Plot/Fairplot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Unable to open EPSG support file gcs.csv.  Try setting the GDAL_DATA environment variable to point to the directory containing EPSG csv files.\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "#stats\n",
    "from scipy import stats\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import logit\n",
    "#plotting libraries\n",
    "import matplotlib.pyplot as plt\n",
    "from plotnine import * \n",
    "\n",
    "#https://www.statsmodels.org/dev/examples/notebooks/generated/discrete_choice_example.html\n",
    "#practice based on model at website"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "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": 10,
   "metadata": {},
   "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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dta = sm.datasets.fair.load_pandas().data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 4,
     "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": [
    "# Histogram of Education\n",
    "dta.educ.hist()\n",
    "plt.title(\"Histogram of Education-Affairs Dataset\")\n",
    "plt.xlabel(\"Education level\")\n",
    "plt.ylabel(\"Frequency\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Mh5bIfzfl8nXAwYXq04D1uXxanXIzMxslVV4NJeBSYHVEfLIwawlweh4/HbihUD5P0j6SZpBOZC+LiA3AVklH53WeVqhjZmajoMrDUMcAbwNWSro7l30EuBBYLOmdwIPAyQARsUrSYuAe0pVUZ0bEjlzvvcBCYALpPMbNFcZtZmY1KksWEfFd6p9vAHhtgzoXABfUKV9OOjluZmYd4P/gNjOzUk4WZmZWysnCzMxKOVmYmVkpJwszMyvlZGFmZqWcLMzMrJSThZmZlXKyMDOzUk4WZmZWysnCzMxKOVmYmVkpJwszMyvlZGFmZqWcLMzMrJSThZmZlWoqWUjyg4fMzMawZvcsPi9pmaT3STqw0ojMzKzrNJUsIuJVwKnAwcBySV+S9LpKIzMzs67R9DmLiLgf+GvgHODVwGck3Svpj6oKzszMukOz5yxeJulTwGrgNcDxEfHSPP6pCuMzM7MuML7J5S4Gvgh8JCKeHCyMiPWS/rqSyMzMrGs0myzeCDwZETsAJO0F7BsRv4yIKyuLzszMukKz5yy+CUwoTO+Xy8zMbAxoNlnsGxEDgxN5fL9qQjIzs27TbLJ4QtJRgxOSZgFPDrG8mZntQZo9Z3EW8GVJ6/P0FOBPqwnJzMy6TVPJIiJ+KOl3gJcAAu6NiKcqjczMzLpGs3sWAK8Apuc6vyuJiLiikqjMzKyrNJUsJF0JvBC4G9iRiwNwsjAzGwOa3bPoAw6NiGh2xZIuA94MbIqIw3PZ+cC7gUfyYh+JiJvyvPOAd5KS0Qci4hu5fBawkHTp7k3AB1uJw8zMhq/Zq6F+Avx2i+teCMytU/6piDgyD4OJ4lBgHnBYrvM5SePy8pcA84GZeai3TjMzq1CzexaTgXskLQO2DRZGxFsaVYiI2yVNb3L9JwCLImIb8ICkNcBsSWuBAyLiDgBJVwAnAjc3uV4zMxsBauaIjqRX1yuPiG+X1JsO3FhzGOoMYAuwHDg7Ih6TdDFwZ0RclZe7lJQQ1gIXRsRxufxY4JyIeHOD9uaT9kLo7e2dtWjRotJtq2dgYICenp626lbJcbXGcbWmyrhWPry57bq9E2Bjm//VdcTUSW23W6asv4azzcMxY9K4Yb2Oc+bMWRERfbXlzV46+21JhwAzI+KbkvYDxpXVq+MS4OOkk+MfBy4C3kG6HHeXZocobxTnAmABQF9fX/T397cRIixdupR261bJcbXGcbWmyrjOOPdrbdc9+4jtXLSylQs3n7H21P622y1T1l/D2ebhWDh3YiWvY7O3KH83cC3whVw0Fbi+1cYiYmNE7IiIp0l3sZ2dZ60jPVhp0DRgfS6fVqfczMxGUbMnuM8EjiEdPhp8ENJzW21M0pTC5EmkE+cAS4B5kvaRNIN0IntZRGwAtko6WpKA04AbWm3XzMyGp9l9u20R8ev0fQ2SxjPE4aC8zDVAPzBZ0jrgo0C/pCNz3bXAewAiYpWkxcA9wHbgzMHboQPv5ZlLZ2/GJ7fNzEZds8ni25I+AkzIz95+H/AfQ1WIiFPqFF86xPIXABfUKV8OHN5knGZmVoFmD0OdS/pHupWkvYGbSM/jNjOzMaDZq6EGT0h/sdpwzMysGzV7b6gHqHOOIiJeMOIRmZlZ12nl3lCD9gVOBp498uGYmVk3auqcRUT8ojA8HBGfBl5TcWxmZtYlmj0MdVRhci/Snsb+lURkZmZdp9nDUBcVxreT/kfi/4x4NGZm1pWavRpqTtWBmJlZ92r2MNRfDjU/Ij45MuGYmVk3auVqqFeQ7uEEcDxwO/BQFUGZmVl3aeXhR0dFxFb4zXMpvhwR76oqMDMz6x7N3u7j+cCvC9O/BqaPeDRmZtaVmt2zuBJYJuk60n9ynwRcUVlUZmbWVZq9GuoCSTcDx+ait0fEj6oLy8zMukmzh6EA9gO2RMS/AOvyQ4rMzGwMaPaxqh8FzgHOy0V7A1dVFZSZmXWXZvcsTgLeAjwBEBHr8e0+zMzGjGaTxa8jIsi3KZc0sbqQzMys2zSbLBZL+gJwoKR3A9/ED0IyMxszmr0a6hP52dtbgJcAfxsRt1QamZmZdY3SZCFpHPCNiDgOcIIwMxuDSg9DRcQO4JeSJo1CPGZm1oWa/Q/uXwErJd1CviIKICI+UElUZmbWVZpNFl/Lg5mZjUFDJgtJz4+IByPi8tEKyMzMuk/ZOYvrB0ckfaXiWMzMrEuVJQsVxl9QZSBmZta9ypJFNBg3M7MxpOwE98slbSHtYUzI4+TpiIgDKo3OzMy6wpB7FhExLiIOiIj9I2J8Hh+cHjJRSLpM0iZJPymUPVvSLZLuz38PKsw7T9IaSfdJen2hfJaklXneZySpti0zM6tWK8+zaNVCYG5N2bnArRExE7g1TyPpUGAecFiu87n8n+MAlwDzgZl5qF2nmZlVrLJkERG3A4/WFJ8ADF6GezlwYqF8UURsi4gHgDXAbElTgAMi4o5819srCnXMzGyUVLlnUU9vRGwAyH+fm8unAg8VlluXy6bm8dpyMzMbRUo/2CtauTQduDEiDs/Tj0fEgYX5j0XEQZL+FbgjIq7K5ZcCNwEPAv+Yb2KIpGOBv4qI4xu0N590yIre3t5ZixYtaivugYEBenp62qpbJcfVmm6Na9Ojm9n4ZGfaPmJq41u8VdlfKx/e3Hbd3gm03V9Dbe9wlfXXcLZ5OGZMGjes13HOnDkrIqKvtrzZ232MlI2SpkTEhnyIaVMuXwccXFhuGrA+l0+rU15XRCwAFgD09fVFf39/W0EuXbqUdutWyXG1plvj+uzVN3DRytH+6CVrT+1vOK/K/jrj3PbvFnT2Edvb7q+htne4yvprONs8HAvnTqzkdRztw1BLgNPz+OnADYXyeZL2kTSDdCJ7WT5UtVXS0fkqqNMKdczMbJRU9vNG0jVAPzBZ0jrgo8CFpKfuvZN0iOlkgIhYJWkxcA+wHTgz3xod4L2kK6smADfnwczMRlFlySIiTmkw67UNlr8AuKBO+XLg8BEMzczMWjTah6HMzGw35GRhZmalnCzMzKyUk4WZmZVysjAzs1JOFmZmVsrJwszMSjlZmJlZKScLMzMr5WRhZmalnCzMzKyUk4WZmZVysjAzs1JOFmZmVsrJwszMSjlZmJlZKScLMzMr5WRhZmalnCzMzKyUk4WZmZVysjAzs1JOFmZmVsrJwszMSjlZmJlZKScLMzMr5WRhZmalnCzMzKyUk4WZmZVysjAzs1JOFmZmVqojyULSWkkrJd0taXkue7akWyTdn/8eVFj+PElrJN0n6fWdiNnMbCzr5J7FnIg4MiL68vS5wK0RMRO4NU8j6VBgHnAYMBf4nKRxnQjYzGys6qbDUCcAl+fxy4ETC+WLImJbRDwArAFmdyA+M7MxSxEx+o1KDwCPAQF8ISIWSHo8Ig4sLPNYRBwk6WLgzoi4KpdfCtwcEdfWWe98YD5Ab2/vrEWLFrUV38DAAD09PW3VrZLjak23xrXp0c1sfLIzbR8xdVLDeVX218qHN7ddt3cCbffXUNs7XGX9NZxtHo4Zk8YN63WcM2fOisIRn98YP6yo2ndMRKyX9FzgFkn3DrGs6pTVzXARsQBYANDX1xf9/f1tBbd06VLarVslx9Wabo3rs1ffwEUrO/PRW3tqf8N5VfbXGed+re26Zx+xve3+Gmp7h6usv4azzcOxcO7ESl7HjhyGioj1+e8m4DrSYaWNkqYA5L+b8uLrgIML1acB60cvWjMzG/VkIWmipP0Hx4E/BH4CLAFOz4udDtyQx5cA8yTtI2kGMBNYNrpRm5mNbZ3YF+4FrpM02P6XIuLrkn4ILJb0TuBB4GSAiFglaTFwD7AdODMidnQgbjOzMWvUk0VE/BR4eZ3yXwCvbVDnAuCCikMzM7MGuunSWTMz61JOFmZmVsrJwszMSjlZmJlZKScLMzMr5WRhZmalOnW7D+sy04d5O4Z2b22w9sI3td2umY0e71mYmVkpJwszMyvlZGFmZqWcLMzMrJSThZmZlXKyMDOzUk4WZmZWysnCzMxKOVmYmVkpJwszMyvlZGFmZqWcLMzMrJSThZmZlXKyMDOzUk4WZmZWysnCzMxKOVmYmVkpJwszMyvlx6rWsfLhzW0/JnQ4/IhRM+tW3rMwM7NSThZmZlbKycLMzEo5WZiZWandJllImivpPklrJJ3b6XjMzMaS3SJZSBoH/CvwBuBQ4BRJh3Y2KjOzsWO3SBbAbGBNRPw0In4NLAJO6HBMZmZjhiKi0zGUkvQnwNyIeFeefhvwyoh4f81y84H5efIlwH1tNjkZ+HmbdavkuFrjuFrjuFqzp8Z1SEQ8p7Zwd/mnPNUp2yXLRcQCYMGwG5OWR0TfcNcz0hxXaxxXaxxXa8ZaXLvLYah1wMGF6WnA+g7FYmY25uwuyeKHwExJMyQ9C5gHLOlwTGZmY8ZucRgqIrZLej/wDWAccFlErKqwyWEfyqqI42qN42qN42rNmIprtzjBbWZmnbW7HIYyM7MOcrIwM7NSYzZZSLpM0iZJP2kwX5I+k28v8mNJR3VJXP2SNku6Ow9/O0pxHSzpNkmrJa2S9ME6y4x6nzUZ16j3maR9JS2T9F85ro/VWaYT/dVMXB15j+W2x0n6kaQb68zryGeyibg69ZlcK2llbnN5nfkj218RMSYH4A+Ao4CfNJj/RuBm0v94HA38oEvi6gdu7EB/TQGOyuP7A/8NHNrpPmsyrlHvs9wHPXl8b+AHwNFd0F/NxNWR91hu+y+BL9Vrv1OfySbi6tRnci0weYj5I9pfY3bPIiJuBx4dYpETgCsiuRM4UNKULoirIyJiQ0Tclce3AquBqTWLjXqfNRnXqMt9MJAn985D7dUkneivZuLqCEnTgDcB/9ZgkY58JpuIq1uNaH+N2WTRhKnAQ4XpdXTBl1D2e/kwws2SDhvtxiVNB36X9Ku0qKN9NkRc0IE+y4cu7gY2AbdERFf0VxNxQWfeY58G/gp4usH8Tr2/yuKCzvRXAP8paYXSrY5qjWh/OVk01tQtRjrgLtK9W14OfBa4fjQbl9QDfAU4KyK21M6uU2VU+qwkro70WUTsiIgjSXccmC3p8JpFOtJfTcQ16v0l6c3ApohYMdRidcoq7a8m4+rUZ/KYiDiKdDfuMyX9Qc38Ee0vJ4vGuvIWIxGxZfAwQkTcBOwtafJotC1pb9IX8tUR8dU6i3Skz8ri6mSf5TYfB5YCc2tmdfQ91iiuDvXXMcBbJK0l3VX6NZKuqlmmE/1VGlen3l8RsT7/3QRcR7o7d9GI9peTRWNLgNPyFQVHA5sjYkOng5L025KUx2eTXsNfjEK7Ai4FVkfEJxssNup91kxcnegzSc+RdGAenwAcB9xbs1gn+qs0rk70V0ScFxHTImI66XY+34qIt9YsNur91UxcHXp/TZS0/+A48IdA7RWUI9pfu8XtPqog6RrSVQyTJa0DPko62UdEfB64iXQ1wRrgl8DbuySuPwHeK2k78CQwL/KlDxU7BngbsDIf7wb4CPD8Qmyd6LNm4upEn00BLld6cNdewOKIuFHSnxfi6kR/NRNXp95ju+iC/momrk70Vy9wXc5R44EvRcTXq+wv3+7DzMxK+TCUmZmVcrIwM7NSThZmZlbKycLMzEo5WZiZWSknC9tjSQpJVxamx0t6RHXuHNrGur8/3HWMBEnTJf1ZYbpP0mc6GZPtmZwsbE/2BHB4/uczgNcBD7eyAknja6bHAUTE749IhG3EUGM68JtkERHLI+IDlQdlY46The3pbibdMRTgFOCawRmSZkv6vtJzCr4v6SW5/AxJX5b0H6QbtfUrPTPjS8DKvMxA/tsj6VZJdyk9W+CEwvr/RtK9km6RdI2kD+fyF0r6utIN4L4j6Xdqg5Z0vqQFkv4TuCLvQXwnt3OXpMFkdSFwrNIzDT6UY72xsI7LJC2V9FNJHyiLzayhZu5j7sHD7jgAA8DLgGuBfYG7KTx7ADgAGJ/HjwO+ksfPIN1X59l5up+0lzKjuO78dzxwQB6fTPpvWQF9ub0JpOds3A98OC93KzAzj7+SdAuJ2tjPB1YAE/L0fsC+eXwmsLwQ242FesXtOx/4PrBPju0XpLsBNIzNg4dGw5i93YeNDRHxY6Vbl59Cuv1B0Wys57MAAAGrSURBVCTSrS9mku7GuXdh3i0RUXyuyLKIeKBOEwL+QemOn0+TbgHdC7wKuCEingTIeymDd8f9feDL+VYNkL7M61kyWD/HdrGkI4EdwIuH2u6Cr0XENmCbpE1DxWY2FCcLGwuWAJ8g/er+rUL5x4HbIuKknFCWFuY9UbOO2ulBpwLPAWZFxFNKdyfdl/q3h4Z06PfxSLcIL1Ns80PARuDleR2/aqI+wLbC+A7SZ75RbGYN+ZyFjQWXAX8XEStryifxzAnvM9pc9yTS8w6ekjQHOCSXfxc4XumZ1z3k8yaRnrXxgKST4TfPSX55k+1siIinSTdOHJfLt5IOJbWibmxmQ3GysD1eRKyLiH+pM+ufgX+U9D2e+fJt1dVAn6TlpL2Me3ObPyTt0fwX8FVgObA51zkVeKek/wJWkR5/WeZzwOmS7iQdghrc6/gxsF3pKW0faibgktjM6vJdZ80qIqknIgYk7QfcDsyP/LzwTuvm2Kw7+ZyFWXUWSDqUdA7j8i77Mu7m2KwLec/CzMxK+ZyFmZmVcrIwM7NSThZmZlbKycLMzEo5WZiZWan/D0cKQuMYdPGqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram of Marriage satisfaction\n",
    "dta.rate_marriage.hist()\n",
    "plt.title(\"Histogram of Marriage-Affairs Dataset\")\n",
    "plt.xlabel(\"Marriage rating\")\n",
    "plt.ylabel(\"Frequency\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 7,
     "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": [
    "pd.crosstab(dta.rate_marriage,dta.affairs.astype(bool)).plot(kind=\"bar\")\n",
    "plt.title(\"Histogram of Marriage by status-Affairs Dataset\")\n",
    "plt.xlabel(\"Marriage rating versus Affairs status\")\n",
    "plt.ylabel(\"Frequency\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\phant\\Miniconda3\\lib\\site-packages\\plotnine\\stats\\stat_bin.py:93: PlotnineWarning: 'stat_bin()' using 'bins = 38'. Pick better value with 'binwidth'.\n",
      "  warn(msg.format(params['bins']), PlotnineWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (-9223371920859119307)>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ggplot style\n",
    "(ggplot (dta)\n",
