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mdekstrand/OddHoroscope.ipynb

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Replication of odd response IDs predicting horoscopes.
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OddHoroscope.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# P-Hacking Hypotheses\n",
"\n",
"This notebook replicates the analysis from [P-hacked Hypotheses Are Deceivingly Robust](http://datacolada.org/48)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup"
]
},
{
"cell_type": "code",
"execution_count": 190,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import scipy.special as sf\n",
"import statsmodels.api as sm\n",
"import seaborn as sb\n",
"from sklearn.metrics import roc_curve, auc\n",
"import pystan"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load Data"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"gss = pd.read_stata('GSS7216_R2.DTA',\n",
" columns=['id', 'year', 'astrolgy', 'colsci', 'age', 'sex', 'god', 'happy'])"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"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>id</th>\n",
" <th>year</th>\n",
" <th>astrolgy</th>\n",
" <th>colsci</th>\n",
" <th>age</th>\n",
" <th>sex</th>\n",
" <th>god</th>\n",
" <th>happy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>53043</th>\n",
" <td>1</td>\n",
" <td>2010</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>31</td>\n",
" <td>male</td>\n",
" <td>know god exists</td>\n",
" <td>pretty happy</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53044</th>\n",
" <td>2</td>\n",
" <td>2010</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>23</td>\n",
" <td>female</td>\n",
" <td>dont believe</td>\n",
" <td>not too happy</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53045</th>\n",
" <td>3</td>\n",
" <td>2010</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>71</td>\n",
" <td>female</td>\n",
" <td>know god exists</td>\n",
" <td>not too happy</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53046</th>\n",
" <td>4</td>\n",
" <td>2010</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>82</td>\n",
" <td>female</td>\n",
" <td>some higher power</td>\n",
" <td>not too happy</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53047</th>\n",
" <td>5</td>\n",
" <td>2010</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>78</td>\n",
" <td>female</td>\n",
" <td>dont believe</td>\n",
" <td>very happy</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id year astrolgy colsci age sex god happy\n",
"53043 1 2010 NaN NaN 31 male know god exists pretty happy\n",
"53044 2 2010 NaN NaN 23 female dont believe not too happy\n",
"53045 3 2010 NaN NaN 71 female know god exists not too happy\n",
"53046 4 2010 NaN NaN 82 female some higher power not too happy\n",
"53047 5 2010 no no 78 female dont believe very happy"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gs10 = gss[gss.year == 2010]\n",
"gs10.head()"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 689\n",
"unique 2\n",
"top yes\n",
"freq 367\n",
"Name: astrolgy, dtype: object"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gs10.astrolgy.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Massage Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Recode the astrology data to a Horoscope logical"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 689\n",
"unique 2\n",
"top True\n",
"freq 367\n",
"Name: horoscope, dtype: object"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"astro = gs10[gs10.astrolgy.notna()].copy()\n",
"astro['horoscope'] = astro.astrolgy == 'yes'\n",
"astro.horoscope.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Code the odd ID"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
"astro['odd'] = astro.id % 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Code the college science question"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"astro['science'] = (astro.colsci == 'yes').astype(np.int32)"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"astro['female'] = (astro.sex == 'female').astype(np.int32)"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [],
"source": [
"astro.age.cat.rename_categories(new_categories={'89 or older': 89.0}, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"outputs": [],
"source": [
"astro['age'] = astro.age.astype(np.float_)"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"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>id</th>\n",
" <th>year</th>\n",
" <th>astrolgy</th>\n",
" <th>colsci</th>\n",
" <th>age</th>\n",
" <th>sex</th>\n",
" <th>god</th>\n",
" <th>happy</th>\n",
" <th>horoscope</th>\n",
