{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generalized Linear Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-19T16:16:26.472100Z",
     "iopub.status.busy": "2024-04-19T16:16:26.471827Z",
     "iopub.status.idle": "2024-04-19T16:16:27.096926Z",
     "shell.execute_reply": "2024-04-19T16:16:27.096209Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-19T16:16:27.101937Z",
     "iopub.status.busy": "2024-04-19T16:16:27.100733Z",
     "iopub.status.idle": "2024-04-19T16:16:28.067687Z",
     "shell.execute_reply": "2024-04-19T16:16:28.066902Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "from scipy import stats\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.rc(\"figure\", figsize=(16,8))\n",
    "plt.rc(\"font\", size=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GLM: Binomial response data\n",
    "\n",
    "### Load Star98 data\n",
    "\n",
    " In this example, we use the Star98 dataset which was taken with permission\n",
    " from Jeff Gill (2000) Generalized linear models: A unified approach. Codebook\n",
    " information can be obtained by typing: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-19T16:16:28.070775Z",
     "iopub.status.busy": "2024-04-19T16:16:28.070283Z",
     "iopub.status.idle": "2024-04-19T16:16:28.074419Z",
     "shell.execute_reply": "2024-04-19T16:16:28.073663Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "::\n",
      "\n",
      "    Number of Observations - 303 (counties in California).\n",
      "\n",
      "    Number of Variables - 13 and 8 interaction terms.\n",
      "\n",
      "    Definition of variables names::\n",
      "\n",
      "        NABOVE   - Total number of students above the national median for the\n",
      "                   math section.\n",
      "        NBELOW   - Total number of students below the national median for the\n",
      "                   math section.\n",
      "        LOWINC   - Percentage of low income students\n",
      "        PERASIAN - Percentage of Asian student\n",
      "        PERBLACK - Percentage of black students\n",
      "        PERHISP  - Percentage of Hispanic students\n",
      "        PERMINTE - Percentage of minority teachers\n",
      "        AVYRSEXP - Sum of teachers' years in educational service divided by the\n",
      "                number of teachers.\n",
      "        AVSALK   - Total salary budget including benefits divided by the number\n",
      "                   of full-time teachers (in thousands)\n",
      "        PERSPENK - Per-pupil spending (in thousands)\n",
      "        PTRATIO  - Pupil-teacher ratio.\n",
      "        PCTAF    - Percentage of students taking UC/CSU prep courses\n",
      "        PCTCHRT  - Percentage of charter schools\n",
      "        PCTYRRND - Percentage of year-round schools\n",
      "\n",
      "        The below variables are interaction terms of the variables defined\n",
      "        above.\n",
      "\n",
      "        PERMINTE_AVYRSEXP\n",
      "        PEMINTE_AVSAL\n",
      "        AVYRSEXP_AVSAL\n",
      "        PERSPEN_PTRATIO\n",
      "        PERSPEN_PCTAF\n",
      "        PTRATIO_PCTAF\n",
      "        PERMINTE_AVTRSEXP_AVSAL\n",
      "        PERSPEN_PTRATIO_PCTAF\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(sm.datasets.star98.NOTE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the data and add a constant to the exogenous (independent) variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-19T16:16:28.076957Z",
     "iopub.status.busy": "2024-04-19T16:16:28.076570Z",
     "iopub.status.idle": "2024-04-19T16:16:28.087983Z",
     "shell.execute_reply": "2024-04-19T16:16:28.087481Z"
    }
   },
   "outputs": [],
   "source": [
    "data = sm.datasets.star98.load()\n",
    "data.exog = sm.add_constant(data.exog, prepend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " The dependent variable is N by 2 (Success: NABOVE, Failure: NBELOW): "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-19T16:16:28.090439Z",
