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@AustinRochford
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MRPyMC3-Multilevel Regression and Poststratification with PyMC3
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"title: MRPyMC3: Multilevel Regression and Poststratification with PyMC3\n",
"tags: PyMC3, Bayesian Statistics\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A few weeks ago, [YouGov](https://today.yougov.com/) correctly [predicted](https://yougov.co.uk/uk-general-election-2017/#/uk-elections-charts-anchor) a hung parliament as a result of the 2017 UK general election, to the astonishment of many commentators. YouGov's predictions were [based](https://yougov.co.uk/news/2017/05/31/how-yougov-model-2017-general-election-works/) on a technique called multilevel regression with poststratification, or MRP for short (Andrew Gelman playfully refers to it as [Mister P](http://andrewgelman.com/2009/05/09/the_next_suprem/)).\n",
"\n",
"I was impressed with YouGov's prediction and decided to work through an MRP example to improve my understanding of this technique. Since all of the applications of MRP I have found online involve `R`'s [`lme4`](https://cran.r-project.org/web/packages/lme4/index.html) package or [Stan](http://mc-stan.org/), I also thought this was a good opportunity to illustrate MRP in Python with PyMC3. This post is essentially a port of [Jonathan Kastellec](http://www.princeton.edu/~jkastell/)'s excellent [MRP primer](http://www.princeton.edu/~jkastell/mrp_primer.html) to Python and PyMC3. I am very grateful for his clear exposition of MRP and willingness to share a relevant data set.\n",
"\n",
"MRP was developed to estimate American state-level opinions from national polls. This sort of estimation is crucial to understanding American politics at the national level, as many of the important political positions of the federal government are impacted by state-level elections:\n",
"\n",
"* the president is chosen by the Electoral College, which (with a few exceptions) votes according to state-level vote totals,\n",
"* senators are chosen by state-level elections,\n",
"* many political and all judicial (notably Supreme Court) appointees require Senate approval, and therefore are subject to indirect state-level elections.\n",
"\n",
"Of course, as YouGov demonstrates, MRP is more widely applicable than estimation of state-level opinion.\n",
"\n",
"In this post, we will follow Kastellec's example of estimating state-level opinion about gay marriage in 2005/2006 using a combination of three national polls. We begin by loading a data set that consists of repsonses to the three national polls."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"#%config InlineBackend.figure_format = 'retina'"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import os\n",
"import us"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib as mpl\n",
"from matplotlib import pyplot as plt\n",
"from matplotlib.colors import Normalize, rgb2hex\n",
"from matplotlib.patches import Polygon\n",
"from matplotlib.ticker import FuncFormatter\n",
"from mpl_toolkits.basemap import Basemap\n",
"import numpy as np\n",
"import pandas as pd\n",
"import pymc3 as pm\n",
"import scipy as sp\n",
"import seaborn as sns\n",
"from theano import shared"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%%bash\n",
"if [ ! -e ./st99_d00.dbf ];\n",
"then\n",
" wget -q https://github.com/matplotlib/basemap/raw/master/examples/st99_d00.dbf\n",
" wget -q https://github.com/matplotlib/basemap/raw/master/examples/st99_d00.shp\n",
" wget -q https://github.com/matplotlib/basemap/raw/master/examples/st99_d00.shx\n",
"fi"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"SEED = 4260026 # from random.org, for reproducibility\n",
"\n",
"np.random.seed(SEED)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We load only the columns which we will use in the analysis and transform categorical variables to be zero-indexed."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def to_zero_indexed(col):\n",
" return lambda df: (df[col] - 1).astype(np.int64)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"DATA_PREFIX = 'http://www.princeton.edu/~jkastell/MRP_primer/'\n",
"\n",
"survey_df = (pd.read_stata(os.path.join(DATA_PREFIX, 'gay_marriage_megapoll.dta'),\n",
" columns=['race_wbh', 'age_cat', 'edu_cat', 'female',\n",
" 'state_initnum', 'state', 'region_cat', 'region', 'statename',\n",
" 'poll', 'yes_of_all'])\n",
" .dropna(subset=['race_wbh', 'age_cat', 'edu_cat', 'state_initnum'])\n",
" .assign(state_initnum=to_zero_indexed('state_initnum'),\n",
" race_wbh=to_zero_indexed('race_wbh'),\n",
" edu_cat=to_zero_indexed('edu_cat'),\n",
" age_cat=to_zero_indexed('age_cat'),\n",
" region_cat=to_zero_indexed('region_cat')))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>race_wbh</th>\n",
" <th>age_cat</th>\n",
" <th>edu_cat</th>\n",
" <th>female</th>\n",
" <th>state_initnum</th>\n",
" <th>state</th>\n",
" <th>region_cat</th>\n",
" <th>region</th>\n",
" <th>statename</th>\n",
" <th>poll</th>\n",
" <th>yes_of_all</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>22</td>\n",
" <td>MI</td>\n",
" <td>1</td>\n",
" <td>midwest</td>\n",
" <td>michigan</td>\n",
" <td>Gall2005Aug22</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>GA</td>\n",
" <td>2</td>\n",
" <td>south</td>\n",
" <td>georgia</td>\n",
" <td>Gall2005Aug22</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>34</td>\n",
" <td>NY</td>\n",
" <td>0</td>\n",
" <td>northeast</td>\n",
" <td>new york</td>\n",
" <td>Gall2005Aug22</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>30</td>\n",
" <td>NH</td>\n",
" <td>0</td>\n",
" <td>northeast</td>\n",
" <td>new hampshire</td>\n",
" <td>Gall2005Aug22</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>14</td>\n",
" <td>IL</td>\n",
" <td>1</td>\n",
" <td>midwest</td>\n",
" <td>illinois</td>\n",
" <td>Gall2005Aug22</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" race_wbh age_cat edu_cat female state_initnum state region_cat \\\n",
"0 0 2 2 1 22 MI 1 \n",
"1 0 2 3 0 10 GA 2 \n",
"2 2 0 3 0 34 NY 0 \n",
"3 0 3 3 1 30 NH 0 \n",
"5 0 3 2 1 14 IL 1 \n",
"\n",
" region statename poll yes_of_all \n",
"0 midwest michigan Gall2005Aug22 0 \n",
"1 south georgia Gall2005Aug22 0 \n",
"2 northeast new york Gall2005Aug22 1 \n",
"3 northeast new hampshire Gall2005Aug22 1 \n",
