th>·<td>1044.375</td>
13351	</tr>	13351	</tr>
13352	<tr>	13352	<tr>
13353	··<th>Sample:</th>·············<td>07-01-1954</td>····<th>··HQIC···············</th>·<td>1034.174</td>	13353	··<th>Sample:</th>·············<td>07-01-1954</td>····<th>··HQIC···············</th>·<td>1034.174</td>
13354	</tr>	13354	</tr>
13355	<tr>	13355	<tr>
13356	··<th></th>···················<td>-·10-01-2010</td>···<th>·····················</th>·····<td>·</td>···	13356	··<th></th>···················<td>-·10-01-2010</td>···<th>·····················</th>·····<td>·</td>···
13357	</tr>	13357	</tr>
Offset 13556, 18 lines modified		Offset 13556, 18 lines modified
13556	<tr>	13556	<tr>
13557	··<th>Dep.·Variable:</th>···········<td>y</td>········<th>··No.·Observations:··</th>····<td>225</td>··	13557	··<th>Dep.·Variable:</th>···········<td>y</td>········<th>··No.·Observations:··</th>····<td>225</td>··
13558	</tr>	13558	</tr>
13559	<tr>	13559	<tr>
13560	··<th>Model:</th>···········<td>MarkovRegression</td>·<th>··Log·Likelihood·····</th>·<td>-264.711</td>	13560	··<th>Model:</th>···········<td>MarkovRegression</td>·<th>··Log·Likelihood·····</th>·<td>-264.711</td>
13561	</tr>	13561	</tr>
13562	<tr>	13562	<tr>
13563	··<th>Date:</th>············<td>~~Fri~~,·06·Mar·2020</td>·<th>··AIC················</th>··<td>543.421</td>	13563	··<th>Date:</th>············<td>Sat,·10·Apr·2021</td>·<th>··AIC················</th>··<td>543.421</td>
13564	</tr>	13564	</tr>
13565	<tr>	13565	<tr>
13566	··<th>Time:</th>················<td>15:40:39</td>·····<th>··BIC················</th>··<td>567.334</td>	13566	··<th>Time:</th>················<td>01:00:11</td>·····<th>··BIC················</th>··<td>567.334</td>
13567	</tr>	13567	</tr>
13568	<tr>	13568	<tr>
13569	··<th>Sample:</th>·············<td>10-01-1954</td>····<th>··HQIC···············</th>··<td>553.073</td>	13569	··<th>Sample:</th>·············<td>10-01-1954</td>····<th>··HQIC···············</th>··<td>553.073</td>
13570	</tr>	13570	</tr>
13571	<tr>	13571	<tr>
13572	··<th></th>···················<td>-·10-01-2010</td>···<th>·····················</th>·····<td>·</td>···	13572	··<th></th>···················<td>-·10-01-2010</td>···<th>·····················</th>·····<td>·</td>···
13573	</tr>	13573	</tr>
Offset 13782, 18 lines modified		Offset 13782, 18 lines modified
13782	<tr>	13782	<tr>
13783	··<th>Dep.·Variable:</th>···········<td>y</td>········<th>··No.·Observations:··</th>····<td>222</td>··	13783	··<th>Dep.·Variable:</th>···········<td>y</td>········<th>··No.·Observations:··</th>····<td>222</td>··
13784	</tr>	13784	</tr>
13785	<tr>	13785	<tr>
13786	··<th>Model:</th>···········<td>MarkovRegression</td>·<th>··Log·Likelihood·····</th>·<td>-229.256</td>	13786	··<th>Model:</th>···········<td>MarkovRegression</td>·<th>··Log·Likelihood·····</th>·<td>-229.256</td>
13787	</tr>	13787	</tr>
13788	<tr>	13788	<tr>
13789	··<th>Date:</th>············<td>~~Fri~~,·06·Mar·2020</td>·<th>··AIC················</th>··<td>480.512</td>	13789	··<th>Date:</th>············<td>Sat,·10·Apr·2021</td>·<th>··AIC················</th>··<td>480.512</td>
13790	</tr>	13790	</tr>
13791	<tr>	13791	<tr>
13792	··<th>Time:</th>················<td>15:40:46</td>·····<th>··BIC················</th>··<td>517.942</td>	13792	··<th>Time:</th>················<td>01:00:14</td>·····<th>··BIC················</th>··<td>517.942</td>
13793	</tr>	13793	</tr>
13794	<tr>	13794	<tr>
13795	··<th>Sample:</th>·············<td>07-01-1955</td>····<th>··HQIC···············</th>··<td>495.624</td>	13795	··<th>Sample:</th>·············<td>07-01-1955</td>····<th>··HQIC···············</th>··<td>495.624</td>
13796	</tr>	13796	</tr>
13797	<tr>	13797	<tr>
13798	··<th></th>···················<td>-·10-01-2010</td>···<th>·····················</th>·····<td>·</td>···	13798	··<th></th>···················<td>-·10-01-2010</td>···<th>·····················</th>·····<td>·</td>···
13799	</tr>	13799	</tr>
Offset 13894, 18 lines modified		Offset 13894, 18 lines modified
13894	<tr>	13894	<tr>
13895	··<th>Dep.·Variable:</th>···········<td>y</td>········<th>··No.·Observations:··</th>····<td>222</td>··	13895	··<th>Dep.·Variable:</th>···········<td>y</td>········<th>··No.·Observations:··</th>····<td>222</td>··
13896	</tr>	13896	</tr>
13897	<tr>	13897	<tr>
13898	··<th>Model:</th>···········<td>MarkovRegression</td>·<th>··Log·Likelihood·····</th>·<td>-180.806</td>	13898	··<th>Model:</th>···········<td>MarkovRegression</td>·<th>··Log·Likelihood·····</th>·<td>-180.806</td>
13899	</tr>	13899	</tr>
13900	<tr>	13900	<tr>
13901	··<th>Date:</th>············<td>~~Fri~~,·06·Mar·2020</td>·<th>··AIC················</th>··<td>399.611</td>	13901	··<th>Date:</th>············<td>Sat,·10·Apr·2021</td>·<th>··AIC················</th>··<td>399.611</td>
13902	</tr>	13902	</tr>
13903	<tr>	13903	<tr>
13904	··<th>Time:</th>················<td>15:40:46</td>·····<th>··BIC················</th>··<td>464.262</td>	13904	··<th>Time:</th>················<td>01:00:14</td>·····<th>··BIC················</th>··<td>464.262</td>
13905	</tr>	13905	</tr>
13906	<tr>	13906	<tr>
13907	··<th>Sample:</th>·············<td>07-01-1955</td>····<th>··HQIC···············</th>··<td>425.713</td>	13907	··<th>Sample:</th>·············<td>07-01-1955</td>····<th>··HQIC···············</th>··<td>425.713</td>
13908	</tr>	13908	</tr>
13909	<tr>	13909	<tr>
13910	··<th></th>···················<td>-·10-01-2010</td>···<th>·····················</th>·····<td>·</td>···	13910	··<th></th>···················<td>-·10-01-2010</td>···<th>·····················</th>·····<td>·</td>···
13911	</tr>	13911	</tr>
Offset 14150, 18 lines modified		Offset 14150, 18 lines modified
14150	<tr>	14150	<tr>
14151	··<th>Dep.·Variable:</th>···········<td>y</td>········<th>··No.·Observations:··</th>····<td>520</td>··	14151	··<th>Dep.·Variable:</th>···········<td>y</td>········<th>··No.·Observations:··</th>····<td>520</td>··
14152	</tr>	14152	</tr>
14153	<tr>	14153	<tr>
14154	··<th>Model:</th>···········<td>MarkovRegression</td>·<th>··Log·Likelihood·····</th>·<td>-745.798</td>	14154	··<th>Model:</th>···········<td>MarkovRegression</td>·<th>··Log·Likelihood·····</th>·<td>-745.798</td>
14155	</tr>	14155	</tr>
14156	<tr>	14156	<tr>
14157	··<th>Date:</th>············<td>~~Fri~~,·06·Mar·2020</td>·<th>··AIC················</th>·<td>1507.595</td>	14157	··<th>Date:</th>············<td>Sat,·10·Apr·2021</td>·<th>··AIC················</th>·<td>1507.595</td>
14158	</tr>	14158	</tr>
14159	<tr>	14159	<tr>
14160	··<th>Time:</th>················<td>15:40:48</td>·····<th>··BIC················</th>·<td>1541.626</td>	14160	··<th>Time:</th>················<td>01:00:15</td>·····<th>··BIC················</th>·<td>1541.626</td>
14161	</tr>	14161	</tr>
14162	<tr>	14162	<tr>
14163	··<th>Sample:</th>·············<td>05-16-2004</td>····<th>··HQIC···············</th>·<td>1520.926</td>	14163	··<th>Sample:</th>·············<td>05-16-2004</td>····<th>··HQIC···············</th>·<td>1520.926</td>
14164	</tr>	14164	</tr>
14165	<tr>	14165	<tr>
14166	··<th></th>···················<td>-·04-27-2014</td>···<th>·····················</th>·····<td>·</td>···	14166	··<th></th>···················<td>-·04-27-2014</td>···<th>·····················</th>·····<td>·</td>···
14167	</tr>	14167	</tr>


Offset 13325, 16 lines modified		Offset 13325, 16 lines modified

13325	<div·class="output_subarea·output_stream·output_stdout·output_text">	13325	<div·class="output_subarea·output_stream·output_stdout·output_text">
13326	<pre>····························OLS·Regression·Results····························	13326	<pre>····························OLS·Regression·Results····························
13327	==============================================================================	13327	==============================================================================
13328	Dep.·Variable:······················y···R-squared:·······················1.000	13328	Dep.·Variable:······················y···R-squared:·······················1.000
13329	Model:····························OLS···Adj.·R-squared:··················1.000	13329	Model:····························OLS···Adj.·R-squared:··················1.000
13330	Method:·················Least·Squares···F-statistic:·················4.020e+06	13330	Method:·················Least·Squares···F-statistic:·················4.020e+06
13331	Date:················~~Fri~~,·06·Mar·2020···Prob·(F-statistic):··········2.83e-239	13331	Date:················Sat,·10·Apr·2021···Prob·(F-statistic):··········2.83e-239
13332	Time:························15:40:03···Log-Likelihood:················-146.51	13332	Time:························01:00:09···Log-Likelihood:················-146.51
13333	No.·Observations:·················100···AIC:·····························299.0	13333	No.·Observations:·················100···AIC:·····························299.0
13334	Df·Residuals:······················97···BIC:·····························306.8	13334	Df·Residuals:······················97···BIC:·····························306.8
13335	Df·Model:···························2·········································	13335	Df·Model:···························2·········································
13336	Covariance·Type:············nonrobust·········································	13336	Covariance·Type:············nonrobust·········································
13337	==============================================================================	13337	==============================================================================
13338	·················coef····std·err··········t······P>\|t\|······[0.025······0.975]	13338	·················coef····std·err··········t······P>\|t\|······[0.025······0.975]
13339	------------------------------------------------------------------------------	13339	------------------------------------------------------------------------------
Offset 13459, 16 lines modified		Offset 13459, 16 lines modified

13459	<div·class="output_subarea·output_stream·output_stdout·output_text">	13459	<div·class="output_subarea·output_stream·output_stdout·output_text">
13460	<pre>····························OLS·Regression·Results····························	13460	<pre>····························OLS·Regression·Results····························
13461	==============================================================================	13461	==============================================================================
13462	Dep.·Variable:······················y···R-squared:·······················0.933	13462	Dep.·Variable:······················y···R-squared:·······················0.933
13463	Model:····························OLS···Adj.·R-squared:··················0.928	13463	Model:····························OLS···Adj.·R-squared:··················0.928
13464	Method:·················Least·Squares···F-statistic:·····················211.8	13464	Method:·················Least·Squares···F-statistic:·····················211.8
13465	Date:················~~Fri~~,·06·Mar·2020···Prob·(F-statistic):···········6.30e-27	13465	Date:················Sat,·10·Apr·2021···Prob·(F-statistic):···········6.30e-27
13466	Time:························15:40:03···Log-Likelihood:················-34.438	13466	Time:························01:00:10···Log-Likelihood:················-34.438
13467	No.·Observations:··················50···AIC:·····························76.88	13467	No.·Observations:··················50···AIC:·····························76.88
13468	Df·Residuals:······················46···BIC:·····························84.52	13468	Df·Residuals:······················46···BIC:·····························84.52
13469	Df·Model:···························3·········································	13469	Df·Model:···························3·········································
13470	Covariance·Type:············nonrobust·········································	13470	Covariance·Type:············nonrobust·········································
13471	==============================================================================	13471	==============================================================================
13472	·················coef····std·err··········t······P>\|t\|······[0.025······0.975]	13472	·················coef····std·err··········t······P>\|t\|······[0.025······0.975]
13473	------------------------------------------------------------------------------	13473	------------------------------------------------------------------------------
Offset 13718, 16 lines modified		Offset 13718, 16 lines modified

13718	<div·class="output_subarea·output_stream·output_stdout·output_text">	13718	<div·class="output_subarea·output_stream·output_stdout·output_text">
13719	<pre>····························OLS·Regression·Results····························	13719	<pre>····························OLS·Regression·Results····························
13720	==============================================================================	13720	==============================================================================
13721	Dep.·Variable:······················y···R-squared:·······················0.978	13721	Dep.·Variable:······················y···R-squared:·······················0.978
13722	Model:····························OLS···Adj.·R-squared:··················0.976	13722	Model:····························OLS···Adj.·R-squared:··················0.976
13723	Method:·················Least·Squares···F-statistic:·····················671.7	13723	Method:·················Least·Squares···F-statistic:·····················671.7
13724	Date:················~~Fri~~,·06·Mar·2020···Prob·(F-statistic):···········5.69e-38	13724	Date:················Sat,·10·Apr·2021···Prob·(F-statistic):···········5.69e-38
13725	Time:························15:40:06···Log-Likelihood:················-64.643	13725	Time:························01:00:10···Log-Likelihood:················-64.643
13726	No.·Observations:··················50···AIC:·····························137.3	13726	No.·Observations:··················50···AIC:·····························137.3
13727	Df·Residuals:······················46···BIC:·····························144.9	13727	Df·Residuals:······················46···BIC:·····························144.9
13728	Df·Model:···························3·········································	13728	Df·Model:···························3·········································
13729	Covariance·Type:············nonrobust·········································	13729	Covariance·Type:············nonrobust·········································
13730	==============================================================================	13730	==============================================================================
13731	·················coef····std·err··········t······P>\|t\|······[0.025······0.975]	13731	·················coef····std·err··········t······P>\|t\|······[0.025······0.975]
13732	------------------------------------------------------------------------------	13732	------------------------------------------------------------------------------
Offset 14030, 16 lines modified		Offset 14030, 16 lines modified