     "iopub.status.busy": "2024-04-19T16:16:28.090033Z",
     "iopub.status.idle": "2024-04-19T16:16:28.096128Z",
     "shell.execute_reply": "2024-04-19T16:16:28.095605Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   NABOVE  NBELOW\n",
      "0   452.0   355.0\n",
      "1   144.0    40.0\n",
      "2   337.0   234.0\n",
      "3   395.0   178.0\n",
      "4     8.0    57.0\n"
     ]
    }
   ],
   "source": [
    "print(data.endog.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " The independent variables include all the other variables described above, as\n",
    " well as the interaction terms:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-19T16:16:28.098388Z",
     "iopub.status.busy": "2024-04-19T16:16:28.098191Z",
     "iopub.status.idle": "2024-04-19T16:16:28.107607Z",
     "shell.execute_reply": "2024-04-19T16:16:28.107058Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     LOWINC   PERASIAN   PERBLACK    PERHISP  PERMINTE  AVYRSEXP    AVSALK  \\\n",
      "0  34.39730  23.299300  14.235280  11.411120  15.91837  14.70646  59.15732   \n",
      "1  17.36507  29.328380   8.234897   9.314884  13.63636  16.08324  59.50397   \n",
      "2  32.64324   9.226386  42.406310  13.543720  28.83436  14.59559  60.56992   \n",
      "3  11.90953  13.883090   3.796973  11.443110  11.11111  14.38939  58.33411   \n",
      "4  36.88889  12.187500  76.875000   7.604167  43.58974  13.90568  63.15364   \n",
      "\n",
      "   PERSPENK   PTRATIO     PCTAF  ...   PCTYRRND  PERMINTE_AVYRSEXP  \\\n",
      "0  4.445207  21.71025  57.03276  ...  22.222220         234.102872   \n",
      "1  5.267598  20.44278  64.62264  ...   0.000000         219.316851   \n",
      "2  5.482922  18.95419  53.94191  ...   0.000000         420.854496   \n",
      "3  4.165093  21.63539  49.06103  ...   7.142857         159.882095   \n",
      "4  4.324902  18.77984  52.38095  ...   0.000000         606.144976   \n",
      "\n",
      "   PERMINTE_AVSAL  AVYRSEXP_AVSAL  PERSPEN_PTRATIO  PERSPEN_PCTAF  \\\n",
      "0       941.68811        869.9948         96.50656      253.52242   \n",
      "1       811.41756        957.0166        107.68435      340.40609   \n",
      "2      1746.49488        884.0537        103.92435      295.75929   \n",
      "3       648.15671        839.3923         90.11341      204.34375   \n",
      "4      2752.85075        878.1943         81.22097      226.54248   \n",
      "\n",
      "   PTRATIO_PCTAF  PERMINTE_AVYRSEXP_AVSAL  PERSPEN_PTRATIO_PCTAF  const  \n",
      "0      1238.1955               13848.8985              5504.0352    1.0  \n",
      "1      1321.0664               13050.2233              6958.8468    1.0  \n",
      "2      1022.4252               25491.1232              5605.8777    1.0  \n",
      "3      1061.4545                9326.5797              4421.0568    1.0  \n",
      "4       983.7059               38280.2616              4254.4314    1.0  \n",
      "\n",
      "[5 rows x 21 columns]\n"
     ]
    }
   ],
   "source": [
    "print(data.exog.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit and summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-19T16:16:28.109847Z",
     "iopub.status.busy": "2024-04-19T16:16:28.109646Z",
     "iopub.status.idle": "2024-04-19T16:16:28.124076Z",
     "shell.execute_reply": "2024-04-19T16:16:28.123464Z"
    }
   },
   "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:                  Fri, 19 Apr 2024   Deviance:                       4078.8\n",
      "Time:                          16:16:28   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",
      "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",
      "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",
      "AVSALK                      0.0804      0.014      5.775      0.000       0.053       0.108\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",
      "PCTAF                      -0.1690      0.033     -5.169      0.000      -0.233      -0.105\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_AVYRSEXP          -0.0141      0.002     -7.391      0.000      -0.018      -0.010\n",