"5 midwest illinois Gall2005Aug22 0 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"survey_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These three surveys collected data from roughly 6,300 respondents during 2005 and 2006."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"6341"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"survey_df.shape[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see that the number of respondents varies widely between states."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def state_plot(state_data, cmap, norm, cbar=True, default=None, ax=None):\n",
" if ax is None:\n",
" fig, ax = plt.subplots(figsize=(8, 6))\n",
" else:\n",
" fig = plt.gcf()\n",
"\n",
" m = Basemap(llcrnrlon=-121, llcrnrlat=20,\n",
" urcrnrlon=-62, urcrnrlat=51,\n",
" projection='lcc',\n",
" lat_1=32, lat_2=45, lon_0=-95)\n",
" m.readshapefile('st99_d00', name='states', drawbounds=True)\n",
"\n",
" for state_info, state_seg in zip(m.states_info, m.states):\n",
" if state_info['NAME'] == 'Alaska':\n",
" state_seg = list(map(lambda xy: (0.35 * xy[0] + 1100000, 0.35 * xy[1] - 1300000), state_seg))\n",
" elif state_info['NAME'] == 'Hawaii' and float(state_info['AREA']) > 0.005:\n",
" state_seg = list(map(lambda xy: (xy[0] + 5100000, xy[1] - 1400000), state_seg))\n",
"\n",
" try:\n",
" state_datum = state_data.loc[us.states.lookup(state_info['NAME']).abbr] \n",
" except KeyError:\n",
" state_datum = default\n",
" \n",
" if state_datum is not None:\n",
" color = rgb2hex(cmap(norm(state_datum))) \n",
"\n",
" poly = Polygon(state_seg, facecolor=color, edgecolor='#000000')\n",
" ax.add_patch(poly)\n",
" \n",
" if cbar:\n",
" cbar_ax = fig.add_axes([0.925, 0.25, 0.04, 0.5])\n",
" mpl.colorbar.ColorbarBase(cbar_ax, cmap=cmap, norm=norm)\n",
" else:\n",
" cbar_ax = None\n",
" \n",
" return fig, ax, cbar_ax"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"state_counts = survey_df.groupby('state').size()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
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gwD5oNBImJibUrFkHH5+25Mlja7A4BUEQhJzjq0sUAgMvU7Bg6mYYPHz4kOvXr1GmTBls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uVl167v4OIyBHv3HmhWJJCO4/P5rH21ZvDgwdi1axeAxp4kNTU1iI2NbfpQOmrUKFy7dg0JCQlw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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f5222bbd6a0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax, cbar_ax = state_plot(state_counts,\n",
" mpl.cm.binary,\n",
" Normalize(0, state_counts.max()),\n",
" default=None)\n",
"\n",
"ax.set_title(\"Number of poll respondents\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notably, there are no respondents from some less populous states, such as Alaska and Hawaii.\n",
"\n",
"Faced with this data set, it is inuitively appealing to estimate state-level opinion by the observed proportion of that state's respondents that supported gay marriage. This approach is known as disaggregation."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"disagg_p = (survey_df.groupby('state')\n",
" .yes_of_all\n",
" .mean())"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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bmoQCoHfv3jRv3pxevXrx008/cfDgQc6cOcOuXbs4cOAA0dHRqsJVAH///TcHDhxIk1AA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Al8KlehmKeXQZ5ArwcGktPNPcvHzUrEEgkqtX6a32WW+g0bITWTERsbi52d3UfbY+PiKFCjOs45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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f52224450f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"p_norm = Normalize(0., 0.6)\n",
"p_cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
"\n",
"fig, ax, cbar_ax = state_plot(disagg_p, p_cmap, p_norm)\n",
"\n",
"p_formatter = FuncFormatter(lambda prop, _: '{:.1%}'.format(p_norm.inverse(prop)))\n",
"cbar_ax.yaxis.set_major_formatter(p_formatter);\n",
"\n",
"ax.set_title(\"Disaggregation estimate of\\nsupport for gay marriage in 2005\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The simplicity of disaggregation is appealing, but it suffers from a number of drawbacks. Obviously, it cannot estimate the state-level support for gay marriage in states with no respondents, such as Alaska and Hawaii. Similarly, for small/low population states with some respondents, the sample size may be too small to produce reliable estimates of opinion. This problem is exacerbated by the fact that for many issues, opinion is quite correlated with demographic factors such as age, race, education, gender, etc. Many more states will not have sufficient sample size for each combination of these factors for the disaggregate estimate to be representative of that state's demographic compositon."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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A8eyG9tGjRyVJUVFRtmUWi4XQBgDAyeyG9rUh7j97bRsAABQOu98eP3bsmPr2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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f5217dc2e48>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = (survey_df.groupby(['state', 'female', 'race_wbh'])\n",
" .size()\n",
" .unstack(level=['female', 'race_wbh'])\n",
" .isnull()\n",
" .sum()\n",
" .unstack(level='female')\n",
" .rename(index={0: 'White', 1: 'Black', 2: 'Hispanic'},\n",
" columns={0: 'Male', 1: 'Female'})\n",
" .rename_axis('Race', axis=0)\n",
" .rename_axis('Gender', axis=1)\n",
" .plot(kind='bar', rot=0, figsize=(8, 6)))\n",
"\n",
"ax.set_yticks(np.arange(0, 21, 5));\n",
"ax.set_ylabel(\"Number of states\");\n",
"\n",
"ax.set_title(\"States with no respondents\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The plot above illustrates this phenomenon; a number of states have no nonwhite male or female respondents. Even more states will have very few such respondents. This lack of data renders the disaggregation estimates suspect. For further discussion and references on disaggregation (as well as an empirical comparison of disaggregation and MRP), consult Lax and Phillip's [_How Should We Estimate Public Opinion in the States?_](http://www.columbia.edu/~jrl2124/Lax%20Phillips%20-%20Estimating%20State%20Public%20Opinion.pdf).\n",
"\n",
"MRP lessens the impact of this per-state respondent sparsity by first building a [multilevel model](https://en.wikipedia.org/wiki/Multilevel_model) of the relationship between respondents' states and demographic characteristics to opinion, and subsequently using the predictions of this multilevel model along with census data about the demographic composition of each state to predict state-level opinion. Intuitively, the multilevel model employed by MRP is a principled statistical method for estimating, for example, how much men in Pennsylvania share opinions with men in other states versus how much they share opinions with women in Pennsylvania. This partial pooling at both the state- and demographic-levels helps MRP impute the opinions of groups present in states that were not surveyed.\n",