" <th>odd</th>\n",
" <th>science</th>\n",
" <th>female</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>53047</th>\n",
" <td>5</td>\n",
" <td>2010</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>78.0</td>\n",
" <td>female</td>\n",
" <td>dont believe</td>\n",
" <td>very happy</td>\n",
" <td>False</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53051</th>\n",
" <td>9</td>\n",
" <td>2010</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>31.0</td>\n",
" <td>female</td>\n",
" <td>know god exists</td>\n",
" <td>very happy</td>\n",
" <td>True</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53052</th>\n",
" <td>10</td>\n",
" <td>2010</td>\n",
" <td>no</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>female</td>\n",
" <td>know god exists</td>\n",
" <td>very happy</td>\n",
" <td>False</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53058</th>\n",
" <td>16</td>\n",
" <td>2010</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>28.0</td>\n",
" <td>female</td>\n",
" <td>know god exists</td>\n",
" <td>very happy</td>\n",
" <td>False</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53064</th>\n",
" <td>22</td>\n",
" <td>2010</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>56.0</td>\n",
" <td>male</td>\n",
" <td>know god exists</td>\n",
" <td>very happy</td>\n",
" <td>True</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id year astrolgy colsci age sex god happy \\\n",
"53047 5 2010 no no 78.0 female dont believe very happy \n",
"53051 9 2010 yes no 31.0 female know god exists very happy \n",
"53052 10 2010 no NaN NaN female know god exists very happy \n",
"53058 16 2010 no no 28.0 female know god exists very happy \n",
"53064 22 2010 yes no 56.0 male know god exists very happy \n",
"\n",
" horoscope odd science female \n",
"53047 False 1 0 1 \n",
"53051 True 1 0 1 \n",
"53052 False 0 0 1 \n",
"53058 False 0 0 1 \n",
"53064 True 0 0 0 "
]
},
"execution_count": 103,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"astro.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Basic Horoscope Regression"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The article uses OLS, so we will start with that."
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {},
"outputs": [],
"source": [
"response = astro.horoscope\n",
"exp_raw = astro.odd\n",
"X_raw = sm.add_constant(exp_raw)"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>horoscope</td> <th> R-squared: </th> <td> 0.012</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.011</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 8.594</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Wed, 14 Nov 2018</td> <th> Prob (F-statistic):</th> <td>0.00349</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>09:59:34</td> <th> Log-Likelihood: </th> <td> -494.32</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 689</td> <th> AIC: </th> <td> 992.6</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 687</td> <th> BIC: </th> <td> 1002.</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 1</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>nonrobust</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>t</th> <th>P>|t|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>const</th> <td> 0.4765</td> <td> 0.027</td> <td> 17.693</td> <td> 0.000</td> <td> 0.424</td> <td> 0.529</td>\n",
"</tr>\n",
"<tr>\n",
" <th>odd</th> <td> 0.1109</td> <td> 0.038</td> <td> 2.931</td> <td> 0.003</td> <td> 0.037</td> <td> 0.185</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td> 1.921</td> <th> Durbin-Watson: </th> <td> 1.990</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.383</td> <th> Jarque-Bera (JB): </th> <td> 109.191</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td>-0.128</td> <th> Prob(JB): </th> <td>1.95e-24</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td> 1.067</td> <th> Cond. No. </th> <td> 2.63</td>\n",
"</tr>\n",
"</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: horoscope R-squared: 0.012\n",
"Model: OLS Adj. R-squared: 0.011\n",
"Method: Least Squares F-statistic: 8.594\n",
"Date: Wed, 14 Nov 2018 Prob (F-statistic): 0.00349\n",
"Time: 09:59:34 Log-Likelihood: -494.32\n",
"No. Observations: 689 AIC: 992.6\n",
"Df Residuals: 687 BIC: 1002.\n",
"Df Model: 1 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const 0.4765 0.027 17.693 0.000 0.424 0.529\n",
"odd 0.1109 0.038 2.931 0.003 0.037 0.185\n",
"==============================================================================\n",
"Omnibus: 1.921 Durbin-Watson: 1.990\n",
"Prob(Omnibus): 0.383 Jarque-Bera (JB): 109.191\n",