      "PERMINTE_AVSAL             -0.0040      0.000     -8.450      0.000      -0.005      -0.003\n",
      "AVYRSEXP_AVSAL             -0.0039      0.001     -4.059      0.000      -0.006      -0.002\n",
      "PERSPEN_PTRATIO             0.0917      0.015      6.321      0.000       0.063       0.120\n",
      "PERSPEN_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",
      "PERMINTE_AVYRSEXP_AVSAL     0.0002   2.99e-05      7.428      0.000       0.000       0.000\n",
      "PERSPEN_PTRATIO_PCTAF      -0.0022      0.000     -6.445      0.000      -0.003      -0.002\n",
      "const                       2.9589      1.547      1.913      0.056      -0.073       5.990\n",
      "===========================================================================================\n"
     ]
    }
   ],
   "source": [
    "glm_binom = sm.GLM(data.endog, data.exog, family=sm.families.Binomial())\n",
    "res = glm_binom.fit()\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quantities of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-19T16:16:28.126776Z",
     "iopub.status.busy": "2024-04-19T16:16:28.126269Z",
     "iopub.status.idle": "2024-04-19T16:16:28.131562Z",
     "shell.execute_reply": "2024-04-19T16:16:28.131008Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of trials: 108418.0\n",
      "Parameters:  LOWINC                    -0.016815\n",
      "PERASIAN                   0.009925\n",
      "PERBLACK                  -0.018724\n",
      "PERHISP                   -0.014239\n",
      "PERMINTE                   0.254487\n",
      "AVYRSEXP                   0.240694\n",
      "AVSALK                     0.080409\n",
      "PERSPENK                  -1.952161\n",
      "PTRATIO                   -0.334086\n",
      "PCTAF                     -0.169022\n",
      "PCTCHRT                    0.004917\n",
      "PCTYRRND                  -0.003580\n",
      "PERMINTE_AVYRSEXP         -0.014077\n",
      "PERMINTE_AVSAL            -0.004005\n",
      "AVYRSEXP_AVSAL            -0.003906\n",
      "PERSPEN_PTRATIO            0.091714\n",
      "PERSPEN_PCTAF              0.048990\n",
      "PTRATIO_PCTAF              0.008041\n",
      "PERMINTE_AVYRSEXP_AVSAL    0.000222\n",
      "PERSPEN_PTRATIO_PCTAF     -0.002249\n",
      "const                      2.958878\n",
      "dtype: float64\n",
      "T-values:  LOWINC                    -38.749083\n",
      "PERASIAN                   16.504736\n",
      "PERBLACK                  -25.182189\n",
      "PERHISP                   -32.817913\n",
      "PERMINTE                    8.498271\n",
      "AVYRSEXP                    4.212479\n",
      "AVSALK                      5.774998\n",
      "PERSPENK                   -6.161911\n",
      "PTRATIO                    -5.453217\n",
      "PCTAF                      -5.168654\n",
      "PCTCHRT                     3.921200\n",
      "PCTYRRND                  -15.878260\n",
      "PERMINTE_AVYRSEXP          -7.390931\n",
      "PERMINTE_AVSAL             -8.449639\n",
      "AVYRSEXP_AVSAL             -4.059162\n",
      "PERSPEN_PTRATIO             6.321099\n",
      "PERSPEN_PCTAF               6.574347\n",
      "PTRATIO_PCTAF               5.362290\n",
      "PERMINTE_AVYRSEXP_AVSAL     7.428064\n",
      "PERSPEN_PTRATIO_PCTAF      -6.445137\n",
      "const                       1.913012\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print('Total number of trials:',  data.endog.iloc[:, 0].sum())\n",
    "print('Parameters: ', res.params)\n",
    "print('T-values: ', res.tvalues)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First differences: We hold all explanatory variables constant at their means and manipulate the percentage of low income households to assess its impact on the response variables: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-19T16:16:28.133806Z",
     "iopub.status.busy": "2024-04-19T16:16:28.133471Z",
     "iopub.status.idle": "2024-04-19T16:16:28.138980Z",