"\n",
"The rest of this post is focused primarily on the execution of MRP in Python with PyMC3. For more detail on the theory and accuracy of MRP, consult the following (very incomplete) list of MRP resources:\n",
"\n",
"* the [MRP primer](http://www.princeton.edu/~jkastell/MRP_primer/mrp_primer.pdf) from which our example is taken,\n",
"* Park, Gelman, and Bafumi's [_Bayesian Multilevel Estimation with Poststratification: State-Level Estimates from National Polls_](https://pdfs.semanticscholar.org/2008/bee9f8c2d7e41ac9c5c54489f41989a0d7ba.pdf), which assesses the accuracy of MRP in predicting the state-level results of the 1998 and 1992 US presidential elections,\n",
"* Section 14.1 of Gelman and Hill's [_Data Analysis Using Regression and Multilevel/Hierarchical Models_](http://www.stat.columbia.edu/~gelman/arm/), which gives an expanded discussion of the example from the previous paper,\n",
"* Lax and Phillips' [_How Should We Estimate Public Opinion in The States?_](http://www.columbia.edu/~jrl2124/Lax%20Phillips%20-%20Estimating%20State%20Public%20Opinion.pdf), which is also mentioned above,\n",
"* Gelman's blog post [Mister P: What’s its secret sauce?](http://andrewgelman.com/2013/10/09/mister-p-whats-its-secret-sauce/), which is an extended discussion of several asssesments of MRP's accuracy ([1](https://academic.oup.com/pan/article-abstract/21/4/449/1544117/How-Does-Multilevel-Regression-and?redirectedFrom=fulltext), [2](http://www.columbia.edu/~jrl2124/mrp2.pdf)).\n",
"\n",
"Following the MRP primer, our multilevel opinion model will include factors for state, race, gender, education, age, and poll. In order to accelerate inference, we count the number of unique combinations of these factors, along with how many respondents with each combination supported gay marriage."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"uniq_survey_df = (survey_df.groupby(['race_wbh', 'female', 'edu_cat', 'age_cat',\n",
" 'region_cat', 'state_initnum', 'poll'])\n",
" .yes_of_all\n",
" .agg({\n",
" 'yes_of_all': 'sum',\n",
" 'n': 'size'\n",
" })\n",
" .reset_index())"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>race_wbh</th>\n",
" <th>female</th>\n",
" <th>edu_cat</th>\n",
" <th>age_cat</th>\n",
" <th>region_cat</th>\n",
" <th>state_initnum</th>\n",
" <th>poll</th>\n",
" <th>yes_of_all</th>\n",
" <th>n</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>Pew 2004Dec01</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>30</td>\n",
" <td>Gall2005Aug22</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>34</td>\n",
" <td>ABC 2004Jan15</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>38</td>\n",
" <td>Pew 2004Dec01</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>12</td>\n",
" <td>ABC 2004Jan15</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" race_wbh female edu_cat age_cat region_cat state_initnum \\\n",
"0 0 0 0 0 0 6 \n",
"1 0 0 0 0 0 30 \n",
"2 0 0 0 0 0 34 \n",
"3 0 0 0 0 0 38 \n",
"4 0 0 0 0 1 12 \n",
"\n",
" poll yes_of_all n \n",
"0 Pew 2004Dec01 0 1 \n",
"1 Gall2005Aug22 0 1 \n",
"2 ABC 2004Jan15 1 1 \n",
"3 Pew 2004Dec01 1 1 \n",
"4 ABC 2004Jan15 0 1 "
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"uniq_survey_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This reduction adds negligible mathematical complexity (several Bernoulli distributions are combined into a single binomial distribution), but reduces the number of rows in the data set by nearly half."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.5824002523261316"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"uniq_survey_df.shape[0] / survey_df.shape[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will refer to each unique combination of state and demographic characteristics as a cell. Let $n_i$ denote the number of respondents in cell $i$, $y_i$ the number of those respondents that supported gay marriage, and $p_i$ the probability that a member of the general population of cell $i$ supports gay marriage. We build a Bayesian multilevel logistic regression model of opinion as follows.\n",
"\n",
"$$\\begin{align*}\n",
"\\eta_i\n",
" & = \\beta_0 + \\alpha^{\\textrm{gender : race}}_{j(i)} + \\alpha^{\\textrm{age}}_{k(i)} + \\alpha^{\\textrm{edu}}_{l(i)} + \\alpha^{\\textrm{age : edu}}_{k(i),\\ l(i)} + \\alpha^{\\textrm{state}}_{s(i)} + \\alpha^{\\textrm{poll}}_{m(i)} \\\\\n",
"\\log \\left(\\frac{p_i}{1 - p_i}\\right)\n",
" & = \\eta_i \\\\\n",
"y_i\n",
" & \\sim \\textrm{Bernoulli}(n_i, p_i)\n",
"\\end{align*}$$\n",
"\n",
"Here each subscript indexed by $i$ is the categorical level of that characteristic for respondents in cell $i$. The prior for the intercept is $\\beta_0 \\sim N(0, 5^2)$. The prior for the effects of the interaction of gender and age is $\\alpha^{\\textrm{gender : race}}_j \\sim N\\left(0, \\sigma_{\\textrm{gender : race}}^2\\right),$ with $\\sigma_{\\textrm{gender : race}} \\sim \\textrm{HalfCauchy}(5)$. The priors on $\\alpha^{\\textrm{age}}_k,$ $\\alpha^{\\textrm{edu}}_l,$ $\\alpha^{\\textrm{age : edu}}_{k,\\ l},$ and $\\alpha^{\\textrm{poll}}_m$ are defined similarly. The prior on the state term, $\\alpha^{\\textrm{state}}_s$, includes state-level predictors for region of the country, religiosity, and support for John Kerry in the 2004 presidential election.\n",
"\n",
"$$\\begin{align*}\n",
"\\alpha^{\\textrm{state}}_s\n",
" & \\sim N\\left(\\alpha^{\\textrm{region}}_s + \\beta^{\\textrm{relig}} x^{\\textrm{relig}}_s + \\beta^{\\textrm{kerry}} x^{\\textrm{kerry}}_s, \\sigma^2_{\\textrm{state}}\\right)\n",
"\\end{align*}$$\n",
"\n",
"Here $x^{\\textrm{relig}}_s$ is the log odds of the proportion of the state's residents that are evangelical Christian or Mormon, and $x^{\\textrm{kerry}}_s$ is the log odds of the proportion of the state's voters that voted for John Kerry in 2004. The priors on $\\alpha^{\\textrm{region}}_s$, $\\beta^{\\textrm{relig}}$, $\\beta^{\\textrm{kerry}}$ are the same as those on the analagous terms in the definition of $\\eta$.\n",
"\n",
"First we encode the respondent information."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def encode_gender_race(female, race_wbh):\n",
" return (3 * female + race_wbh).values\n",
"\n",
"def encode_age_edu(age, edu):\n",
" return (4 * age + edu).values"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"gender_race = encode_gender_race(uniq_survey_df.female, uniq_survey_df.race_wbh)\n",
"n_gender_race = np.unique(gender_race).size\n",
"\n",
"age = uniq_survey_df.age_cat.values\n",
"n_age = np.unique(age).size\n",