14030	<div·class="output_subarea·output_stream·output_stdout·output_text">	14030	<div·class="output_subarea·output_stream·output_stdout·output_text">
14031	<pre>····························OLS·Regression·Results····························	14031	<pre>····························OLS·Regression·Results····························
14032	==============================================================================	14032	==============================================================================
14033	Dep.·Variable:·················TOTEMP···R-squared:·······················0.995	14033	Dep.·Variable:·················TOTEMP···R-squared:·······················0.995
14034	Model:····························OLS···Adj.·R-squared:··················0.992	14034	Model:····························OLS···Adj.·R-squared:··················0.992
14035	Method:·················Least·Squares···F-statistic:·····················330.3	14035	Method:·················Least·Squares···F-statistic:·····················330.3
14036	Date:················~~Fri~~,·06·Mar·2020···Prob·(F-statistic):···········4.98e-10	14036	Date:················Sat,·10·Apr·2021···Prob·(F-statistic):···········4.98e-10
14037	Time:························15:40:08···Log-Likelihood:················-109.62	14037	Time:························01:00:11···Log-Likelihood:················-109.62
14038	No.·Observations:··················16···AIC:·····························233.2	14038	No.·Observations:··················16···AIC:·····························233.2
14039	Df·Residuals:·······················9···BIC:·····························238.6	14039	Df·Residuals:·······················9···BIC:·····························238.6
14040	Df·Model:···························6·········································	14040	Df·Model:···························6·········································
14041	Covariance·Type:············nonrobust·········································	14041	Covariance·Type:············nonrobust·········································
14042	==============================================================================	14042	==============================================================================
14043	·················coef····std·err··········t······P>\|t\|······[0.025······0.975]	14043	·················coef····std·err··········t······P>\|t\|······[0.025······0.975]
14044	------------------------------------------------------------------------------	14044	------------------------------------------------------------------------------


Offset 13397, 15 lines modified		Offset 13397, 15 lines modified

13397	····<div·class="prompt"></div>	13397	····<div·class="prompt"></div>




13398	<div·class="output_png·output_subarea·">	13398	<div·class="output_png·output_subarea·">
13399	<img·src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAmcAAAHcCAYAAACTVw06AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzsvXm~~YG+~~WZ~~7n2X~~VLvWb~~qkXr+AGryx22A0kAR~~J~~OCB7~~ikAnGPR~~BgIMmBIQ7O4POxZEjOhEyAb8xgYmb~~gSwI~~HE4g~~N5~~EDIxC~~QM~~k4kzJCzBgLGNl7at3rul1~~r4v~~3VJ~~9~~f6glq9Va~~S~~mptJb2/68qVWKqqfv~~Xm~~VdWj57~~2~~f+6EkSQKBQCAQCAQCoT~~F~~Q1XsA~~BAKB~~QCAQCISTkOCMQC~~A~~QCAQCoYEgwRm~~B~~QCAQCARCA0GCMwK~~B~~QCAQCIQ~~G~~ggRn~~BAKB~~QCA~~Q~~CA0ECc4~~IBAKBQC~~AQG~~oiqB~~WcUR~~T~~1DUdQERVEHM15rpyjqTYqijk3/d9v0~~6~~xRFUT+m~~KOo4R~~VH7~~K~~Yo6p1rjIhAIB~~A~~KBQGhkqpk5~~exb~~AVVmv3Qvg~~9~~5IkLQX~~w~~++l/~~A8AXA~~Syd~~/~~s83~~A~~TxZxXERC~~AQCg~~UAgNCxVC84kSfpvAK6sl9cD2DH~~9~~v3cA+HLG689JSd4FYKQoal61xkY~~gEAgEA~~oHQqNA1/ntdkiSNT/9vK4C~~u~~6f+9AMBwxnE~~j~~06+NowBms1k69~~dRTK~~z1G~~Ao~~FAIB~~A~~IhIrzwQcfO~~CR~~J6ih2XK~~2~~DszS~~S~~JEkURZXcO4qiqG8iufWJxYsXY+/evRUfG4F~~AIBAIB~~EKloShqUM5xta7W~~t~~KW2K6f~~/~~e2L69VEAizKOWzj92iw~~k~~Sfq~~J~~JEnnSZJ0XkdH~~0eCT~~QC~~AQCAQCQV~~HUOjj7NYCbp~~//~~3zQBey3j~~9~~pumqzYsA~~eDO2P~~wkE~~AoFAI~~BBa~~h~~qpt~~a~~1IU~~tRPA~~ZQDMFEWN~~A~~Pg+gIc~~B~~vERR1G0~~A~~BgFsmD78dQBXAzgO~~I~~ATgb6s1L~~g~~KBQC~~AQ~~CIRGpmrBmS~~R~~JvXne+lyOYyU~~A~~d1ZrL~~AQCgUAgEAh~~KgX~~Q~~IIBAIBAK~~BQ~~GggSHBGIBAIBA~~KB0ECQ~~4IxAI~~BAIB~~AKhgSDBGYFA~~IBAIB~~EID~~Q~~YIzAoFA~~IBA~~IhAa~~C~~BGcEAoF~~AIBAIDQ~~QJzggEAoF~~AIBAaCBKcEQ~~gEAoF~~AI~~DQQJDgj~~EAgEA~~oFAaC~~BIcEYgEAgEA~~oHQQJDgjE~~AgEAgEAqGB~~qFpvzVYmG~~o~~3ivffeQyKRA~~M~~MwWLt2LVQqEgcTakN/fz8~~GBwcBA~~J/61Kd~~[·...·truncated·by·diffoscope;·len:·104589,·SHA1:·~~3d7a8c8e8~~0~~b640203f4fa81f9e78a58d~~fd18~~5839~~·...·]	13399	<img·src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAmcAAAHcCAYAAACTVw06AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzsvXmcXFWZ//+5VXevtbs63dnI0mYhYYsYJWwOLiiCGHRk6QEJX/w6ghiIJk4AB5fJIIRJfgSjI258hUkmQR0UZ4I6jDOgDoIEDFv2dHrvqq593+v+/qiuSnXVrapbXeutOu/Xixfk1q2bU4dzz33ucz7n81CSJIFAIBAIBAKB0Bpomt0AAoFAIBAIBMIZSHBGIBAIBAKB0EKQ4IxAIBAIBAKhhSDBGYFAIBAIBEILQYIzAoFAIBAIhBaCBGcEAoFAIBAILUTdgjOKop6gKGqKoqi3c451UxT1PEVRJ6b/3TV9nKIo6tsURZ2kKOpNiqIurFe7CAQCgUAgEFqZembOfgLgqrxj9wL4nSRJywH8bvrPAPAxAMun//lbAN+rY7sIBAKBQCAQWpa6BWeSJP0egCvv8HoAT07/95MArss5/pSU5mUAZoqi5tWrbQQCgUAgEAitCt3gv69PkqTJ6f+2Auib/u8FAEZzzhubPjaJEvT09EhLliypdRsJBAKBQCAQas5rr73mkCRpTrnzGh2cZZEkSaIoquLaURRF/S3SS59YtGgRDh48WPO2EQgEAoFAINQaiqKGlZzX6N2atsxy5fS/p6aPjwM4K+e8hdPHCpAk6QeSJK2VJGntnDllg08CgUAgEAgEVdHo4OxXADZM//cGAM/mHL91etfmOgDenOVPAoFAIBAIhI6hbsuaFEXtA3AFgB6KosYAfB3AwwB+SlHUZwEMA7hh+vTnAFwN4CSAEID/U692EQgEAoFAILQydQvOJEkaKPLRh2TOlQDcVa+2EAgEAoFAIKgFUiGAQCAQCAQCoYUgwRmBQCAQCARCC0GCMwKBQCAQCIQWggRnBAKBQCAQCC0ECc4IBAKBQCAQWggSnBEIBAKBQCC0ECQ4IxAIBAKBQGghSHBGIBAIBAKB0EKQ4IxAIBAIBAKhhSDBGYFAIBAIBEILQYIzAoFAIBAIhBaibrU1O5loNIpXXnkFqVQKDMPg4osvhkZD4mBCYzh9+jSGh4cBAO9+97thMpm[·...·truncated·by·diffoscope;·len:·104501,·SHA1:·f5c13522b05a8dc6b88535e03286b3141f212dda·...·]
13400	"	13400	"
13401	>	13401	>
13402	</div>	13402	</div>

13403	</div>	13403	</div>

13404	</div>	13404	</div>
Offset 13537, 15 lines modified		Offset 13537, 15 lines modified

13537	····<div·class="prompt"></div>	13537	····<div·class="prompt"></div>




13538	<div·class="output_png·output_subarea·">	13538	<div·class="output_png·output_subarea·">
13539	<img·src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAmcAAAHcCAYAAACTVw06AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzsnXucW2Wd/z8ny~~bnm~~O~~pPMTO+0lba0Al~~atl~~uvSn8oKBW1RCh2pVGF1QSwU290KLCJWKUg~~rRU~~TYXWEtUsp~~F~~wQpFlFUQVqBLWUopvUw~~7~~91tmcr/fc35~~/~~ZJJmMrmcZJ~~J~~MTvK8Xy9e0OSc9D~~k~~Pzz~~nP~~93yfz/~~P~~5UqIogk~~AgEAgEAo~~FQGyimu~~gEEAoFAIBAIh~~FOQ4Ix~~AIBAI~~BAKhhiDBGYF~~AIBA~~IBE~~INQYIzAoFA~~IBAIhBqCBGc~~EAoFA~~IBA~~I~~NQQJzg~~gEAo~~FAIBBqiIoFZxRFPU5R1~~Ch~~FU~~Yf~~TPmumKOpViqJ~~O~~jP2~~7~~aexziqKon1MUdZ~~K~~iqEMURX2qUu0iEAgE~~A~~oFAq~~GU~~qmTn7NYBLMj77PoC~~/i~~KK4AMB~~f~~xv4MAJ~~cCWD~~D2z7cBPFLBdhEIB~~A~~KBQCDULBULzkRRfAO~~APe~~PjVQB2jf33LgCr0z5~~/~~QkzwDgADRVHTK9U~~2~~AoFA~~IBA~~IhFpFVe~~W~~/r00UxeGx~~/~~zYDaBv77~~5~~kA+tOOGxj7bBh5MJlM4ty5c8vdRgKB~~QCAQCI~~Sy895771lFUWwpdFy1g7MUoiiKFEU~~V~~XTuKoqhvI7H0iTlz5uDAgQNlbxuBQ~~CAQCA~~RCuaEoqlfKcdXe~~r~~TmSXK4c+/~~fo2OeDA~~GanHTdr7LMJiK~~L~~4H6IoLhNFcVlLS8Hgk0~~A~~gE~~A~~gEAkFWVDs4+wO~~A9WP/~~vR7~~A~~3rTPrx3btXkO~~A~~Ffa8ieBQC~~AQCA~~RCw1CxZ~~U~~2KovY~~A~~WAHA~~R~~FHU~~A~~IC7~~ANwL~~4FmKoq4H0~~A~~vgqrHDXwa~~wEsBJAH4~~A36xUuwgE~~AoFA~~IBBqmYoFZ6Iotuf46vNZjhUB3FSpth~~AIBAKBQCDI~~BVIhgE~~AgEA~~gEAqGG~~I~~MEZgU~~AgEAg~~EQ~~g1B~~gjM~~CgU~~AgE~~A~~iEGoIEZw~~QCgUAgEAg~~1BAnO~~CAQC~~gUAgE~~G~~oIEpwR~~CAQC~~gUA~~g~~1BAkOCM~~QCAQC~~gUCoIUhwRiA~~QCAQCgVB~~DkOCM~~QCA~~QCA~~Q~~CoYYgwRm~~BQ~~CAQCARCDUGCMw~~KBQ~~CAQC~~I~~Qaom~~K~~1NRu~~Z~~UCiE/fv~~3~~Ix6Pg6ZpnH~~v~~uuVAoSBxMq~~A~~7d3d3o7e0F~~A~~Hzyk5~~+E~~Xq+f4h~~[·...·truncated·by·diffoscope;·len:·104570,·SHA1:·~~c7d8f17~~2d0~~9e5aa78~~be~~420~~a~~3174~~2~~4d4646d~~7401a·...·]	13539	<img·src="data:image/png;base64,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[·...·truncated·by·diffoscope;·len:·105546,·SHA1:·7035d99f2e04c92e39be93fa58b6125a652b574b·...·]
13540	"	13540	"
13541	>	13541	>
13542	</div>	13542	</div>

13543	</div>	13543	</div>

13544	</div>	13544	</div>
Offset 13572, 15 lines modified		Offset 13572, 15 lines modified

13572	····<div·class="prompt"></div>	13572	····<div·class="prompt"></div>




13573	<div·class="output_png·output_subarea·">	13573	<div·class="output_png·output_subarea·">
13574	<img·src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAmcAAAHcCAYAAACTVw06AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzsnX~~l8FP~~X9/~~1+zc~~+~~1uL~~gL~~hRo4op6g~~p~~onjUilZaBTV~~q~~UckPBItHsXwRLCjqt6hfsQI11C~~hSrVUrCg~~1g1dJ+QVv~~6~~RaoWoYIGua9AOQMk2XN2Z2d2d35/bHbZO7uQ7~~MG8n4+~~HD8lmgu~~98~~/Mx8~~XvM~~+GU~~3~~TQBAEQRAE~~Q~~eQG~~hmw~~bQB~~AE~~QRAE~~Q~~ZyBxBlBEARBEEQO~~Q~~eKM~~I~~AiCIA~~g~~ihyBxRhAEQRA~~EkU~~OQO~~CM~~IgiAIgsghSJwR~~B~~EEQBEHkEO0m~~z~~hiGeYdhmFMMw2wP+6~~wj~~wzBr~~G~~YbZ1~~/Lv~~0pbPGYZhXmU~~Y~~Zj/D~~M~~N8xDDOsvewi~~CIIgC~~ILIZdrTc/Yug~~Juj~~PnsSwP9pmtYfwP+1f~~A~~0AtwDo3~~/L~~PwwBeb0e7C~~IIg~~CIIgc~~p~~Z2E2eapn0OoDnq40o~~A~~S1r+vATAHWGfv6cF2AigA8Mw3dvLNoIgC~~IIg~~iFyFy~~/B/~~r6umaSda~~/~~twAoGvLn3s~~C~~OBJ23dGWz04gCWVlZVr~~f~~vn3b2kaCIAiC~~I~~Ig2Z8uWLY~~2~~apnVu~~7~~bpMi7MQmqZpDMOkPTuKYZiHEQh9onfv3ti8eXOb20YQBEEQBNHW~~M~~Azzn1Suy3S~~15~~slguLLl36daPj~~8~~G4IKw6~~3~~q1fBaDpmlvapo2XNO04Z07tyo+CYIg~~C~~IIg8opMi7O/~~A~~JjU8udJ~~A~~FaF~~fT6~~xpWrzKgC2~~s~~PAnQRAEQR~~CE~~bmi3sCb~~DMLUARg~~IoYxjmKI~~BnA~~cwHsJJhmA~~c~~A/~~AfA~~PS2XrwEwGsB+~~AC4A~~P20v~~u~~wiCIAiCIHKZdhNnmqZVJf~~jWD+Ncq~~wGY2l62EARBEARB~~5As0I~~YAgCIIgCCKHIHFGEAR~~BEAS~~RQ5A~~4~~IwiCIAiCy~~CFI~~nBEE~~Q~~RAEQeQQJM4~~I~~giAIgi~~ByCB~~JnBEEQ~~BEEQ~~OQSJM4I~~gCI~~IgiByCxBlB~~EARBEE~~QOQeKMIAi~~C~~IAgihyBxRhAEQRAEk~~UO~~QOCMIgiAIgsgh2m22pt~~7~~ZtW~~s~~XTp48iT59+qBfv37ZNofQES6XC19//TWMRiNG~~jBiRbX~~MIgiCINCFx1k78Ze2nOO5x4HvHy0mcERnl2LFj+PiLf8~~Dr~~dOPKK68E~~[·...·truncated·by·diffoscope;·len:·103~~260~~,·SHA1:·c14~~837d~~2fb67~~0a10d46be157f7ee089483c4f380~~·...·]	13574	<img·src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAmcAAAHcCAYAAACTVw06AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzs3XmYU+XZP/Dv2U+WWVgEBGQZRBapTBVF7aJ1aRWqgKI4gmC1bi+lhYKivvVnW94qWKhUpLVWrVDssCiKLW64VaqVinbcWDUgDjBbMtlOcnKynN8fmYTsC85kMffnurwKkwx98lzJOXee537um9F1HYQQQgghpDSwxR4AIYQQQgg5hoIzQgghhJASQsEZIYQQQkgJoeCMEEIIIaSEUHBGCCGEEFJCKDgjhBBCCCkhPRacMQzzBMMwbQzDfBLzs94Mw2xjGGZ/1//26vo5wzDMQwzDfMYwzEcMw5zeU+MihBBCCCllPbly9iSASxJ+dieA13RdHwngta6/A8ClAEZ2/XczgD/24LgIIYQQQkpWjwVnuq6/BcCW8OMpANZ0/XkNgKkxP1+rh70LoJZhmBN7amyEEEIIIaWKL/D/X39d1492/bkFQP+uPw8C8GXM85q7fnYUGfTt21cfNmxYd4+REEIIIaTbvf/++x26rp+Q7XmFDs6idF3XGYbJu3cUwzA3I7z1iSFDhmDnzp3dPjZCCCGEkO7GMMwXuTyv0Kc1WyPblV3/29b188MATop53uCunyXRdf1RXdcn6Lo+4YQTsgafhBBCCCFlpdDB2fMA5nT9eQ6ALTE/n911avNsAI6Y7U9CCCGEkIrRY9uaDMM0AjgfQF+GYZoB3AtgKYCNDMPcCOALAFd3Pf0FAJMAfAbAA+BHPTUuQgghhJBS1mPBma7rDWkeujDFc3UAc3tqLIQQQggh5YI6BBBCCCGElBAKzgghhBBCSggFZ4QQQgghJYSCM0IIIYSQEkLBGSGEEEJICaHgjBBCCCGkhFBwRgghhBBSQig4I4QQQggpIRScEUIIIYSUEArOCCGEEEJKCAVnhBBCCCElpMd6a1a63bt3o7W1FUOHDsXw4cOLPRxSQTweD9577z3IsoyJEycWeziEEELyRMFZD3l+20s44nPhm0fqKDgjBXX48GE8u/1VBNxenHXWWWAYpthDIoQQkgfa1uwhmubHoBHDoGlasYdC[·...·truncated·by·diffoscope;·len:·103404,·SHA1:·014ec45529262c552030c334431d9c3c70259c5a·...·]
13575	"	13575	"
13576	>	13576	>
13577	</div>	13577	</div>

