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fonnesbeck/Random Effect Model.ipynb

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Random Effect Model.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%load_ext watermark"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPython 3.4.2\n",
"IPython 4.0.0-dev\n",
"\n",
"numpy 1.10.0.dev-fb898ce\n",
"pandas 0.15.2-50-g4aa0e0a\n",
"pymc 2.3.4\n",
"\n",
"compiler : GCC 4.2.1 Compatible Apple LLVM 6.0 (clang-600.0.54)\n",
"system : Darwin\n",
"release : 14.3.0\n",
"machine : x86_64\n",
"processor : i386\n",
"CPU cores : 4\n",
"interpreter: 64bit\n",
"Git hash : \n"
]
}
],
"source": [
"%watermark -v -m -p numpy,pandas,pymc -g"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import numpy as np\n",
"import pymc as pm\n",
"\n",
"from dateutil.parser import parse\n",
"from datetime import datetime"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.4/site-packages/pandas/io/parsers.py:1159: DtypeWarning: Columns (40,111,112,132,188,193) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" data = self._reader.read(nrows)\n"
]
},
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>COUNTY</th>\n",
" <th>STATEID</th>\n",
" <th>BIRTH</th>\n",
" <th>AGE</th>\n",
" <th>UNIT</th>\n",
" <th>SEX</th>\n",
" <th>RACE</th>\n",
" <th>ETHNIC</th>\n",
" <th>OUTCOME</th>\n",
" <th>BACTEREM</th>\n",
" <th>...</th>\n",
" <th>regioneast</th>\n",
" <th>agegroup</th>\n",
" <th>_clinsynd</th>\n",
" <th>vaccineserotype</th>\n",
" <th>PENR</th>\n",
" <th>PENS</th>\n",
" <th>LEVR</th>\n",
" <th>LEVS</th>\n",
" <th>ERYTHR</th>\n",
" <th>ERYTHS</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> WILLIAMSON</td>\n",
" <td> TNF6092</td>\n",
" <td> 05nov1972</td>\n",
" <td> 37</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 9</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
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" <td> 1</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> SHELBY</td>\n",
" <td> TND6006</td>\n",
" <td> 27jun1927</td>\n",
" <td> 80</td>\n",
" <td> 3</td>\n",
" <td> 2</td>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
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" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> DAVIDSON</td>\n",
" <td> TN43378</td>\n",
" <td> 15apr1958</td>\n",
" <td> 45</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td>NaN</td>\n",
" <td> 9</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
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" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> SHELBY</td>\n",
" <td> TNC6014</td>\n",
" <td> 28jun1950</td>\n",
" <td> 56</td>\n",
" <td> 3</td>\n",
" <td> 2</td>\n",
" <td> 1</td>\n",
" <td> 9</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" <td> 9</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> RUTHERFORD</td>\n",
" <td> TNG6394</td>\n",
" <td> 23sep1953</td>\n",
" <td> 58</td>\n",
" <td> 3</td>\n",
" <td> 2</td>\n",
" <td> 4</td>\n",
" <td> 2</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" <td> 3</td>\n",
" <td> 2</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 269 columns</p>\n",
"</div>"
],
"text/plain": [
" COUNTY STATEID BIRTH AGE UNIT SEX RACE ETHNIC OUTCOME \\\n",
"0 WILLIAMSON TNF6092 05nov1972 37 3 1 1 9 1 \n",
"1 SHELBY TND6006 27jun1927 80 3 2 1 2 1 \n",
"2 DAVIDSON TN43378 15apr1958 45 3 1 NaN 9 1 \n",
"3 SHELBY TNC6014 28jun1950 56 3 2 1 9 1 \n",
"4 RUTHERFORD TNG6394 23sep1953 58 3 2 4 2 1 \n",
"\n",
" BACTEREM ... regioneast agegroup _clinsynd vaccineserotype PENR \\\n",
"0 0 ... 0 3 3 2 1 \n",
"1 0 ... 0 4 3 5 0 \n",
"2 1 ... 0 3 1 3 0 \n",
"3 0 ... 0 3 9 NaN NaN \n",
"4 0 ... 0 3 3 2 1 \n",
"\n",
" PENS LEVR LEVS ERYTHR ERYTHS \n",
"0 0 0 1 1 0 \n",
"1 1 0 1 0 1 \n",
"2 1 0 1 0 1 \n",
"3 NaN NaN NaN NaN NaN \n",
"4 0 0 1 1 0 \n",
"\n",
"[5 rows x 269 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_dir = './data/'\n",
"_adult_data = pd.read_csv(data_dir+'adult_data.csv')\n",
"_adult_data.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"_adult_2013 = pd.read_csv(data_dir+'2013 adult merged CRF and lab info.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Recode RACE variable"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"_adult_2013['RACE'] = 1\n",
"_adult_2013.loc[_adult_2013.BLACK==1, 'RACE'] = 2\n",
"_adult_2013.loc[_adult_2013.AMERIND==1, 'RACE'] = 3\n",
"_adult_2013.loc[_adult_2013.ASIAN==1, 'RACE'] = 4\n",
"_adult_2013.loc[_adult_2013.PACIFIC==1, 'RACE'] = 5\n",
"_adult_2013.loc[_adult_2013.UNKRACE==1, 'RACE'] = 9"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"_adult_data = _adult_data.append(_adult_2013, ignore_index=True)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"keep_columns = ['COUNTY','STATEID','RACE','calcage','agegroup','regioneast','CULT','year',\n",
" 'pneumo7','pneumo13','pneumo23','pneumononPCV','PENR','LEVR','ERYTHR']\n",
"adult_data = _adult_data[keep_columns].copy()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def parse_date(d):\n",
" if d.find('-')==-1:\n",
" return datetime.strptime(d, '%d%b%Y')\n",
" return datetime.strptime(d, '%d-%b-%Y')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"adult_data['culture'] = adult_data.CULT.apply(parse_date)\n",
"#adult_data['culture'] = [datetime.strptime(d, '%d%b%Y') for d in adult_data.CULT]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"adult_data['over64'] = (adult_data.agegroup==4).astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3 3548\n",
"4 1933\n",
"dtype: int64"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"adult_data.agegroup.value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Obtain subset of interest"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#data_subset = adult_data[(adult_data.over64==0) & (adult_data.regioneast==1)]\n",
"data_subset = adult_data.copy()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"covnames = ['over64', 'regioneast']\n",
"# covnames = ['over64','regioneast','pneumo7',\n",
"# 'pneumo13','pneumo23','pneumononPCV','PENR','LEVR','ERYTHR'"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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" <th></th>\n",
" <th>year</th>\n",
" <th>over64</th>\n",
" <th>regioneast</th>\n",
" <th>count</th>\n",
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" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td> 11</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 71</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td> 11</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 84</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td> 11</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td> 12</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 151</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td> 12</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td> 12</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 92</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td> 12</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 39</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year over64 regioneast count\n",
"0 0 0 0 207\n",
"1 0 0 1 66\n",
"2 0 1 0 119\n",
"3 0 1 1 60\n",
"4 1 0 0 188\n",
"5 1 0 1 53\n",
"6 1 1 0 88\n",
"7 1 1 1 40\n",
"8 2 0 0 175\n",
"9 2 0 1 60\n",
"10 2 1 0 110\n",
"11 2 1 1 39\n",
"12 3 0 0 161\n",
"13 3 0 1 50\n",
"14 3 1 0 87\n",
"15 3 1 1 39\n",
"16 4 0 0 221\n",
"17 4 0 1 91\n",
"18 4 1 0 90\n",
"19 4 1 1 53\n",
"20 5 0 0 234\n",
"21 5 0 1 79\n",
"22 5 1 0 100\n",
"23 5 1 1 61\n",
"24 6 0 0 219\n",
"25 6 0 1 81\n",
"26 6 1 0 99\n",
"27 6 1 1 55\n",
"28 7 0 0 232\n",
"29 7 0 1 110\n",
"30 7 1 0 117\n",
"31 7 1 1 61\n",
"32 8 0 0 214\n",
"33 8 0 1 93\n",
"34 8 1 0 95\n",
"35 8 1 1 43\n",
"36 9 0 0 198\n",
"37 9 0 1 81\n",
"38 9 1 0 106\n",
"39 9 1 1 48\n",
"40 10 0 0 186\n",
"41 10 0 1 82\n",
"42 10 1 0 104\n",
"43 10 1 1 61\n",
"44 11 0 0 170\n",
"45 11 0 1 71\n",
"46 11 1 0 84\n",
"47 11 1 1 43\n",
"48 12 0 0 151\n",
"49 12 0 1 75\n",
"50 12 1 0 92\n",
"51 12 1 1 39"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"adult_resampled = data_subset.groupby([pd.Grouper(freq='AS', key='culture'), 'over64', 'regioneast'])['CULT'].count()\n",
"\n",
"adult_resampled = adult_resampled.reset_index().rename(columns={'CULT':'count', 'culture':'year'})\n",
"adult_resampled['year'] = [d.year-2001 for d in adult_resampled.year]\n",
"adult_resampled"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# adult_data = pd.DataFrame(np.c_[[a.year-2001 for a in adult_resampled.index], adult_resampled.values], \n",
"# columns=['year', 'count'])"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>year</th>\n",
" <th>over64</th>\n",
" <th>regioneast</th>\n",
" <th>count</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>47</th>\n",
" <td> 11</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td> 12</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 151</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td> 12</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td> 12</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 92</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td> 12</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 39</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year over64 regioneast count\n",