"Skew: -0.128 Prob(JB): 1.95e-24\n",
"Kurtosis: 1.067 Cond. No. 2.63\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"\"\"\""
]
},
"execution_count": 112,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mod_raw = sm.OLS(response, X_raw)\n",
"fit_raw = mod_raw.fit()\n",
"fit_raw.summary()"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {},
"outputs": [],
"source": [
"exp_demo = astro.loc[:, ['odd', 'age', 'female']]\n",
"d_mask = exp_demo.age.notna()\n",
"X_demo = sm.add_constant(exp_demo[d_mask])"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>horoscope</td> <th> R-squared: </th> <td> 0.076</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.072</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 18.75</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Wed, 14 Nov 2018</td> <th> Prob (F-statistic):</th> <td>1.07e-11</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>10:00:42</td> <th> Log-Likelihood: </th> <td> -469.82</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 687</td> <th> AIC: </th> <td> 947.6</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 683</td> <th> BIC: </th> <td> 965.8</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 3</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>nonrobust</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>t</th> <th>P>|t|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>const</th> <td> 0.3919</td> <td> 0.061</td> <td> 6.397</td> <td> 0.000</td> <td> 0.272</td> <td> 0.512</td>\n",
"</tr>\n",
"<tr>\n",
" <th>odd</th> <td> 0.1172</td> <td> 0.037</td> <td> 3.193</td> <td> 0.001</td> <td> 0.045</td> <td> 0.189</td>\n",
"</tr>\n",
"<tr>\n",
" <th>age</th> <td> -0.0012</td> <td> 0.001</td> <td> -1.133</td> <td> 0.257</td> <td> -0.003</td> <td> 0.001</td>\n",
"</tr>\n",
"<tr>\n",
" <th>female</th> <td> 0.2482</td> <td> 0.037</td> <td> 6.702</td> <td> 0.000</td> <td> 0.175</td> <td> 0.321</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td> 2.047</td> <th> Durbin-Watson: </th> <td> 2.018</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.359</td> <th> Jarque-Bera (JB): </th> <td> 81.254</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td>-0.133</td> <th> Prob(JB): </th> <td>2.27e-18</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td> 1.336</td> <th> Cond. No. </th> <td> 179.</td>\n",
"</tr>\n",
"</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: horoscope R-squared: 0.076\n",
"Model: OLS Adj. R-squared: 0.072\n",
"Method: Least Squares F-statistic: 18.75\n",
"Date: Wed, 14 Nov 2018 Prob (F-statistic): 1.07e-11\n",
"Time: 10:00:42 Log-Likelihood: -469.82\n",
"No. Observations: 687 AIC: 947.6\n",
"Df Residuals: 683 BIC: 965.8\n",
"Df Model: 3 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const 0.3919 0.061 6.397 0.000 0.272 0.512\n",
"odd 0.1172 0.037 3.193 0.001 0.045 0.189\n",
"age -0.0012 0.001 -1.133 0.257 -0.003 0.001\n",
"female 0.2482 0.037 6.702 0.000 0.175 0.321\n",
"==============================================================================\n",
"Omnibus: 2.047 Durbin-Watson: 2.018\n",
"Prob(Omnibus): 0.359 Jarque-Bera (JB): 81.254\n",
"Skew: -0.133 Prob(JB): 2.27e-18\n",
"Kurtosis: 1.336 Cond. No. 179.\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"\"\"\""
]
},
"execution_count": 117,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mod_demo = sm.OLS(response[d_mask], X_demo)\n",
"fit_demo = mod_demo.fit()\n",
"fit_demo.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Good! We're seeing the same coefficients and $p$-values that the article found. Useful confirmation that we are doing this correctly for replication purposes.\n",
"\n",
"But - let's look at those model assumptions - first fitted x residuals:"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x24896e60c18>"
]
},
"execution_count": 118,
"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": [
"sb.scatterplot(fit_demo.resid, fit_demo.fittedvalues)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Everybody now, do the heteroskedastic!\n",
"\n",
"And the normality of the residuals:"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"execution_count": 119,
"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": [
"sm.qqplot(fit_demo.resid, line='s')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As expected, a mess. Don't use OLS for binary outcomes, people..."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Logistic Regression"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, let's do this more correctly. Binomial outcome data should be fit with a logistic or probit regression in order for the $p$-values to be meaningful."