"\n",
"edu = uniq_survey_df.edu_cat.values\n",
"n_edu = np.unique(edu).size\n",
"\n",
"age_edu = encode_age_edu(uniq_survey_df.age_cat, uniq_survey_df.edu_cat)\n",
"n_age_edu = np.unique(age_edu).size\n",
"\n",
"poll, poll_map = uniq_survey_df.poll.factorize()\n",
"n_poll = poll_map.size\n",
"\n",
"region = uniq_survey_df.region_cat.values\n",
"n_region = np.unique(region).size\n",
"\n",
"state = uniq_survey_df.state_initnum.values\n",
"n_state = 51\n",
"\n",
"n = uniq_survey_df.n.values\n",
"yes_of_all = uniq_survey_df.yes_of_all.values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next we load the state-level data and encode $x^{\\textrm{relig}}$ and $x^{\\textrm{kerry}}$."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"STATE_URL = 'http://www.princeton.edu/~jkastell/MRP_primer/state_level_update.dta'\n",
"\n",
"state_df = (pd.read_stata(STATE_URL,\n",
" columns=['sstate_initnum', 'sstate',\n",
" 'p_evang', 'p_mormon', 'kerry_04'])\n",
" .rename(columns={'sstate_initnum': 'state_initnum', 'sstate': 'state'})\n",
" .assign(state_initnum=to_zero_indexed('state_initnum'),\n",
" p_relig=lambda df: df.p_evang + df.p_mormon))"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>state_initnum</th>\n",
" <th>state</th>\n",
" <th>p_evang</th>\n",
" <th>p_mormon</th>\n",
" <th>kerry_04</th>\n",
" <th>p_relig</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>AK</td>\n",
" <td>12.440000</td>\n",
" <td>3.003126</td>\n",
" <td>35.500000</td>\n",
" <td>15.443126</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>AL</td>\n",
" <td>40.549999</td>\n",
" <td>0.458273</td>\n",
" <td>36.799999</td>\n",
" <td>41.008274</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>AR</td>\n",
" <td>43.070000</td>\n",
" <td>0.560113</td>\n",
" <td>44.599998</td>\n",
" <td>43.630112</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>AZ</td>\n",
" <td>9.410000</td>\n",
" <td>4.878735</td>\n",
" <td>44.400002</td>\n",
" <td>14.288734</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>CA</td>\n",
" <td>7.160000</td>\n",
" <td>1.557627</td>\n",
" <td>54.299999</td>\n",
" <td>8.717627</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" state_initnum state p_evang p_mormon kerry_04 p_relig\n",
"0 0 AK 12.440000 3.003126 35.500000 15.443126\n",
"1 1 AL 40.549999 0.458273 36.799999 41.008274\n",
"2 2 AR 43.070000 0.560113 44.599998 43.630112\n",
"3 3 AZ 9.410000 4.878735 44.400002 14.288734\n",
"4 4 CA 7.160000 1.557627 54.299999 8.717627"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"state_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"state_kerry = sp.special.logit(state_df.kerry_04.values / 100.)\n",
"state_relig = sp.special.logit(state_df.p_relig.values / 100.)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The state-level data doesn't contain region information, so we load census data in order to build a mapping between state and region."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"CENSUS_URL = 'http://www.princeton.edu/~jkastell/MRP_primer/poststratification%202000.dta'\n",
"\n",
"census_df = (pd.read_stata(CENSUS_URL)\n",
" .rename(columns=lambda s: s.lstrip('c_').lower())\n",
" .assign(race_wbh=to_zero_indexed('race_wbh'),\n",
" edu_cat=to_zero_indexed('edu_cat'),\n",
" age_cat=to_zero_indexed('age_cat')))"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>race_wbh</th>\n",
" <th>age_cat</th>\n",
" <th>edu_cat</th>\n",
" <th>female</th>\n",
" <th>state</th>\n",
" <th>freq</th>\n",
" <th>freq_state</th>\n",
" <th>percent_state</th>\n",
" <th>region</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>AK</td>\n",
" <td>467</td>\n",
" <td>21222.0</td>\n",
" <td>0.022005</td>\n",
" <td>west</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>AK</td>\n",
" <td>377</td>\n",
" <td>21222.0</td>\n",
" <td>0.017765</td>\n",
" <td>west</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>AK</td>\n",
" <td>419</td>\n",
" <td>21222.0</td>\n",
" <td>0.019744</td>\n",
" <td>west</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>AK</td>\n",
" <td>343</td>\n",
" <td>21222.0</td>\n",
" <td>0.016162</td>\n",
" <td>west</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>AK</td>\n",
" <td>958</td>\n",
" <td>21222.0</td>\n",
" <td>0.045142</td>\n",
" <td>west</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" race_wbh age_cat edu_cat female state freq freq_state percent_state \\\n",
"0 0 0 0 0 AK 467 21222.0 0.022005 \n",
"1 0 1 0 0 AK 377 21222.0 0.017765 \n",
"2 0 2 0 0 AK 419 21222.0 0.019744 \n",
"3 0 3 0 0 AK 343 21222.0 0.016162 \n",
"4 0 0 1 0 AK 958 21222.0 0.045142 \n",
"\n",
" region \n",
"0 west \n",
"1 west \n",
"2 west \n",
"3 west \n",
"4 west "
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"census_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"state_df = (pd.merge(\n",
" pd.merge((survey_df.groupby('region')\n",
" .region_cat\n",
" .first()\n",
" .reset_index()),\n",
" (census_df[['state', 'region']].drop_duplicates()),\n",
" on='region')[['state', 'region_cat']],\n",
" state_df, on='state')\n",
" .set_index('state_initnum')\n",
" .sort_index())"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>state</th>\n",
" <th>region_cat</th>\n",
" <th>p_evang</th>\n",
" <th>p_mormon</th>\n",
" <th>kerry_04</th>\n",
" <th>p_relig</th>\n",
" </tr>\n",
" <tr>\n",
" <th>state_initnum</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>0</th>\n",
" <td>AK</td>\n",
" <td>3</td>\n",
" <td>12.440000</td>\n",
" <td>3.003126</td>\n",
" <td>35.500000</td>\n",
" <td>15.443126</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>AL</td>\n",
" <td>2</td>\n",
" <td>40.549999</td>\n",
" <td>0.458273</td>\n",
" <td>36.799999</td>\n",
" <td>41.008274</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>AR</td>\n",
" <td>2</td>\n",
" <td>43.070000</td>\n",
" <td>0.560113</td>\n",
" <td>44.599998</td>\n",
" <td>43.630112</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>AZ</td>\n",
" <td>3</td>\n",
" <td>9.410000</td>\n",
" <td>4.878735</td>\n",
" <td>44.400002</td>\n",
" <td>14.288734</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>CA</td>\n",
" <td>3</td>\n",
" <td>7.160000</td>\n",
" <td>1.557627</td>\n",
" <td>54.299999</td>\n",
" <td>8.717627</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" state region_cat p_evang p_mormon kerry_04 p_relig\n",
"state_initnum \n",
"0 AK 3 12.440000 3.003126 35.500000 15.443126\n",
"1 AL 2 40.549999 0.458273 36.799999 41.008274\n",
"2 AR 2 43.070000 0.560113 44.599998 43.630112\n",
"3 AZ 3 9.410000 4.878735 44.400002 14.288734\n",
