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   "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",
    "plt.rc(\"figure\", figsize=(16,8))\n",
    "plt.rc(\"font\", size=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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   "outputs": [],
   "source": [
    "import statsmodels.stats.tests.test_influence\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",
   "execution_count": 3,
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    {
     "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:                Tue, 02 Feb 2021   Deviance:                       29.227\n",
      "Time:                        06:55:19   Pearson chi2:                     34.2\n",
      "No. Iterations:                     7                                         \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(df['constrict'], df[['const', 'log_rate', 'log_volumne']],\n",
    "          family=families.Binomial()).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": {
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    "execution": {
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     "shell.execute_reply": "2021-02-02T06:55:19.461921Z"
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   },
   "outputs": [],
   "source": [
    "infl = res.get_influence(observed=False)"
   ]
  },
  {
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    "execution": {
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       "</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": 1,
     "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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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = infl.plot_influence()\n",
    "fig.tight_layout(pad=1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2021-02-02T06:55:19.845010Z",
     "iopub.status.busy": "2021-02-02T06:55:19.843316Z",
     "iopub.status.idle": "2021-02-02T06:55:20.118314Z",
     "shell.execute_reply": "2021-02-02T06:55:20.119282Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2021-02-02T06:55:20.123616Z",
     "iopub.status.busy": "2021-02-02T06:55:20.122373Z",
     "iopub.status.idle": "2021-02-02T06:55:20.438463Z",
     "shell.execute_reply": "2021-02-02T06:55:20.439454Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2021-02-02T06:55:20.444153Z",
     "iopub.status.busy": "2021-02-02T06:55:20.442742Z",
     "iopub.status.idle": "2021-02-02T06:55:20.747650Z",
     "shell.execute_reply": "2021-02-02T06:55:20.748778Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2021-02-02T06:55:20.753433Z",
     "iopub.status.busy": "2021-02-02T06:55:20.752049Z",
     "iopub.status.idle": "2021-02-02T06:55:21.032050Z",
     "shell.execute_reply": "2021-02-02T06:55:21.032904Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2021-02-02T06:55:21.036777Z",
     "iopub.status.busy": "2021-02-02T06:55:21.035615Z",
     "iopub.status.idle": "2021-02-02T06:55:21.319906Z",
     "shell.execute_reply": "2021-02-02T06:55:21.320805Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": {
    "collapsed": true
   },
   "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}