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>Generalized Linear Model Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>horoscope</td> <th> No. Observations: </th> <td> 689</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>GLM</td> <th> Df Residuals: </th> <td> 687</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model Family:</th> <td>Binomial</td> <th> Df Model: </th> <td> 1</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Link Function:</th> <td>logit</td> <th> Scale: </th> <td> 1.0000</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>IRLS</td> <th> Log-Likelihood: </th> <td> -471.84</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Wed, 14 Nov 2018</td> <th> Deviance: </th> <td> 943.69</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>10:01:02</td> <th> Pearson chi2: </th> <td> 689.</td> \n",
"</tr>\n",
"<tr>\n",
" <th>No. Iterations:</th> <td>4</td> <th> Covariance Type: </th> <td>nonrobust</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>const</th> <td> -0.0942</td> <td> 0.109</td> <td> -0.867</td> <td> 0.386</td> <td> -0.307</td> <td> 0.119</td>\n",
"</tr>\n",
"<tr>\n",
" <th>odd</th> <td> 0.4474</td> <td> 0.154</td> <td> 2.911</td> <td> 0.004</td> <td> 0.146</td> <td> 0.749</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" Generalized Linear Model Regression Results \n",
"==============================================================================\n",
"Dep. Variable: horoscope No. Observations: 689\n",
"Model: GLM Df Residuals: 687\n",
"Model Family: Binomial Df Model: 1\n",
"Link Function: logit Scale: 1.0000\n",
"Method: IRLS Log-Likelihood: -471.84\n",
"Date: Wed, 14 Nov 2018 Deviance: 943.69\n",
"Time: 10:01:02 Pearson chi2: 689.\n",
"No. Iterations: 4 Covariance Type: nonrobust\n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const -0.0942 0.109 -0.867 0.386 -0.307 0.119\n",
"odd 0.4474 0.154 2.911 0.004 0.146 0.749\n",
"==============================================================================\n",
"\"\"\""
]
},
"execution_count": 120,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gl_raw = sm.GLM(response, X_raw, family=sm.families.Binomial())\n",
"fit_gl_raw = gl_raw.fit()\n",
"fit_gl_raw.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We still see a significant $p$-value on the odd explanatory variable. This supports the original analysis, for better or worse.\n",
"\n",
"Test predictiveness:"
]
},
{
"cell_type": "code",
"execution_count": 186,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x24896ade0f0>"
]
},
"execution_count": 186,
"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": [
"tpr, fpr, thresh = roc_curve(response, fit_gl_raw.fittedvalues)\n",
"sb.lineplot(tpr, fpr)"
]
},
{
"cell_type": "code",
"execution_count": 187,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.5556890686614653"
]
},
"execution_count": 187,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"auc(tpr, fpr)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That model is... not good."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Demographics"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>Generalized Linear Model Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>horoscope</td> <th> No. Observations: </th> <td> 687</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>GLM</td> <th> Df Residuals: </th> <td> 683</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model Family:</th> <td>Binomial</td> <th> Df Model: </th> <td> 3</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Link Function:</th> <td>logit</td> <th> Scale: </th> <td> 1.0000</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>IRLS</td> <th> Log-Likelihood: </th> <td> -447.90</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Wed, 14 Nov 2018</td> <th> Deviance: </th> <td> 895.80</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>10:01:11</td> <th> Pearson chi2: </th> <td> 688.</td> \n",
"</tr>\n",
"<tr>\n",