     "shell.execute_reply": "2024-04-19T16:16:28.138366Z"
    }
   },
   "outputs": [],
   "source": [
    "means = data.exog.mean(axis=0)\n",
    "means25 = means.copy()\n",
    "means25.iloc[0] = stats.scoreatpercentile(data.exog.iloc[:,0], 25)\n",
    "means75 = means.copy()\n",
    "means75.iloc[0] = lowinc_75per = stats.scoreatpercentile(data.exog.iloc[:,0], 75)\n",
    "resp_25 = res.predict(means25)\n",
    "resp_75 = res.predict(means75)\n",
    "diff = resp_75 - resp_25\n"
   ]
  },
  {
   "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": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-19T16:16:28.141470Z",
     "iopub.status.busy": "2024-04-19T16:16:28.141098Z",
     "iopub.status.idle": "2024-04-19T16:16:28.144511Z",
     "shell.execute_reply": "2024-04-19T16:16:28.143875Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-11.8753%\n"
     ]
    }
   ],
   "source": [
    "print(\"%2.4f%%\" % (diff.iloc[0]*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plots\n",
    "\n",
    " We extract information that will be used to draw some interesting plots: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-19T16:16:28.146820Z",
     "iopub.status.busy": "2024-04-19T16:16:28.146582Z",
     "iopub.status.idle": "2024-04-19T16:16:28.150493Z",
     "shell.execute_reply": "2024-04-19T16:16:28.149916Z"
    }
   },
   "outputs": [],
   "source": [
    "nobs = res.nobs\n",
    "y = data.endog.iloc[:,0]/data.endog.sum(1)\n",
    "yhat = res.mu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot yhat vs y:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-19T16:16:28.152675Z",
     "iopub.status.busy": "2024-04-19T16:16:28.152478Z",
     "iopub.status.idle": "2024-04-19T16:16:28.155344Z",
     "shell.execute_reply": "2024-04-19T16:16:28.154820Z"
    }
   },
   "outputs": [],
   "source": [
    "from statsmodels.graphics.api import abline_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-19T16:16:28.157506Z",
     "iopub.status.busy": "2024-04-19T16:16:28.157312Z",
     "iopub.status.idle": "2024-04-19T16:16:28.487539Z",
     "shell.execute_reply": "2024-04-19T16:16:28.486766Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(yhat, y)\n",
    "line_fit = sm.OLS(y, sm.add_constant(yhat, prepend=True)).fit()\n",
    "abline_plot(model_results=line_fit, ax=ax)\n",
    "\n",
    "\n",
    "ax.set_title('Model Fit Plot')\n",
    "ax.set_ylabel('Observed values')\n",
    "ax.set_xlabel('Fitted values');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot yhat vs. Pearson residuals:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-19T16:16:28.492422Z",
     "iopub.status.busy": "2024-04-19T16:16:28.490892Z",
     "iopub.status.idle": "2024-04-19T16:16:28.788400Z",
     "shell.execute_reply": "2024-04-19T16:16:28.787704Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Fitted values')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.scatter(yhat, res.resid_pearson)\n",
    "ax.hlines(0, 0, 1)\n",
    "ax.set_xlim(0, 1)\n",
    "ax.set_title('Residual Dependence Plot')\n",
    "ax.set_ylabel('Pearson Residuals')\n",
    "ax.set_xlabel('Fitted values')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Histogram of standardized deviance residuals:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-19T16:16:28.792321Z",
     "iopub.status.busy": "2024-04-19T16:16:28.791788Z",
     "iopub.status.idle": "2024-04-19T16:16:29.258313Z",
     "shell.execute_reply": "2024-04-19T16:16:29.257376Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "resid = res.resid_deviance.copy()\n",
    "resid_std = stats.zscore(resid)\n",
    "ax.hist(resid_std, bins=25)\n",
    "ax.set_title('Histogram of standardized deviance residuals');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "QQ Plot of Deviance Residuals:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-19T16:16:29.265732Z",
     "iopub.status.busy": "2024-04-19T16:16:29.263624Z",
     "iopub.status.idle": "2024-04-19T16:16:29.771543Z",
     "shell.execute_reply": "2024-04-19T16:16:29.770818Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels import graphics\n",