"47 11 1 1 43\n",
"48 12 0 0 151\n",
"49 12 0 1 75\n",
"50 12 1 0 92\n",
"51 12 1 1 39"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"adult_resampled.tail()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>year</th>\n",
" <th>_newagegroupadult</th>\n",
" <th>regioneast</th>\n",
" <th>n</th>\n",
" <th>over64</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 0</td>\n",
" <td> 18to65</td>\n",
" <td> 0</td>\n",
" <td> 1377668.2</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 1</td>\n",
" <td> 18to65</td>\n",
" <td> 0</td>\n",
" <td> 1393999.8</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 2</td>\n",
" <td> 18to65</td>\n",
" <td> 0</td>\n",
" <td> 1410903.8</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 3</td>\n",
" <td> 18to65</td>\n",
" <td> 0</td>\n",
" <td> 1432392.4</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 4</td>\n",
" <td> 18to65</td>\n",
" <td> 0</td>\n",
" <td> 1456641.2</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year _newagegroupadult regioneast n over64\n",
"0 0 18to65 0 1377668.2 0\n",
"1 1 18to65 0 1393999.8 0\n",
"2 2 18to65 0 1410903.8 0\n",
"3 3 18to65 0 1432392.4 0\n",
"4 4 18to65 0 1456641.2 0"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"denom = pd.read_csv(data_dir+'adult_denominator.csv')\n",
"denom['over64'] = (denom._newagegroupadult!='18to65').astype(int)\n",
"denom['year'] = denom.year - 1\n",
"denom.rename(columns={'_newagegroupadultpop':'n'}, inplace=True)\n",
"denom = denom[denom._newagegroupadult!='total']\n",
"denom.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Add 2013 census data"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"denom13_raw = pd.read_csv(data_dir+'2013 adult census data.csv')"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"denom13 = denom13_raw.drop('COUNTY', axis=1).groupby(['_newagegroupadult', 'year', 'regioneast']).sum().reset_index()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>year</th>\n",
" <th>regioneast</th>\n",
" <th>n</th>\n",
" <th>over64</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 12</td>\n",
" <td> 0</td>\n",
" <td> 1617941.2</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 12</td>\n",
" <td> 1</td>\n",
" <td> 503405.0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 12</td>\n",
" <td> 0</td>\n",
" <td> 287483.0</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 12</td>\n",
" <td> 1</td>\n",
" <td> 117198.0</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year regioneast n over64\n",
"0 12 0 1617941.2 0\n",
"1 12 1 503405.0 0\n",
"2 12 0 287483.0 1\n",
"3 12 1 117198.0 1"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"denom13['over64'] = (denom13._newagegroupadult!='18to65').astype(int)\n",
"denom13['year'] = denom.year.max() + 1\n",
"denom13.rename(columns={'_newagegroupadultpop':'n'}, inplace=True)\n",
"denom13 = denom13[denom13._newagegroupadult!='total'].drop('_newagegroupadult', axis=1)\n",
"denom13.head()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# denom_subset = denom.ix[(denom.over64==0) & (denom.regioneast==1), ['year', 'n']]\n",
"denom_subset = denom[['year', 'n', 'regioneast', 'over64']].append(denom13, ignore_index=True)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data_merged = pd.merge(denom_subset, adult_resampled, on=['year', 'regioneast', 'over64'])\n",
"data_merged = data_merged[['year', 'n', 'count', 'regioneast', 'over64']]\n",
"data_merged['post_2010'] = (data_merged.year > 9).astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"count = data_merged['count'].values\n",
"year = data_merged['year'].values\n",
"n = data_merged['n'].values\n",
"X = data_merged[['regioneast', 'over64']].values\n",
"post_2010 = data_merged.post_2010"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"nyears = data_merged.year.unique().shape[0]"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"13"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nyears"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(52, 2)"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X.shape"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x1187595f8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data_merged.groupby(['regioneast', 'over64']).plot(x='year', y='count', ax=plt.subplot(), legend=False);"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>year</th>\n",
" <th>n</th>\n",
" <th>count</th>\n",
" <th>regioneast</th>\n",
" <th>over64</th>\n",
" <th>post_2010</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 0</td>\n",
" <td> 1377668.2</td>\n",
" <td> 207</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 1</td>\n",
" <td> 1393999.8</td>\n",
" <td> 188</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 2</td>\n",
" <td> 1410903.8</td>\n",
" <td> 175</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 3</td>\n",
" <td> 1432392.4</td>\n",
" <td> 161</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 4</td>\n",
" <td> 1456641.2</td>\n",
" <td> 221</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year n count regioneast over64 post_2010\n",
"0 0 1377668.2 207 0 0 0\n",
"1 1 1393999.8 188 0 0 0\n",
"2 2 1410903.8 175 0 0 0\n",
"3 3 1432392.4 161 0 0 0\n",
"4 4 1456641.2 221 0 0 0"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_merged.head()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(52, 6)"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_merged.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Model year effect as first-order autocorrelated random effect"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pcv13 = pm.Normal('pcv13', 0, 0.01, value=0)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"mu_alpha = pm.Normal('mu_alpha', 0, 0.01, value=0)\n",
"sigma_alpha = pm.Uniform('sigma_alpha', 0, 100, value=10)\n",
"rho = pm.Uniform('rho', -1, 1, value=0)\n",
"\n",
"mu = pm.Lambda('mu', lambda mu=mu_alpha, d=pcv13: [mu]*nyears + d*post_2010[:nyears])\n",
"off_diag = np.eye(nyears, k=1)\n",
"Sigma = pm.Lambda('Sigma', lambda s=sigma_alpha, r=rho: (np.eye(nyears) + off_diag*r + off_diag.T*r)*(s**2))\n",
"\n",
"alpha = pm.MvNormalCov('alpha', mu, Sigma, value=[0]*nyears)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"mu_alpha = pm.Normal('mu_alpha', 0, 0.01, value=0)\n",
"sigma_alpha = pm.Uniform('sigma_alpha', 0, 100, value=10)\n",
"alpha = pm.Normal('alpha', mu_alpha, sigma_alpha**-2, value=[0]*nyears)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Covariate effects"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"beta = pm.Normal('beta', 0, 0.001, value=[0]*X.shape[1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Poisson regression expected value"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"rate = pm.Lambda('rate', lambda alpha=alpha, beta=beta: np.exp(alpha[year] + X.dot(beta)))"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"rate_west_under65 = pm.Lambda('rate_west_under65', lambda r=rate: r[:12])\n",
"rate_west_over64 = pm.Lambda('rate_west_over64', lambda r=rate: r[12:24])\n",
"rate_east_under65 = pm.Lambda('rate_east_under65', lambda r=rate: r[24:36])\n",
"rate_east_over64 = pm.Lambda('rate_east_over64', lambda r=rate: r[36:])"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"theta = pm.Lambda('theta', lambda rate=rate: rate * n)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Likelihood function"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"cases = pm.Poisson('cases', theta, value=count, observed=True)\n",
"# cases = pm.Binomial('cases', n.astype(int), rate, value=count, observed=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Posterior predictive distribution for all observations (for goodness-of-fit)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cases_pred = pm.Poisson('cases_pred', theta, value=count)\n",
"# cases_pred = pm.Binomial('cases_pred', n.astype(int), rate, value=count)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run MCMC sampler"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"M = pm.MCMC(locals())"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 50000 of 50000 complete in 45.2 sec"
]
}
],
"source": [
"M.sample(50000, 40000)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Posterior mean"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n"
]
},
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x118719f28>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.summary_plot(alpha)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Plotting beta_0\n",
"Plotting beta_1\n"
]
},
{
"data": {
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"/+yNVRy1Fgr/AAAAAElFTkSuQmCC\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x118d729e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.plot(beta)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Plotting pcv13\n"
]
},
{
"data": {
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"SUVORK5CYII=\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x118bec9e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.plot(pcv13)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"beta:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t0.187 0.029 0.001 [ 0.132 0.244]\n",