"4 CA 3 7.160000 1.557627 54.299999 8.717627"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"state_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"state_region = state_df.region_cat.values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we are ready to specify the model with PyMC3. First, we wrap the predictors in `theano.shared` so that we can eventually replace the survey respondent's predictors with census predictors for posterior prediction (the poststratification step of MRP)."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"gender_race_ = shared(gender_race)\n",
"age_ = shared(age)\n",
"edu_ = shared(edu)\n",
"age_edu_ = shared(age_edu)\n",
"poll_ = shared(poll)\n",
"state_ = shared(state)\n",
"use_poll_ = shared(1)\n",
"n_ = shared(n)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We specify the model for $\\alpha^{\\textrm{state}}$."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def hierarchical_normal(name, shape, μ=0.):\n",
" Δ = pm.Normal('Δ_{}'.format(name), 0., 1., shape=shape)\n",
" σ = pm.HalfCauchy('σ_{}'.format(name), 5.)\n",
" \n",
" return pm.Deterministic(name, μ + Δ * σ)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"with pm.Model() as model:\n",
" α_region = hierarchical_normal('region', n_region)\n",
" β_relig = pm.Normal('relig', 0., 5.)\n",
" β_kerry = pm.Normal('kerry', 0., 5.)\n",
" μ_state = α_region[state_region] + β_relig * state_relig + β_kerry * state_kerry\n",
" α_state = hierarchical_normal('state', n_state, μ=μ_state)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Throughout, we use a [non-centered parametrization](http://twiecki.github.io/blog/2017/02/08/bayesian-hierchical-non-centered/) for our hierarchical normal priors for more efficient sampling. We now specify the rest of $\\eta_i$."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"with model:\n",
" β0 = pm.Normal('β0', 0., 5.,\n",
" testval=sp.special.logit(survey_df.yes_of_all.mean()))\n",
" α_gender_race = hierarchical_normal('gender_race', n_gender_race)\n",
" α_age = hierarchical_normal('age', n_age)\n",
" α_edu = hierarchical_normal('edu', n_edu)\n",
" α_age_edu = hierarchical_normal('age_edu', n_age_edu)\n",
" α_poll = hierarchical_normal('poll', n_poll)\n",
" \n",
" η = β0 \\\n",
" + α_gender_race[gender_race_] \\\n",
" + α_age[age_] \\\n",
" + α_edu[edu_] \\\n",
" + α_age_edu[age_edu_] \\\n",
" + α_state[state_] \\\n",
" + use_poll_ * α_poll[poll_]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here the `theano.shared` variable `use_poll_` will allow us to ignore poll effects when we do posterior predictive sampling with census data.\n",
"\n",
"Finally, we specify the likelihood and sample from the model using NUTS."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"with model:\n",
" p = pm.math.sigmoid(η)\n",
" obs = pm.Binomial('obs', n_, p, observed=yes_of_all)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Auto-assigning NUTS sampler...\n",
"Initializing NUTS using ADVI...\n",
"Average Loss = 2,800: 19%|█▉ | 37824/200000 [00:44<03:05, 876.09it/s] \n",
"Convergence archived at 37900\n",
"Interrupted at 37,900 [18%]: Average Loss = 3,804.3\n",
"100%|██████████| 1500/1500 [08:29<00:00, 3.77it/s]\n"
]
}
],
"source": [
"NUTS_KWARGS = {\n",
" 'target_accept': 0.99\n",
"} \n",
"\n",
"with model:\n",
" trace = pm.sample(draws=1000, random_seed=SEED,\n",
" nuts_kwargs=NUTS_KWARGS, njobs=3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The marginal energy and energy transition distributions are fairly close, showing no obvious problem with NUTS."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
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H++//IV/+8jcoK9vIrbf++7zTSyYnJzhw4DUeeeSPBINBnn76Kf76r99z2u93cr9bRVH4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\n9A6u6BQAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f522204e1d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.energyplot(trace);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Gelman-Rubin statistics for all parameters are quite close to one, indicating convergence."
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.0088100577623547"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"max(np.max(score) for score in pm.gelman_rubin(trace).values())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We are now ready for the post-stratification step of MRP. First we combine the census and state-level data."
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ps_df = pd.merge(census_df,\n",
" state_df[['state', 'region_cat']].reset_index(),\n",
" on='state')"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>race_wbh</th>\n",
" <th>age_cat</th>\n",
" <th>edu_cat</th>\n",
" <th>female</th>\n",
" <th>state</th>\n",
" <th>freq</th>\n",
" <th>freq_state</th>\n",
" <th>percent_state</th>\n",
" <th>region</th>\n",
" <th>state_initnum</th>\n",
" <th>region_cat</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>AK</td>\n",
" <td>467</td>\n",
" <td>21222.0</td>\n",
" <td>0.022005</td>\n",
" <td>west</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>AK</td>\n",
" <td>377</td>\n",
" <td>21222.0</td>\n",
" <td>0.017765</td>\n",
" <td>west</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>AK</td>\n",
" <td>419</td>\n",
" <td>21222.0</td>\n",
" <td>0.019744</td>\n",
" <td>west</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>AK</td>\n",
" <td>343</td>\n",
" <td>21222.0</td>\n",
" <td>0.016162</td>\n",
" <td>west</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>AK</td>\n",
" <td>958</td>\n",
" <td>21222.0</td>\n",
" <td>0.045142</td>\n",
" <td>west</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" race_wbh age_cat edu_cat female state freq freq_state percent_state \\\n",
"0 0 0 0 0 AK 467 21222.0 0.022005 \n",
"1 0 1 0 0 AK 377 21222.0 0.017765 \n",
"2 0 2 0 0 AK 419 21222.0 0.019744 \n",
"3 0 3 0 0 AK 343 21222.0 0.016162 \n",
"4 0 0 1 0 AK 958 21222.0 0.045142 \n",
"\n",
" region state_initnum region_cat \n",
"0 west 0 3 \n",
"1 west 0 3 \n",
"2 west 0 3 \n",
"3 west 0 3 \n",
"4 west 0 3 "
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ps_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next we encode this combined data as before."
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ps_gender_race = encode_gender_race(ps_df.female, ps_df.race_wbh)\n",
"ps_age = ps_df.age_cat.values\n",
"ps_edu = ps_df.edu_cat.values\n",
"ps_age_edu = encode_age_edu(ps_df.age_cat, ps_df.edu_cat)\n",
"ps_region = ps_df.region_cat.values\n",
"ps_state = ps_df.state_initnum.values\n",
"ps_n = ps_df.freq.values.astype(np.int64)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We now set the values of the `theano.shared` variables in our PyMC3 model to the poststratification data and sample from the posterior predictive distribution."
