{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# Influence Measures for GLM Logit\n",
    "\n",
    "\n",
    "Based on draft version for GLMInfluence, which will also apply to discrete Logit, Probit and Poisson, and eventually be extended to cover most models outside of time series analysis.\n",
    "\n",
    "The example for logistic regression was used by Pregibon (1981) \"Logistic Regression diagnostics\" and is based on data by Finney (1947).\n",
    "\n",
    "GLMInfluence includes the basic influence measures but still misses some measures described in Pregibon (1981), for example those related to deviance and effects on confidence intervals."
   ]
  },
  {
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   "source": [
    "import os.path\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from statsmodels.genmod.generalized_linear_model import GLM\n",
    "from statsmodels.genmod import families\n",
    "\n",
    "plt.rc(\"figure\", figsize=(16, 8))\n",
    "plt.rc(\"font\", size=14)"
   ]
  },
  {
   "cell_type": "code",
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    },
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   "outputs": [],
   "source": [
    "import statsmodels.stats.tests.test_influence\n",
    "\n",
    "test_module = statsmodels.stats.tests.test_influence.__file__\n",
    "cur_dir = cur_dir = os.path.abspath(os.path.dirname(test_module))\n",
    "\n",
    "file_name = \"binary_constrict.csv\"\n",
    "file_path = os.path.join(cur_dir, \"results\", file_name)\n",
    "df = pd.read_csv(file_path, index_col=0)"
   ]
  },
  {
   "cell_type": "code",
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    "execution": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Generalized Linear Model Regression Results                  \n",
      "==============================================================================\n",
      "Dep. Variable:              constrict   No. Observations:                   39\n",
      "Model:                            GLM   Df Residuals:                       36\n",
      "Model Family:                Binomial   Df Model:                            2\n",
      "Link Function:                  Logit   Scale:                          1.0000\n",
      "Method:                          IRLS   Log-Likelihood:                -14.614\n",
      "Date:                Wed, 02 Nov 2022   Deviance:                       29.227\n",
      "Time:                        17:10:15   Pearson chi2:                     34.2\n",
      "No. Iterations:                     7   Pseudo R-squ. (CS):             0.4707\n",
      "Covariance Type:            nonrobust                                         \n",
      "===============================================================================\n",
      "                  coef    std err          z      P>|z|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------\n",
      "const          -2.8754      1.321     -2.177      0.029      -5.464      -0.287\n",
      "log_rate        4.5617      1.838      2.482      0.013       0.959       8.164\n",
      "log_volumne     5.1793      1.865      2.777      0.005       1.524       8.834\n",
      "===============================================================================\n"
     ]
    }
   ],
   "source": [
    "res = GLM(\n",
    "    df[\"constrict\"],\n",
    "    df[[\"const\", \"log_rate\", \"log_volumne\"]],\n",
    "    family=families.Binomial(),\n",
    ").fit(attach_wls=True, atol=1e-10)\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## get the influence measures\n",
    "\n",
    "GLMResults has a `get_influence` method similar to OLSResults, that returns and instance of the GLMInfluence class. This class has methods and (cached) attributes to inspect influence and outlier measures.\n",
    "\n",
    "This measures are based on a one-step approximation to the the results for deleting one observation. One-step approximations are usually accurate for small changes but underestimate the magnitude of large changes. Event though large changes are underestimated, they still show clearly the effect of influential observations\n",
    "\n",
    "In this example observation 4 and 18 have a large standardized residual and large Cook's distance, but not a large leverage. Observation 13 has the largest leverage but only small Cook's distance and not a large studentized residual.\n",
    "\n",
    "Only the two observations 4 and 18 have a large impact on the parameter estimates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2022-11-02T17:10:15.629967Z"
    }
   },
   "outputs": [],
   "source": [
    "infl = res.get_influence(observed=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dfb_const</th>\n",
       "      <th>dfb_log_rate</th>\n",
       "      <th>dfb_log_volumne</th>\n",
       "      <th>cooks_d</th>\n",
       "      <th>standard_resid</th>\n",
       "      <th>hat_diag</th>\n",
       "      <th>dffits_internal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Case</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.073359</td>\n",
       "      <td>-0.930200</td>\n",
       "      <td>-1.017953</td>\n",
       "      <td>0.429085</td>\n",
       "      <td>3.681352</td>\n",
       "      <td>0.086745</td>\n",
       "      <td>1.134573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.959508</td>\n",
       "      <td>-0.827905</td>\n",
       "      <td>-0.847666</td>\n",
       "      <td>0.328152</td>\n",
       "      <td>3.055542</td>\n",
       "      <td>0.095386</td>\n",
       "      <td>0.992197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-0.259120</td>\n",
       "      <td>0.202363</td>\n",
       "      <td>-0.004883</td>\n",
       "      <td>0.066770</td>\n",
       "      <td>-1.150095</td>\n",
       "      <td>0.131521</td>\n",
       "      <td>-0.447560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.236747</td>\n",