" <th>No. Iterations:</th> <td>4</td> <th> Covariance Type: </th> <td>nonrobust</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>const</th> <td> -0.4462</td> <td> 0.265</td> <td> -1.686</td> <td> 0.092</td> <td> -0.965</td> <td> 0.072</td>\n",
"</tr>\n",
"<tr>\n",
" <th>odd</th> <td> 0.5063</td> <td> 0.160</td> <td> 3.168</td> <td> 0.002</td> <td> 0.193</td> <td> 0.819</td>\n",
"</tr>\n",
"<tr>\n",
" <th>age</th> <td> -0.0052</td> <td> 0.005</td> <td> -1.135</td> <td> 0.257</td> <td> -0.014</td> <td> 0.004</td>\n",
"</tr>\n",
"<tr>\n",
" <th>female</th> <td> 1.0338</td> <td> 0.161</td> <td> 6.432</td> <td> 0.000</td> <td> 0.719</td> <td> 1.349</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" Generalized Linear Model Regression Results \n",
"==============================================================================\n",
"Dep. Variable: horoscope No. Observations: 687\n",
"Model: GLM Df Residuals: 683\n",
"Model Family: Binomial Df Model: 3\n",
"Link Function: logit Scale: 1.0000\n",
"Method: IRLS Log-Likelihood: -447.90\n",
"Date: Wed, 14 Nov 2018 Deviance: 895.80\n",
"Time: 10:01:11 Pearson chi2: 688.\n",
"No. Iterations: 4 Covariance Type: nonrobust\n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const -0.4462 0.265 -1.686 0.092 -0.965 0.072\n",
"odd 0.5063 0.160 3.168 0.002 0.193 0.819\n",
"age -0.0052 0.005 -1.135 0.257 -0.014 0.004\n",
"female 1.0338 0.161 6.432 0.000 0.719 1.349\n",
"==============================================================================\n",
"\"\"\""
]
},
"execution_count": 121,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gl_demo = sm.GLM(response[d_mask], X_demo, family=sm.families.Binomial())\n",
"fit_gl_demo = gl_demo.fit()\n",
"fit_gl_demo.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Age is no longer predictive, but odd remains a statistically significant predictor."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll use ROC curves again - how well does it fit?"
]
},
{
"cell_type": "code",
"execution_count": 185,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x24896973cc0>"
]
},
"execution_count": 185,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"tpr, fpr, thresh = roc_curve(response[d_mask], fit_gl_demo.fittedvalues)\n",
"sb.lineplot(tpr, fpr)"
]
},
{
"cell_type": "code",
"execution_count": 181,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.6570461512261581"
]
},
"execution_count": 181,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"auc(tpr, fpr)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That is not terribly good fit."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Bayesian\n",
"\n",
"Now, for the real purpose of this exercise in being nerd-sniped. I wanted to see what happens when we apply Bayesian methods to this problem, so let's do a Bayesian logistic regression:\n",
"\n",
"$$\\begin{align*}\n",
"y_i & \\sim \\mathrm{Bernoulli}(\\theta_i) \\\\\n",
"\\theta_i & = \\sigma^{-1}(\\beta_0 + \\sum_{k=1}^{m} \\beta_k x_k)\n",
"\\end{align*}$$\n",
"\n",
"$\\sigma$ is the logit function."
]
},
{
"cell_type": "code",
"execution_count": 148,
"metadata": {},
"outputs": [],
"source": [
"lg_code = '''\n",
"data {\n",
" int N;\n",
" int M;\n",
" int<lower=0,upper=1> y[N];\n",
" vector[M] X[N];\n",
"}\n",
"parameters {\n",
" real intercept;\n",
" vector[M] coeff;\n",
"}\n",
"transformed parameters {\n",
" real theta[N];\n",
" for (i in 1:N) {\n",
" theta[i] = inv_logit(intercept + dot_product(coeff, X[i]));\n",
" }\n",
"}\n",
"model {\n",
" intercept ~ normal(0, 100);\n",
" coeff ~ normal(0, 100);\n",
" y ~ bernoulli(theta);\n",
"}\n",
"'''"
]
},
{
"cell_type": "code",
"execution_count": 149,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_4d29e3c4c365581ac559eebf84ef5c06 NOW.\n",
"D:\\Anaconda3\\lib\\site-packages\\Cython\\Compiler\\Main.py:367: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: C:\\Users\\MICHAE~1\\AppData\\Local\\Temp\\tmpyzttr1uy\\stanfit4anon_model_4d29e3c4c365581ac559eebf84ef5c06_7878976334359051040.pyx\n",