"\t1.141 0.029 0.002 [ 1.085 1.198]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t0.128 0.169 0.188 0.207 0.242\n",
"\t1.084 1.122 1.14 1.162 1.198\n",
"\t\n"
]
}
],
"source": [
"beta.summary()"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Plotting mu_alpha\n"
]
},
{
"data": {
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"AAAAAElFTkSuQmCC\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x11ccbb208>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.plot(mu_alpha)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n"
]
},
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x11cce5080>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.summary_plot(rate)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Goodness of fit plots (posterior predictive checks)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#pm.Matplot.gof_plot(cases_pred, count)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Estimate rates of resistance and serotypes\n",
"\n",
"* pneumo7\n",
"* pneumo13\n",
"* pneumo23\n",
"* pneumononPCV\n",
"* PENR\n",
"* LEVR\n",
"* ERYTHR"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data_subset = adult_data.copy()"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>COUNTY</th>\n",
" <th>STATEID</th>\n",
" <th>RACE</th>\n",
" <th>calcage</th>\n",
" <th>agegroup</th>\n",
" <th>regioneast</th>\n",
" <th>CULT</th>\n",
" <th>year</th>\n",
" <th>pneumo7</th>\n",
" <th>pneumo13</th>\n",
" <th>pneumo23</th>\n",
" <th>pneumononPCV</th>\n",
" <th>PENR</th>\n",
" <th>LEVR</th>\n",
" <th>ERYTHR</th>\n",
" <th>culture</th>\n",
" <th>over64</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> WILLIAMSON</td>\n",
" <td> TNF6092</td>\n",
" <td> 1</td>\n",
" <td> 37.275837</td>\n",
" <td> 3</td>\n",
" <td> 0</td>\n",
" <td> 14feb2010</td>\n",
" <td> 10</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td>2010-02-14</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> SHELBY</td>\n",
" <td> TND6006</td>\n",
" <td> 1</td>\n",
" <td> 80.528404</td>\n",
" <td> 4</td>\n",
" <td> 0</td>\n",
" <td> 06jan2008</td>\n",
" <td> 8</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>2008-01-06</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> DAVIDSON</td>\n",
" <td> TN43378</td>\n",
" <td>NaN</td>\n",
" <td> 45.889118</td>\n",
" <td> 3</td>\n",
" <td> 0</td>\n",
" <td> 05mar2004</td>\n",
" <td> 4</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>2004-03-05</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> SHELBY</td>\n",
" <td> TNC6014</td>\n",
" <td> 1</td>\n",
" <td> 56.594112</td>\n",
" <td> 3</td>\n",
" <td> 0</td>\n",
" <td> 31jan2007</td>\n",
" <td> 7</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2007-01-31</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> RUTHERFORD</td>\n",
" <td> TNG6394</td>\n",
" <td> 4</td>\n",
" <td> 58.028748</td>\n",
" <td> 3</td>\n",
" <td> 0</td>\n",
" <td> 04oct2011</td>\n",
" <td> 11</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td>2011-10-04</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" COUNTY STATEID RACE calcage agegroup regioneast CULT \\\n",
"0 WILLIAMSON TNF6092 1 37.275837 3 0 14feb2010 \n",
"1 SHELBY TND6006 1 80.528404 4 0 06jan2008 \n",
"2 DAVIDSON TN43378 NaN 45.889118 3 0 05mar2004 \n",
"3 SHELBY TNC6014 1 56.594112 3 0 31jan2007 \n",
"4 RUTHERFORD TNG6394 4 58.028748 3 0 04oct2011 \n",
"\n",
" year pneumo7 pneumo13 pneumo23 pneumononPCV PENR LEVR ERYTHR \\\n",
"0 10 0 1 0 0 1 0 1 \n",
"1 8 0 0 1 1 0 0 0 \n",
"2 4 0 0 0 1 0 0 0 \n",
"3 7 NaN NaN NaN NaN NaN NaN NaN \n",
"4 11 0 1 0 0 1 0 1 \n",
"\n",
" culture over64 \n",
"0 2010-02-14 0 \n",
"1 2008-01-06 1 \n",
"2 2004-03-05 0 \n",
"3 2007-01-31 0 \n",
"4 2011-10-04 0 "
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_subset.head()"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data_grouped = data_subset.groupby([pd.Grouper(freq='AS', key='culture'), 'over64', 'regioneast'])['LEVR']\n",
"# data_grouped = data_subset.groupby([pd.Grouper(freq='AS', key='culture'), 'over64'])['LEVR']"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
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" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td> 12</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td> 12</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td> 12</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year over64 regioneast count post_2010\n",
"0 0 0 0 0 0\n",
"1 0 0 1 1 0\n",
"2 0 1 0 3 0\n",
"3 0 1 1 0 0\n",
"4 1 0 0 0 0\n",
"5 1 0 1 0 0\n",
"6 1 1 0 0 0\n",
"7 1 1 1 0 0\n",
"8 2 0 0 1 0\n",
"9 2 0 1 0 0\n",
"10 2 1 0 3 0\n",
"11 2 1 1 0 0\n",
"12 3 0 0 0 0\n",
"13 3 0 1 0 0\n",
"14 3 1 0 0 0\n",
"15 3 1 1 0 0\n",
"16 4 0 0 2 0\n",
"17 4 0 1 0 0\n",
"18 4 1 0 1 0\n",
"19 4 1 1 0 0\n",
"20 5 0 0 0 0\n",
"21 5 0 1 0 0\n",
"22 5 1 0 1 0\n",
"23 5 1 1 0 0\n",
"24 6 0 0 1 0\n",
"25 6 0 1 0 0\n",
"26 6 1 0 2 0\n",
"27 6 1 1 0 0\n",
"28 7 0 0 3 0\n",
"29 7 0 1 0 0\n",
"30 7 1 0 0 0\n",
"31 7 1 1 0 0\n",
"32 8 0 0 0 0\n",
"33 8 0 1 0 0\n",
"34 8 1 0 1 0\n",
"35 8 1 1 0 0\n",
"36 9 0 0 0 0\n",
"37 9 0 1 0 0\n",
"38 9 1 0 0 0\n",
"39 9 1 1 0 0\n",
"40 10 0 0 0 1\n",
"41 10 0 1 0 1\n",
"42 10 1 0 2 1\n",
"43 10 1 1 0 1\n",
"44 11 0 0 2 1\n",
"45 11 0 1 0 1\n",
"46 11 1 0 0 1\n",
"47 11 1 1 1 1\n",
"48 12 0 0 1 1\n",
"49 12 0 1 0 1\n",
"50 12 1 0 0 1\n",
"51 12 1 1 0 1"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"adult_resampled = pd.DataFrame(dict(count = data_grouped.sum())).reset_index().rename(columns={'culture':'year'})\n",
"adult_resampled['year'] = [d.year-2001 for d in adult_resampled.year]\n",
"adult_resampled['post_2010'] = (adult_resampled.year > 9).astype(int)\n",
"adult_resampled"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data_merged = pd.merge(denom_subset, adult_resampled, on=['year', 'regioneast', 'over64'])\n",
"data_merged = data_merged[['year', 'n', 'count', 'regioneast', 'over64']]\n",
"data_merged['post_2010'] = (data_merged.year > 9).astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>year</th>\n",
" <th>n</th>\n",
" <th>count</th>\n",
" <th>regioneast</th>\n",
" <th>over64</th>\n",
" <th>post_2010</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 0</td>\n",
" <td> 1377668.2</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 1</td>\n",
" <td> 1393999.8</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 2</td>\n",
" <td> 1410903.8</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 3</td>\n",
" <td> 1432392.4</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
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" <td> 4</td>\n",
" <td> 1456641.2</td>\n",
" <td> 2</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year n count regioneast over64 post_2010\n",
"0 0 1377668.2 0 0 0 0\n",
"1 1 1393999.8 0 0 0 0\n",
"2 2 1410903.8 1 0 0 0\n",
"3 3 1432392.4 0 0 0 0\n",
"4 4 1456641.2 2 0 0 0"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_merged.head()"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def resample_data(variable, regional=True):\n",
" \n",
" groupby_ind = [pd.Grouper(freq='AS', key='culture'), 'over64']\n",
" if regional:\n",
" groupby_ind += ['regioneast']\n",
" data_grouped = data_subset.groupby(groupby_ind)[variable]\n",
" \n",
" adult_resampled = pd.DataFrame(dict(n = data_grouped.count(), \n",
" count = data_grouped.sum())).reset_index().rename(columns={'culture':'year'})\n",
" adult_resampled['year'] = [d.year-2001 for d in adult_resampled.year]\n",
" adult_resampled['post_2010'] = (adult_resampled.year > 9).astype(int)\n",
" \n",
" return adult_resampled"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>over64</th>\n",
" <th>year</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> 1822327.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1 </th>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 1843273.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2 </th>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" <td> 1864774.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3 </th>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" <td> 1891701.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4 </th>\n",
" <td> 0</td>\n",
" <td> 4</td>\n",
" <td> 1921488.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td> 0</td>\n",
" <td> 5</td>\n",
" <td> 1958459.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 </th>\n",
" <td> 0</td>\n",
" <td> 6</td>\n",
" <td> 1991248.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> 0</td>\n",
" <td> 7</td>\n",
" <td> 2014026.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td> 0</td>\n",
" <td> 8</td>\n",
" <td> 2038777.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> 0</td>\n",
" <td> 9</td>\n",
" <td> 2062440.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td> 0</td>\n",
" <td> 10</td>\n",
" <td> 2086422.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td> 0</td>\n",
" <td> 11</td>\n",
" <td> 2107241.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td> 0</td>\n",
" <td> 12</td>\n",
" <td> 2121346.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 306863.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 307936.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" <td> 311000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td> 1</td>\n",