       "      <td>-0.194984</td>\n",
       "      <td>0.028643</td>\n",
       "      <td>0.065370</td>\n",
       "      <td>0.984729</td>\n",
       "      <td>0.168219</td>\n",
       "      <td>0.442844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.116501</td>\n",
       "      <td>-0.099602</td>\n",
       "      <td>0.132197</td>\n",
       "      <td>0.055382</td>\n",
       "      <td>0.713771</td>\n",
       "      <td>0.245917</td>\n",
       "      <td>0.407609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-0.128107</td>\n",
       "      <td>0.041039</td>\n",
       "      <td>-0.100410</td>\n",
       "      <td>0.051950</td>\n",
       "      <td>-1.420261</td>\n",
       "      <td>0.071721</td>\n",
       "      <td>-0.394777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.093083</td>\n",
       "      <td>-0.009463</td>\n",
       "      <td>0.177532</td>\n",
       "      <td>0.046492</td>\n",
       "      <td>-0.847046</td>\n",
       "      <td>0.162757</td>\n",
       "      <td>-0.373465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-0.196119</td>\n",
       "      <td>0.127482</td>\n",
       "      <td>0.035689</td>\n",
       "      <td>0.031168</td>\n",
       "      <td>-1.065576</td>\n",
       "      <td>0.076085</td>\n",
       "      <td>-0.305786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>-0.088174</td>\n",
       "      <td>-0.013657</td>\n",
       "      <td>-0.002161</td>\n",
       "      <td>0.027481</td>\n",
       "      <td>-1.238185</td>\n",
       "      <td>0.051031</td>\n",
       "      <td>-0.287130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.092235</td>\n",
       "      <td>0.170980</td>\n",
       "      <td>0.038080</td>\n",
       "      <td>0.026230</td>\n",
       "      <td>0.661478</td>\n",
       "      <td>0.152431</td>\n",
       "      <td>0.280520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      dfb_const  dfb_log_rate  dfb_log_volumne   cooks_d  standard_resid  \\\n",
       "Case                                                                       \n",
       "4      1.073359     -0.930200        -1.017953  0.429085        3.681352   \n",
       "18     0.959508     -0.827905        -0.847666  0.328152        3.055542   \n",
       "19    -0.259120      0.202363        -0.004883  0.066770       -1.150095   \n",
       "29     0.236747     -0.194984         0.028643  0.065370        0.984729   \n",
       "31     0.116501     -0.099602         0.132197  0.055382        0.713771   \n",
       "24    -0.128107      0.041039        -0.100410  0.051950       -1.420261   \n",
       "13    -0.093083     -0.009463         0.177532  0.046492       -0.847046   \n",
       "23    -0.196119      0.127482         0.035689  0.031168       -1.065576   \n",
       "33    -0.088174     -0.013657        -0.002161  0.027481       -1.238185   \n",
       "6     -0.092235      0.170980         0.038080  0.026230        0.661478   \n",
       "\n",
       "      hat_diag  dffits_internal  \n",
       "Case                             \n",
       "4     0.086745         1.134573  \n",
       "18    0.095386         0.992197  \n",
       "19    0.131521        -0.447560  \n",
       "29    0.168219         0.442844  \n",
       "31    0.245917         0.407609  \n",
       "24    0.071721        -0.394777  \n",
       "13    0.162757        -0.373465  \n",
       "23    0.076085        -0.305786  \n",
       "33    0.051031        -0.287130  \n",
       "6     0.152431         0.280520  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summ_df = infl.summary_frame()\n",
    "summ_df.sort_values(\"cooks_d\", ascending=False)[:10]"
   ]
  },
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    {
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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = infl.plot_influence()\n",
    "fig.tight_layout(pad=1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:10:16.094747Z",
     "iopub.status.busy": "2022-11-02T17:10:16.094303Z",
     "iopub.status.idle": "2022-11-02T17:10:16.608091Z",
     "shell.execute_reply": "2022-11-02T17:10:16.607333Z"
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = infl.plot_index(y_var=\"cooks\", threshold=2 * infl.cooks_distance[0].mean())\n",
    "fig.tight_layout(pad=1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:10:16.612373Z",
     "iopub.status.busy": "2022-11-02T17:10:16.611960Z",
     "iopub.status.idle": "2022-11-02T17:10:16.984530Z",
     "shell.execute_reply": "2022-11-02T17:10:16.983658Z"
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = infl.plot_index(y_var=\"resid\", threshold=1)\n",
    "fig.tight_layout(pad=1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:10:16.988807Z",
     "iopub.status.busy": "2022-11-02T17:10:16.988539Z",
     "iopub.status.idle": "2022-11-02T17:10:17.351604Z",
     "shell.execute_reply": "2022-11-02T17:10:17.350813Z"
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = infl.plot_index(y_var=\"dfbeta\", idx=1, threshold=0.5)\n",
    "fig.tight_layout(pad=1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:10:17.356033Z",
     "iopub.status.busy": "2022-11-02T17:10:17.355754Z",
     "iopub.status.idle": "2022-11-02T17:10:17.765220Z",
     "shell.execute_reply": "2022-11-02T17:10:17.764572Z"
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = infl.plot_index(y_var=\"dfbeta\", idx=2, threshold=0.5)\n",
    "fig.tight_layout(pad=1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:10:17.769122Z",
     "iopub.status.busy": "2022-11-02T17:10:17.768863Z",
     "iopub.status.idle": "2022-11-02T17:10:18.301512Z",
     "shell.execute_reply": "2022-11-02T17:10:18.300550Z"
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = infl.plot_index(y_var=\"dfbeta\", idx=0, threshold=0.5)\n",
    "fig.tight_layout(pad=1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}