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"gender_race_.set_value(ps_gender_race)\n",
"age_.set_value(ps_age)\n",
"edu_.set_value(ps_edu)\n",
"age_edu_.set_value(ps_age_edu)\n",
"poll_.set_value(np.zeros_like(ps_gender_race))\n",
"state_.set_value(ps_state)\n",
"use_poll_.set_value(0)\n",
"n_.set_value(ps_n)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 1000/1000 [00:01<00:00, 568.29it/s]\n"
]
}
],
"source": [
"with model:\n",
" pp_trace = pm.sample_ppc(trace, random_seed=SEED)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"PP_COLS = ['pp_yes_of_all_{}'.format(i) for i in range(pp_trace['obs'].shape[0])]\n",
"\n",
"pp_df = pd.merge(ps_df,\n",
" pd.DataFrame(pp_trace['obs'].T, columns=PP_COLS),\n",
" left_index=True, right_index=True)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>race_wbh</th>\n",
" <th>age_cat</th>\n",
" <th>edu_cat</th>\n",
" <th>female</th>\n",
" <th>state</th>\n",
" <th>freq</th>\n",
" <th>freq_state</th>\n",
" <th>percent_state</th>\n",
" <th>region</th>\n",
" <th>state_initnum</th>\n",
" <th>...</th>\n",
" <th>pp_yes_of_all_990</th>\n",
" <th>pp_yes_of_all_991</th>\n",
" <th>pp_yes_of_all_992</th>\n",
" <th>pp_yes_of_all_993</th>\n",
" <th>pp_yes_of_all_994</th>\n",
" <th>pp_yes_of_all_995</th>\n",
" <th>pp_yes_of_all_996</th>\n",
" <th>pp_yes_of_all_997</th>\n",
" <th>pp_yes_of_all_998</th>\n",
" <th>pp_yes_of_all_999</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>AK</td>\n",
" <td>467</td>\n",
" <td>21222.0</td>\n",
" <td>0.022005</td>\n",
" <td>west</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>205</td>\n",
" <td>151</td>\n",
" <td>144</td>\n",
" <td>185</td>\n",
" <td>137</td>\n",
" <td>122</td>\n",
" <td>145</td>\n",
" <td>139</td>\n",
" <td>171</td>\n",
" <td>186</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>AK</td>\n",
" <td>377</td>\n",
" <td>21222.0</td>\n",
" <td>0.017765</td>\n",
" <td>west</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>86</td>\n",
" <td>71</td>\n",
" <td>83</td>\n",
" <td>95</td>\n",
" <td>84</td>\n",
" <td>96</td>\n",
" <td>66</td>\n",
" <td>79</td>\n",
" <td>64</td>\n",
" <td>121</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>AK</td>\n",
" <td>419</td>\n",
" <td>21222.0</td>\n",
" <td>0.019744</td>\n",
" <td>west</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>94</td>\n",
" <td>77</td>\n",
" <td>45</td>\n",
" <td>86</td>\n",
" <td>80</td>\n",
" <td>61</td>\n",
" <td>47</td>\n",
" <td>54</td>\n",
" <td>50</td>\n",
" <td>65</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>AK</td>\n",
" <td>343</td>\n",
" <td>21222.0</td>\n",
" <td>0.016162</td>\n",
" <td>west</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>69</td>\n",
" <td>38</td>\n",
" <td>33</td>\n",
" <td>40</td>\n",
" <td>39</td>\n",
" <td>18</td>\n",
" <td>32</td>\n",
" <td>35</td>\n",
" <td>32</td>\n",
" <td>39</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>AK</td>\n",
" <td>958</td>\n",
" <td>21222.0</td>\n",
" <td>0.045142</td>\n",
" <td>west</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>430</td>\n",
" <td>287</td>\n",
" <td>342</td>\n",
" <td>430</td>\n",
" <td>342</td>\n",
" <td>348</td>\n",
" <td>307</td>\n",
" <td>382</td>\n",
" <td>312</td>\n",
" <td>450</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 1011 columns</p>\n",
"</div>"
],
"text/plain": [
" race_wbh age_cat edu_cat female state freq freq_state percent_state \\\n",
"0 0 0 0 0 AK 467 21222.0 0.022005 \n",
"1 0 1 0 0 AK 377 21222.0 0.017765 \n",
"2 0 2 0 0 AK 419 21222.0 0.019744 \n",
"3 0 3 0 0 AK 343 21222.0 0.016162 \n",
"4 0 0 1 0 AK 958 21222.0 0.045142 \n",
"\n",
" region state_initnum ... pp_yes_of_all_990 \\\n",
"0 west 0 ... 205 \n",
"1 west 0 ... 86 \n",
"2 west 0 ... 94 \n",
"3 west 0 ... 69 \n",
"4 west 0 ... 430 \n",
"\n",
" pp_yes_of_all_991 pp_yes_of_all_992 pp_yes_of_all_993 pp_yes_of_all_994 \\\n",
"0 151 144 185 137 \n",
"1 71 83 95 84 \n",
"2 77 45 86 80 \n",
"3 38 33 40 39 \n",
"4 287 342 430 342 \n",
"\n",
" pp_yes_of_all_995 pp_yes_of_all_996 pp_yes_of_all_997 pp_yes_of_all_998 \\\n",
"0 122 145 139 171 \n",
"1 96 66 79 64 \n",
"2 61 47 54 50 \n",
"3 18 32 35 32 \n",
"4 348 307 382 312 \n",
"\n",
" pp_yes_of_all_999 \n",
"0 186 \n",
"1 121 \n",
"2 65 \n",
"3 39 \n",
"4 450 \n",
"\n",
"[5 rows x 1011 columns]"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pp_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We complete the poststratification step by taking a weighted sum across the demographic cells within each state, to produce posterior predictive samples from the state-level opinion distribution."
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ps_prob = (pp_df.groupby('state')\n",
" .apply(lambda df: df[PP_COLS].sum(axis=0) / df.freq.sum()))"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>pp_yes_of_all_0</th>\n",
" <th>pp_yes_of_all_1</th>\n",
" <th>pp_yes_of_all_2</th>\n",
" <th>pp_yes_of_all_3</th>\n",
" <th>pp_yes_of_all_4</th>\n",
" <th>pp_yes_of_all_5</th>\n",
" <th>pp_yes_of_all_6</th>\n",
" <th>pp_yes_of_all_7</th>\n",
" <th>pp_yes_of_all_8</th>\n",
" <th>pp_yes_of_all_9</th>\n",
" <th>...</th>\n",
" <th>pp_yes_of_all_990</th>\n",
" <th>pp_yes_of_all_991</th>\n",
" <th>pp_yes_of_all_992</th>\n",
" <th>pp_yes_of_all_993</th>\n",
" <th>pp_yes_of_all_994</th>\n",
" <th>pp_yes_of_all_995</th>\n",
" <th>pp_yes_of_all_996</th>\n",
" <th>pp_yes_of_all_997</th>\n",
" <th>pp_yes_of_all_998</th>\n",
" <th>pp_yes_of_all_999</th>\n",
" </tr>\n",
" <tr>\n",
" <th>state</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></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>AK</th>\n",
" <td>0.306380</td>\n",
" <td>0.390585</td>\n",
" <td>0.361229</td>\n",
" <td>0.275893</td>\n",
" <td>0.374140</td>\n",
" <td>0.410847</td>\n",
" <td>0.362360</td>\n",
" <td>0.389172</td>\n",
" <td>0.372491</td>\n",
" <td>0.302281</td>\n",
" <td>...</td>\n",
" <td>0.411413</td>\n",
" <td>0.290312</td>\n",
" <td>0.297521</td>\n",
" <td>0.387287</td>\n",
" <td>0.365988</td>\n",
" <td>0.328386</td>\n",
" <td>0.316982</td>\n",
" <td>0.357035</td>\n",
" <td>0.280417</td>\n",
" <td>0.373433</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AL</th>\n",
" <td>0.174168</td>\n",
" <td>0.199505</td>\n",
" <td>0.203892</td>\n",
" <td>0.155924</td>\n",
" <td>0.214749</td>\n",
" <td>0.190090</td>\n",
" <td>0.204509</td>\n",
" <td>0.203507</td>\n",
" <td>0.188764</td>\n",
" <td>0.147419</td>\n",
" <td>...</td>\n",
" <td>0.217865</td>\n",
" <td>0.145274</td>\n",
" <td>0.153651</td>\n",