" <td> 3</td>\n",
" <td> 314250.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td> 1</td>\n",
" <td> 4</td>\n",
" <td> 320068.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td> 1</td>\n",
" <td> 5</td>\n",
" <td> 327686.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td> 1</td>\n",
" <td> 6</td>\n",
" <td> 334378.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td> 1</td>\n",
" <td> 7</td>\n",
" <td> 344093.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td> 1</td>\n",
" <td> 8</td>\n",
" <td> 352267.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td> 1</td>\n",
" <td> 9</td>\n",
" <td> 360882.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td> 1</td>\n",
" <td> 10</td>\n",
" <td> 369262.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td> 1</td>\n",
" <td> 11</td>\n",
" <td> 387854.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td> 1</td>\n",
" <td> 12</td>\n",
" <td> 404681.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" over64 year n\n",
"0 0 0 1822327.6\n",
"1 0 1 1843273.0\n",
"2 0 2 1864774.0\n",
"3 0 3 1891701.6\n",
"4 0 4 1921488.6\n",
"5 0 5 1958459.8\n",
"6 0 6 1991248.6\n",
"7 0 7 2014026.8\n",
"8 0 8 2038777.6\n",
"9 0 9 2062440.8\n",
"10 0 10 2086422.2\n",
"11 0 11 2107241.4\n",
"12 0 12 2121346.2\n",
"13 1 0 306863.0\n",
"14 1 1 307936.0\n",
"15 1 2 311000.0\n",
"16 1 3 314250.0\n",
"17 1 4 320068.0\n",
"18 1 5 327686.0\n",
"19 1 6 334378.0\n",
"20 1 7 344093.0\n",
"21 1 8 352267.0\n",
"22 1 9 360882.0\n",
"23 1 10 369262.0\n",
"24 1 11 387854.0\n",
"25 1 12 404681.0"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"denom_subset.groupby(['over64', 'year']).n.sum().reset_index()"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def rate_model(variable, regional=True):\n",
" \n",
" groupby_ind = [pd.Grouper(freq='AS', key='culture'), 'over64']\n",
" if regional:\n",
" groupby_ind += ['regioneast']\n",
" data_grouped = data_subset.groupby(groupby_ind)[variable]\n",
" \n",
" adult_resampled = pd.DataFrame(dict(count = data_grouped.sum())).reset_index().rename(columns={'culture':'year'})\n",
" adult_resampled['year'] = [d.year-2001 for d in adult_resampled.year]\n",
" adult_resampled['post_2010'] = (adult_resampled.year > 9).astype(int)\n",
" \n",
" if regional:\n",
" data_merged = pd.merge(denom_subset, adult_resampled, on=['year', 'regioneast', 'over64'])\n",
" data_merged = data_merged[['year', 'n', 'count', 'regioneast', 'over64']]\n",
" else:\n",
" denom_subset_state = denom_subset.groupby(['over64', 'year']).n.sum().reset_index()\n",
" data_merged = pd.merge(denom_subset_state, adult_resampled, on=['year', 'over64'])\n",
" data_merged = data_merged[['year', 'n', 'count', 'over64']]\n",
" data_merged['post_2010'] = (data_merged.year > 9).astype(int)\n",
" \n",
" count = data_merged['count'].values\n",
" year = data_merged['year'].values\n",
" # Make rates per 1000\n",
" n = (data_merged['n']/1000).astype(int).values\n",
"\n",
" if regional:\n",
" X = data_merged[['regioneast', 'over64']].values\n",
" regioneast = X[:,0].astype(bool)\n",
" regionwest = ~regioneast\n",
" k = X.shape[1]\n",
" over64 = X[:,1].astype(bool)\n",
" under65 = ~over64\n",
" else:\n",
" X = data_merged['over64'].values\n",
" k = 1\n",
" over64 = X.astype(bool)\n",
" under65 = ~over64\n",
"\n",
" \n",
" nyears = len(set(year))\n",
" \n",
" mu_alpha = pm.Normal('mu_alpha', 0, 0.01, value=0)\n",
" sigma_alpha = pm.Uniform('sigma_alpha', 0, 100, value=10)\n",
" rho = pm.Uniform('rho', -1, 1, value=0)\n",
"\n",
"# mu = pm.Lambda('mu', lambda mu=mu_alpha, d=pcv13: [mu]*nyears + d*post_2010[:12])\n",
" off_diag = np.eye(nyears, k=1)\n",
" Sigma = pm.Lambda('Sigma', lambda s=sigma_alpha, r=rho: (np.eye(nyears) + off_diag*r + off_diag.T*r)*(s**2))\n",
"\n",
" alpha = pm.MvNormalCov('alpha', [mu_alpha]*nyears, Sigma, value=[0]*nyears)\n",
"\n",
" mu_alpha = pm.Normal('mu_alpha', 0, 0.01, value=0)\n",
" sigma_alpha = pm.Uniform('sigma_alpha', 0, 100, value=10)\n",
" alpha = pm.Normal('alpha', mu_alpha, sigma_alpha**-2, value=[0]*nyears)\n",
"\n",
"\n",
" # Covariate effects\n",
" beta = pm.Normal('beta', 0, 0.001, value=[0]*k)\n",
" \n",
" # Noise term\n",
" tau = pm.Exponential('tau', 1, value=1)\n",
" epsilon = pm.Normal('epsilon', 0, tau, value=np.zeros(len(n)))\n",
"\n",
"\n",
" # Poisson regression expected value\n",
" if regional:\n",
" rate = pm.Lambda('rate', lambda alpha=alpha, beta=beta, e=epsilon: pm.invlogit(alpha[year] + X.dot(beta) + e))\n",
" else:\n",
" rate = pm.Lambda('rate', lambda alpha=alpha, beta=beta, e=epsilon: pm.invlogit(alpha[year] + X*beta + e))\n",
"\n",
" # Calculate region/age-specific rates (per 100K)\n",
" if regional:\n",
" rate_west_under65 = pm.Lambda('rate_west_under65', lambda r=rate: r[regionwest & under65]*100)\n",
" rate_west_over64 = pm.Lambda('rate_west_over64', lambda r=rate: r[regionwest & over64]*100)\n",
" rate_east_under65 = pm.Lambda('rate_east_under65', lambda r=rate: r[regioneast & under65]*100)\n",
" rate_east_over64 = pm.Lambda('rate_east_over64', lambda r=rate: r[regioneast & over64]*100)\n",
" else:\n",
" rate_under65 = pm.Lambda('rate_under65', lambda r=rate: r[under65]*100)\n",
" rate_over64 = pm.Lambda('rate_over64', lambda r=rate: r[over64]*100)\n",
"\n",
"\n",
" # Likelihood function\n",
" y = pm.Binomial('y', n, rate, value=count, observed=True)\n",
"\n",
" # Posterior predictive distribution for all observations (for goodness-of-fit)\n",
" y_pred = pm.Binomial('y_pred', n, rate, value=count)\n",
" \n",
" return(locals())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Pneumo7"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"M_pneumo7 = pm.MCMC(rate_model('pneumo7'))"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 50000 of 50000 complete in 28.9 sec"
]
}
],
"source": [
"M_pneumo7.sample(50000, 40000)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 50000 of 50000 complete in 27.2 sec"
]
}
],
"source": [
"M_pneumo7.sample(50000, 40000)"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x11cfcdef0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.summary_plot(M_pneumo7.alpha)"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"rate_east_under65:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t6.563 1.154 0.072 [ 4.385 8.867]\n",
"\t4.349 0.93 0.059 [ 2.529 6.148]\n",
"\t4.456 0.951 0.063 [ 2.744 6.447]\n",
"\t1.657 0.501 0.035 [ 0.758 2.621]\n",
"\t1.984 0.513 0.035 [ 1.102 3.111]\n",
"\t1.141 0.373 0.028 [ 0.539 1.897]\n",
"\t0.992 0.376 0.03 [ 0.349 1.735]\n",
"\t0.792 0.289 0.023 [ 0.334 1.381]\n",
"\t0.651 0.235 0.018 [ 0.237 1.126]\n",
"\t0.331 0.171 0.014 [ 0.071 0.678]\n",
"\t0.397 0.178 0.014 [ 0.101 0.755]\n",
"\t0.383 0.195 0.017 [ 0.099 0.779]\n",
"\t0.399 0.183 0.015 [ 0.11 0.762]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t4.503 5.769 6.507 7.275 9.024\n",
"\t2.696 3.687 4.28 4.944 6.341\n",
"\t2.756 3.776 4.395 5.075 6.473\n",
"\t0.841 1.295 1.617 1.962 2.795\n",
"\t1.094 1.621 1.941 2.308 3.104\n",
"\t0.574 0.864 1.083 1.374 1.976\n",
"\t0.461 0.731 0.923 1.18 1.975\n",
"\t0.374 0.577 0.748 0.961 1.483\n",
"\t0.284 0.483 0.623 0.775 1.234\n",
"\t0.088 0.211 0.298 0.415 0.767\n",
"\t0.131 0.265 0.369 0.498 0.815\n",
"\t0.125 0.237 0.344 0.481 0.855\n",
"\t0.142 0.266 0.366 0.493 0.881\n",
"\t\n"
]
}
],
"source": [
"M_pneumo7.rate_east_under65.summary()"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"rate_west_under65:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t5.825 0.611 0.027 [ 4.602 7.018]\n",
"\t4.094 0.517 0.025 [ 3.1 5.095]\n",
"\t2.971 0.437 0.024 [ 2.144 3.829]\n",
"\t1.9 0.324 0.018 [ 1.324 2.578]\n",
"\t2.305 0.368 0.019 [ 1.635 3.026]\n",
"\t1.599 0.304 0.018 [ 1.014 2.226]\n",
"\t0.853 0.214 0.015 [ 0.458 1.292]\n",
"\t0.596 0.169 0.012 [ 0.302 0.936]\n",
"\t0.547 0.171 0.013 [ 0.246 0.873]\n",
"\t0.334 0.122 0.009 [ 0.132 0.576]\n",
"\t0.36 0.127 0.01 [ 0.16 0.609]\n",
"\t0.179 0.077 0.006 [ 0.046 0.328]\n",
"\t0.243 0.096 0.008 [ 0.091 0.429]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t4.648 5.418 5.804 6.214 7.091\n",
"\t3.165 3.728 4.074 4.415 5.17\n",
"\t2.181 2.663 2.943 3.261 3.879\n",
"\t1.332 1.667 1.876 2.107 2.595\n",
"\t1.666 2.037 2.281 2.545 3.069\n",
"\t1.028 1.388 1.585 1.791 2.249\n",
"\t0.488 0.7 0.832 0.983 1.332\n",
"\t0.324 0.47 0.576 0.7 0.981\n",
"\t0.273 0.423 0.529 0.645 0.93\n",
"\t0.161 0.245 0.313 0.399 0.632\n",
"\t0.182 0.265 0.339 0.433 0.662\n",
"\t0.064 0.12 0.169 0.225 0.36\n",
"\t0.104 0.171 0.228 0.3 0.458\n",
"\t\n"
]
}
],
"source": [
"M_pneumo7.rate_west_under65.summary()"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"rate_east_over64:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t20.474 3.786 0.264 [ 13.695 28.37 ]\n",
"\t8.339 2.423 0.189 [ 3.952 13.102]\n",
"\t13.345 2.97 0.205 [ 7.595 19.214]\n",
"\t9.866 2.832 0.225 [ 4.749 15.748]\n",
"\t5.846 2.066 0.165 [ 2.123 9.93 ]\n",
"\t4.076 1.531 0.127 [ 1.456 7.031]\n",
"\t2.907 1.363 0.116 [ 0.869 5.497]\n",
"\t1.652 0.689 0.056 [ 0.531 2.987]\n",
"\t1.586 0.675 0.054 [ 0.467 2.874]\n",
"\t1.152 0.489 0.038 [ 0.317 2.141]\n",
"\t1.109 0.549 0.045 [ 0.269 2.138]\n",
"\t0.899 0.691 0.062 [ 0.128 2.263]\n",
"\t0.92 0.481 0.041 [ 0.138 1.822]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t13.876 17.781 20.181 22.804 28.641\n",