    " + geom_histogram(aes('rate_marriage',fill='factor(affair)'))\n",
    ")+ggtitle('Histogram of Marriage by status-Affairs Dataset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (115995656512)>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ggplot style\n",
    "(ggplot (dta)\n",
    " + geom_point(aes('yrs_married', 'rate_marriage',color='factor(affair)',shape='factor(religious)'))\n",
    ")+labs(color=\"Affair\",shape='Religous')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.545314\n",
      "         Iterations 6\n"
     ]
    }
   ],
   "source": [
    "#affair is transformed into binary variable for logistic regression\n",
    "affair_mod = logit(\"affair ~ occupation + educ + occupation_husb\"\n",
    "                   \"+ rate_marriage + age + yrs_married + children\"\n",
    "                   \" + religious\", dta).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "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:                Thu, 14 May 2020   Pseudo R-squ.:                  0.1327\n",
      "Time:                        22:17:02   Log-Likelihood:                -3471.5\n",
      "converged:                       True   LL-Null:                       -4002.5\n",
      "Covariance Type:            nonrobust   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": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3882.,  431.],\n",
       "       [1326.,  727.]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "affair_mod.pred_table()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000    0.518782\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "respondent1000 = dta.iloc[[1000]]\n",
    "affair_mod.predict(respondent1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07516159285059443"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "affair_mod.fittedvalues[1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5187815572121549"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "affair_mod.model.cdf(affair_mod.fittedvalues[1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "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": 13,
   "metadata": {},
   "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();"
   ]
  }
 ],
 "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": 2
}