" tree = Parsing.p_module(s, pxd, full_module_name)\n"
]
}
],
"source": [
"lg_model = pystan.StanModel(model_code=lg_code)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Common Data"
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {},
"outputs": [],
"source": [
"n_obs = len(astro)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Raw Model"
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {},
"outputs": [],
"source": [
"raw_data = dict(N=n_obs, M=1, y=response.astype(np.int32),\n",
" X=astro.horoscope.astype(np.float_).values.reshape((n_obs, 1)))"
]
},
{
"cell_type": "code",
"execution_count": 162,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:pystan:25 of 4000 iterations ended with a divergence (0.625%).\n",
"WARNING:pystan:Try running with adapt_delta larger than 0.99 to remove the divergences.\n"
]
}
],
"source": [
"raw_fit = lg_model.sampling(data=raw_data, iter=2000, chains=4, control=dict(adapt_delta=0.99))"
]
},
{
"cell_type": "code",
"execution_count": 166,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Inference for Stan model: anon_model_4d29e3c4c365581ac559eebf84ef5c06.\n",
"4 chains, each with iter=2000; warmup=1000; thin=1; \n",
"post-warmup draws per chain=1000, total post-warmup draws=4000.\n",
"\n",
" mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat\n",
"intercept -50.26 1.97 37.36 -144.4 -70.5 -40.78 -21.19 -7.2 360 1.0\n",
"coeff[1] 121.74 3.2 61.06 28.66 75.1 114.76 159.36 260.92 363 1.01\n",
"\n",
"Samples were drawn using NUTS at Wed Nov 14 10:54:02 2018.\n",
"For each parameter, n_eff is a crude measure of effective sample size,\n",
"and Rhat is the potential scale reduction factor on split chains (at \n",
"convergence, Rhat=1).\n"
]
}
],
"source": [
"print(raw_fit.stansummary(pars=['intercept', 'coeff']))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Demographic Model"
]
},
{
"cell_type": "code",
"execution_count": 173,
"metadata": {},
"outputs": [],
"source": [
"demo_data = dict(N=d_mask.sum(), M=3, y=response[d_mask].astype(np.int32),\n",
" X=np.array(exp_demo[d_mask]))"
]
},
{
"cell_type": "code",
"execution_count": 174,
"metadata": {},
"outputs": [],
"source": [
"demo_fit = lg_model.sampling(data=demo_data, iter=2000, chains=4, control=dict(adapt_delta=0.9))"
]
},
{
"cell_type": "code",
"execution_count": 175,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Inference for Stan model: anon_model_4d29e3c4c365581ac559eebf84ef5c06.\n",
"4 chains, each with iter=2000; warmup=1000; thin=1; \n",
"post-warmup draws per chain=1000, total post-warmup draws=4000.\n",
"\n",
" mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat\n",
"intercept -0.45 6.8e-3 0.27 -0.99 -0.62 -0.45 -0.27 0.07 1567 1.0\n",
"coeff[1] 0.51 3.2e-3 0.16 0.2 0.4 0.51 0.62 0.83 2474 1.0\n",
"coeff[2] -5.2e-3 1.0e-4 4.6e-3 -0.01-8.3e-3-5.4e-3-2.1e-3 4.0e-3 2057 1.0\n",
"coeff[3] 1.04 3.3e-3 0.16 0.73 0.93 1.04 1.15 1.35 2383 1.0\n",
"\n",
"Samples were drawn using NUTS at Wed Nov 14 10:57:20 2018.\n",
"For each parameter, n_eff is a crude measure of effective sample size,\n",
"and Rhat is the potential scale reduction factor on split chains (at \n",
"convergence, Rhat=1).\n"
]
}
],
"source": [
"print(demo_fit.stansummary(pars=['intercept', 'coeff']))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is pretty close to what we were seeing with the frequentist logistic regression. Same coefficient; also the 95% credible interval excludes 0."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Bayesian with PyMC3"
]
},
{
"cell_type": "code",
"execution_count": 189,
"metadata": {},
"outputs": [],
"source": [
"demo_resp = response[d_mask]\n",
"demo_exp = exp_demo[d_mask]"
]
},
{
"cell_type": "code",
"execution_count": 188,
"metadata": {},
"outputs": [],
"source": [
"import pymc3 as pm"
]
},
{
"cell_type": "code",
"execution_count": 201,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:pymc3:Auto-assigning NUTS sampler...\n",