    "graphics.gofplots.qqplot(resid, line='r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GLM: Gamma for proportional count response\n",
    "\n",
    "### Load Scottish Parliament Voting data\n",
    "\n",
    " In the example above, we printed the ``NOTE`` attribute to learn about the\n",
    " Star98 dataset. statsmodels datasets ships with other useful information. For\n",
    " example: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-19T16:16:29.776925Z",
     "iopub.status.busy": "2024-04-19T16:16:29.775657Z",
     "iopub.status.idle": "2024-04-19T16:16:29.782697Z",
     "shell.execute_reply": "2024-04-19T16:16:29.781906Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "This data is based on the example in Gill and describes the proportion of\n",
      "voters who voted Yes to grant the Scottish Parliament taxation powers.\n",
      "The data are divided into 32 council districts.  This example's explanatory\n",
      "variables include the amount of council tax collected in pounds sterling as\n",
      "of April 1997 per two adults before adjustments, the female percentage of\n",
      "total claims for unemployment benefits as of January, 1998, the standardized\n",
      "mortality rate (UK is 100), the percentage of labor force participation,\n",
      "regional GDP, the percentage of children aged 5 to 15, and an interaction term\n",
      "between female unemployment and the council tax.\n",
      "\n",
      "The original source files and variable information are included in\n",
      "/scotland/src/\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(sm.datasets.scotland.DESCRLONG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Load the data and add a constant to the exogenous variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-19T16:16:29.787623Z",
     "iopub.status.busy": "2024-04-19T16:16:29.786318Z",
     "iopub.status.idle": "2024-04-19T16:16:29.806067Z",
     "shell.execute_reply": "2024-04-19T16:16:29.805252Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   COUTAX  UNEMPF    MOR   ACT      GDP   AGE  COUTAX_FEMALEUNEMP  const\n",
      "0   712.0    21.0  105.0  82.4  13566.0  12.3             14952.0    1.0\n",
      "1   643.0    26.5   97.0  80.2  13566.0  15.3             17039.5    1.0\n",
      "2   679.0    28.3  113.0  86.3   9611.0  13.9             19215.7    1.0\n",
      "3   801.0    27.1  109.0  80.4   9483.0  13.6             21707.1    1.0\n",
      "4   753.0    22.0  115.0  64.7   9265.0  14.6             16566.0    1.0\n",
      "0    60.3\n",
      "1    52.3\n",
      "2    53.4\n",
      "3    57.0\n",
      "4    68.7\n",
      "Name: YES, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "data2 = sm.datasets.scotland.load()\n",
    "data2.exog = sm.add_constant(data2.exog, prepend=False)\n",
    "print(data2.exog.head())\n",
    "print(data2.endog.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Fit and summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-19T16:16:29.811131Z",
     "iopub.status.busy": "2024-04-19T16:16:29.809790Z",
     "iopub.status.idle": "2024-04-19T16:16:29.828884Z",
     "shell.execute_reply": "2024-04-19T16:16:29.828207Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Generalized Linear Model Regression Results                  \n",
      "==============================================================================\n",
      "Dep. Variable:                    YES   No. Observations:                   32\n",
      "Model:                            GLM   Df Residuals:                       24\n",
      "Model Family:                   Gamma   Df Model:                            7\n",
      "Link Function:                    Log   Scale:                       0.0035927\n",
      "Method:                          IRLS   Log-Likelihood:                -83.110\n",
      "Date:                Fri, 19 Apr 2024   Deviance:                     0.087988\n",
      "Time:                        16:16:29   Pearson chi2:                   0.0862\n",