13578	</div>	13578	</div>

13579	</div>	13579	</div>
Offset 13809, 15 lines modified		Offset 13809, 15 lines modified

13809	····<div·class="prompt"></div>	13809	····<div·class="prompt"></div>




13810	<div·class="output_png·output_subarea·">	13810	<div·class="output_png·output_subarea·">
13811	<img·src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAmcAAAHoCAYAAAAMvEiBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzsvXmUI2d57/99~~tbeWl~~tRq~~9TZLj7E9NsbYLGY~~G~~4ww~~hQMIWQ~~nJu~~IMmPx~~eGScI1vlnOzweVk4f6S8CPbTd~~g~~mDgSIuSGs4SaEEIzDaoMz~~g~~23Adox~~n7e~~lV3a3Wvqslvb8/StVTX~~V0lVUlVUpX~~0fM7pM9Oq7elXb~~9~~X71LMy~~z~~jkIg~~iAIgiAI~~a+A~~Y~~tgAEQ~~RAEQRDE~~VUg5Iwi~~CIAiC~~sBCknBE~~EQRAE~~QVgIUs4I~~giAIgi~~AsBClnBE~~EQBEEQ~~FoKUM4I~~g~~CI~~IgCA~~tByhlBGAhjr~~M~~gYe5rJ13gRY2y~~t~~x2P/j~~jH2~~Ryb~~I~~dA9~~j7~~PeM~~P~~i8x~~GjDGf~~pEx~~9~~qBJ~~5~~z7FGDsn+f0Gxtj3GW~~M~~Fxtiv~~m~~TU3GW~~P~~vZIz~~9~~rdHnJQiAlDPCZjDGOGPsOtln72KM~~/b3~~k93cyxpbaitIaY+zT~~J~~snyDcbYL0k/4~~5wHOe~~eXzbieVVBaaDnnd3HO/9CEa72LMfYuye+/xB~~i~~72P5uv8wY~~W5Dt/x~~zG2L~~f~~a27cYY7~~9u~~gAzfYI~~xl~~GGP~~e~~fs/V5Tp~~vYIx~~dYYz~~l~~GWNnGGOHu+z~~/LsbY~~bvtvzTLGvsMYu7~~2P~~619hjL20x2OPte9NV~~6~~/X~~13Gt~~fc8Azvk~~Dn~~PMbJLv8DoCvc85DnPP3GTE3lV6IOOfv5pz~~/~~ktoxBNEPpJwRIwVj7E4Ab~~w~~TwUs55EMBt~~AL~~46XKkII2CMvQjAuwG8~~BsA~~UgCU~~A~~n5RsnwbwZ~~QB/AyA~~G4Do~~A~~X+nzmscAn~~ALAA~~fxUP+fqcp0ggI~~8~~BeCuACI~~B~~fAVDVcOin~~2/~~M8DuBBAJ9njDGd1zZd~~o~~RowiwD+~~c9hCEEQ~~/kHJGjBr~~PA3A~~f5/wSAHD~~O~~NznnH1LbuW0t+~~C~~3G2GO~~MsRxj~~7NOMMV97W5Q~~x9k~~XGWL~~J~~tOfmia~~M~~1gjP0xhEX~~7A~~23LxQfa~~n~~++91TP~~G~~woyxj7ePX2aM/S5jzNHe9ouMsQcZY3/e~~PvcSY+~~wVErnezBj7Y~~ds1c~~5kx~~9t~~+0Dg~~Bj7E~~bG2P~~2MsTRj7Bxj7~~HUd9v3~~J~~tgtItL~~zc~~Itl2hDH2+bb8Kcb~~YBx~~hjTwdwD4Db~~R~~YtN~~e999~~7lLG2C+3LVxpxtgXpBau9hjdxRi~~[·...·truncated·by·diffoscope;·len:·8~~3161~~,·SHA1:·f10569c~~9a57df~~5~~55883dd2~~9d8bf~~202d085~~4~~db699~~·...·]AAAAAElFTkSuQmCC	13811	<img·src="data:image/png;base64,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[·...·truncated·by·diffoscope;·len:·82949,·SHA1:·20bcc4e48e50e91f01e9d15f117b561a49672535·...·]AAAAAElFTkSuQmCC
13812	"	13812	"
13813	>	13813	>
13814	</div>	13814	</div>

13815	</div>	13815	</div>

13816	</div>	13816	</div>
Offset 13890, 15 lines modified		Offset 13890, 15 lines modified

13890	····<div·class="prompt"></div>	13890	····<div·class="prompt"></div>




13891	<div·class="output_png·output_subarea·">	13891	<div·class="output_png·output_subarea·">
13892	<img·src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAmcAAAHoCAYAAAAMvEiBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzsnXl8W+WZ73+vVmvzvjtxEoIDYQlJ~~SUMDDUkIUKDppaW0lG0KA~~y~~1LZyi3HaZTu~~L~~1wZ2hKO22ZMjPttKULbWkJTdmZgZI~~E~~J4GEkDjBNiFxvO+yZM~~vyI~~ku2Jb3~~3jyMps~~i1L~~R9K~~Rjo70~~f~~D8ff2yf9dGr95zznGd~~lnHMQBEEQ~~BE~~EQm~~YFKbgEIgi~~AI~~giCIM5B~~y~~RhAEQR~~A~~EkUGQckY~~Q~~BEEQBJFBkHJGEARBEASRQZB~~y~~RhAEQR~~A~~EkUGQckY~~Q~~BEEQBJFBkHJGEBLCGJtkj~~J~~2V4nNsYYz1Jbjvbxljj~~6dAp~~v9ij~~H1H~~6uMS2QF~~j~~7A7G2DspOv~~Y~~mxlhL~~2~~P/nMM~~Y~~+YIxNMMYeSNXcZIw~~9z~~Bh7WurjEgRAyhmhMBhjnDF29r~~x~~ljzHG/hD2/8OMsc6Ao~~tTHG~~NuZIlnqGWN3hy/j~~n~~Js55x2pOF+mE~~O~~lByzm~~/~~l3P+Lyk412OMsc~~f~~C/~~r~~+bMdYW+~~G~~7fYIxVz9v+Y4yx/YH1Q4~~yx~~r0sgQz1~~jb~~JQxpk/2WDHOcx~~tjrIs~~xNs4~~Y~~O8w~~YW~~xJj+8cYY7OBz+pkjB1kjG1M4vxdjLErE9x3eeD~~a~~1CR6/jjONecewDk/wDk/J~~2yTf~~wTw~~N~~ufcwjl/Soq5G~~em~~FiHO+g~~3~~N+92L7EEQykH~~JGZBWMs~~S8DuB3AlZx~~zM~~4D1APbIKxUhBYyxLQB2~~A~~LgeQDG~~A~~TgB/CltfCuA~~NAD8HUA~~LgbA~~B~~/TfKcywFs~~AsAB~~/K9kjhXjPGYAv~~wHwVQCFA~~P4O~~g~~EfErjsD87wMwDs~~AXmC~~MsTjPnXKFKs0sA3BCbiEIIhlIOSO~~y~~jY8~~DeJ~~Nz3g4~~An~~HMr~~5/w~~Xi20c~~s~~Bb8A2OsiTE2xhjbyRjLC6wrY~~oy~~9xh~~i~~zBywnrwWtGYyx70J4aP9HwHLxH4Hlobd6xlg~~B~~Y+x~~3~~gf27G~~WP~~/hzGmCqy7gzH2DmP~~sh4Fj~~dzLGrg2T607G2MmAa6aDMXaP~~2AFg~~jJ3LGHuLMe~~Z~~gjLUw~~xr~~4YZdvtA~~RdQ~~0PK~~y~~Jmz~~dUsbY~~CwH~~5Rxhj~~/8E~~YWw3g~~vwBsDFpsAtvOcZcyxr4SsHA5GGO~~vhFu4~~AmN~~0~~L2O~~s~~NXDe/xS~~p~~UGwH8GfO+QnO+Qy~~A~~fwF~~wOWNsZW[·...·truncated·by·diffoscope;·len:·86333,·SHA1:·~~487ae934870~~3~~5598ca~~2~~2ce671~~c~~8e783~~51f48~~a780~~·...·]=	13892	<img·src="data:image/png;base64,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[·...·truncated·by·diffoscope;·len:·86133,·SHA1:·c961b8f85ac3977f408259b9a7c550985b482aca·...·]=
13893	"	13893	"
13894	>	13894	>
13895	</div>	13895	</div>

13896	</div>	13896	</div>

13897	</div>	13897	</div>


Offset 13297, 35 lines modified		Offset 13297, 35 lines modified

13297	····<div·class="prompt"></div>	13297	····<div·class="prompt"></div>


13298	<div·class="output_subarea·output_stream·output_stdout·output_text">	13298	<div·class="output_subarea·output_stream·output_stdout·output_text">
13299	<pre>····························OLS·Regression·Results····························	13299	<pre>····························OLS·Regression·Results····························
13300	==============================================================================	13300	==============================================================================
13301	Dep.·Variable:······················y···R-squared:·······················0.986	13301	Dep.·Variable:······················y···R-squared:·······················0.984
13302	Model:····························OLS···Adj.·R-squared:··················0.985	13302	Model:····························OLS···Adj.·R-squared:··················0.983
13303	Method:·················Least·Squares···F-statistic:·····················~~1052.~~	13303	Method:·················Least·Squares···F-statistic:·····················968.4
13304	Date:················~~Fri~~,·06·Mar·2020···Prob·(F-statistic):···········2.27e-42	13304	Date:················Sat,·10·Apr·2021···Prob·(F-statistic):···········1.48e-41
13305	Time:························15:40:24···Log-Likelihood:·················4.~~5668~~	13305	Time:························01:00:12···Log-Likelihood:·················1.8725
13306	No.·Observations:··················50···AIC:····························~~-1~~.~~134~~	13306	No.·Observations:··················50···AIC:·····························4.255
13307	Df·Residuals:······················46···BIC:·····························~~6.514~~	13307	Df·Residuals:······················46···BIC:·····························11.90
13308	Df·Model:···························3·········································	13308	Df·Model:···························3·········································
13309	Covariance·Type:············nonrobust·········································	13309	Covariance·Type:············nonrobust·········································
13310	==============================================================================	13310	==============================================================================
13311	·················coef····std·err··········t······P>\|t\|······[0.025······0.975]	13311	·················coef····std·err··········t······P>\|t\|······[0.025······0.975]
13312	------------------------------------------------------------------------------	13312	------------------------------------------------------------------------------
13313	const··········5.0~~650~~······0.078·····64.~~539~~······0.000·······4.~~907~~·······5.~~223~~	13313	const··········5.0007······0.083·····60.377······0.000·······4.834·······5.167
13314	x1·············0.4839······0.012·····39.~~979~~······0.000·······0.460·······0.508	13314	x1·············0.5039······0.013·····39.451······0.000·······0.478·······0.530
13315	x2·············0.5~~638~~······0.048·····11.848······0.000·······0.468·······0.660	13315	x2·············0.5066······0.050·····10.088······0.000·······0.405·······0.608
13316	x3············-0.0~~184~~······0.001····-17.~~327~~······0.000······-0.021······-0.016	13316	x3············-0.0203······0.001····-18.093······0.000······-0.023······-0.018
13317	==============================================================================	13317	==============================================================================
13318	Omnibus:························2.~~544~~···Durbin-Watson:···················2.~~534~~	13318	Omnibus:························0.966···Durbin-Watson:···················2.449
13319	Prob(Omnibus):··················0.~~280~~···Jarque-Bera·(JB):················1.~~892~~	13319	Prob(Omnibus):··················0.617···Jarque-Bera·(JB):················0.353
13320	Skew:···························0.~~472~~···Prob(JB):························0.388	13320	Skew:··························-0.136···Prob(JB):························0.838
13321	Kurtosis:·······················3.129···Cond.·No.·························221.	13321	Kurtosis:·······················3.309···Cond.·No.·························221.
13322	==============================================================================	13322	==============================================================================

13323	Warnings:	13323	Warnings:
13324	[1]·Standard·Errors·assume·that·the·covariance·matrix·of·the·errors·is·correctly·specified.	13324	[1]·Standard·Errors·assume·that·the·covariance·matrix·of·the·errors·is·correctly·specified.
13325	</pre>	13325	</pre>
13326	</div>	13326	</div>
13327	</div>	13327	</div>
Offset 13360, 23 lines modified		Offset 13360, 23 lines modified

