{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", " \n", "
\n", "\n", "# Beetle Mortality Data\n", "Author(s): Paul Miles | Date Created: June 18, 2018\n", "\n", "This is a binomial logistic regression example with dose-response data.\n", "\n", "A. Dobson, \"An Introduction to Generalized Linear Models\", Chapman & Hall/CRC, 2002." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.9.0\n" ] } ], "source": [ "# import required packages\n", "import numpy as np\n", "import math\n", "from pymcmcstat.MCMC import MCMC\n", "import matplotlib.pyplot as plt\n", "import pymcmcstat\n", "print(pymcmcstat.__version__)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Define mortality data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Beetle mortality data\n", "dose = np.array([1.6907, 1.7242, 1.7552, 1.7842,\n", " 1.8113, 1.8369, 1.8610, 1.8839])\n", "number_of_beetles = np.array([59, 60, 62, 56, 63, 59, 62, 60])\n", "number_of_beetles_killed = np.array([6, 13, 18, 28, 52, 53, 61, 60])\n", "\n", "x = np.array([dose, number_of_beetles]).T\n", "y = number_of_beetles_killed" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Define model options\n", "The user is given several modeling options. The user chooses the desired options by defining the appropriate keywork to `beetle_link`.\n", "\n", "- `logitmodelfun`: \n", "$$y(d,\\theta) = \\frac{1}{1 + \\exp(\\theta_0 + \\theta_1d)}$$\n", "- `loglogmodelfun`:\n", "$$y(d,\\theta) = 1 - \\exp(-\\exp(\\theta_0 + \\theta_1d))$$\n", "- `probitmodelfun`:\n", "$$y(d,\\theta) = \\frac{1}{2}\\Big[1 + \\text{erf}\\Big(\\frac{\\theta_0 + \\theta_1d -\\mu}{\\sigma\\sqrt{2}}\\Big)\\Big], \\quad \\mu = 0, \\quad \\sigma^2 = 1$$" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def logitmodelfun(d, t):\n", " return 1/(1+np.exp(t[0]+t[1]*d))\n", "def loglogmodelfun(d, t):\n", " return 1 - np.exp(-np.exp(t[0] + t[1]*d))\n", "def nordf(x, mu = 0, sigma2 = 1):\n", " # NORDF the standard normal (Gaussian) cumulative distribution.\n", " # NORPF(x,mu,sigma2) x quantile, mu mean, sigma2 variance\n", " # Marko Laine \n", " # $Revision: 1.5 $ $Date: 2007/12/04 08:57:00 $\n", " # adapted for Python by Paul Miles, November 8, 2017\n", " return 0.5 + 0.5*math.erf((x-mu)/math.sqrt(sigma2)/math.sqrt(2))\n", "def probitmodelfun(d, t):\n", " tmp = np.vectorize(nordf)\n", " return tmp(t[0] + t[1]*d)\n", "\n", "beetle_link_dictionary = dict(\n", " logit={'theta0': [60, -35], 'modelfun': logitmodelfun, \n", " 'label': 'Beetle data with logit link'},\n", " loglog={'theta0': [-40, 22], 'modelfun': loglogmodelfun, \n", " 'label': 'Beetle data with loglog link'},\n", " probit={'theta0': [-35, 20], 'modelfun': probitmodelfun, \n", " 'label': 'Beetle data with loglog link'},\n", ")\n", "# specify model type\n", "beetle_link = 'loglog' \n", "beetle_model_object = beetle_link_dictionary[beetle_link]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Setup MCMC Simulation\n", "Note, $\\theta_0 = b_0$ and $\\theta_1 = b_1$." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Initialize MCMC object\n", "mcstat = MCMC()\n", "# initialize data structure \n", "mcstat.data.add_data_set(\n", " x, y, user_defined_object=beetle_model_object)\n", "# initialize parameter array\n", "theta0 = beetle_model_object['theta0']\n", "mcstat.parameters.add_model_parameter(name='$b_0$',\n", " theta0=theta0[0])\n", "mcstat.parameters.add_model_parameter(name='$b_1$',\n", " theta0=theta0[1])\n", "# Generate options\n", "mcstat.simulation_options.define_simulation_options(nsimu=5.0e3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The sum-of-squares function is somewhat involved as you must make sure the data component are the appropriate sizes." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# define sum-of-squares model function\n", "def ssfun(theta,data):\n", " # unpack data\n", " ss = np.zeros([1])\n", " y = data.ydata[0]\n", " ndp = len(y)\n", " dose = data.xdata[0][:,0].reshape(ndp, 1)\n", " n = data.xdata[0][:,1].reshape(ndp, 1)\n", " obj = data.user_defined_object[0]\n", " model = obj['modelfun']\n", " # evaluate model\n", " p = model(dose, theta)\n", " # calculate loglikelihood\n", " ss = -2*(y*np.log(p) + (n-y)*np.log(1-p)).sum()\n", " return ss\n", "\n", "# Define model object:\n", "mcstat.model_settings.define_model_settings(sos_function = ssfun)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Run simulation\n", "We observe several error messages associated with numerical overflow. This is acceptable in light of those points being rejected during the simulation." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Sampling these parameters:\n", " name start [ min, max] N( mu, sigma^2)\n", " $b_0$: -40.00 [ -inf, inf] N( 0.00e+00, inf)\n", " $b_1$: 22.00 [ -inf, inf] N( 0.00e+00, inf)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/anaconda3/envs/pymcmcstat_190/lib/python3.6/site-packages/ipykernel_launcher.py:14: RuntimeWarning: divide by zero encountered in log\n", " \n", "/anaconda3/envs/pymcmcstat_190/lib/python3.6/site-packages/ipykernel_launcher.py:14: RuntimeWarning: invalid value encountered in multiply\n", " \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " [-----------------100%-----------------] 5000 of 5000 complete in 0.9 sec" ] } ], "source": [ "# Run mcmcrun\n", "mcstat.run_simulation()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Extract results and display chain statistics" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "------------------------------\n", "name : mean std MC_err tau geweke\n", "$b_0$ : -39.9298 3.1084 0.1059 6.8859 0.9892\n", "$b_1$ : 22.2399 1.7278 0.0592 6.9157 0.9890\n", "------------------------------\n", "==============================\n", "Acceptance rate information\n", "---------------\n", "Results dictionary:\n", "Stage 1: 37.82%\n", "Stage 2: 48.14%\n", "Net : 85.96% -> 4298/5000\n", "---------------\n", "Chain provided:\n", "Net : 85.96% -> 4298/5000\n", "---------------\n", "Note, the net acceptance rate from the results dictionary\n", "may be different if you only provided a subset of the chain,\n", "e.g., removed the first part for burnin-in.\n", "------------------------------\n" ] } ], "source": [ "# Extract results\n", "results = mcstat.simulation_results.results\n", "names = results['names']\n", "chain = results['chain']\n", "s2chain = results['s2chain']\n", "names = results['names'] # parameter names\n", "# display chain stats\n", "mcstat.chainstats(chain, results)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Plot the chain and pairwise correlation panel" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from pymcmcstat import mcmcplot as mcp\n", "# plot chain panel\n", "mcp.plot_chain_panel(chain, names)\n", "# pairwise correlation\n", "settings = dict(fig=dict(figsize=(4, 4)))\n", "f = mcp.plot_pairwise_correlation_panel(chain, names, settings=settings)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Generate credible intervals\n", "## Option 1" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generating credible/prediction intervals:\n", "\n", "\n", "Interval generation complete\n", "\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Define function to generate credible intervals\n", "def predmodelfun(data, theta):\n", " dose = data.xdata[0][:, 0]\n", " obj = data.user_defined_object[0]\n", " model = obj['modelfun']\n", " # evaluate model\n", " p = model(dose, theta)\n", " return p\n", "\n", "# define data structure for prediction\n", "predmcmc = MCMC()\n", "xmod = np.linspace(1.5,2)\n", "predmcmc.data.add_data_set(x=xmod,\n", " y=xmod,\n", " user_defined_object=beetle_model_object)\n", "\n", "mcstat.PI.setup_prediction_interval_calculation(\n", " results=results,\n", " data=predmcmc.data,\n", " modelfunction=predmodelfun);\n", "mcstat.PI.generate_prediction_intervals(\n", " nsample=500,\n", " calc_pred_int=False);\n", "# plot credible intervals\n", "mcstat.PI.plot_prediction_intervals(adddata=False)\n", "\n", "plt.plot(dose, number_of_beetles_killed/number_of_beetles,\n", " 'ok', label = 'data'); # add data points to the plot\n", "plt.xlabel('log(dose)', Fontsize=20)\n", "plt.xticks(Fontsize=20)\n", "plt.ylabel('proportion killed', Fontsize=20)\n", "plt.yticks(Fontsize=20)\n", "plt.title('Beetle data with loglog link', Fontsize=20)\n", "ax = plt.gca()\n", "handles, labels = ax.get_legend_handles_labels()\n", "plt.legend(handles, labels, loc='upper left')\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Option 2" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\r", " [-----------------100%-----------------] 500 of 500 complete in 0.0 sec" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Define function to generate credible intervals\n", "def predmodelfun2(q, data):\n", " dose = data.xdata[0][:, 0]\n", " obj = data.user_defined_object[0]\n", " model = obj['modelfun']\n", " # evaluate model\n", " p = model(dose, q)\n", " return p\n", "\n", "# define data structure for prediction\n", "from pymcmcstat import propagation as up\n", "from pymcmcstat.MCMC import DataStructure\n", "\n", "pdata = DataStructure()\n", "xmod = np.linspace(1.5,2)\n", "pdata.add_data_set(x=xmod, y=xmod,\n", " user_defined_object=beetle_model_object)\n", "\n", "intervals = up.calculate_intervals(chain=chain, results=results, data=pdata,\n", " model=predmodelfun2, nsample=500)\n", "ciset = dict(\n", " limits=[50, 90, 95, 99],\n", " )\n", "data_display = dict(\n", " color='k',\n", " marker='o')\n", "fig, ax = up.plot_intervals(intervals, xmod,\n", " ydata=number_of_beetles_killed/number_of_beetles,\n", " xdata=dose,\n", " adddata=True,\n", " addprediction=False,\n", " ciset=ciset,\n", " data_display=data_display)\n", "for tick in ax.xaxis.get_major_ticks():\n", " tick.label.set_fontsize(20)\n", "for tick in ax.yaxis.get_major_ticks():\n", " tick.label.set_fontsize(20)\n", "ax.set_xlabel('log(dose)', Fontsize=20)\n", "ax.set_ylabel('proportion killed', Fontsize=20)\n", "ax.set_title('Beetle data with loglog link', Fontsize=20)\n", "fig.tight_layout()" ] } ], "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": "3.6.8" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autoclose": false, "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": true, "user_envs_cfg": false }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": true } }, "nbformat": 4, "nbformat_minor": 2 }