"\t4.232 6.571 8.067 9.876 13.522\n",
"\t8.071 11.212 13.189 15.238 19.809\n",
"\t5.157 7.879 9.498 11.535 16.275\n",
"\t2.611 4.305 5.515 7.168 10.599\n",
"\t1.614 3.054 3.923 4.878 7.492\n",
"\t1.133 2.007 2.672 3.483 6.493\n",
"\t0.674 1.127 1.539 2.049 3.26\n",
"\t0.548 1.088 1.502 1.982 3.137\n",
"\t0.411 0.803 1.087 1.4 2.334\n",
"\t0.387 0.736 1.011 1.333 2.574\n",
"\t0.234 0.489 0.698 1.052 2.8\n",
"\t0.2 0.585 0.872 1.179 2.07\n",
"\t\n"
]
}
],
"source": [
"M_pneumo7.rate_east_over64.summary()"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"rate_west_over64:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t19.778 2.447 0.123 [ 15.132 24.547]\n",
"\t13.049 2.175 0.128 [ 8.75 17.372]\n",
"\t11.408 2.014 0.123 [ 7.434 15.313]\n",
"\t5.277 1.343 0.092 [ 2.897 7.964]\n",
"\t4.082 1.098 0.074 [ 2.171 6.4 ]\n",
"\t2.688 0.849 0.062 [ 1.224 4.431]\n",
"\t2.375 0.758 0.057 [ 1.063 3.941]\n",
"\t1.584 0.55 0.041 [ 0.649 2.67 ]\n",
"\t1.443 0.603 0.048 [ 0.386 2.566]\n",
"\t1.098 0.511 0.043 [ 0.289 2.086]\n",
"\t0.958 0.446 0.037 [ 0.282 1.829]\n",
"\t0.539 0.242 0.019 [ 0.165 1.039]\n",
"\t0.658 0.307 0.025 [ 0.175 1.234]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t15.236 18.04 19.715 21.466 24.676\n",
"\t8.91 11.568 12.955 14.426 17.565\n",
"\t7.749 10.003 11.342 12.668 15.722\n",
"\t3.096 4.295 5.132 6.077 8.285\n",
"\t2.199 3.3 4.011 4.771 6.473\n",
"\t1.305 2.076 2.568 3.209 4.573\n",
"\t1.122 1.829 2.294 2.853 4.063\n",
"\t0.746 1.173 1.509 1.914 2.841\n",
"\t0.508 1.008 1.392 1.764 2.842\n",
"\t0.395 0.742 0.988 1.367 2.323\n",
"\t0.361 0.623 0.872 1.194 2.006\n",
"\t0.201 0.362 0.495 0.664 1.151\n",
"\t0.221 0.418 0.609 0.844 1.37\n",
"\t\n"
]
}
],
"source": [
"M_pneumo7.rate_west_over64.summary()"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def make_rates_df(mod):\n",
" rate_west_over64 = pd.DataFrame(mod.rate_west_over64.trace())\n",
" rate_west_over64['Region'] = 'West'\n",
" rate_west_over64['Age'] = 'Over 64'\n",
"\n",
" rate_east_over64 = pd.DataFrame(mod.rate_east_over64.trace())\n",
" rate_east_over64['Region'] = 'East'\n",
" rate_east_over64['Age'] = 'Over 64'\n",
"\n",
" rate_west_under65 = pd.DataFrame(mod.rate_west_under65.trace())\n",
" rate_west_under65['Region'] = 'West'\n",
" rate_west_under65['Age'] = 'Under 65'\n",
"\n",
" rate_east_under65 = pd.DataFrame(mod.rate_east_under65.trace())\n",
" rate_east_under65['Region'] = 'East'\n",
" rate_east_under65['Age'] = 'Under 65'\n",
"\n",
" rates = pd.concat([rate_west_over64, rate_east_over64, rate_west_under65, rate_east_under65], \n",
" ignore_index=True)\n",
" \n",
" names = rates.columns.values.copy()\n",
" names[:13] += 2001\n",
" rates.columns = names\n",
" \n",
" return rates\n"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import seaborn as sb\n",
"\n",
"def make_factorplot(data_frame):\n",
" sb.factorplot(row=\"Age\", col=\"Region\", data=data_frame, kind='box',\n",
" row_order=['Under 65', 'Over 64'],\n",
" col_order=['West', 'East'],\n",
" palette=\"YlGnBu_d\", linewidth=0.7, fliersize=0, aspect=1.25).despine(left=True)"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": false
},
"outputs": [
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x122626b00>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pneumo7_rates = make_rates_df(M_pneumo7)\n",
"make_factorplot(pneumo7_rates)"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x12319ee80>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# sb.set_context(\"talk\", font_scale=0.8)\n",
"sb.set_style(\"whitegrid\")\n",
"\n",
"f, axes = plt.subplots(figsize=(16,4), sharey=True)\n",
"sb.boxplot(data=M_pneumo7.rate_east_over64.trace(), linewidth=0.2, fliersize=0, color='Red')\n",
"sb.boxplot(data=M_pneumo7.rate_west_over64.trace(), linewidth=0.2, fliersize=0, color='Blue')\n",
"\n",
"# plt.xlabel('Age group'); plt.ylabel('Susceptibles');"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1232ebb00>"
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
},
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x1232eba20>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import seaborn as sb\n",
"# sb.set_context(\"talk\", font_scale=0.8)\n",
"sb.set_style(\"whitegrid\")\n",
"\n",
"f, axes = plt.subplots(figsize=(16,4), sharey=True)\n",
"sb.boxplot(data=M_pneumo7.rate_east_under65.trace(), linewidth=0.2, fliersize=0, color='Red')\n",
"sb.boxplot(data=M_pneumo7.rate_west_under65.trace(), linewidth=0.2, fliersize=0, color='Blue')"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pneumo7_east_over64 = pd.DataFrame(M_pneumo7.rate_east_over64.trace())\n",
"pneumo7_east_over64['Region'] = 'East'\n",
"pneumo7_east_over64['Age'] = 'Over 64'\n",
"pneumo7_west_over64 = pd.DataFrame(M_pneumo7.rate_west_over64.trace())\n",
"pneumo7_west_over64['Region'] = 'West'\n",
"pneumo7_west_over64['Age'] = 'Over 64'\n",
"pneumo7_east_under65 = pd.DataFrame(M_pneumo7.rate_east_under65.trace())\n",
"pneumo7_east_under65['Region'] = 'East'\n",
"pneumo7_east_under65['Age'] = 'Under 65'\n",
"pneumo7_west_under65 = pd.DataFrame(M_pneumo7.rate_west_under65.trace())\n",
"pneumo7_west_under65['Region'] = 'West'\n",
"pneumo7_west_under65['Age'] = 'Under 65'\n",
"\n",
"pneumo7 = pd.concat([pneumo7_east_over64, pneumo7_east_under65, \n",
" pneumo7_west_over64, pneumo7_west_under65], ignore_index=True)"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x12319e7f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.summary_plot(M_pneumo7.rate_east_over64)"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x123830160>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.summary_plot(M_pneumo7.rate_west_over64)"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"AAAASUVORK5CYII=\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x11d20e8d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.summary_plot(M_pneumo7.rate_east_under65)"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x123825160>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.summary_plot(M_pneumo7.rate_west_under65)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Pneumo7 Tennessee-wide"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 50000 of 50000 complete in 28.8 sec"
]
}
],
"source": [
"M_pneumo7_tenn = pm.MCMC(rate_model('pneumo7', regional=False))\n",
"M_pneumo7_tenn.sample(50000, 40000)"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Plotting beta_0\n"
]
},
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x1239c2160>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.plot(M_pneumo7_tenn.beta)"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n"
]
},
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x11d0f91d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.summary_plot(M_pneumo7_tenn.rate_over64)"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n"
]
},
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x120f3e908>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.summary_plot(M_pneumo7_tenn.rate_under65)"
]
},
{
"cell_type": "code",
"execution_count": 153,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"rate_under65:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t5.933 0.541 0.026 [ 4.949 7.111]\n",
"\t4.227 0.473 0.026 [ 3.273 5.148]\n",
"\t3.385 0.418 0.023 [ 2.598 4.177]\n",
"\t1.746 0.312 0.02 [ 1.202 2.42 ]\n",
"\t2.263 0.346 0.021 [ 1.601 2.946]\n",
"\t1.504 0.277 0.018 [ 0.981 2.049]\n",
"\t0.914 0.2 0.014 [ 0.545 1.297]\n",
"\t0.64 0.153 0.012 [ 0.36 0.968]\n",
"\t0.59 0.151 0.012 [ 0.335 0.908]\n",
"\t0.321 0.111 0.01 [ 0.114 0.545]\n",
"\t0.361 0.118 0.01 [ 0.138 0.594]\n",
"\t0.238 0.109 0.01 [ 0.061 0.429]\n",
"\t0.27 0.104 0.009 [ 0.112 0.516]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t4.912 5.571 5.908 6.287 7.079\n",
"\t3.334 3.9 4.207 4.537 5.23\n",
"\t2.647 3.101 3.362 3.637 4.276\n",
"\t1.184 1.524 1.72 1.942 2.409\n",
"\t1.619 2.009 2.247 2.49 2.983\n",
"\t1.046 1.301 1.469 1.68 2.139\n",
"\t0.578 0.768 0.895 1.043 1.349\n",
"\t0.378 0.53 0.626 0.731 1.0\n",
"\t0.346 0.477 0.573 0.685 0.928\n",
"\t0.128 0.244 0.307 0.386 0.591\n",
"\t0.151 0.277 0.356 0.435 0.617\n",
"\t0.084 0.16 0.227 0.285 0.51\n",
"\t0.119 0.199 0.25 0.323 0.534\n",
"\t\n"
]
}
],
"source": [
"M_pneumo7_tenn.rate_under65.summary()"
]
},
{
"cell_type": "code",
"execution_count": 154,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"rate_over64:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t20.374 2.407 0.138 [ 16.088 25.37 ]\n",
"\t11.172 1.832 0.129 [ 7.553 14.709]\n",
"\t11.758 1.802 0.109 [ 8.446 15.281]\n",
"\t7.285 1.3 0.085 [ 4.806 9.767]\n",
"\t4.23 1.204 0.098 [ 2.138 6.54 ]\n",
"\t3.11 0.89 0.072 [ 1.565 4.826]\n",
"\t2.46 0.837 0.07 [ 0.972 4.204]\n",
"\t1.597 0.505 0.043 [ 0.735 2.592]\n",
"\t1.354 0.528 0.046 [ 0.439 2.304]\n",
"\t1.19 0.47 0.041 [ 0.434 2.116]\n",
"\t0.977 0.484 0.044 [ 0.127 1.794]\n",
"\t0.725 0.346 0.031 [ 0.242 1.444]\n",
"\t0.806 0.424 0.039 [ 0.161 1.651]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t15.935 18.667 20.281 22.02 25.264\n",
"\t7.783 9.876 11.125 12.327 15.064\n",
"\t8.601 10.415 11.642 12.962 15.623\n",
"\t4.924 6.351 7.235 8.134 9.995\n",
"\t2.323 3.362 4.097 4.901 7.057\n",
"\t1.654 2.465 3.046 3.649 5.054\n",