"INFO:pymc3:Initializing NUTS using jitter+adapt_diag...\n",
"INFO:pymc3:Multiprocess sampling (4 chains in 4 jobs)\n",
"INFO:pymc3:NUTS: [c_sex, c_age, c_odd, int]\n",
"Sampling 4 chains: 100%|████████████████████████████████| 12000/12000 [00:26<00:00, 445.24draws/s]\n"
]
}
],
"source": [
"with pm.Model() as model:\n",
" intercept = pm.Normal('int', mu=0, sd=100)\n",
" c_odd = pm.Normal('c_odd', mu=0, sd=100)\n",
" c_age = pm.Normal('c_age', mu=0, sd=100)\n",
" c_sex = pm.Normal('c_sex', mu=0, sd=100)\n",
" loth = intercept + c_odd * demo_exp.odd + c_age * demo_exp.age + c_sex * demo_exp.female\n",
" theta = pm.math.sigmoid(loth)\n",
" pm.Bernoulli('y', p=theta, observed=demo_resp)\n",
" \n",
" trace = pm.sample(2000, tune=1000, chains=4, target_accept=0.9)"
]
},
{
"cell_type": "code",
"execution_count": 202,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"D:\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py:3455: MatplotlibDeprecationWarning: \n",
"The `ymin` argument was deprecated in Matplotlib 3.0 and will be removed in 3.2. Use `bottom` instead.\n",
" alternative='`bottom`', obj_type='argument')\n",
"D:\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py:3455: MatplotlibDeprecationWarning: \n",
"The `ymin` argument was deprecated in Matplotlib 3.0 and will be removed in 3.2. Use `bottom` instead.\n",
" alternative='`bottom`', obj_type='argument')\n",
"D:\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py:3455: MatplotlibDeprecationWarning: \n",
"The `ymin` argument was deprecated in Matplotlib 3.0 and will be removed in 3.2. Use `bottom` instead.\n",
" alternative='`bottom`', obj_type='argument')\n",
"D:\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py:3455: MatplotlibDeprecationWarning: \n",
"The `ymin` argument was deprecated in Matplotlib 3.0 and will be removed in 3.2. Use `bottom` instead.\n",
" alternative='`bottom`', obj_type='argument')\n"
]
},
{
"data": {
"text/plain": [
"array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000002489B7460B8>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x000002489B7352E8>],\n",
" [<matplotlib.axes._subplots.AxesSubplot object at 0x000002489B664518>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x000002488E4D3128>],\n",
" [<matplotlib.axes._subplots.AxesSubplot object at 0x000002489B6AC4A8>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x000002489B6CE6D8>],\n",
" [<matplotlib.axes._subplots.AxesSubplot object at 0x000002489B6F4908>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x000002489B779B00>]],\n",
" dtype=object)"
]
},
"execution_count": 202,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x576 with 8 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"pm.traceplot(trace)"
]
},
{
"cell_type": "code",
"execution_count": 203,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'int': 1.0005234014599007,\n",
" 'c_odd': 1.0003512462497184,\n",
" 'c_age': 1.000394817681039,\n",
" 'c_sex': 1.0003697358404449}"
]
},
"execution_count": 203,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pm.gelman_rubin(trace)"
]
},
{
"cell_type": "code",
"execution_count": 204,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([<matplotlib.axes._subplots.AxesSubplot object at 0x00000248992E6BA8>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x000002489BA9C470>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x000002489B616E10>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x000002489B55D240>],\n",
" dtype=object)"
]
},
"execution_count": 204,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x360 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"pm.plot_posterior(trace)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Very comparable results to the STAN model :)"
]
}
],
"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": "3.6.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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