13360	<div·class="output_area">	13360	<div·class="output_area">

13361	····<div·class="prompt"></div>	13361	····<div·class="prompt"></div>


13362	<div·class="output_subarea·output_stream·output_stdout·output_text">	13362	<div·class="output_subarea·output_stream·output_stdout·output_text">
13363	<pre>[·4.~~60466~~883··5.0980318···5.~~54849514~~··5.925~~33477~~··6.2089~~148~~···6.393~~9136~~1	13363	<pre>[·4.49340827··4.9796044···5.42600456··5.80500128··6.0989505···6.30307041
13364	··6.4~~9019815~~··6.~~521202~~89··6.52~~0079~~58··6.52425~~047~~··6.~~56926···~~··6.~~68293492~~	13364	··6.42622709··6.48947785··6.52261154··6.55925439··6.63134523··6.76388811
13365	··6.8808~~1243~~··7.~~16358~~748··7.5~~1699857~~··7.9~~14170~~89··8.3~~2003177~~··8.69707~~303~~	13365	··6.97084416··7.25283783··7.59705429··7.97934488··8.36819465··8.72990019
13366	··9.0~~1151343~~··9.238~~84889~~··9.3~~6787841~~··9.402~~54434~~··9.~~36128472~~··9.~~2740039~~	13366	··9.03410701··9.25879666··9.39390437··9.44297258··9.422569····9.35956452
13367	··9.17715~~938~~··9.107~~77195~~··9.09734324··9.1~~666~~8014··9.~~32247623~~··9.~~55621212~~	13367	··9.28671845··9.23729623··9.23960401··9.31233806··9.46151299··9.67947356
13368	··9.84555~~508~~·10.~~15802829~~·10.45634671·10.7045~~4284~~·10.8~~7387613~~·10.9~~4755498~~	13368	··9.94615249·10.23236734·10.50461521·10.73057711·10.88442789·10.95107925
13369	·10.92~~34955~~··10.8~~1466392~~·10.646946~~3·~~·10.4~~548~~9~~548~~·10.~~276054~~14·10.~~144786~~7	13369	·10.9286587··10.8288172··10.67481496·10.4976998··10.33120617·10.20621257
13370	·10.~~086~~6333··10.1~~1411~~363·10.2~~2466~~998·10.~~40108~~7~~5·~~·10.614~~32249~~·10.~~82827425~~	13370	·10.14566839·10.16082335·10.2493792··10.39586731·10.57419016·10.75190904
13371	·11.0057163··11.~~1144~~1~~258~~]	13371	·10.89557369·10.97621802]
13372	</pre>	13372	</pre>
13373	</div>	13373	</div>
13374	</div>	13374	</div>

13375	</div>	13375	</div>
13376	</div>	13376	</div>

Offset 13410, 16 lines modified		Offset 13410, 16 lines modified

13410	<div·class="output_area">	13410	<div·class="output_area">

13411	····<div·class="prompt"></div>	13411	····<div·class="prompt"></div>


13412	<div·class="output_subarea·output_stream·output_stdout·output_text">	13412	<div·class="output_subarea·output_stream·output_stdout·output_text">
13413	<pre>[11.~~122840~~18·10.~~984~~47209·10.~~72141659~~·10.384~~055~~63·10.038~~70959~~··9.~~75139~~974	13413	<pre>[10.96123313·10.81247049·10.54979574·10.21848016··9.87811667··9.58802945
13414	··9.~~5716839·~~··9.~~520523··~~··9.~~5851~~4908··9.~~72219136~~]	13414	··9.39274923··9.31111069··9.33164105··9.41536898]
13415	</pre>	13415	</pre>
13416	</div>	13416	</div>
13417	</div>	13417	</div>

13418	</div>	13418	</div>
13419	</div>	13419	</div>

Offset 13457, 15 lines modified		Offset 13457, 15 lines modified

13457	····<div·class="prompt"></div>	13457	····<div·class="prompt"></div>




13458	<div·class="output_png·output_subarea·">	13458	<div·class="output_png·output_subarea·">
13459	<img·src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzt3X~~dclW~~UbwPH~~fzRJQFAeYW~~5~~y5RdHMl~~S~~vtTXNbudKs0Mw0LcvKcjTE1L~~Ryl~~OUs~~R~~44kX33Lm~~V~~qWW9wzRQUX4gBkc+73jyMICIJw4Kz~~r+~~/nwAR6ec5~~7r~~4ejFfe7n~~eq5~~baa0RQghh/RzMHYAQQg~~jT~~kIQuhBA2Q~~h~~K6EELYC~~EnoQghhI~~ySh~~CyGE~~jZCELoQ~~QNkI~~SuhB~~C2Ah~~J6E~~II~~YSMko~~QshhI~~1wys+DlShRQ~~le~~sWDE/DymEEFZv//79N7TWX~~l~~ntl68Jv~~W~~LFiuzbty8/~~D~~ymEEFZPKXUhO/vJlIsQQtgI~~SehCC~~GEjJKEL~~IYSNy~~Nc59IwkJCQQE~~h~~JCbGysuUMR2eDq6krZsmVxdnY2dyhCiH~~T~~MntBDQkLw8PCgYsWKKK~~X~~MHY54C~~K~~014eHhhIS~~E~~4OPjY+5w~~h~~BDpmD2hx8bGSjK3EkopihcvTlhY~~m~~LlDEV~~Y~~g8GAoUzac4vLtGEp7u~~jG6~~Q3W6+pYxd1g2zewJHZBkbk~~X~~ktRLZEXgwlPd/OUJ~~MQhI~~AobdjeP+XIwCS1POQXBQVQpjclA2nUpJ5spiEJKZsOGWmiOyDJHT~~A~~0dGR+v~~X~~rU6t~~W~~LerVq8e0ad~~M~~wGAw~~P~~fUxw~~cDB~~Lly7NpwiFsC6~~X~~b8c80nZhGhY~~x5~~fIo8mJezs3NjaCgI~~AC~~uX79O~~n~~z59iIiIYMKECZk+Jj~~m~~h9+nTJ1fHFsIWlfZ0~~I~~zSD5F3a080M0dgPq~~xqhJ8~~/L~~h~~d6OQXN/~~Xi7~~wYKjJjuHt7c3cuXOZOXMmWmuCg4Np~~0~~aIFDRo0oEGDBvz99~~9~~8AjB~~kz~~hj///JP69es~~z~~ffr0TPc~~T~~wh6N7lAd~~N2fH~~NNvcn~~B0Z3a~~G6mSKyD1Y1Qn/YvJwpL7RUqlS~~J~~pKQkr~~l~~+/jre3N5s2~~b~~cLV1ZUzZ87~~Qu~~3dv9u3bR0BAAFOnTmXdunUAREdHZ7if~~E~~PYo+f+jVLnkL6tK6OaYl0tISGDYsGEEBQXh6OjI6dOnc7WfEPaiq28ZSeD5zKoSe~~n~~7Ny507dw5HR0e8vb2ZMGECJUuW5NC~~hQ~~xgMBlx~~d~~XTN8zPTp07O1nxCpS~~a~~22MCWrSuijO1RPU9sKpp~~[·...·truncated·by·diffoscope;·len:·17553,·SHA1:·~~3ca~~e~~320~~1~~14dfe34~~5~~3a8df7724ead44~~92~~03fd545e~~·...·]=	13459	<img·src="data:image/png;base64,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[·...·truncated·by·diffoscope;·len:·16957,·SHA1:·59ee2fa319090d0856c4081a8bcb5aa912aacc31·...·]=
13460	"	13460	"
13461	>	13461	>
13462	</div>	13462	</div>

13463	</div>	13463	</div>

13464	</div>	13464	</div>
Offset 13532, 18 lines modified		Offset 13532, 18 lines modified

13532	····<div·class="prompt·output_prompt">Out[8]:</div>	13532	····<div·class="prompt·output_prompt">Out[8]:</div>




13533	<div·class="output_text·output_subarea·output_execute_result">	13533	<div·class="output_text·output_subarea·output_execute_result">
13534	<pre>Intercept···········5.065009	13534	<pre>Intercept···········5.000693
13535	x1··················0.483~~889~~	13535	x1··················0.503931
13536	np.sin(x1)··········0.5~~63753~~	13536	np.sin(x1)··········0.506567
13537	I((x1·-·5)·**·2)···-0.0~~18414~~	13537	I((x1·-·5)·**·2)···-0.020291
13538	dtype:·float64</pre>	13538	dtype:·float64</pre>
13539	</div>	13539	</div>

13540	</div>	13540	</div>

13541	</div>	13541	</div>
13542	</div>	13542	</div>
Offset 13577, 24 lines modified		Offset 13577, 24 lines modified

13577	····<div·class="prompt·output_prompt">Out[9]:</div>	13577	····<div·class="prompt·output_prompt">Out[9]:</div>




13578	<div·class="output_text·output_subarea·output_execute_result">	13578	<div·class="output_text·output_subarea·output_execute_result">
13579	<pre>0····11.~~122840~~	13579	<pre>0····10.961233
13580	1····10.~~984~~472	13580	1····10.812470
13581	2····10.~~721417~~	13581	2····10.549796
13582	3····10.384056	13582	3····10.218480
13583	4····10.038710	13583	4·····9.878117
13584	5·····9.751400	13584	5·····9.588029
13585	6·····9.~~571684~~	13585	6·····9.392749
13586	7·····9.~~520523~~	13586	7·····9.311111
Max diff block lines reached; 36/43490 bytes (0.08%) of diff not shown.


Offset 13357, 16 lines modified		Offset 13357, 16 lines modified

13357	<div·class="output_subarea·output_stream·output_stdout·output_text">	13357	<div·class="output_subarea·output_stream·output_stdout·output_text">
13358	<pre>·························QuantReg·Regression·Results··························	13358	<pre>·························QuantReg·Regression·Results··························
13359	==============================================================================	13359	==============================================================================
13360	Dep.·Variable:················foodexp···Pseudo·R-squared:···············0.6206	13360	Dep.·Variable:················foodexp···Pseudo·R-squared:···············0.6206
13361	Model:·······················QuantReg···Bandwidth:·······················64.51	13361	Model:·······················QuantReg···Bandwidth:·······················64.51
13362	Method:·················Least·Squares···Sparsity:························209.3	13362	Method:·················Least·Squares···Sparsity:························209.3
13363	Date:················~~Fri~~,·06·Mar·2020···No.·Observations:··················235	13363	Date:················Sat,·10·Apr·2021···No.·Observations:··················235
13364	Time:························15:40:14···Df·Residuals:······················233	13364	Time:························01:00:05···Df·Residuals:······················233
13365	········································Df·Model:····························1	13365	········································Df·Model:····························1
13366	==============================================================================	13366	==============================================================================
13367	·················coef····std·err··········t······P>\|t\|······[0.025······0.975]	13367	·················coef····std·err··········t······P>\|t\|······[0.025······0.975]
13368	------------------------------------------------------------------------------	13368	------------------------------------------------------------------------------
13369	Intercept·····81.4823·····14.634······5.568······0.000······52.649·····110.315	13369	Intercept·····81.4823·····14.634······5.568······0.000······52.649·····110.315
13370	income·········0.5602······0.013·····42.516······0.000·······0.534·······0.586	13370	income·········0.5602······0.013·····42.516······0.000·······0.534·······0.586
13371	==============================================================================	13371	==============================================================================


Offset 13352, 16 lines modified		Offset 13352, 16 lines modified


13352	<div·class="output_subarea·output_stream·output_stdout·output_text">	13352	<div·class="output_subarea·output_stream·output_stdout·output_text">
13353	<pre>···························Statespace·Model·Results···························	13353	<pre>···························Statespace·Model·Results···························
13354	==============================================================================	13354	==============================================================================
13355	Dep.·Variable:·······WORLDCONSUMPTION···No.·Observations:···················25	13355	Dep.·Variable:·······WORLDCONSUMPTION···No.·Observations:···················25
13356	Model:····················RecursiveLS···Log·Likelihood················-153.737	13356	Model:····················RecursiveLS···Log·Likelihood················-153.737
13357	Date:················~~Fri~~,·06·Mar·2020···AIC····························317.474	13357	Date:················Sat,·10·Apr·2021···AIC····························317.474
13358	Time:························15:40:37···BIC····························323.568	13358	Time:························01:00:05···BIC····························323.568
13359	Sample:····················01-01-1951···HQIC···························319.164	13359	Sample:····················01-01-1951···HQIC···························319.164
13360	·························-·01-01-1975·········································	13360	·························-·01-01-1975·········································
13361	Covariance·Type:············nonrobust·········································	13361	Covariance·Type:············nonrobust·········································
13362	==================================================================================	13362	==================================================================================
13363	·····················coef····std·err··········z······P>\|z\|······[0.025······0.975]	13363	·····················coef····std·err··········z······P>\|z\|······[0.025······0.975]
13364	----------------------------------------------------------------------------------	13364	----------------------------------------------------------------------------------
13365	const··········-6513.9911···2367.692·····-2.751······0.006···-1.12e+04···-1873.400	13365	const··········-6513.9911···2367.692·····-2.751······0.006···-1.12e+04···-1873.400


Offset 13262, 16 lines modified		Offset 13262, 16 lines modified

13262	<div·class="output_subarea·output_stream·output_stdout·output_text">	13262	<div·class="output_subarea·output_stream·output_stdout·output_text">
13263	<pre>····························OLS·Regression·Results····························	13263	<pre>····························OLS·Regression·Results····························
13264	==============================================================================	13264	==============================================================================
13265	Dep.·Variable:················Lottery···R-squared:·······················0.348	13265	Dep.·Variable:················Lottery···R-squared:·······················0.348
13266	Model:····························OLS···Adj.·R-squared:··················0.333	13266	Model:····························OLS···Adj.·R-squared:··················0.333
13267	Method:·················Least·Squares···F-statistic:·····················22.20	13267	Method:·················Least·Squares···F-statistic:·····················22.20
13268	Date:················~~Fri~~,·06·Mar·2020···Prob·(F-statistic):···········1.90e-08	13268	Date:················Sat,·10·Apr·2021···Prob·(F-statistic):···········1.90e-08
13269	Time:························15:40:19···Log-Likelihood:················-379.82	13269	Time:························01:00:09···Log-Likelihood:················-379.82
13270	No.·Observations:··················86···AIC:·····························765.6	13270	No.·Observations:··················86···AIC:·····························765.6
13271	Df·Residuals:······················83···BIC:·····························773.0	13271	Df·Residuals:······················83···BIC:·····························773.0
13272	Df·Model:···························2·········································	13272	Df·Model:···························2·········································
13273	Covariance·Type:············nonrobust·········································	13273	Covariance·Type:············nonrobust·········································
13274	===================================================================================	13274	===================================================================================
13275	······················coef····std·err··········t······P>\|t\|······[0.025······0.975]	13275	······················coef····std·err··········t······P>\|t\|······[0.025······0.975]
13276	-----------------------------------------------------------------------------------	13276	-----------------------------------------------------------------------------------