"\t1.101 1.876 2.346 2.919 4.449\n",
"\t0.823 1.208 1.533 1.917 2.708\n",
"\t0.537 0.955 1.289 1.691 2.496\n",
"\t0.504 0.83 1.103 1.485 2.285\n",
"\t0.187 0.615 0.958 1.3 1.972\n",
"\t0.307 0.491 0.641 0.872 1.668\n",
"\t0.21 0.469 0.737 1.049 1.813\n",
"\t\n"
]
}
],
"source": [
"M_pneumo7_tenn.rate_over64.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Pneumo13"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 50000 of 50000 complete in 29.2 sec"
]
}
],
"source": [
"M_pneumo13 = pm.MCMC(rate_model('pneumo13'))\n",
"M_pneumo13.sample(50000, 40000)\n",
"# M_pneumo13.sample(50000, 40000)"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pneumo13_rates = make_rates_df(M_pneumo13)"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x1268ff358>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"make_factorplot(pneumo13_rates)"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n"
]
},
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x127fbc6a0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.summary_plot(M_pneumo13.rate_east_under65)"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n"
]
},
{
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"RU5ErkJggg==\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x128005668>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.summary_plot(M_pneumo13.rate_east_over64)"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n"
]
},
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x12824e4a8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.summary_plot(M_pneumo13.rate_west_under65)"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n"
]
},
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x12840b630>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.summary_plot(M_pneumo13.rate_west_over64)"
]
},
{
"cell_type": "code",
"execution_count": 155,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"rate_east_under65:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t2.455 0.648 0.058 [ 1.313 3.738]\n",
"\t2.408 0.523 0.044 [ 1.327 3.427]\n",
"\t3.026 0.601 0.049 [ 1.877 4.244]\n",
"\t2.627 0.558 0.046 [ 1.556 3.666]\n",
"\t5.38 1.0 0.082 [ 3.513 7.393]\n",
"\t6.16 0.966 0.074 [ 4.287 8.047]\n",
"\t4.557 0.778 0.062 [ 3.106 6.094]\n",
"\t7.371 1.06 0.08 [ 5.283 9.34 ]\n",
"\t7.475 1.098 0.087 [ 5.245 9.642]\n",
"\t5.186 1.002 0.086 [ 3.365 7.298]\n",
"\t4.091 0.751 0.062 [ 2.668 5.488]\n",
"\t2.816 0.576 0.048 [ 1.784 3.943]\n",
"\t1.806 0.506 0.046 [ 0.9 2.836]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t1.459 1.999 2.352 2.797 4.109\n",
"\t1.479 2.057 2.356 2.696 3.658\n",
"\t1.983 2.601 2.97 3.405 4.406\n",
"\t1.572 2.218 2.627 3.019 3.695\n",
"\t3.556 4.691 5.315 6.053 7.475\n",
"\t4.43 5.476 6.102 6.762 8.284\n",
"\t3.167 3.997 4.516 5.061 6.188\n",
"\t5.533 6.602 7.298 8.078 9.631\n",
"\t5.395 6.71 7.418 8.189 9.846\n",
"\t3.408 4.513 5.1 5.802 7.359\n",
"\t2.783 3.54 4.036 4.568 5.695\n",
"\t1.824 2.385 2.777 3.227 3.995\n",
"\t0.987 1.457 1.751 2.071 3.025\n",
"\t\n"
]
}
],
"source": [
"M_pneumo13.rate_east_under65.summary()"
]
},
{
"cell_type": "code",
"execution_count": 156,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"rate_west_under65:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t5.825 0.611 0.027 [ 4.602 7.018]\n",
"\t4.094 0.517 0.025 [ 3.1 5.095]\n",
"\t2.971 0.437 0.024 [ 2.144 3.829]\n",
"\t1.9 0.324 0.018 [ 1.324 2.578]\n",
"\t2.305 0.368 0.019 [ 1.635 3.026]\n",
"\t1.599 0.304 0.018 [ 1.014 2.226]\n",
"\t0.853 0.214 0.015 [ 0.458 1.292]\n",
"\t0.596 0.169 0.012 [ 0.302 0.936]\n",
"\t0.547 0.171 0.013 [ 0.246 0.873]\n",
"\t0.334 0.122 0.009 [ 0.132 0.576]\n",
"\t0.36 0.127 0.01 [ 0.16 0.609]\n",
"\t0.179 0.077 0.006 [ 0.046 0.328]\n",
"\t0.243 0.096 0.008 [ 0.091 0.429]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t4.648 5.418 5.804 6.214 7.091\n",
"\t3.165 3.728 4.074 4.415 5.17\n",
"\t2.181 2.663 2.943 3.261 3.879\n",
"\t1.332 1.667 1.876 2.107 2.595\n",
"\t1.666 2.037 2.281 2.545 3.069\n",
"\t1.028 1.388 1.585 1.791 2.249\n",
"\t0.488 0.7 0.832 0.983 1.332\n",
"\t0.324 0.47 0.576 0.7 0.981\n",
"\t0.273 0.423 0.529 0.645 0.93\n",
"\t0.161 0.245 0.313 0.399 0.632\n",
"\t0.182 0.265 0.339 0.433 0.662\n",
"\t0.064 0.12 0.169 0.225 0.36\n",
"\t0.104 0.171 0.228 0.3 0.458\n",
"\t\n"
]
}
],
"source": [
"M_pneumo7.rate_west_under65.summary()"
]
},
{
"cell_type": "code",
"execution_count": 157,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"rate_east_over64:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t20.474 3.786 0.264 [ 13.695 28.37 ]\n",
"\t8.339 2.423 0.189 [ 3.952 13.102]\n",
"\t13.345 2.97 0.205 [ 7.595 19.214]\n",
"\t9.866 2.832 0.225 [ 4.749 15.748]\n",
"\t5.846 2.066 0.165 [ 2.123 9.93 ]\n",
"\t4.076 1.531 0.127 [ 1.456 7.031]\n",
"\t2.907 1.363 0.116 [ 0.869 5.497]\n",
"\t1.652 0.689 0.056 [ 0.531 2.987]\n",
"\t1.586 0.675 0.054 [ 0.467 2.874]\n",
"\t1.152 0.489 0.038 [ 0.317 2.141]\n",
"\t1.109 0.549 0.045 [ 0.269 2.138]\n",
"\t0.899 0.691 0.062 [ 0.128 2.263]\n",
"\t0.92 0.481 0.041 [ 0.138 1.822]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t13.876 17.781 20.181 22.804 28.641\n",
"\t4.232 6.571 8.067 9.876 13.522\n",
"\t8.071 11.212 13.189 15.238 19.809\n",
"\t5.157 7.879 9.498 11.535 16.275\n",
"\t2.611 4.305 5.515 7.168 10.599\n",
"\t1.614 3.054 3.923 4.878 7.492\n",
"\t1.133 2.007 2.672 3.483 6.493\n",
"\t0.674 1.127 1.539 2.049 3.26\n",
"\t0.548 1.088 1.502 1.982 3.137\n",
"\t0.411 0.803 1.087 1.4 2.334\n",
"\t0.387 0.736 1.011 1.333 2.574\n",
"\t0.234 0.489 0.698 1.052 2.8\n",
"\t0.2 0.585 0.872 1.179 2.07\n",
"\t\n"
]
}
],
"source": [
"M_pneumo7.rate_east_over64.summary()"
]
},
{
"cell_type": "code",
"execution_count": 158,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"rate_west_over64:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t19.778 2.447 0.123 [ 15.132 24.547]\n",
"\t13.049 2.175 0.128 [ 8.75 17.372]\n",
"\t11.408 2.014 0.123 [ 7.434 15.313]\n",
"\t5.277 1.343 0.092 [ 2.897 7.964]\n",
"\t4.082 1.098 0.074 [ 2.171 6.4 ]\n",
"\t2.688 0.849 0.062 [ 1.224 4.431]\n",
"\t2.375 0.758 0.057 [ 1.063 3.941]\n",
"\t1.584 0.55 0.041 [ 0.649 2.67 ]\n",
"\t1.443 0.603 0.048 [ 0.386 2.566]\n",
"\t1.098 0.511 0.043 [ 0.289 2.086]\n",
"\t0.958 0.446 0.037 [ 0.282 1.829]\n",
"\t0.539 0.242 0.019 [ 0.165 1.039]\n",
"\t0.658 0.307 0.025 [ 0.175 1.234]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t15.236 18.04 19.715 21.466 24.676\n",
"\t8.91 11.568 12.955 14.426 17.565\n",
"\t7.749 10.003 11.342 12.668 15.722\n",
"\t3.096 4.295 5.132 6.077 8.285\n",
"\t2.199 3.3 4.011 4.771 6.473\n",
"\t1.305 2.076 2.568 3.209 4.573\n",
"\t1.122 1.829 2.294 2.853 4.063\n",
"\t0.746 1.173 1.509 1.914 2.841\n",
"\t0.508 1.008 1.392 1.764 2.842\n",
"\t0.395 0.742 0.988 1.367 2.323\n",
"\t0.361 0.623 0.872 1.194 2.006\n",
"\t0.201 0.362 0.495 0.664 1.151\n",
"\t0.221 0.418 0.609 0.844 1.37\n",
"\t\n"
]
}
],
"source": [
"M_pneumo7.rate_west_over64.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Pneumo13 Tennessee-wide"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 50000 of 50000 complete in 28.5 sec"
]
}
],
"source": [
"M_pneumo13_tenn = pm.MCMC(rate_model('pneumo13', regional=False))\n",
"M_pneumo13_tenn.sample(50000, 40000)"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n"
]
},
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x127918048>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.summary_plot(M_pneumo13_tenn.alpha)"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Plotting beta_0\n"
]
},
{
"data": {
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"G5oAAAAASUVORK5CYII=\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x128410e10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.plot(M_pneumo13_tenn.beta)"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n"
]
},
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x12825f6a0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.summary_plot(M_pneumo13_tenn.rate_over64)"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n"
]
},
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x12690f240>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.summary_plot(M_pneumo13_tenn.rate_under65)"
]
},
{
"cell_type": "code",
"execution_count": 162,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"rate_under65:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t2.386 0.333 0.027 [ 1.8 3.073]\n",
"\t2.728 0.368 0.029 [ 2.026 3.427]\n",
"\t2.892 0.36 0.026 [ 2.255 3.663]\n",
"\t2.552 0.333 0.026 [ 1.919 3.212]\n",
"\t4.029 0.433 0.031 [ 3.209 4.895]\n",
"\t5.545 0.482 0.031 [ 4.664 6.547]\n",
"\t4.859 0.474 0.033 [ 3.988 5.846]\n",
"\t6.011 0.55 0.038 [ 4.994 7.134]\n",
"\t5.79 0.539 0.038 [ 4.705 6.797]\n",
"\t4.433 0.41 0.026 [ 3.604 5.186]\n",
"\t3.855 0.39 0.027 [ 3.111 4.636]\n",
"\t2.287 0.302 0.024 [ 1.724 2.948]\n",
"\t1.674 0.272 0.022 [ 1.104 2.198]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t1.83 2.149 2.357 2.588 3.119\n",
"\t2.051 2.465 2.72 2.976 3.459\n",
"\t2.242 2.638 2.87 3.125 3.656\n",
"\t1.991 2.324 2.528 2.746 3.307\n",
"\t3.172 3.735 4.013 4.339 4.888\n",
"\t4.638 5.219 5.534 5.848 6.531\n",
"\t3.988 4.526 4.843 5.186 5.846\n",
"\t4.98 5.648 6.0 6.371 7.131\n",
"\t4.744 5.413 5.791 6.141 6.853\n",
