{
 "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": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "from scipy import stats\n",
    "from statsmodels.formula.api import logit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "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": 4,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "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": 5,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [],
   "source": [
    "dta = sm.datasets.fair.load_pandas().data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "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": 7,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "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": 8,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.545314\n",
      "         Iterations 6\n"
     ]
    }
   ],
   "source": [
    "affair_mod = logit(\n",
    "    \"affair ~ occupation + educ + occupation_husb\"\n",
    "    \"+ rate_marriage + age + yrs_married + children\"\n",
    "    \" + religious\",\n",
    "    dta,\n",
    ").fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "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, 18 Dec 2025   Pseudo R-squ.:                  0.1327\n",
      "Time:                        07:37:30   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": "markdown",
   "metadata": {},
   "source": [
    "How well are we predicting?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3882.,  431.],\n",
       "       [1326.,  727.]])"
      ]
     },
     "execution_count": 10,
     "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": 11,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "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": 12,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "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"
     ]
    }
   ],
   "source": [
    "respondent1000 = dta.iloc[1000]\n",
    "print(respondent1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "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(\n",
    "    zip(\n",
    "        range(1, 9),\n",
    "        respondent1000[\n",
    "            [\n",
    "                \"occupation\",\n",
    "                \"educ\",\n",
    "                \"occupation_husb\",\n",
    "                \"rate_marriage\",\n",
    "                \"age\",\n",
    "                \"yrs_married\",\n",
    "                \"children\",\n",
    "                \"religious\",\n",
    "            ]\n",
    "        ].tolist(),\n",
    "    )\n",
    ")\n",
    "resp.update({0: 1})\n",
    "print(resp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "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": "markdown",
   "metadata": {},
   "source": [
    "`predict` expects a `DataFrame` since `patsy` is used to select columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000    0.518782\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "respondent1000 = dta.iloc[[1000]]\n",
    "affair_mod.predict(respondent1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.07516159285055934)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "affair_mod.fittedvalues[1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.5187815572121461)"
      ]
     },
     "execution_count": 17,
     "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": 18,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xadde5de1e8ed>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": 19,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xadde5de1e8ed>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": [
    "### Generalized Linear Model Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "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": 21,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "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": 22,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "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": 23,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "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": 24,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "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(\n",
    "    dta[\n",
    "        [\"NABOVE\", \"NBELOW\", \"LOWINC\", \"PERASIAN\", \"PERBLACK\", \"PERHISP\", \"PERMINTE\"]\n",
    "    ].head(10)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "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(\n",
    "    dta[\n",
    "        [\"AVYRSEXP\", \"AVSALK\", \"PERSPENK\", \"PTRATIO\", \"PCTAF\", \"PCTCHRT\", \"PCTYRRND\"]\n",
    "    ].head(10)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "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": 27,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.16075102880658423)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.binom(5, 1.0 / 6).pmf(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.1607510288065844)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.special import comb\n",
    "\n",
    "comb(5, 2) * (1 / 6.0) ** 2 * (5 / 6.0) ** 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [],
   "source": [
    "from statsmodels.formula.api import glm\n",
    "\n",
    "glm_mod = glm(formula, dta, family=sm.families.Binomial()).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "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:                          1.0000\n",
      "Method:                            IRLS   Log-Likelihood:                -2998.6\n",
      "Date:                  Thu, 18 Dec 2025   Deviance:                       4078.8\n",
      "Time:                          07:37:30   Pearson chi2:                 4.05e+03\n",
      "No. Iterations:                       5   Pseudo R-squ. (CS):              1.000\n",
      "Covariance Type:              nonrobust                                         \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": 31,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      807.0\n",
       "1      184.0\n",
       "2      571.0\n",
       "3      573.0\n",
       "4       65.0\n",
       "       ...  \n",
       "298    342.0\n",
       "299    154.0\n",
       "300    595.0\n",
       "301    709.0\n",
       "302    156.0\n",
       "Length: 303, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glm_mod.model.data.orig_endog.sum(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      470.732584\n",
       "1      138.266178\n",
       "2      285.832629\n",
       "3      392.702917\n",
       "4       20.963146\n",
       "          ...    \n",
       "298    111.464708\n",
       "299     61.037884\n",
       "300    235.517446\n",
       "301    290.952508\n",
       "302     53.312851\n",
       "Length: 303, dtype: float64"
      ]
     },
     "execution_count": 32,
     "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": 33,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [],
   "source": [
    "exog = glm_mod.model.data.orig_exog  # get the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "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": 35,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "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(0.25)\n",
    "print(means25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "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(0.75)\n",
    "print(means75)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, `predict` expects a `DataFrame` since `patsy` is used to select columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [],
   "source": [
    "resp25 = glm_mod.predict(pd.DataFrame(means25).T)\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": 38,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-11.8863%\n"
     ]
    }
   ],
   "source": [
    "print(\"%2.4f%%\" % (diff[0] * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [],
   "source": [
    "nobs = glm_mod.nobs\n",
    "y = glm_mod.model.endog\n",
    "yhat = glm_mod.mu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.graphics.api import abline_plot\n",
    "\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": 41,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xadde5de1e8ed>]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 8))\n",
    "ax = fig.add_subplot(\n",
    "    111,\n",
    "    title=\"Residual Dependence Plot\",\n",
    "    xlabel=\"Fitted Values\",\n",
    "    ylabel=\"Pearson Residuals\",\n",
    ")\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 overlaid"
   ]
  },
  {
   "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": 42,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<statsmodels.nonparametric.kde.KDEUnivariate at 0xadde5de1e8ed>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 43,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xadde5de1e8ed>]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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, density=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": 44,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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 (ipykernel)",
   "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": "3.13.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