Offset 14764, 16 lines modified		Offset 14764, 16 lines modified

14764	<div·class="output_subarea·output_stream·output_stdout·output_text">	14764	<div·class="output_subarea·output_stream·output_stdout·output_text">
14765	<pre>····························OLS·Regression·Results····························	14765	<pre>····························OLS·Regression·Results····························
14766	==============================================================================	14766	==============================================================================
14767	Dep.·Variable:···············prestige···R-squared:·······················0.828	14767	Dep.·Variable:···············prestige···R-squared:·······················0.828
14768	Model:····························OLS···Adj.·R-squared:··················0.820	14768	Model:····························OLS···Adj.·R-squared:··················0.820
14769	Method:·················Least·Squares···F-statistic:·····················101.2	14769	Method:·················Least·Squares···F-statistic:·····················101.2
14770	Date:················~~Fri~~,·06·Mar·2020···Prob·(F-statistic):···········8.65e-17	14770	Date:················Sat,·10·Apr·2021···Prob·(F-statistic):···········8.65e-17
14771	Time:························15:39:59···Log-Likelihood:················-178.98	14771	Time:························01:00:11···Log-Likelihood:················-178.98
14772	No.·Observations:··················45···AIC:·····························364.0	14772	No.·Observations:··················45···AIC:·····························364.0
14773	Df·Residuals:······················42···BIC:·····························369.4	14773	Df·Residuals:······················42···BIC:·····························369.4
14774	Df·Model:···························2·········································	14774	Df·Model:···························2·········································
14775	Covariance·Type:············nonrobust·········································	14775	Covariance·Type:············nonrobust·········································
14776	==============================================================================	14776	==============================================================================
14777	·················coef····std·err··········t······P>\|t\|······[0.025······0.975]	14777	·················coef····std·err··········t······P>\|t\|······[0.025······0.975]
14778	------------------------------------------------------------------------------	14778	------------------------------------------------------------------------------
Offset 15168, 16 lines modified		Offset 15168, 16 lines modified
15168	==============================================================================	15168	==============================================================================
15169	Dep.·Variable:···············prestige···No.·Observations:···················45	15169	Dep.·Variable:···············prestige···No.·Observations:···················45
15170	Model:····························RLM···Df·Residuals:·······················42	15170	Model:····························RLM···Df·Residuals:·······················42
15171	Method:··························IRLS···Df·Model:····························2	15171	Method:··························IRLS···Df·Model:····························2
15172	Norm:··························HuberT·········································	15172	Norm:··························HuberT·········································
15173	Scale·Est.:·······················mad·········································	15173	Scale·Est.:·······················mad·········································
15174	Cov·Type:··························H1·········································	15174	Cov·Type:··························H1·········································
15175	Date:················~~Fri~~,·06·Mar·2020·········································	15175	Date:················Sat,·10·Apr·2021·········································
15176	Time:························15:40:02·········································	15176	Time:························01:00:11·········································
15177	No.·Iterations:····················18·········································	15177	No.·Iterations:····················18·········································
15178	==============================================================================	15178	==============================================================================
15179	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]	15179	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]
15180	------------------------------------------------------------------------------	15180	------------------------------------------------------------------------------
15181	Intercept·····-7.1107······3.879·····-1.833······0.067·····-14.713·······0.492	15181	Intercept·····-7.1107······3.879·····-1.833······0.067·····-14.713·······0.492
15182	income·········0.7015······0.109······6.456······0.000·······0.489·······0.914	15182	income·········0.7015······0.109······6.456······0.000·······0.489·······0.914
15183	education······0.4854······0.089······5.441······0.000·······0.311·······0.660	15183	education······0.4854······0.089······5.441······0.000·······0.311·······0.660


Offset 13454, 15 lines modified		Offset 13454, 15 lines modified
13454	<span·class="ansi-green-intense-fg·ansi-bold">····182</span>·········self<span·class="ansi-blue-fg">.</span>_prepare_conn<span·class="ansi-blue-fg">(</span>conn<span·class="ansi-blue-fg">)</span>	13454	<span·class="ansi-green-intense-fg·ansi-bold">····182</span>·········self<span·class="ansi-blue-fg">.</span>_prepare_conn<span·class="ansi-blue-fg">(</span>conn<span·class="ansi-blue-fg">)</span>

13455	<span·class="ansi-green-fg">/usr/lib/python3/dist-packages/urllib3/connection.py</span>·in·<span·class="ansi-cyan-fg">_new_conn</span><span·class="ansi-blue-fg">(self)</span>	13455	<span·class="ansi-green-fg">/usr/lib/python3/dist-packages/urllib3/connection.py</span>·in·<span·class="ansi-cyan-fg">_new_conn</span><span·class="ansi-blue-fg">(self)</span>
13456	<span·class="ansi-green-intense-fg·ansi-bold">····167</span>·············raise·NewConnectionError(	13456	<span·class="ansi-green-intense-fg·ansi-bold">····167</span>·············raise·NewConnectionError(
13457	<span·class="ansi-green-fg">-->·168</span><span·class="ansi-red-fg">·················self,·"Failed·to·establish·a·new·connection:·%s"·%·e)	13457	<span·class="ansi-green-fg">-->·168</span><span·class="ansi-red-fg">·················self,·"Failed·to·establish·a·new·connection:·%s"·%·e)
13458	</span><span·class="ansi-green-intense-fg·ansi-bold">····169</span>·	13458	</span><span·class="ansi-green-intense-fg·ansi-bold">····169</span>·

13459	<span·class="ansi-red-fg">NewConnectionError</span>:·<urllib3.connection.HTTPConnection·object·at·0xac~~8fc~~76c>:·Failed·to·establish·a·new·connection:·[Errno·111]·Connection·refused	13459	<span·class="ansi-red-fg">NewConnectionError</span>:·<urllib3.connection.HTTPConnection·object·at·0xec9067cc>:·Failed·to·establish·a·new·connection:·[Errno·111]·Connection·refused

13460	During·handling·of·the·above·exception,·another·exception·occurred:	13460	During·handling·of·the·above·exception,·another·exception·occurred:

13461	<span·class="ansi-red-fg">MaxRetryError</span>·····························Traceback·(most·recent·call·last)	13461	<span·class="ansi-red-fg">MaxRetryError</span>·····························Traceback·(most·recent·call·last)
13462	<span·class="ansi-green-fg">/usr/lib/python3/dist-packages/requests/adapters.py</span>·in·<span·class="ansi-cyan-fg">send</span><span·class="ansi-blue-fg">(self,·request,·stream,·timeout,·verify,·cert,·proxies)</span>	13462	<span·class="ansi-green-fg">/usr/lib/python3/dist-packages/requests/adapters.py</span>·in·<span·class="ansi-cyan-fg">send</span><span·class="ansi-blue-fg">(self,·request,·stream,·timeout,·verify,·cert,·proxies)</span>
13463	<span·class="ansi-green-intense-fg·ansi-bold">····448</span>·····················retries<span·class="ansi-blue-fg">=</span>self<span·class="ansi-blue-fg">.</span>max_retries<span·class="ansi-blue-fg">,</span>	13463	<span·class="ansi-green-intense-fg·ansi-bold">····448</span>·····················retries<span·class="ansi-blue-fg">=</span>self<span·class="ansi-blue-fg">.</span>max_retries<span·class="ansi-blue-fg">,</span>
13464	<span·class="ansi-green-fg">-->·449</span><span·class="ansi-red-fg">·····················</span>timeout<span·class="ansi-blue-fg">=</span>timeout	13464	<span·class="ansi-green-fg">-->·449</span><span·class="ansi-red-fg">·····················</span>timeout<span·class="ansi-blue-fg">=</span>timeout
Offset 13474, 15 lines modified		Offset 13474, 15 lines modified
13474	</span><span·class="ansi-green-intense-fg·ansi-bold">····639</span>·············retries<span·class="ansi-blue-fg">.</span>sleep<span·class="ansi-blue-fg">(</span><span·class="ansi-blue-fg">)</span>	13474	</span><span·class="ansi-green-intense-fg·ansi-bold">····639</span>·············retries<span·class="ansi-blue-fg">.</span>sleep<span·class="ansi-blue-fg">(</span><span·class="ansi-blue-fg">)</span>

13475	<span·class="ansi-green-fg">/usr/lib/python3/dist-packages/urllib3/util/retry.py</span>·in·<span·class="ansi-cyan-fg">increment</span><span·class="ansi-blue-fg">(self,·method,·url,·response,·error,·_pool,·_stacktrace)</span>	13475	<span·class="ansi-green-fg">/usr/lib/python3/dist-packages/urllib3/util/retry.py</span>·in·<span·class="ansi-cyan-fg">increment</span><span·class="ansi-blue-fg">(self,·method,·url,·response,·error,·_pool,·_stacktrace)</span>
13476	<span·class="ansi-green-intense-fg·ansi-bold">····397</span>·········<span·class="ansi-green-fg">if</span>·new_retry<span·class="ansi-blue-fg">.</span>is_exhausted<span·class="ansi-blue-fg">(</span><span·class="ansi-blue-fg">)</span><span·class="ansi-blue-fg">:</span>	13476	<span·class="ansi-green-intense-fg·ansi-bold">····397</span>·········<span·class="ansi-green-fg">if</span>·new_retry<span·class="ansi-blue-fg">.</span>is_exhausted<span·class="ansi-blue-fg">(</span><span·class="ansi-blue-fg">)</span><span·class="ansi-blue-fg">:</span>
13477	<span·class="ansi-green-fg">-->·398</span><span·class="ansi-red-fg">·············</span><span·class="ansi-green-fg">raise</span>·MaxRetryError<span·class="ansi-blue-fg">(</span>_pool<span·class="ansi-blue-fg">,</span>·url<span·class="ansi-blue-fg">,</span>·error·<span·class="ansi-green-fg">or</span>·ResponseError<span·class="ansi-blue-fg">(</span>cause<span·class="ansi-blue-fg">)</span><span·class="ansi-blue-fg">)</span>	13477	<span·class="ansi-green-fg">-->·398</span><span·class="ansi-red-fg">·············</span><span·class="ansi-green-fg">raise</span>·MaxRetryError<span·class="ansi-blue-fg">(</span>_pool<span·class="ansi-blue-fg">,</span>·url<span·class="ansi-blue-fg">,</span>·error·<span·class="ansi-green-fg">or</span>·ResponseError<span·class="ansi-blue-fg">(</span>cause<span·class="ansi-blue-fg">)</span><span·class="ansi-blue-fg">)</span>
13478	<span·class="ansi-green-intense-fg·ansi-bold">····399</span>·	13478	<span·class="ansi-green-intense-fg·ansi-bold">····399</span>·

13479	<span·class="ansi-red-fg">MaxRetryError</span>:·HTTPConnectionPool(host='127.0.0.1',·port=9):·Max·retries·exceeded·with·url:·http://staff.feweb.vu.nl/koopman/projects/ckbook/OxCodeAll.zip·(Caused·by·ProxyError('Cannot·connect·to·proxy.',·NewConnectionError('<urllib3.connection.HTTPConnection·object·at·0xac~~8fc~~76c>:·Failed·to·establish·a·new·connection:·[Errno·111]·Connection·refused')))	13479	<span·class="ansi-red-fg">MaxRetryError</span>:·HTTPConnectionPool(host='127.0.0.1',·port=9):·Max·retries·exceeded·with·url:·http://staff.feweb.vu.nl/koopman/projects/ckbook/OxCodeAll.zip·(Caused·by·ProxyError('Cannot·connect·to·proxy.',·NewConnectionError('<urllib3.connection.HTTPConnection·object·at·0xec9067cc>:·Failed·to·establish·a·new·connection:·[Errno·111]·Connection·refused')))

13480	During·handling·of·the·above·exception,·another·exception·occurred:	13480	During·handling·of·the·above·exception,·another·exception·occurred:

13481	<span·class="ansi-red-fg">ProxyError</span>································Traceback·(most·recent·call·last)	13481	<span·class="ansi-red-fg">ProxyError</span>································Traceback·(most·recent·call·last)
13482	<span·class="ansi-green-fg"><ipython-input-3-adacc4910a58></span>·in·<span·class="ansi-cyan-fg"><module></span><span·class="ansi-blue-fg">()</span>	13482	<span·class="ansi-green-fg"><ipython-input-3-adacc4910a58></span>·in·<span·class="ansi-cyan-fg"><module></span><span·class="ansi-blue-fg">()</span>
13483	<span·class="ansi-green-intense-fg·ansi-bold">······4</span>·	13483	<span·class="ansi-green-intense-fg·ansi-bold">······4</span>·
13484	<span·class="ansi-green-intense-fg·ansi-bold">······5</span>·<span·class="ansi-red-fg">#·Download·the·dataset</span>	13484	<span·class="ansi-green-intense-fg·ansi-bold">······5</span>·<span·class="ansi-red-fg">#·Download·the·dataset</span>
Offset 13521, 15 lines modified		Offset 13521, 15 lines modified
13521	<span·class="ansi-green-fg">/usr/lib/python3/dist-packages/requests/adapters.py</span>·in·<span·class="ansi-cyan-fg">send</span><span·class="ansi-blue-fg">(self,·request,·stream,·timeout,·verify,·cert,·proxies)</span>	13521	<span·class="ansi-green-fg">/usr/lib/python3/dist-packages/requests/adapters.py</span>·in·<span·class="ansi-cyan-fg">send</span><span·class="ansi-blue-fg">(self,·request,·stream,·timeout,·verify,·cert,·proxies)</span>
13522	<span·class="ansi-green-intense-fg·ansi-bold">····508</span>·	13522	<span·class="ansi-green-intense-fg·ansi-bold">····508</span>·
13523	<span·class="ansi-green-intense-fg·ansi-bold">····509</span>·············<span·class="ansi-green-fg">if</span>·isinstance<span·class="ansi-blue-fg">(</span>e<span·class="ansi-blue-fg">.</span>reason<span·class="ansi-blue-fg">,</span>·_ProxyError<span·class="ansi-blue-fg">)</span><span·class="ansi-blue-fg">:</span>	13523	<span·class="ansi-green-intense-fg·ansi-bold">····509</span>·············<span·class="ansi-green-fg">if</span>·isinstance<span·class="ansi-blue-fg">(</span>e<span·class="ansi-blue-fg">.</span>reason<span·class="ansi-blue-fg">,</span>·_ProxyError<span·class="ansi-blue-fg">)</span><span·class="ansi-blue-fg">:</span>
13524	<span·class="ansi-green-fg">-->·510</span><span·class="ansi-red-fg">·················</span><span·class="ansi-green-fg">raise</span>·ProxyError<span·class="ansi-blue-fg">(</span>e<span·class="ansi-blue-fg">,</span>·request<span·class="ansi-blue-fg">=</span>request<span·class="ansi-blue-fg">)</span>	13524	<span·class="ansi-green-fg">-->·510</span><span·class="ansi-red-fg">·················</span><span·class="ansi-green-fg">raise</span>·ProxyError<span·class="ansi-blue-fg">(</span>e<span·class="ansi-blue-fg">,</span>·request<span·class="ansi-blue-fg">=</span>request<span·class="ansi-blue-fg">)</span>
13525	<span·class="ansi-green-intense-fg·ansi-bold">····511</span>·	13525	<span·class="ansi-green-intense-fg·ansi-bold">····511</span>·
13526	<span·class="ansi-green-intense-fg·ansi-bold">····512</span>·············<span·class="ansi-green-fg">if</span>·isinstance<span·class="ansi-blue-fg">(</span>e<span·class="ansi-blue-fg">.</span>reason<span·class="ansi-blue-fg">,</span>·_SSLError<span·class="ansi-blue-fg">)</span><span·class="ansi-blue-fg">:</span>	13526	<span·class="ansi-green-intense-fg·ansi-bold">····512</span>·············<span·class="ansi-green-fg">if</span>·isinstance<span·class="ansi-blue-fg">(</span>e<span·class="ansi-blue-fg">.</span>reason<span·class="ansi-blue-fg">,</span>·_SSLError<span·class="ansi-blue-fg">)</span><span·class="ansi-blue-fg">:</span>