"\t3.671 4.167 4.425 4.692 5.281\n",
"\t3.12 3.597 3.838 4.106 4.654\n",
"\t1.691 2.097 2.274 2.471 2.929\n",
"\t1.153 1.475 1.665 1.841 2.251\n",
"\t\n"
]
}
],
"source": [
"M_pneumo13_tenn.rate_under65.summary()"
]
},
{
"cell_type": "code",
"execution_count": 163,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"rate_over64:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t10.905 1.559 0.123 [ 7.996 13.661]\n",
"\t10.226 1.647 0.139 [ 7.18 13.481]\n",
"\t11.451 1.645 0.127 [ 8.516 14.86 ]\n",
"\t10.294 1.35 0.099 [ 7.885 12.909]\n",
"\t12.215 1.645 0.125 [ 9.078 15.588]\n",
"\t12.409 1.743 0.14 [ 9.207 15.959]\n",
"\t15.947 2.028 0.158 [ 12.244 20.392]\n",
"\t13.887 1.784 0.142 [ 10.732 17.451]\n",
"\t9.957 1.42 0.114 [ 7.619 12.878]\n",
"\t12.889 1.615 0.123 [ 9.922 16.332]\n",
"\t9.989 1.471 0.12 [ 7.098 12.874]\n",
"\t6.488 1.177 0.101 [ 4.204 8.86 ]\n",
"\t4.374 0.87 0.074 [ 2.827 6.114]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t8.101 9.732 10.918 12.017 13.847\n",
"\t7.408 9.049 10.145 11.247 13.772\n",
"\t8.606 10.252 11.287 12.541 15.002\n",
"\t7.943 9.309 10.209 11.209 13.044\n",
"\t9.336 11.053 12.105 13.228 15.884\n",
"\t9.307 11.166 12.326 13.536 16.113\n",
"\t12.101 14.545 15.764 17.206 20.316\n",
"\t10.859 12.65 13.696 14.953 17.826\n",
"\t7.611 8.902 9.888 10.892 12.877\n",
"\t9.801 11.81 12.819 13.916 16.23\n",
"\t7.269 8.944 9.961 10.959 13.154\n",
"\t4.36 5.696 6.41 7.145 9.066\n",
"\t2.883 3.796 4.267 4.867 6.26\n",
"\t\n"
]
}
],
"source": [
"M_pneumo13_tenn.rate_over64.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Pneumo23"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 50000 of 50000 complete in 30.5 sec"
]
}
],
"source": [
"M_pneumo23 = pm.MCMC(rate_model('pneumo23'))\n",
"M_pneumo23.sample(50000, 40000)\n",
"# M_pneumo23.sample(50000, 40000)"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"collapsed": false
},
"outputs": [
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x12856af98>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pneumo23_rates = make_rates_df(M_pneumo23)\n",
"make_factorplot(pneumo23_rates)"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n"
]
},
{
"data": {
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"/wctrrTpETaJDAAAAABJRU5ErkJggg==\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x127c02e10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.summary_plot(M_pneumo23.alpha)"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n"
]
},
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x12bf91978>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.summary_plot(M_pneumo23.rate_west_over64)"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n"
]
},
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x12c2b57f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.summary_plot(M_pneumo23.rate_west_under65)"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n"
]
},
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x12c259cc0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.summary_plot(M_pneumo23.rate_east_over64)"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n"
]
},
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x12c282e10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.summary_plot(M_pneumo23.rate_east_under65)"
]
},
{
"cell_type": "code",
"execution_count": 164,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"rate_east_under65:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t2.026 0.491 0.043 [ 1.06 2.993]\n",
"\t2.159 0.456 0.038 [ 1.273 2.969]\n",
"\t2.338 0.539 0.048 [ 1.435 3.475]\n",
"\t3.422 0.807 0.07 [ 2.021 5.046]\n",
"\t5.625 0.944 0.078 [ 3.941 7.502]\n",
"\t4.266 0.746 0.06 [ 2.663 5.665]\n",
"\t6.613 0.99 0.075 [ 4.692 8.519]\n",
"\t4.773 0.859 0.071 [ 3.147 6.31 ]\n",
"\t4.887 0.755 0.056 [ 3.405 6.309]\n",
"\t5.281 0.958 0.079 [ 3.542 7.108]\n",
"\t5.037 0.803 0.061 [ 3.628 6.674]\n",
"\t5.32 0.805 0.059 [ 3.822 6.908]\n",
"\t6.416 0.998 0.079 [ 4.58 8.43]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t1.208 1.713 1.952 2.273 3.257\n",
"\t1.307 1.842 2.157 2.47 3.019\n",
"\t1.502 1.936 2.24 2.662 3.556\n",
"\t2.117 2.836 3.332 3.892 5.281\n",
"\t4.002 4.937 5.564 6.218 7.646\n",
"\t2.843 3.754 4.246 4.73 5.868\n",
"\t4.834 5.907 6.54 7.255 8.707\n",
"\t3.236 4.183 4.726 5.358 6.503\n",
"\t3.488 4.38 4.88 5.353 6.451\n",
"\t3.651 4.558 5.226 5.911 7.295\n",
"\t3.638 4.441 4.959 5.576 6.691\n",
"\t3.84 4.722 5.294 5.873 6.948\n",
"\t4.657 5.735 6.303 7.054 8.555\n",
"\t\n"
]
}
],
"source": [
"M_pneumo23.rate_east_under65.summary()"
]
},
{
"cell_type": "code",
"execution_count": 165,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"rate_west_under65:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t2.663 0.358 0.027 [ 2.002 3.377]\n",
"\t2.672 0.382 0.029 [ 1.964 3.442]\n",
"\t3.074 0.435 0.033 [ 2.262 3.983]\n",
"\t3.252 0.423 0.03 [ 2.378 4.012]\n",
"\t5.824 0.601 0.041 [ 4.707 7.091]\n",
"\t4.251 0.47 0.029 [ 3.349 5.159]\n",
"\t3.228 0.423 0.031 [ 2.367 4.052]\n",
"\t3.254 0.441 0.034 [ 2.429 4.122]\n",
"\t3.154 0.391 0.029 [ 2.479 3.972]\n",
"\t3.727 0.473 0.034 [ 2.834 4.704]\n",
"\t3.91 0.485 0.036 [ 2.971 4.849]\n",
"\t4.055 0.48 0.033 [ 3.166 5.002]\n",
"\t3.623 0.409 0.029 [ 2.867 4.394]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t2.04 2.405 2.635 2.894 3.44\n",
"\t1.954 2.409 2.665 2.926 3.435\n",
"\t2.221 2.789 3.066 3.349 3.954\n",
"\t2.414 2.967 3.243 3.544 4.061\n",
"\t4.691 5.427 5.787 6.205 7.082\n",
"\t3.377 3.931 4.234 4.55 5.252\n",
"\t2.462 2.934 3.201 3.494 4.158\n",
"\t2.437 2.943 3.243 3.556 4.134\n",
"\t2.46 2.864 3.138 3.419 3.96\n",
"\t2.834 3.416 3.693 4.03 4.704\n",
"\t3.007 3.57 3.917 4.232 4.903\n",
"\t3.214 3.714 4.027 4.358 5.071\n",
"\t2.89 3.321 3.619 3.901 4.423\n",
"\t\n"
]
}
],
"source": [
"M_pneumo23.rate_west_under65.summary()"
]
},
{
"cell_type": "code",
"execution_count": 166,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"rate_east_over64:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t7.238 1.597 0.137 [ 4.181 10.077]\n",
"\t5.606 1.636 0.15 [ 2.762 8.225]\n",
"\t7.961 1.828 0.153 [ 4.595 11.4 ]\n",
"\t7.811 1.772 0.155 [ 4.78 11.639]\n",
"\t9.571 1.715 0.142 [ 6.433 13.075]\n",
"\t11.645 2.431 0.211 [ 7.372 16.558]\n",
"\t13.184 2.636 0.231 [ 8.533 18.527]\n",
"\t12.789 2.429 0.201 [ 8.633 18.003]\n",
"\t11.389 1.988 0.164 [ 7.817 15.089]\n",
"\t11.332 1.776 0.139 [ 7.854 14.791]\n",
"\t13.345 2.61 0.227 [ 8.266 18.13 ]\n",
"\t12.4 2.373 0.201 [ 7.944 16.85 ]\n",
"\t11.735 2.257 0.188 [ 7.323 16.31 ]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t4.31 6.032 7.261 8.313 10.379\n",
"\t3.036 4.447 5.524 6.494 9.35\n",
"\t5.106 6.672 7.738 9.005 12.126\n",
"\t5.023 6.553 7.534 8.825 12.095\n",
"\t6.634 8.368 9.449 10.623 13.408\n",
"\t7.769 9.941 11.291 13.008 17.202\n",
"\t8.572 11.25 13.101 14.886 18.635\n",
"\t8.846 11.093 12.592 14.167 18.62\n",
"\t7.959 9.878 11.267 12.846 15.302\n",
"\t8.06 10.096 11.28 12.477 15.138\n",
"\t8.457 11.505 13.157 15.252 18.391\n",
"\t8.239 10.607 12.35 14.034 17.218\n",
"\t7.627 10.123 11.62 13.142 16.672\n",
"\t\n"
]
}
],
"source": [
"M_pneumo23.rate_east_over64.summary()"
]
},
{
"cell_type": "code",
"execution_count": 167,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"rate_west_over64:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t7.122 1.3 0.107 [ 4.683 9.606]\n",
"\t4.772 1.178 0.104 [ 2.763 7.138]\n",
"\t7.327 1.364 0.111 [ 4.804 9.936]\n",
"\t5.852 1.099 0.091 [ 3.854 8.257]\n",
"\t10.114 1.548 0.122 [ 7.206 13.169]\n",
"\t11.651 1.899 0.157 [ 8.267 15.487]\n",
"\t10.352 1.824 0.151 [ 6.848 13.672]\n",
"\t10.543 1.862 0.155 [ 7.231 14.141]\n",
"\t7.974 1.627 0.143 [ 4.965 11.135]\n",
"\t10.822 1.858 0.152 [ 7.387 14.432]\n",
"\t11.853 1.784 0.145 [ 8.588 15.533]\n",
"\t10.8 1.703 0.137 [ 7.228 13.881]\n",
"\t9.893 1.548 0.119 [ 7.054 13.14 ]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t4.852 6.173 6.997 7.971 9.996\n",
"\t2.907 3.927 4.624 5.468 7.472\n",
"\t4.861 6.351 7.271 8.294 10.025\n",
"\t3.931 5.113 5.779 6.491 8.374\n",
"\t7.397 8.986 10.015 11.126 13.384\n",
"\t8.344 10.287 11.457 12.965 15.636\n",
"\t7.186 9.067 10.262 11.484 14.13\n",
"\t7.343 9.164 10.421 11.829 14.349\n",
"\t5.325 6.864 7.723 8.857 12.148\n",
"\t7.465 9.484 10.709 12.091 14.542\n",
"\t8.664 10.585 11.754 12.961 15.626\n",
"\t7.57 9.58 10.784 11.88 14.321\n",
"\t7.054 8.802 9.815 10.864 13.14\n",
"\t\n"
]
}
],
"source": [
"M_pneumo23.rate_west_over64.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Pneumo23 Tennessee-wide"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 50000 of 50000 complete in 25.9 sec"
]
}
],
"source": [
"M_pneumo23_tenn = pm.MCMC(rate_model('pneumo23', regional=False))\n",
"M_pneumo23_tenn.sample(50000, 40000)"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n"
]
},
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x12c26bb70>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.summary_plot(M_pneumo23_tenn.rate_over64)"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n"
]
},
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x12c529940>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.summary_plot(M_pneumo23_tenn.rate_under65)"
]
},
{