      "No. Iterations:                     7   Pseudo R-squ. (CS):             0.9797\n",
      "Covariance Type:            nonrobust                                         \n",
      "======================================================================================\n",
      "                         coef    std err          z      P>|z|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------\n",
      "COUTAX                -0.0024      0.001     -2.466      0.014      -0.004      -0.000\n",
      "UNEMPF                -0.1005      0.031     -3.269      0.001      -0.161      -0.040\n",
      "MOR                    0.0048      0.002      2.946      0.003       0.002       0.008\n",
      "ACT                   -0.0067      0.003     -2.534      0.011      -0.012      -0.002\n",
      "GDP                 8.173e-06   7.19e-06      1.136      0.256   -5.93e-06    2.23e-05\n",
      "AGE                    0.0298      0.015      2.009      0.045       0.001       0.059\n",
      "COUTAX_FEMALEUNEMP     0.0001   4.33e-05      2.724      0.006    3.31e-05       0.000\n",
      "const                  5.6581      0.680      8.318      0.000       4.325       6.991\n",
      "======================================================================================\n"
     ]
    }
   ],
   "source": [
    "glm_gamma = sm.GLM(data2.endog, data2.exog, family=sm.families.Gamma(sm.families.links.Log()))\n",
    "glm_results = glm_gamma.fit()\n",
    "print(glm_results.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GLM: Gaussian distribution with a noncanonical link\n",
    "\n",
    "### Artificial data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-19T16:16:29.833827Z",
     "iopub.status.busy": "2024-04-19T16:16:29.832641Z",
     "iopub.status.idle": "2024-04-19T16:16:29.839444Z",
     "shell.execute_reply": "2024-04-19T16:16:29.838696Z"
    }
   },
   "outputs": [],
   "source": [
    "nobs2 = 100\n",
    "x = np.arange(nobs2)\n",
    "np.random.seed(54321)\n",
    "X = np.column_stack((x,x**2))\n",
    "X = sm.add_constant(X, prepend=False)\n",
    "lny = np.exp(-(.03*x + .0001*x**2 - 1.0)) + .001 * np.random.rand(nobs2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit and summary (artificial data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-19T16:16:29.844341Z",
     "iopub.status.busy": "2024-04-19T16:16:29.843007Z",
     "iopub.status.idle": "2024-04-19T16:16:29.859441Z",
     "shell.execute_reply": "2024-04-19T16:16:29.858671Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Generalized Linear Model Regression Results                  \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   No. Observations:                  100\n",
      "Model:                            GLM   Df Residuals:                       97\n",
      "Model Family:                Gaussian   Df Model:                            2\n",
      "Link Function:                    Log   Scale:                      1.0531e-07\n",
      "Method:                          IRLS   Log-Likelihood:                 662.92\n",
      "Date:                Fri, 19 Apr 2024   Deviance:                   1.0215e-05\n",
      "Time:                        16:16:29   Pearson chi2:                 1.02e-05\n",
      "No. Iterations:                     7   Pseudo R-squ. (CS):              1.000\n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "x1            -0.0300    5.6e-06  -5361.316      0.000      -0.030      -0.030\n",
      "x2         -9.939e-05   1.05e-07   -951.091      0.000   -9.96e-05   -9.92e-05\n",
      "const          1.0003   5.39e-05   1.86e+04      0.000       1.000       1.000\n",
      "==============================================================================\n"
     ]
    }
   ],
   "source": [
    "gauss_log = sm.GLM(lny, X, family=sm.families.Gaussian(sm.families.links.Log()))\n",
    "gauss_log_results = gauss_log.fit()\n",
    "print(gauss_log_results.summary())"
   ]
  }
 ],
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