13527	<span·class="ansi-red-fg">ProxyError</span>:·HTTPConnectionPool(host='127.0.0.1',·port=9):·Max·retries·exceeded·with·url:·http://staff.feweb.vu.nl/koopman/projects/ckbook/OxCodeAll.zip·(Caused·by·ProxyError('Cannot·connect·to·proxy.',·NewConnectionError('<urllib3.connection.HTTPConnection·object·at·0xac~~8fc~~76c>:·Failed·to·establish·a·new·connection:·[Errno·111]·Connection·refused')))</pre>	13527	<span·class="ansi-red-fg">ProxyError</span>:·HTTPConnectionPool(host='127.0.0.1',·port=9):·Max·retries·exceeded·with·url:·http://staff.feweb.vu.nl/koopman/projects/ckbook/OxCodeAll.zip·(Caused·by·ProxyError('Cannot·connect·to·proxy.',·NewConnectionError('<urllib3.connection.HTTPConnection·object·at·0xec9067cc>:·Failed·to·establish·a·new·connection:·[Errno·111]·Connection·refused')))</pre>
13528	</div>	13528	</div>
13529	</div>	13529	</div>

13530	</div>	13530	</div>
13531	</div>	13531	</div>

13532	</div>	13532	</div>


Offset 13360, 15 lines modified		Offset 13360, 15 lines modified
13360	<span·class="ansi-green-intense-fg·ansi-bold">····182</span>·········self<span·class="ansi-blue-fg">.</span>_prepare_conn<span·class="ansi-blue-fg">(</span>conn<span·class="ansi-blue-fg">)</span>	13360	<span·class="ansi-green-intense-fg·ansi-bold">····182</span>·········self<span·class="ansi-blue-fg">.</span>_prepare_conn<span·class="ansi-blue-fg">(</span>conn<span·class="ansi-blue-fg">)</span>

13361	<span·class="ansi-green-fg">/usr/lib/python3/dist-packages/urllib3/connection.py</span>·in·<span·class="ansi-cyan-fg">_new_conn</span><span·class="ansi-blue-fg">(self)</span>	13361	<span·class="ansi-green-fg">/usr/lib/python3/dist-packages/urllib3/connection.py</span>·in·<span·class="ansi-cyan-fg">_new_conn</span><span·class="ansi-blue-fg">(self)</span>
13362	<span·class="ansi-green-intense-fg·ansi-bold">····167</span>·············raise·NewConnectionError(	13362	<span·class="ansi-green-intense-fg·ansi-bold">····167</span>·············raise·NewConnectionError(
13363	<span·class="ansi-green-fg">-->·168</span><span·class="ansi-red-fg">·················self,·"Failed·to·establish·a·new·connection:·%s"·%·e)	13363	<span·class="ansi-green-fg">-->·168</span><span·class="ansi-red-fg">·················self,·"Failed·to·establish·a·new·connection:·%s"·%·e)
13364	</span><span·class="ansi-green-intense-fg·ansi-bold">····169</span>·	13364	</span><span·class="ansi-green-intense-fg·ansi-bold">····169</span>·

13365	<span·class="ansi-red-fg">NewConnectionError</span>:·<urllib3.connection.HTTPConnection·object·at·0xac8~~c146~~c>:·Failed·to·establish·a·new·connection:·[Errno·111]·Connection·refused	13365	<span·class="ansi-red-fg">NewConnectionError</span>:·<urllib3.connection.HTTPConnection·object·at·0xec82c50c>:·Failed·to·establish·a·new·connection:·[Errno·111]·Connection·refused

13366	During·handling·of·the·above·exception,·another·exception·occurred:	13366	During·handling·of·the·above·exception,·another·exception·occurred:

13367	<span·class="ansi-red-fg">MaxRetryError</span>·····························Traceback·(most·recent·call·last)	13367	<span·class="ansi-red-fg">MaxRetryError</span>·····························Traceback·(most·recent·call·last)
13368	<span·class="ansi-green-fg">/usr/lib/python3/dist-packages/requests/adapters.py</span>·in·<span·class="ansi-cyan-fg">send</span><span·class="ansi-blue-fg">(self,·request,·stream,·timeout,·verify,·cert,·proxies)</span>	13368	<span·class="ansi-green-fg">/usr/lib/python3/dist-packages/requests/adapters.py</span>·in·<span·class="ansi-cyan-fg">send</span><span·class="ansi-blue-fg">(self,·request,·stream,·timeout,·verify,·cert,·proxies)</span>
13369	<span·class="ansi-green-intense-fg·ansi-bold">····448</span>·····················retries<span·class="ansi-blue-fg">=</span>self<span·class="ansi-blue-fg">.</span>max_retries<span·class="ansi-blue-fg">,</span>	13369	<span·class="ansi-green-intense-fg·ansi-bold">····448</span>·····················retries<span·class="ansi-blue-fg">=</span>self<span·class="ansi-blue-fg">.</span>max_retries<span·class="ansi-blue-fg">,</span>
13370	<span·class="ansi-green-fg">-->·449</span><span·class="ansi-red-fg">·····················</span>timeout<span·class="ansi-blue-fg">=</span>timeout	13370	<span·class="ansi-green-fg">-->·449</span><span·class="ansi-red-fg">·····················</span>timeout<span·class="ansi-blue-fg">=</span>timeout
Offset 13380, 15 lines modified		Offset 13380, 15 lines modified
13380	</span><span·class="ansi-green-intense-fg·ansi-bold">····639</span>·············retries<span·class="ansi-blue-fg">.</span>sleep<span·class="ansi-blue-fg">(</span><span·class="ansi-blue-fg">)</span>	13380	</span><span·class="ansi-green-intense-fg·ansi-bold">····639</span>·············retries<span·class="ansi-blue-fg">.</span>sleep<span·class="ansi-blue-fg">(</span><span·class="ansi-blue-fg">)</span>

13381	<span·class="ansi-green-fg">/usr/lib/python3/dist-packages/urllib3/util/retry.py</span>·in·<span·class="ansi-cyan-fg">increment</span><span·class="ansi-blue-fg">(self,·method,·url,·response,·error,·_pool,·_stacktrace)</span>	13381	<span·class="ansi-green-fg">/usr/lib/python3/dist-packages/urllib3/util/retry.py</span>·in·<span·class="ansi-cyan-fg">increment</span><span·class="ansi-blue-fg">(self,·method,·url,·response,·error,·_pool,·_stacktrace)</span>
13382	<span·class="ansi-green-intense-fg·ansi-bold">····397</span>·········<span·class="ansi-green-fg">if</span>·new_retry<span·class="ansi-blue-fg">.</span>is_exhausted<span·class="ansi-blue-fg">(</span><span·class="ansi-blue-fg">)</span><span·class="ansi-blue-fg">:</span>	13382	<span·class="ansi-green-intense-fg·ansi-bold">····397</span>·········<span·class="ansi-green-fg">if</span>·new_retry<span·class="ansi-blue-fg">.</span>is_exhausted<span·class="ansi-blue-fg">(</span><span·class="ansi-blue-fg">)</span><span·class="ansi-blue-fg">:</span>
13383	<span·class="ansi-green-fg">-->·398</span><span·class="ansi-red-fg">·············</span><span·class="ansi-green-fg">raise</span>·MaxRetryError<span·class="ansi-blue-fg">(</span>_pool<span·class="ansi-blue-fg">,</span>·url<span·class="ansi-blue-fg">,</span>·error·<span·class="ansi-green-fg">or</span>·ResponseError<span·class="ansi-blue-fg">(</span>cause<span·class="ansi-blue-fg">)</span><span·class="ansi-blue-fg">)</span>	13383	<span·class="ansi-green-fg">-->·398</span><span·class="ansi-red-fg">·············</span><span·class="ansi-green-fg">raise</span>·MaxRetryError<span·class="ansi-blue-fg">(</span>_pool<span·class="ansi-blue-fg">,</span>·url<span·class="ansi-blue-fg">,</span>·error·<span·class="ansi-green-fg">or</span>·ResponseError<span·class="ansi-blue-fg">(</span>cause<span·class="ansi-blue-fg">)</span><span·class="ansi-blue-fg">)</span>
13384	<span·class="ansi-green-intense-fg·ansi-bold">····399</span>·	13384	<span·class="ansi-green-intense-fg·ansi-bold">····399</span>·

13385	<span·class="ansi-red-fg">MaxRetryError</span>:·HTTPConnectionPool(host='127.0.0.1',·port=9):·Max·retries·exceeded·with·url:·http://www.ssfpack.com/files/DK-data.zip·(Caused·by·ProxyError('Cannot·connect·to·proxy.',·NewConnectionError('<urllib3.connection.HTTPConnection·object·at·0xac8~~c146~~c>:·Failed·to·establish·a·new·connection:·[Errno·111]·Connection·refused')))	13385	<span·class="ansi-red-fg">MaxRetryError</span>:·HTTPConnectionPool(host='127.0.0.1',·port=9):·Max·retries·exceeded·with·url:·http://www.ssfpack.com/files/DK-data.zip·(Caused·by·ProxyError('Cannot·connect·to·proxy.',·NewConnectionError('<urllib3.connection.HTTPConnection·object·at·0xec82c50c>:·Failed·to·establish·a·new·connection:·[Errno·111]·Connection·refused')))

13386	During·handling·of·the·above·exception,·another·exception·occurred:	13386	During·handling·of·the·above·exception,·another·exception·occurred:

13387	<span·class="ansi-red-fg">ProxyError</span>································Traceback·(most·recent·call·last)	13387	<span·class="ansi-red-fg">ProxyError</span>································Traceback·(most·recent·call·last)
13388	<span·class="ansi-green-fg"><ipython-input-3-074aec8a1161></span>·in·<span·class="ansi-cyan-fg"><module></span><span·class="ansi-blue-fg">()</span>	13388	<span·class="ansi-green-fg"><ipython-input-3-074aec8a1161></span>·in·<span·class="ansi-cyan-fg"><module></span><span·class="ansi-blue-fg">()</span>
13389	<span·class="ansi-green-intense-fg·ansi-bold">······4</span>·	13389	<span·class="ansi-green-intense-fg·ansi-bold">······4</span>·
13390	<span·class="ansi-green-intense-fg·ansi-bold">······5</span>·<span·class="ansi-red-fg">#·Download·the·dataset</span>	13390	<span·class="ansi-green-intense-fg·ansi-bold">······5</span>·<span·class="ansi-red-fg">#·Download·the·dataset</span>
Offset 13427, 15 lines modified		Offset 13427, 15 lines modified
13427	<span·class="ansi-green-fg">/usr/lib/python3/dist-packages/requests/adapters.py</span>·in·<span·class="ansi-cyan-fg">send</span><span·class="ansi-blue-fg">(self,·request,·stream,·timeout,·verify,·cert,·proxies)</span>	13427	<span·class="ansi-green-fg">/usr/lib/python3/dist-packages/requests/adapters.py</span>·in·<span·class="ansi-cyan-fg">send</span><span·class="ansi-blue-fg">(self,·request,·stream,·timeout,·verify,·cert,·proxies)</span>
13428	<span·class="ansi-green-intense-fg·ansi-bold">····508</span>·	13428	<span·class="ansi-green-intense-fg·ansi-bold">····508</span>·
13429	<span·class="ansi-green-intense-fg·ansi-bold">····509</span>·············<span·class="ansi-green-fg">if</span>·isinstance<span·class="ansi-blue-fg">(</span>e<span·class="ansi-blue-fg">.</span>reason<span·class="ansi-blue-fg">,</span>·_ProxyError<span·class="ansi-blue-fg">)</span><span·class="ansi-blue-fg">:</span>	13429	<span·class="ansi-green-intense-fg·ansi-bold">····509</span>·············<span·class="ansi-green-fg">if</span>·isinstance<span·class="ansi-blue-fg">(</span>e<span·class="ansi-blue-fg">.</span>reason<span·class="ansi-blue-fg">,</span>·_ProxyError<span·class="ansi-blue-fg">)</span><span·class="ansi-blue-fg">:</span>
13430	<span·class="ansi-green-fg">-->·510</span><span·class="ansi-red-fg">·················</span><span·class="ansi-green-fg">raise</span>·ProxyError<span·class="ansi-blue-fg">(</span>e<span·class="ansi-blue-fg">,</span>·request<span·class="ansi-blue-fg">=</span>request<span·class="ansi-blue-fg">)</span>	13430	<span·class="ansi-green-fg">-->·510</span><span·class="ansi-red-fg">·················</span><span·class="ansi-green-fg">raise</span>·ProxyError<span·class="ansi-blue-fg">(</span>e<span·class="ansi-blue-fg">,</span>·request<span·class="ansi-blue-fg">=</span>request<span·class="ansi-blue-fg">)</span>
13431	<span·class="ansi-green-intense-fg·ansi-bold">····511</span>·	13431	<span·class="ansi-green-intense-fg·ansi-bold">····511</span>·
13432	<span·class="ansi-green-intense-fg·ansi-bold">····512</span>·············<span·class="ansi-green-fg">if</span>·isinstance<span·class="ansi-blue-fg">(</span>e<span·class="ansi-blue-fg">.</span>reason<span·class="ansi-blue-fg">,</span>·_SSLError<span·class="ansi-blue-fg">)</span><span·class="ansi-blue-fg">:</span>	13432	<span·class="ansi-green-intense-fg·ansi-bold">····512</span>·············<span·class="ansi-green-fg">if</span>·isinstance<span·class="ansi-blue-fg">(</span>e<span·class="ansi-blue-fg">.</span>reason<span·class="ansi-blue-fg">,</span>·_SSLError<span·class="ansi-blue-fg">)</span><span·class="ansi-blue-fg">:</span>

13433	<span·class="ansi-red-fg">ProxyError</span>:·HTTPConnectionPool(host='127.0.0.1',·port=9):·Max·retries·exceeded·with·url:·http://www.ssfpack.com/files/DK-data.zip·(Caused·by·ProxyError('Cannot·connect·to·proxy.',·NewConnectionError('<urllib3.connection.HTTPConnection·object·at·0xac8~~c146~~c>:·Failed·to·establish·a·new·connection:·[Errno·111]·Connection·refused')))</pre>	13433	<span·class="ansi-red-fg">ProxyError</span>:·HTTPConnectionPool(host='127.0.0.1',·port=9):·Max·retries·exceeded·with·url:·http://www.ssfpack.com/files/DK-data.zip·(Caused·by·ProxyError('Cannot·connect·to·proxy.',·NewConnectionError('<urllib3.connection.HTTPConnection·object·at·0xec82c50c>:·Failed·to·establish·a·new·connection:·[Errno·111]·Connection·refused')))</pre>
13434	</div>	13434	</div>
13435	</div>	13435	</div>

13436	</div>	13436	</div>
13437	</div>	13437	</div>

13438	</div>	13438	</div>


Offset 14412, 28 lines modified		Offset 14412, 14 lines modified


14412	<div·class="output_area">	14412	<div·class="output_area">

14413	····<div·class="prompt"></div>	14413	····<div·class="prompt"></div>


14414	<div·class="output_subarea·output_stream·output_stderr·output_text">
14415	<pre>/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:646:·FutureWarning:·Conversion·of·the·second·argument·of·issubdtype·from·`float`·to·`np.floating`·is·deprecated.·In·future,·it·will·be·treated·as·`np.float64·==·np.dtype(float).type`.
14416	··if·issubdtype(paramsdtype,·float):
14417	/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:650:·FutureWarning:·Conversion·of·the·second·argument·of·issubdtype·from·`complex`·to·`np.complexfloating`·is·deprecated.·In·future,·it·will·be·treated·as·`np.complex128·==·np.dtype(complex).type`.
14418	··elif·issubdtype(paramsdtype,·complex):
14419	</pre>
14420	</div>
14421	</div>