" <td>0.220120</td>\n",
" <td>0.185526</td>\n",
" <td>0.187560</td>\n",
" <td>0.101283</td>\n",
" <td>0.175781</td>\n",
" <td>0.117774</td>\n",
" <td>0.218250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AR</th>\n",
" <td>0.142486</td>\n",
" <td>0.207379</td>\n",
" <td>0.219824</td>\n",
" <td>0.221326</td>\n",
" <td>0.229756</td>\n",
" <td>0.204580</td>\n",
" <td>0.221235</td>\n",
" <td>0.239279</td>\n",
" <td>0.193739</td>\n",
" <td>0.198919</td>\n",
" <td>...</td>\n",
" <td>0.210138</td>\n",
" <td>0.164843</td>\n",
" <td>0.146860</td>\n",
" <td>0.238502</td>\n",
" <td>0.185973</td>\n",
" <td>0.245931</td>\n",
" <td>0.114837</td>\n",
" <td>0.189018</td>\n",
" <td>0.164965</td>\n",
" <td>0.232096</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AZ</th>\n",
" <td>0.353140</td>\n",
" <td>0.395125</td>\n",
" <td>0.388163</td>\n",
" <td>0.361972</td>\n",
" <td>0.394620</td>\n",
" <td>0.378743</td>\n",
" <td>0.387904</td>\n",
" <td>0.375209</td>\n",
" <td>0.385323</td>\n",
" <td>0.443167</td>\n",
" <td>...</td>\n",
" <td>0.390827</td>\n",
" <td>0.318305</td>\n",
" <td>0.340562</td>\n",
" <td>0.411255</td>\n",
" <td>0.376126</td>\n",
" <td>0.455857</td>\n",
" <td>0.318835</td>\n",
" <td>0.387193</td>\n",
" <td>0.329390</td>\n",
" <td>0.405663</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CA</th>\n",
" <td>0.384078</td>\n",
" <td>0.463444</td>\n",
" <td>0.463495</td>\n",
" <td>0.405385</td>\n",
" <td>0.468195</td>\n",
" <td>0.475593</td>\n",
" <td>0.463783</td>\n",
" <td>0.474011</td>\n",
" <td>0.429405</td>\n",
" <td>0.431427</td>\n",
" <td>...</td>\n",
" <td>0.468451</td>\n",
" <td>0.384701</td>\n",
" <td>0.374767</td>\n",
" <td>0.486855</td>\n",
" <td>0.434450</td>\n",
" <td>0.475072</td>\n",
" <td>0.378866</td>\n",
" <td>0.471518</td>\n",
" <td>0.378529</td>\n",
" <td>0.496080</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 1000 columns</p>\n",
"</div>"
],
"text/plain": [
" pp_yes_of_all_0 pp_yes_of_all_1 pp_yes_of_all_2 pp_yes_of_all_3 \\\n",
"state \n",
"AK 0.306380 0.390585 0.361229 0.275893 \n",
"AL 0.174168 0.199505 0.203892 0.155924 \n",
"AR 0.142486 0.207379 0.219824 0.221326 \n",
"AZ 0.353140 0.395125 0.388163 0.361972 \n",
"CA 0.384078 0.463444 0.463495 0.405385 \n",
"\n",
" pp_yes_of_all_4 pp_yes_of_all_5 pp_yes_of_all_6 pp_yes_of_all_7 \\\n",
"state \n",
"AK 0.374140 0.410847 0.362360 0.389172 \n",
"AL 0.214749 0.190090 0.204509 0.203507 \n",
"AR 0.229756 0.204580 0.221235 0.239279 \n",
"AZ 0.394620 0.378743 0.387904 0.375209 \n",
"CA 0.468195 0.475593 0.463783 0.474011 \n",
"\n",
" pp_yes_of_all_8 pp_yes_of_all_9 ... pp_yes_of_all_990 \\\n",
"state ... \n",
"AK 0.372491 0.302281 ... 0.411413 \n",
"AL 0.188764 0.147419 ... 0.217865 \n",
"AR 0.193739 0.198919 ... 0.210138 \n",
"AZ 0.385323 0.443167 ... 0.390827 \n",
"CA 0.429405 0.431427 ... 0.468451 \n",
"\n",
" pp_yes_of_all_991 pp_yes_of_all_992 pp_yes_of_all_993 \\\n",
"state \n",
"AK 0.290312 0.297521 0.387287 \n",
"AL 0.145274 0.153651 0.220120 \n",
"AR 0.164843 0.146860 0.238502 \n",
"AZ 0.318305 0.340562 0.411255 \n",
"CA 0.384701 0.374767 0.486855 \n",
"\n",
" pp_yes_of_all_994 pp_yes_of_all_995 pp_yes_of_all_996 \\\n",
"state \n",
"AK 0.365988 0.328386 0.316982 \n",
"AL 0.185526 0.187560 0.101283 \n",
"AR 0.185973 0.245931 0.114837 \n",
"AZ 0.376126 0.455857 0.318835 \n",
"CA 0.434450 0.475072 0.378866 \n",
"\n",
" pp_yes_of_all_997 pp_yes_of_all_998 pp_yes_of_all_999 \n",
"state \n",
"AK 0.357035 0.280417 0.373433 \n",
"AL 0.175781 0.117774 0.218250 \n",
"AR 0.189018 0.164965 0.232096 \n",
"AZ 0.387193 0.329390 0.405663 \n",
"CA 0.471518 0.378529 0.496080 \n",
"\n",
"[5 rows x 1000 columns]"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ps_prob.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The simplest summary of state-level opinion is the posterior expected mean, shown below."
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": true,
"scrolled": false
},
"outputs": [],
"source": [
"ps_mean = ps_prob.mean(axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"state\n",
"AK 0.365962\n",
"AL 0.189076\n",
"AR 0.201302\n",
"AZ 0.395071\n",
"CA 0.459842\n",
"dtype: float64"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ps_mean.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following choropleth maps show the disaggregation and MRP estimates of support for gay marriage by state."
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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RcRBK8kVEREREREQchJJ8EREREREREQehJF9ERERERETEQSjJFxEREREREXEQ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\nvXxo0b6jbvBJpnNyMmE0Ghj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37MSrkC9tOnTOtGMDXLt6lYJeXpjyUHcluaVyzVps2ryR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\nrOhLXHIyOTnZfPvdT4V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3NGvG7C9Wsu3nnzlyLJmioiJeHDWa/YcPEVq/IavX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\nEREREQehIl9ERERERETEQfwfCowJb+t4Yi4AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f51e6bda160>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, (disagg_ax, mrp_ax) = plt.subplots(ncols=2, sharex=True, sharey=True, figsize=(16, 6))\n",
"\n",
"fig, disagg_ax, _ = state_plot(disagg_p, p_cmap, p_norm, cbar=False, ax=disagg_ax)\n",
"disagg_ax.set_title(\"Disaggregation\");\n",
"\n",
"fig, mrp_ax, cbar_ax = state_plot(ps_mean, p_cmap, p_norm, ax=mrp_ax)\n",
"cbar_ax.yaxis.set_major_formatter(p_formatter);\n",
"mrp_ax.set_title(\"MRP\");\n",
"\n",
"fig.suptitle(\"Estimated support for gay marriage in 2005\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notably, MRP produces opinion estimates for Alaska and Hawaii, which disaggregation does not. The following scatter plot makes it easier to see how the estimate for each state differs between disaggregation and MRP."
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"disagg_p_aligned, ps_mean_aligned = disagg_p.align(ps_mean)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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jatSogY+PDzk5Ofj7+3P27FlCQ0OLPCY0NJSUlBTb7aSkJEJCQoiOjiY6OpoT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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f51f00a7e10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(8, 8))\n",
"ax.set_aspect('equal');\n",
"\n",
"pct_formatter = FuncFormatter(lambda prop, _: '{:.1%}'.format(prop))\n",
"\n",
"ax.plot([0.1, 0.7], [0.1, 0.7], '--', c='k', label=\"No change\");\n",
"ax.scatter(disagg_p_aligned, ps_mean_aligned);\n",
"\n",
"ax.set_xlim(0.1, 0.7);\n",
"ax.xaxis.set_major_formatter(pct_formatter);\n",
