{ "cells": [ { "cell_type": "markdown", "metadata": {}, "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." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2024-03-18T09:21:21.935155Z", "iopub.status.busy": "2024-03-18T09:21:21.934849Z", "iopub.status.idle": "2024-03-18T09:21:23.508224Z", "shell.execute_reply": "2024-03-18T09:21:23.507356Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [], "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", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2024-03-18T09:21:23.513859Z", "iopub.status.busy": "2024-03-18T09:21:23.512313Z", "iopub.status.idle": "2024-03-18T09:21:23.605964Z", "shell.execute_reply": "2024-03-18T09:21:23.605017Z" }, "jupyter": { "outputs_hidden": false } }, "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", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2024-03-18T09:21:23.610681Z", "iopub.status.busy": "2024-03-18T09:21:23.610234Z", "iopub.status.idle": "2024-03-18T09:21:23.631575Z", "shell.execute_reply": "2024-03-18T09:21:23.630725Z" }, "jupyter": { "outputs_hidden": false } }, "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: Mon, 18 Mar 2024 Deviance: 29.227\n", "Time: 09:21:23 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": { "iopub.execute_input": "2024-03-18T09:21:23.636222Z", "iopub.status.busy": "2024-03-18T09:21:23.635544Z", "iopub.status.idle": "2024-03-18T09:21:23.642876Z", "shell.execute_reply": "2024-03-18T09:21:23.642119Z" } }, "outputs": [], "source": [ "infl = res.get_influence(observed=False)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2024-03-18T09:21:23.646276Z", "iopub.status.busy": "2024-03-18T09:21:23.645728Z", "iopub.status.idle": "2024-03-18T09:21:23.667374Z", "shell.execute_reply": "2024-03-18T09:21:23.666503Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "text/html": [ "
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dfb_constdfb_log_ratedfb_log_volumnecooks_dstandard_residhat_diagdffits_internal
Case
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" ], "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]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2024-03-18T09:21:23.670724Z", "iopub.status.busy": "2024-03-18T09:21:23.670201Z", "iopub.status.idle": "2024-03-18T09:21:24.076928Z", "shell.execute_reply": "2024-03-18T09:21:24.076169Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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" ] }, "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": "2024-03-18T09:21:24.080241Z", "iopub.status.busy": "2024-03-18T09:21:24.079874Z", "iopub.status.idle": "2024-03-18T09:21:24.920406Z", "shell.execute_reply": "2024-03-18T09:21:24.919693Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "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": "2024-03-18T09:21:24.926929Z", "iopub.status.busy": "2024-03-18T09:21:24.924721Z", "iopub.status.idle": "2024-03-18T09:21:25.561167Z", "shell.execute_reply": "2024-03-18T09:21:25.560492Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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" ] }, "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": "2024-03-18T09:21:25.564460Z", "iopub.status.busy": "2024-03-18T09:21:25.564099Z", "iopub.status.idle": "2024-03-18T09:21:26.246527Z", "shell.execute_reply": "2024-03-18T09:21:26.245842Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "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": "2024-03-18T09:21:26.249988Z", "iopub.status.busy": "2024-03-18T09:21:26.249619Z", "iopub.status.idle": "2024-03-18T09:21:26.844598Z", "shell.execute_reply": "2024-03-18T09:21:26.843829Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "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": "2024-03-18T09:21:26.848205Z", "iopub.status.busy": "2024-03-18T09:21:26.847870Z", "iopub.status.idle": "2024-03-18T09:21:27.413618Z", "shell.execute_reply": "2024-03-18T09:21:27.412849Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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" ] }, "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": "3.10.13" } }, "nbformat": 4, "nbformat_minor": 4 }