14422	<div·class="output_area">

14423	····<div·class="prompt"></div>


14424	<div·class="output_subarea·output_stream·output_stdout·output_text">	14414	<div·class="output_subarea·output_stream·output_stdout·output_text">
14425	<pre>············AC···········Q······Prob(>Q)	14415	<pre>············AC···········Q······Prob(>Q)
14426	lag·····································	14416	lag·····································
14427	1.0···0.254921···32.687661··1.082221e-08	14417	1.0···0.254921···32.687661··1.082221e-08
14428	2.0··-0.172416···47.670720··4.450765e-11	14418	2.0··-0.172416···47.670720··4.450765e-11
14429	3.0··-0.420945··137.159375··1.548479e-29	14419	3.0··-0.420945··137.159375··1.548479e-29
14430	4.0··-0.046875··138.271282··6.617765e-29	14420	4.0··-0.046875··138.271282··6.617765e-29
Offset 14479, 15 lines modified		Offset 14465, 19 lines modified

14479	<div·class="output_area">	14465	<div·class="output_area">

14480	····<div·class="prompt"></div>	14466	····<div·class="prompt"></div>


14481	<div·class="output_subarea·output_stream·output_stderr·output_text">	14467	<div·class="output_subarea·output_stream·output_stderr·output_text">
14482	<pre>/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:~~577~~:·FutureWarning:·Conversion·of·the·second·argument·of·issubdtype·from·`float`·to·`np.floating`·is·deprecated.·In·future,·it·will·be·treated·as·`np.float64·==·np.dtype(float).type`.	14468	<pre>/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:646:·FutureWarning:·Conversion·of·the·second·argument·of·issubdtype·from·`float`·to·`np.floating`·is·deprecated.·In·future,·it·will·be·treated·as·`np.float64·==·np.dtype(float).type`.
		14469	··if·issubdtype(paramsdtype,·float):
		14470	/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:650:·FutureWarning:·Conversion·of·the·second·argument·of·issubdtype·from·`complex`·to·`np.complexfloating`·is·deprecated.·In·future,·it·will·be·treated·as·`np.complex128·==·np.dtype(complex).type`.
		14471	··elif·issubdtype(paramsdtype,·complex):
		14472	/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:577:·FutureWarning:·Conversion·of·the·second·argument·of·issubdtype·from·`float`·to·`np.floating`·is·deprecated.·In·future,·it·will·be·treated·as·`np.float64·==·np.dtype(float).type`.
14483	··if·issubdtype(paramsdtype,·float):	14473	··if·issubdtype(paramsdtype,·float):
14484	</pre>	14474	</pre>
14485	</div>	14475	</div>
14486	</div>	14476	</div>

14487	</div>	14477	</div>
14488	</div>	14478	</div>


Offset 13358, 16 lines modified		Offset 13358, 16 lines modified

13358	<div·class="output_subarea·output_stream·output_stdout·output_text">	13358	<div·class="output_subarea·output_stream·output_stdout·output_text">
13359	<pre>······························ARMA·Model·Results······························	13359	<pre>······························ARMA·Model·Results······························
13360	==============================================================================	13360	==============================================================================
13361	Dep.·Variable:······················y···No.·Observations:··················250	13361	Dep.·Variable:······················y···No.·Observations:··················250
13362	Model:·····················ARMA(2,·2)···Log·Likelihood················-353.445	13362	Model:·····················ARMA(2,·2)···Log·Likelihood················-353.445
13363	Method:·······················css-mle···S.D.·of·innovations··············0.990	13363	Method:·······················css-mle···S.D.·of·innovations··············0.990
13364	Date:················~~Fri~~,·06·Mar·2020···AIC····························716.891	13364	Date:················Sat,·10·Apr·2021···AIC····························716.891
13365	Time:························15:39:45···BIC····························734.498	13365	Time:························01:00:05···BIC····························734.498
13366	Sample:····················01-31-1980···HQIC···························723.977	13366	Sample:····················01-31-1980···HQIC···························723.977
13367	·························-·10-31-2000·········································	13367	·························-·10-31-2000·········································
13368	==============================================================================	13368	==============================================================================
13369	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]	13369	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]
13370	------------------------------------------------------------------------------	13370	------------------------------------------------------------------------------
13371	ar.L1.y········0.7904······0.134······5.878······0.000·······0.527·······1.054	13371	ar.L1.y········0.7904······0.134······5.878······0.000·······0.527·······1.054
13372	ar.L2.y·······-0.2314······0.113·····-2.044······0.042······-0.453······-0.009	13372	ar.L2.y·······-0.2314······0.113·····-2.044······0.042······-0.453······-0.009


Offset 13320, 32 lines modified		Offset 13320, 14 lines modified
13320	<span·class="n">pandas_ar_res</span>·<span·class="o">=</span>·<span·class="n">ar_model</span><span·class="o">.</span><span·class="n">fit</span><span·class="p">(</span><span·class="n">maxlag</span><span·class="o">=</span><span·class="mi">9</span><span·class="p">,</span>·<span·class="n">method</span><span·class="o">=</span><span·class="s1">'mle'</span><span·class="p">,</span>·<span·class="n">disp</span><span·class="o">=-</span><span·class="mi">1</span><span·class="p">)</span>	13320	<span·class="n">pandas_ar_res</span>·<span·class="o">=</span>·<span·class="n">ar_model</span><span·class="o">.</span><span·class="n">fit</span><span·class="p">(</span><span·class="n">maxlag</span><span·class="o">=</span><span·class="mi">9</span><span·class="p">,</span>·<span·class="n">method</span><span·class="o">=</span><span·class="s1">'mle'</span><span·class="p">,</span>·<span·class="n">disp</span><span·class="o">=-</span><span·class="mi">1</span><span·class="p">)</span>
13321	</pre></div>	13321	</pre></div>

13322	····</div>	13322	····</div>
13323	</div>	13323	</div>
13324	</div>	13324	</div>

13325	<div·class="output_wrapper">
13326	<div·class="output">


13327	<div·class="output_area">

13328	····<div·class="prompt"></div>


13329	<div·class="output_subarea·output_stream·output_stdout·output_text">
13330	<pre>The·history·saving·thread·hit·an·unexpected·error·(OperationalError('database·is·locked')).History·will·not·be·written·to·the·database.
13331	</pre>
13332	</div>
13333	</div>

13334	</div>
13335	</div>

13336	</div>	13325	</div>
13337	<div·class="cell·border-box-sizing·text_cell·rendered"><div·class="prompt·input_prompt">	13326	<div·class="cell·border-box-sizing·text_cell·rendered"><div·class="prompt·input_prompt">
13338	</div><div·class="inner_cell">	13327	</div><div·class="inner_cell">
13339	<div·class="text_cell_render·border-box-sizing·rendered_html">	13328	<div·class="text_cell_render·border-box-sizing·rendered_html">
13340	<p>Out-of-sample·prediction</p>	13329	<p>Out-of-sample·prediction</p>

13341	</div>	13330	</div>


Offset 13315, 16 lines modified		Offset 13315, 16 lines modified

13315	<div·class="output_subarea·output_stream·output_stdout·output_text">	13315	<div·class="output_subarea·output_stream·output_stdout·output_text">
13316	<pre>····························WLS·Regression·Results····························	13316	<pre>····························WLS·Regression·Results····························
13317	==============================================================================	13317	==============================================================================
13318	Dep.·Variable:······················y···R-squared:·······················0.910	13318	Dep.·Variable:······················y···R-squared:·······················0.910
13319	Model:····························WLS···Adj.·R-squared:··················0.909	13319	Model:····························WLS···Adj.·R-squared:··················0.909
13320	Method:·················Least·Squares···F-statistic:·····················487.9	13320	Method:·················Least·Squares···F-statistic:·····················487.9
13321	Date:················~~Fri~~,·06·Mar·2020···Prob·(F-statistic):···········8.52e-27	13321	Date:················Sat,·10·Apr·2021···Prob·(F-statistic):···········8.52e-27
13322	Time:························15:40:14···Log-Likelihood:················-57.048	13322	Time:························01:00:05···Log-Likelihood:················-57.048
13323	No.·Observations:··················50···AIC:·····························118.1	13323	No.·Observations:··················50···AIC:·····························118.1
13324	Df·Residuals:······················48···BIC:·····························121.9	13324	Df·Residuals:······················48···BIC:·····························121.9
13325	Df·Model:···························1·········································	13325	Df·Model:···························1·········································
13326	Covariance·Type:············nonrobust·········································	13326	Covariance·Type:············nonrobust·········································
13327	==============================================================================	13327	==============================================================================
13328	·················coef····std·err··········t······P>\|t\|······[0.025······0.975]	13328	·················coef····std·err··········t······P>\|t\|······[0.025······0.975]
13329	------------------------------------------------------------------------------	13329	------------------------------------------------------------------------------
Offset 13573, 16 lines modified		Offset 13573, 16 lines modified

13573	<div·class="output_subarea·output_stream·output_stdout·output_text">	13573	<div·class="output_subarea·output_stream·output_stdout·output_text">
13574	<pre>····························WLS·Regression·Results····························	13574	<pre>····························WLS·Regression·Results····························
13575	==============================================================================	13575	==============================================================================
13576	Dep.·Variable:······················y···R-squared:·······················0.914	13576	Dep.·Variable:······················y···R-squared:·······················0.914
13577	Model:····························WLS···Adj.·R-squared:··················0.912	13577	Model:····························WLS···Adj.·R-squared:··················0.912
13578	Method:·················Least·Squares···F-statistic:·····················507.1	13578	Method:·················Least·Squares···F-statistic:·····················507.1
13579	Date:················~~Fri~~,·06·Mar·2020···Prob·(F-statistic):···········3.65e-27	13579	Date:················Sat,·10·Apr·2021···Prob·(F-statistic):···········3.65e-27
13580	Time:························15:40:16···Log-Likelihood:················-55.777	13580	Time:························01:00:06···Log-Likelihood:················-55.777
13581	No.·Observations:··················50···AIC:·····························115.6	13581	No.·Observations:··················50···AIC:·····························115.6
13582	Df·Residuals:······················48···BIC:·····························119.4	13582	Df·Residuals:······················48···BIC:·····························119.4
13583	Df·Model:···························1·········································	13583	Df·Model:···························1·········································
13584	Covariance·Type:············nonrobust·········································	13584	Covariance·Type:············nonrobust·········································
13585	==============================================================================	13585	==============================================================================
13586	·················coef····std·err··········t······P>\|t\|······[0.025······0.975]	13586	·················coef····std·err··········t······P>\|t\|······[0.025······0.975]
13587	------------------------------------------------------------------------------	13587	------------------------------------------------------------------------------


Offset 105, 16 lines modified		Offset 105, 16 lines modified
105	<span·class="go">===================================================================================</span>	105	<span·class="go">===================================================================================</span>
106	<span·class="go">Dep.·Variable:···························y···No.·Observations:··················236</span>	106	<span·class="go">Dep.·Variable:···························y···No.·Observations:··················236</span>
107	<span·class="go">Model:·································GEE···No.·clusters:·······················59</span>	107	<span·class="go">Model:·································GEE···No.·clusters:·······················59</span>
108	<span·class="go">Method:························Generalized···Min.·cluster·size:···················4</span>	108	<span·class="go">Method:························Generalized···Min.·cluster·size:···················4</span>
109	<span·class="go">······················Estimating·Equations···Max.·cluster·size:···················4</span>	109	<span·class="go">······················Estimating·Equations···Max.·cluster·size:···················4</span>
110	<span·class="go">Family:····························Poisson···Mean·cluster·size:·················4.0</span>	110	<span·class="go">Family:····························Poisson···Mean·cluster·size:·················4.0</span>
111	<span·class="go">Dependence·structure:·········Exchangeable···Num.·iterations:····················51</span>	111	<span·class="go">Dependence·structure:·········Exchangeable···Num.·iterations:····················51</span>
112	<span·class="go">Date:·····················~~Fri~~,·06·Mar·2020···Scale:···························1.000</span>	112	<span·class="go">Date:·····················Sat,·10·Apr·2021···Scale:···························1.000</span>
113	<span·class="go">Covariance·type:····················robust···Time:·························15:42:30</span>	113	<span·class="go">Covariance·type:····················robust···Time:·························01:00:45</span>
114	<span·class="go">====================================================================================</span>	114	<span·class="go">====================================================================================</span>
115	<span·class="go">·······················coef····std·err··········z······P>\|z\|······[0.025······0.975]</span>	115	<span·class="go">·······················coef····std·err··········z······P>\|z\|······[0.025······0.975]</span>
116	<span·class="gt">------------------------------------------------------------------------------------</span>	116	<span·class="gt">------------------------------------------------------------------------------------</span>
117	<span·class="n">Intercept</span>············<span·class="mf">0.5730</span>······<span·class="mf">0.361</span>······<span·class="mf">1.589</span>······<span·class="mf">0.112</span>······<span·class="o">-</span><span·class="mf">0.134</span>·······<span·class="mf">1.280</span>	117	<span·class="n">Intercept</span>············<span·class="mf">0.5730</span>······<span·class="mf">0.361</span>······<span·class="mf">1.589</span>······<span·class="mf">0.112</span>······<span·class="o">-</span><span·class="mf">0.134</span>·······<span·class="mf">1.280</span>
118	<span·class="n">trt</span><span·class="p">[</span><span·class="n">T</span><span·class="o">.</span><span·class="n">progabide</span><span·class="p">]</span>····<span·class="o">-</span><span·class="mf">0.1519</span>······<span·class="mf">0.171</span>·····<span·class="o">-</span><span·class="mf">0.888</span>······<span·class="mf">0.375</span>······<span·class="o">-</span><span·class="mf">0.487</span>·······<span·class="mf">0.183</span>	118	<span·class="n">trt</span><span·class="p">[</span><span·class="n">T</span><span·class="o">.</span><span·class="n">progabide</span><span·class="p">]</span>····<span·class="o">-</span><span·class="mf">0.1519</span>······<span·class="mf">0.171</span>·····<span·class="o">-</span><span·class="mf">0.888</span>······<span·class="mf">0.375</span>······<span·class="o">-</span><span·class="mf">0.487</span>·······<span·class="mf">0.183</span>
119	<span·class="n">age</span>··················<span·class="mf">0.0223</span>······<span·class="mf">0.011</span>······<span·class="mf">1.960</span>······<span·class="mf">0.050</span>····<span·class="mf">2.11e-06</span>·······<span·class="mf">0.045</span>	119	<span·class="n">age</span>··················<span·class="mf">0.0223</span>······<span·class="mf">0.011</span>······<span·class="mf">1.960</span>······<span·class="mf">0.050</span>····<span·class="mf">2.11e-06</span>·······<span·class="mf">0.045</span>
120	<span·class="n">base</span>·················<span·class="mf">0.0226</span>······<span·class="mf">0.001</span>·····<span·class="mf">18.451</span>······<span·class="mf">0.000</span>·······<span·class="mf">0.020</span>·······<span·class="mf">0.025</span>	120	<span·class="n">base</span>·················<span·class="mf">0.0226</span>······<span·class="mf">0.001</span>·····<span·class="mf">18.451</span>······<span·class="mf">0.000</span>·······<span·class="mf">0.020</span>·······<span·class="mf">0.025</span>