"ax.set_xlabel(\"Disaggregation estimate\");\n",
"\n",
"ax.set_ylim(0.1, 0.7);\n",
"ax.yaxis.set_major_formatter(pct_formatter);\n",
"ax.set_ylabel(\"MRP estimate\");\n",
"\n",
"ax.legend(loc=2);\n",
"ax.set_title(\"Estimated support for gay marriage in 2005\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see that the MRP estimates tend to be higher than the disaggregation estimates, possibly due to under-sampling of supportive demographic cells in many states.\n",
"\n",
"An additional advantage of MRP is that we can produce better opinion estimates for demographic subsets than disaggregation. For example, we plot below the disaggregation and MRP estimates of support for gay marriage among black men. From above, we know disaggregation will not be able to produce an estimate for many states."
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"black_men_disagg_p = (survey_df[(survey_df.race_wbh == 1) & (survey_df.female == 0)]\n",
" .groupby('state')\n",
" .yes_of_all\n",
" .mean())\n",
"\n",
"black_men_ps_mean = (pp_df[(pp_df.race_wbh == 1) & (pp_df.female == 0)]\n",
" .groupby('state')\n",
" .apply(lambda df: (df[PP_COLS].sum(axis=0) / df.freq.sum()))\n",
" .mean(axis=1))"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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fJk+enGxTv9S0adOGTp060b59+4wKM9P88/0A6NatG6VKleLDDz/MuqBERERyOE3XFxER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haNCgQC0u/FcCnsLDWC6hB98wYnFy+i981onERiSH+1SkcEEjPIIwe3VasxtbQuVIIPYPWfJDhe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IMNbBg5NSrmVwd3HhXmoSMiNjnu3bSMtO3WnX3I8a1YYTfOUKebnZRMbG03rOHNatW8WNkEv8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\nAAAohMgHAACAQvwJh1xF/y/rJ9EAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f51e9106ac8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, (disagg_ax, mrp_ax) = plt.subplots(ncols=2, sharex=True, sharey=True, figsize=(16, 6))\n",
"\n",
"fig, disagg_ax, _ = state_plot(black_men_disagg_p, p_cmap, p_norm, cbar=False, ax=disagg_ax)\n",
"disagg_ax.set_title(\"Disaggregation\");\n",
"\n",
"fig, mrp_ax, cbar_ax = state_plot(black_men_ps_mean, p_cmap, p_norm, ax=mrp_ax)\n",
"cbar_ax.yaxis.set_major_formatter(p_formatter);\n",
"mrp_ax.set_title(\"MRP\");\n",
"\n",
"fig.suptitle(\"Estimated support for gay marriage\\namong black men in 2005\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In addition to the gaps in the disaggregation map above, it seems highly unlikely that not a single black man in Minnesota, Arizona, New Mexico, etc. supported gay marriage in 2005. These disaggregation estimates are due to polling few black men in these states, which MRP attempts to counteract. For further discussion of estimating the opinions of demographic subgroups using MRP, consult Ghitza and Gelman's [_Deep Interactions with MRP: Election Turnout and Voting Patterns Among Small Electoral Subgroups_](http://www.stat.columbia.edu/~gelman/research/published/misterp.pdf).\n",
"\n",
"One advantage of using the fully Bayesian approach we have taken to MRP via PyMC3 is that we have access to the full posterior distribution of each state's opinion, in addition to the posterior expected values shown in the above choropleth maps."
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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AeJel+/DdqG7dutq7d6+3swAAAMAHLM3wTZ06tdDjkydP6sKFCz4JBAAAAO+y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qqacUERGhQYMGafr06QoODpYkNW3aVFu2bLE0IAAAAKrGVOF77733ZBiGIiMjJUkHDhzQo48+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\nD9vjd2DFl+vrAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f51e926e240>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"grid = sns.FacetGrid(pd.melt(ps_prob.reset_index(),\n",
" id_vars='state', value_vars=PP_COLS,\n",
" var_name='pp_sample', value_name='p'),\n",
" col='state', col_wrap=3, size=2, aspect=1.5)\n",
"grid.map(plt.hist, 'p', bins=30);\n",
"\n",
"grid.set_xlabels(\"Posterior distribution of support\\nfor gay marriage in 2005\");\n",
"\n",
"for ax in grid.axes.flat:\n",
" ax.set_xticks(np.linspace(0, 1, 5));\n",
" ax.xaxis.set_major_formatter(pct_formatter);\n",
" plt.setp(ax.get_xticklabels(), visible=True);\n",
"\n",
"grid.set_yticklabels([]);\n",
"grid.set_ylabels(\"Frequency\");\n",
"\n",
"grid.fig.tight_layout();\n",
"grid.set_titles('{col_name}');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Specifying this model in PyMC3 would certainly have been simpler using [Bambi](https://github.com/bambinos/bambi), which I intend to learn soon for exactly that reason.\n",
"\n",
"I am particularly eager to see what applications MRP will find outside of political science in the coming years.\n",
"\n",
"This post is available as a Jupyter notebook [here](https://gist.github.com/AustinRochford/bfc20cb3262169b41b730bd9faf74477)."
]
}
],
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"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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@tmastny
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tmastny commented Nov 9, 2017

Great work! You mention that "all of the applications of MRP I have found online involve R's lme4 package or Stan." I've seen the MRP primer and Gelman and Hill's example in their book; do you have any other examples you could share?

@jaircastruita
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jaircastruita commented May 9, 2018

During cell 34, fitting the model, gives me the following error:

Bad initial energy: inf. The model might be misspecified. NaN occurred in optimization.

Could the dataset or the internals on library pyMC3 change enough to give this? I,m using pymc3 version 3.3 with python version 3.6

@nss2108
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nss2108 commented Nov 21, 2018

During cell 34, fitting the model, gives me the following error:

Bad initial energy: inf. The model might be misspecified. NaN occurred in optimization.

Could the dataset or the internals on library pyMC3 change enough to give this? I,m using pymc3 version 3.3 with python version 3.6

was this resolved? I got a similar result.

@jaircastruita
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jaircastruita commented Dec 7, 2018

Looks like new versions of PyMC3 used jittering as a default initializing method. To replicate the notebook exactly as it is you now have to specify which method you want, in this case NUTS using ADVI:

with model: trace = pm.sample(draws=1000, random_seed=SEED, nuts_kwargs=NUTS_KWARGS, init='advi', njobs=3)

Hope this works for you

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