Offset 95, 16 lines modified		Offset 95, 16 lines modified
95	<span·class="go">··················Generalized·Linear·Model·Regression·Results···················</span>	95	<span·class="go">··················Generalized·Linear·Model·Regression·Results···················</span>
96	<span·class="go">================================================================================</span>	96	<span·class="go">================================================================================</span>
97	<span·class="go">Dep.·Variable:······················y···No.·Observations:·····················32</span>	97	<span·class="go">Dep.·Variable:······················y···No.·Observations:·····················32</span>
98	<span·class="go">Model:····························GLM···Df·Residuals:·························24</span>	98	<span·class="go">Model:····························GLM···Df·Residuals:·························24</span>
99	<span·class="go">Model·Family:···················Gamma···Df·Model:······························7</span>	99	<span·class="go">Model·Family:···················Gamma···Df·Model:······························7</span>
100	<span·class="go">Link·Function:··········inverse_power···Scale:··············0.003584283173493395</span>	100	<span·class="go">Link·Function:··········inverse_power···Scale:··············0.003584283173493395</span>
101	<span·class="go">Method:··························IRLS···Log-Likelihood:··················-83.017</span>	101	<span·class="go">Method:··························IRLS···Log-Likelihood:··················-83.017</span>
102	<span·class="go">Date:················~~Fri~~,·06·Mar·2020···Deviance:·······················0.087389</span>	102	<span·class="go">Date:················Sat,·10·Apr·2021···Deviance:·······················0.087389</span>
103	<span·class="go">Time:························15:47:39···Pearson·chi2:·····················0.0860</span>	103	<span·class="go">Time:························01:02:19···Pearson·chi2:·····················0.0860</span>
104	<span·class="go">No.·Iterations:·····················4···········································</span>	104	<span·class="go">No.·Iterations:·····················4···········································</span>
105	<span·class="go">==============================================================================</span>	105	<span·class="go">==============================================================================</span>
106	<span·class="go">·················coef····std·err··········z······P>\|z\|······[0.025······0.975]</span>	106	<span·class="go">·················coef····std·err··········z······P>\|z\|······[0.025······0.975]</span>
107	<span·class="gt">------------------------------------------------------------------------------</span>	107	<span·class="gt">------------------------------------------------------------------------------</span>
108	<span·class="n">const</span>·········<span·class="o">-</span><span·class="mf">0.0178</span>······<span·class="mf">0.011</span>·····<span·class="o">-</span><span·class="mf">1.548</span>······<span·class="mf">0.122</span>······<span·class="o">-</span><span·class="mf">0.040</span>·······<span·class="mf">0.005</span>	108	<span·class="n">const</span>·········<span·class="o">-</span><span·class="mf">0.0178</span>······<span·class="mf">0.011</span>·····<span·class="o">-</span><span·class="mf">1.548</span>······<span·class="mf">0.122</span>······<span·class="o">-</span><span·class="mf">0.040</span>·······<span·class="mf">0.005</span>
109	<span·class="n">x1</span>··········<span·class="mf">4.962e-05</span>···<span·class="mf">1.62e-05</span>······<span·class="mf">3.060</span>······<span·class="mf">0.002</span>····<span·class="mf">1.78e-05</span>····<span·class="mf">8.14e-05</span>	109	<span·class="n">x1</span>··········<span·class="mf">4.962e-05</span>···<span·class="mf">1.62e-05</span>······<span·class="mf">3.060</span>······<span·class="mf">0.002</span>····<span·class="mf">1.78e-05</span>····<span·class="mf">8.14e-05</span>
110	<span·class="n">x2</span>·············<span·class="mf">0.0020</span>······<span·class="mf">0.001</span>······<span·class="mf">3.824</span>······<span·class="mf">0.000</span>·······<span·class="mf">0.001</span>·······<span·class="mf">0.003</span>	110	<span·class="n">x2</span>·············<span·class="mf">0.0020</span>······<span·class="mf">0.001</span>······<span·class="mf">3.824</span>······<span·class="mf">0.000</span>·······<span·class="mf">0.001</span>·······<span·class="mf">0.003</span>


Offset 99, 16 lines modified		Offset 99, 16 lines modified
99	<span·class="go">#·Inspect·the·results</span>	99	<span·class="go">#·Inspect·the·results</span>
100	<span·class="gp">In·[6]:·</span><span·class="k">print</span><span·class="p">(</span><span·class="n">results</span><span·class="o">.</span><span·class="n">summary</span><span·class="p">())</span>	100	<span·class="gp">In·[6]:·</span><span·class="k">print</span><span·class="p">(</span><span·class="n">results</span><span·class="o">.</span><span·class="n">summary</span><span·class="p">())</span>
101	<span·class="go">····························OLS·Regression·Results····························</span>	101	<span·class="go">····························OLS·Regression·Results····························</span>
102	<span·class="go">==============================================================================</span>	102	<span·class="go">==============================================================================</span>
103	<span·class="go">Dep.·Variable:················Lottery···R-squared:·······················0.348</span>	103	<span·class="go">Dep.·Variable:················Lottery···R-squared:·······················0.348</span>
104	<span·class="go">Model:····························OLS···Adj.·R-squared:··················0.333</span>	104	<span·class="go">Model:····························OLS···Adj.·R-squared:··················0.333</span>
105	<span·class="go">Method:·················Least·Squares···F-statistic:·····················22.20</span>	105	<span·class="go">Method:·················Least·Squares···F-statistic:·····················22.20</span>
106	<span·class="go">Date:················~~Fri~~,·06·Mar·2020···Prob·(F-statistic):···········1.90e-08</span>	106	<span·class="go">Date:················Sat,·10·Apr·2021···Prob·(F-statistic):···········1.90e-08</span>
107	<span·class="go">Time:························15:47:41···Log-Likelihood:················-379.82</span>	107	<span·class="go">Time:························01:02:19···Log-Likelihood:················-379.82</span>
108	<span·class="go">No.·Observations:··················86···AIC:·····························765.6</span>	108	<span·class="go">No.·Observations:··················86···AIC:·····························765.6</span>
109	<span·class="go">Df·Residuals:······················83···BIC:·····························773.0</span>	109	<span·class="go">Df·Residuals:······················83···BIC:·····························773.0</span>
110	<span·class="go">Df·Model:···························2·········································</span>	110	<span·class="go">Df·Model:···························2·········································</span>
111	<span·class="go">Covariance·Type:············nonrobust·········································</span>	111	<span·class="go">Covariance·Type:············nonrobust·········································</span>
112	<span·class="go">===================================================================================</span>	112	<span·class="go">===================================================================================</span>
113	<span·class="go">······················coef····std·err··········t······P>\|t\|······[0.025······0.975]</span>	113	<span·class="go">······················coef····std·err··········t······P>\|t\|······[0.025······0.975]</span>
114	<span·class="gt">-----------------------------------------------------------------------------------</span>	114	<span·class="gt">-----------------------------------------------------------------------------------</span>
Offset 150, 16 lines modified		Offset 150, 16 lines modified
150	<span·class="go">#·Inspect·the·results</span>	150	<span·class="go">#·Inspect·the·results</span>
151	<span·class="gp">In·[16]:·</span><span·class="k">print</span><span·class="p">(</span><span·class="n">results</span><span·class="o">.</span><span·class="n">summary</span><span·class="p">())</span>	151	<span·class="gp">In·[16]:·</span><span·class="k">print</span><span·class="p">(</span><span·class="n">results</span><span·class="o">.</span><span·class="n">summary</span><span·class="p">())</span>
152	<span·class="go">····························OLS·Regression·Results····························</span>	152	<span·class="go">····························OLS·Regression·Results····························</span>
153	<span·class="go">==============================================================================</span>	153	<span·class="go">==============================================================================</span>
154	<span·class="go">Dep.·Variable:······················y···R-squared:·······················0.260</span>	154	<span·class="go">Dep.·Variable:······················y···R-squared:·······················0.260</span>
155	<span·class="go">Model:····························OLS···Adj.·R-squared:··················0.245</span>	155	<span·class="go">Model:····························OLS···Adj.·R-squared:··················0.245</span>
156	<span·class="go">Method:·················Least·Squares···F-statistic:·····················17.06</span>	156	<span·class="go">Method:·················Least·Squares···F-statistic:·····················17.06</span>
157	<span·class="go">Date:················~~Fri~~,·06·Mar·2020···Prob·(F-statistic):···········4.49e-07</span>	157	<span·class="go">Date:················Sat,·10·Apr·2021···Prob·(F-statistic):···········4.49e-07</span>
158	<span·class="go">Time:························15:47:41···Log-Likelihood:················-23.039</span>	158	<span·class="go">Time:························01:02:19···Log-Likelihood:················-23.039</span>
159	<span·class="go">No.·Observations:·················100···AIC:·····························52.08</span>	159	<span·class="go">No.·Observations:·················100···AIC:·····························52.08</span>
160	<span·class="go">Df·Residuals:······················97···BIC:·····························59.89</span>	160	<span·class="go">Df·Residuals:······················97···BIC:·····························59.89</span>
161	<span·class="go">Df·Model:···························2·········································</span>	161	<span·class="go">Df·Model:···························2·········································</span>
162	<span·class="go">Covariance·Type:············nonrobust·········································</span>	162	<span·class="go">Covariance·Type:············nonrobust·········································</span>
163	<span·class="go">==============================================================================</span>	163	<span·class="go">==============================================================================</span>
164	<span·class="go">·················coef····std·err··········t······P>\|t\|······[0.025······0.975]</span>	164	<span·class="go">·················coef····std·err··········t······P>\|t\|······[0.025······0.975]</span>
165	<span·class="gt">------------------------------------------------------------------------------</span>	165	<span·class="gt">------------------------------------------------------------------------------</span>


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98	<span·class="gp">In·[7]:·</span><span·class="k">print</span><span·class="p">(</span><span·class="n">res</span><span·class="o">.</span><span·class="n">summary</span><span·class="p">())</span>	98	<span·class="gp">In·[7]:·</span><span·class="k">print</span><span·class="p">(</span><span·class="n">res</span><span·class="o">.</span><span·class="n">summary</span><span·class="p">())</span>
99	<span·class="go">····························OLS·Regression·Results····························</span>	99	<span·class="go">····························OLS·Regression·Results····························</span>
100	<span·class="go">==============================================================================</span>	100	<span·class="go">==============================================================================</span>
101	<span·class="go">Dep.·Variable:······················y···R-squared:·······················0.416</span>	101	<span·class="go">Dep.·Variable:······················y···R-squared:·······················0.416</span>
102	<span·class="go">Model:····························OLS···Adj.·R-squared:··················0.353</span>	102	<span·class="go">Model:····························OLS···Adj.·R-squared:··················0.353</span>
103	<span·class="go">Method:·················Least·Squares···F-statistic:·····················6.646</span>	103	<span·class="go">Method:·················Least·Squares···F-statistic:·····················6.646</span>
104	<span·class="go">Date:················~~Fri~~,·06·Mar·2020···Prob·(F-statistic):············0.00157</span>	104	<span·class="go">Date:················Sat,·10·Apr·2021···Prob·(F-statistic):············0.00157</span>
105	<span·class="go">Time:························15:47:43···Log-Likelihood:················-12.978</span>	105	<span·class="go">Time:························01:02:20···Log-Likelihood:················-12.978</span>
106	<span·class="go">No.·Observations:··················32···AIC:·····························33.96</span>	106	<span·class="go">No.·Observations:··················32···AIC:·····························33.96</span>
107	<span·class="go">Df·Residuals:······················28···BIC:·····························39.82</span>	107	<span·class="go">Df·Residuals:······················28···BIC:·····························39.82</span>
108	<span·class="go">Df·Model:···························3·········································</span>	108	<span·class="go">Df·Model:···························3·········································</span>
109	<span·class="go">Covariance·Type:············nonrobust·········································</span>	109	<span·class="go">Covariance·Type:············nonrobust·········································</span>
110	<span·class="go">==============================================================================</span>	110	<span·class="go">==============================================================================</span>
111	<span·class="go">·················coef····std·err··········t······P>\|t\|······[0.025······0.975]</span>	111	<span·class="go">·················coef····std·err··········t······P>\|t\|······[0.025······0.975]</span>
112	<span·class="gt">------------------------------------------------------------------------------</span>	112	<span·class="gt">------------------------------------------------------------------------------</span>


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00002910:·2020·2020·2020·2020·2020·2020·2020·2020··················		00002910:·2020·2020·2020·2020·2020·2020·2020·2020··················
00002920:·2020·2020·5641·523c·2f73·7061·6e3e·0a3c······VAR</span>.<		00002920:·2020·2020·5641·523c·2f73·7061·6e3e·0a3c······VAR</span>.<
00002930:·7370·616e·2063·6c61·7373·3d22·676f·223e··span·class="go">		00002930:·7370·616e·2063·6c61·7373·3d22·676f·223e··span·class="go">
00002940:·4d65·7468·6f64·3a20·2020·2020·2020·2020··Method:·········		00002940:·4d65·7468·6f64·3a20·2020·2020·2020·2020··Method:·········
00002950:·2020·2020·2020·2020·2020·2020·2020·204f·················O		00002950:·2020·2020·2020·2020·2020·2020·2020·204f·················O
00002960:·4c53·3c2f·7370·616e·3e0a·3c73·7061·6e20··LS</span>.<span·		00002960:·4c53·3c2f·7370·616e·3e0a·3c73·7061·6e20··LS</span>.<span·
00002970:·636c·6173·733d·2267·6f22·3e44·6174·653a··class="go">Date:		00002970:·636c·6173·733d·2267·6f22·3e44·6174·653a··class="go">Date:
00002980:·2020·2020·2020·2020·2020·2046·~~7269~~·2c20·············~~Fri~~,·		00002980:·2020·2020·2020·2020·2020·2053·6174·2c20·············Sat,·
00002990:·3036·2c20·4~~d61~~·722c·2032·3032·303c·2f73··06,·Mar,·2020</s		00002990:·3130·2c20·4170·722c·2032·3032·313c·2f73··10,·Apr,·2021</s
000029a0:·7061·6e3e·0a3c·7370·616e·2063·6c61·7373··pan>.<span·class		000029a0:·7061·6e3e·0a3c·7370·616e·2063·6c61·7373··pan>.<span·class
000029b0:·3d22·676f·223e·5469·6d65·3a20·2020·2020··="go">Time:·····		000029b0:·3d22·676f·223e·5469·6d65·3a20·2020·2020··="go">Time:·····
000029c0:·2020·2020·2020·2020·2020·2020·2020·2020··················		000029c0:·2020·2020·2020·2020·2020·2020·2020·2020··················
000029d0:·3135·3a34·373a·3534·3c2f·7370·616e·3e0a··15:47:54</span>.		000029d0:·3031·3a30·323a·3234·3c2f·7370·616e·3e0a··01:02:24</span>.
000029e0:·3c73·7061·6e20·636c·6173·733d·2267·7422··<span·class="gt"		000029e0:·3c73·7061·6e20·636c·6173·733d·2267·7422··<span·class="gt"
000029f0:·3e2d·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d··>---------------		000029f0:·3e2d·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d··>---------------
00002a00:·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d··----------------		00002a00:·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d··----------------
00002a10:·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d··----------------		00002a10:·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d··----------------
00002a20:·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d··----------------		00002a20:·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d··----------------
00002a30:·2d2d·2d2d·2d3c·2f73·7061·6e3e·0a3c·7370··-----</span>.<sp		00002a30:·2d2d·2d2d·2d3c·2f73·7061·6e3e·0a3c·7370··-----</span>.<sp
00002a40:·616e·2063·6c61·7373·3d22·6e22·3e4e·6f3c··an·class="n">No<		00002a40:·616e·2063·6c61·7373·3d22·6e22·3e4e·6f3c··an·class="n">No<


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947	drwxr-xr-x···0·root·········(0)·root·········(0)········0·2019-03-02·14:59:35.000000·./usr/share/doc/python-statsmodels/examples/	947	drwxr-xr-x···0·root·········(0)·root·········(0)········0·2019-03-02·14:59:35.000000·./usr/share/doc/python-statsmodels/examples/
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