"cell_type": "code",
"execution_count": 168,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"rate_under65:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t2.438 0.335 0.026 [ 1.798 3.085]\n",
"\t2.57 0.347 0.028 [ 1.94 3.245]\n",
"\t2.751 0.326 0.024 [ 2.127 3.398]\n",
"\t3.345 0.407 0.031 [ 2.592 4.166]\n",
"\t5.884 0.502 0.031 [ 4.931 6.88 ]\n",
"\t4.218 0.445 0.032 [ 3.344 5.059]\n",
"\t3.951 0.402 0.027 [ 3.227 4.809]\n",
"\t3.527 0.414 0.029 [ 2.74 4.335]\n",
"\t3.578 0.359 0.024 [ 2.897 4.292]\n",
"\t4.042 0.404 0.028 [ 3.338 4.869]\n",
"\t4.112 0.42 0.031 [ 3.386 4.991]\n",
"\t4.409 0.423 0.027 [ 3.609 5.287]\n",
"\t4.336 0.442 0.031 [ 3.446 5.228]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t1.836 2.212 2.426 2.651 3.125\n",
"\t1.947 2.321 2.553 2.822 3.254\n",
"\t2.135 2.515 2.751 2.97 3.411\n",
"\t2.627 3.063 3.315 3.601 4.231\n",
"\t4.95 5.54 5.869 6.217 6.91\n",
"\t3.375 3.92 4.206 4.508 5.105\n",
"\t3.206 3.675 3.92 4.217 4.799\n",
"\t2.75 3.246 3.51 3.8 4.359\n",
"\t2.9 3.328 3.58 3.824 4.296\n",
"\t3.336 3.76 4.022 4.303 4.869\n",
"\t3.389 3.824 4.075 4.364 5.024\n",
"\t3.59 4.141 4.393 4.674 5.273\n",
"\t3.512 4.05 4.301 4.602 5.307\n",
"\t\n"
]
}
],
"source": [
"M_pneumo23_tenn.rate_under65.summary()"
]
},
{
"cell_type": "code",
"execution_count": 169,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"rate_over64:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t7.056 1.427 0.124 [ 4.597 10.023]\n",
"\t5.227 1.041 0.092 [ 3.094 7.17 ]\n",
"\t8.58 1.431 0.117 [ 6.065 11.581]\n",
"\t6.589 1.095 0.089 [ 4.625 8.775]\n",
"\t9.642 1.409 0.111 [ 6.945 12.436]\n",
"\t10.671 1.675 0.135 [ 7.255 14.054]\n",
"\t11.569 1.571 0.12 [ 8.517 14.638]\n",
"\t11.389 1.738 0.144 [ 8.28 14.732]\n",
"\t8.412 1.26 0.099 [ 5.933 10.807]\n",
"\t10.781 1.499 0.119 [ 7.784 13.639]\n",
"\t12.245 1.633 0.127 [ 8.795 15.372]\n",
"\t11.172 1.495 0.11 [ 8.42 14.23]\n",
"\t10.469 1.412 0.11 [ 7.664 13.235]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t4.764 6.019 6.852 7.942 10.227\n",
"\t3.231 4.477 5.24 5.926 7.348\n",
"\t5.959 7.504 8.546 9.552 11.478\n",
"\t4.767 5.794 6.49 7.271 9.102\n",
"\t7.065 8.663 9.63 10.537 12.682\n",
"\t7.474 9.604 10.568 11.705 14.394\n",
"\t8.533 10.519 11.555 12.62 14.665\n",
"\t8.391 10.111 11.306 12.521 15.013\n",
"\t6.03 7.539 8.396 9.24 10.936\n",
"\t8.01 9.718 10.743 11.792 13.898\n",
"\t9.088 11.118 12.165 13.307 15.742\n",
"\t8.457 10.161 11.075 12.127 14.29\n",
"\t7.801 9.484 10.473 11.358 13.401\n",
"\t\n"
]
}
],
"source": [
"M_pneumo23_tenn.rate_over64.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### PneumononPCV"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 50000 of 50000 complete in 31.7 sec"
]
}
],
"source": [
"M_pneumononPCV = pm.MCMC(rate_model('pneumononPCV'))\n",
"M_pneumononPCV.sample(50000, 40000)\n",
"# M_pneumononPCV.sample(50000, 40000)"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {
"collapsed": false
},
"outputs": [
{
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"AAAASUVORK5CYII=\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x12bf89d30>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pneumononpcv_rates = make_rates_df(M_pneumononPCV)\n",
"make_factorplot(pneumononpcv_rates)"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n"
]
},
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x130199e10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.summary_plot(M_pneumononPCV.rate_west_over64)"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n"
]
},
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x12bbc4cc0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.summary_plot(M_pneumononPCV.rate_west_under65)"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n"
]
},
{
"data": {
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"RK5CYII=\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x13032bd68>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.summary_plot(M_pneumononPCV.rate_east_over64)"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n"
]
},
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x127880b70>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.summary_plot(M_pneumononPCV.rate_east_under65)"
]
},
{
"cell_type": "code",
"execution_count": 170,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"rate_east_under65:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t3.136 0.636 0.056 [ 1.938 4.371]\n",
"\t2.9 0.564 0.05 [ 1.809 3.989]\n",
"\t3.538 0.629 0.055 [ 2.355 4.724]\n",
"\t4.64 1.021 0.094 [ 2.606 6.408]\n",
"\t6.535 1.07 0.092 [ 4.37 8.541]\n",
"\t6.265 0.772 0.062 [ 4.788 7.826]\n",
"\t8.575 1.184 0.099 [ 6.044 10.671]\n",
"\t8.081 1.173 0.1 [ 5.942 10.597]\n",
"\t7.634 1.143 0.1 [ 5.519 9.872]\n",
"\t7.896 1.042 0.085 [ 5.922 9.916]\n",
"\t8.195 1.242 0.106 [ 5.925 10.565]\n",
"\t7.88 1.108 0.092 [ 5.658 9.971]\n",
"\t8.983 1.121 0.089 [ 6.776 11.036]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t2.07 2.701 3.057 3.508 4.62\n",
"\t1.839 2.516 2.863 3.267 4.089\n",
"\t2.409 3.099 3.527 3.948 4.824\n",
"\t2.79 3.916 4.575 5.352 6.78\n",
"\t4.689 5.799 6.514 7.148 8.982\n",
"\t4.757 5.741 6.26 6.786 7.818\n",
"\t6.323 7.753 8.572 9.331 11.041\n",
"\t6.087 7.278 7.976 8.755 10.811\n",
"\t5.631 6.819 7.548 8.387 10.12\n",
"\t5.989 7.165 7.836 8.632 10.024\n",
"\t5.999 7.263 8.13 9.098 10.664\n",
"\t5.753 7.101 7.915 8.606 10.092\n",
"\t6.896 8.189 8.94 9.769 11.22\n",
"\t\n"
]
}
],
"source": [
"M_pneumononPCV.rate_east_under65.summary()"
]
},
{
"cell_type": "code",
"execution_count": 171,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"rate_west_under65:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t3.86 0.511 0.041 [ 2.83 4.822]\n",
"\t4.2 0.498 0.041 [ 3.258 5.132]\n",
"\t5.015 0.551 0.043 [ 3.903 6.021]\n",
"\t5.643 0.594 0.046 [ 4.574 6.871]\n",
"\t7.987 0.657 0.046 [ 6.744 9.309]\n",
"\t7.543 0.669 0.049 [ 6.156 8.823]\n",
"\t6.56 0.592 0.042 [ 5.451 7.76 ]\n",
"\t6.439 0.564 0.04 [ 5.413 7.628]\n",
"\t5.734 0.529 0.039 [ 4.694 6.805]\n",
"\t6.479 0.6 0.043 [ 5.287 7.595]\n",
"\t6.246 0.56 0.04 [ 5.195 7.395]\n",
"\t6.675 0.589 0.041 [ 5.509 7.796]\n",
"\t5.868 0.515 0.035 [ 4.919 6.972]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t2.896 3.531 3.847 4.199 4.932\n",
"\t3.288 3.851 4.185 4.547 5.178\n",
"\t4.006 4.63 5.001 5.372 6.224\n",
"\t4.628 5.217 5.578 6.02 6.976\n",
"\t6.833 7.532 7.927 8.402 9.428\n",
"\t6.159 7.124 7.552 7.977 8.836\n",
"\t5.478 6.15 6.545 6.933 7.823\n",
"\t5.423 6.043 6.417 6.802 7.658\n",
"\t4.671 5.381 5.729 6.075 6.802\n",
"\t5.269 6.083 6.487 6.906 7.584\n",
"\t5.197 5.858 6.224 6.598 7.426\n",
"\t5.531 6.276 6.659 7.067 7.835\n",
"\t4.772 5.525 5.882 6.212 6.851\n",
"\t\n"
]
}
],
"source": [
"M_pneumononPCV.rate_west_under65.summary()"
]
},
{
"cell_type": "code",
"execution_count": 172,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"rate_east_over64:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t11.389 2.662 0.247 [ 6.039 16.589]\n",
"\t13.413 2.4 0.215 [ 8.388 18.061]\n",
"\t11.775 2.837 0.265 [ 6.775 17.42 ]\n",
"\t17.079 3.494 0.318 [ 10.933 23.805]\n",
"\t21.895 3.621 0.327 [ 15.517 29.513]\n",
"\t30.855 3.864 0.338 [ 23.844 39.233]\n",
"\t26.829 3.815 0.337 [ 20.033 33.841]\n",
"\t26.722 3.498 0.302 [ 20.399 33.821]\n",
"\t21.614 2.854 0.245 [ 16.261 27.248]\n",
"\t20.633 3.465 0.315 [ 13.337 26.773]\n",
"\t29.564 3.559 0.306 [ 22.705 36.875]\n",
"\t24.831 3.277 0.28 [ 18.784 31.394]\n",
"\t18.538 2.77 0.242 [ 13.367 23.897]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t6.908 9.631 11.039 12.964 17.777\n",
"\t8.826 11.861 13.278 14.953 18.575\n",
"\t6.834 9.637 11.698 13.62 17.589\n",
"\t11.09 14.29 17.037 19.553 24.227\n",
"\t15.709 19.291 21.503 24.23 29.767\n",
"\t22.333 28.404 30.836 33.498 38.128\n",
"\t19.848 23.93 26.835 29.75 33.684\n",
"\t20.273 24.317 26.588 29.111 33.748\n",
"\t16.82 19.483 21.421 23.398 28.21\n",
"\t14.057 18.148 20.761 22.972 27.534\n",
"\t22.673 27.172 29.487 31.826 36.866\n",
"\t18.647 22.478 24.775 27.075 31.342\n",
"\t13.501 16.585 18.397 20.419 24.184\n",
"\t\n"
]
}
],
"source": [
"M_pneumononPCV.rate_east_over64.summary()"
]
},
{
"cell_type": "code",
"execution_count": 173,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"rate_west_over64:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t12.085 2.097 0.187 [ 8.228 16.473]\n",
"\t11.144 2.108 0.192 [ 6.918 15.107]\n",
"\t17.338 2.181 0.185 [ 13.003 21.258]\n",
"\t17.507 2.275 0.198 [ 13.092 21.762]\n",
"\t20.48 2.608 0.225 [ 15.228 25.851]\n",
"\t20.591 2.502 0.209 [ 15.727 25.255]\n",
"\t20.172 2.42 0.204 [ 15.706 24.941]\n",
"\t25.796 2.537 0.202 [ 21.128 31.012]\n",
"\t22.035 2.306 0.18 [ 17.655 26.498]\n",
"\t22.672 2.298 0.185 [ 18.499 27.253]\n",
"\t23.131 2.49 0.198 [ 18.086 27.993]\n",
"\t18.618 2.217 0.187 [ 14.472 22.733]\n",
"\t22.82 2.15 0.169 [ 18.787 27.008]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t8.561 10.608 11.864 13.207 17.137\n",
"\t7.315 9.684 11.05 12.517 15.622\n",
"\t13.115 15.786 17.341 18.933 21.419\n",
"\t13.153 15.918 17.509 19.109 21.86\n",
"\t14.762 18.79 20.516 22.155 25.472\n",
"\t16.206 18.857 20.497 22.212 25.813\n",
"\t16.143 18.443 19.882 21.714 25.597\n",
"\t21.068 24.042 25.638 27.52 30.987\n",
"\t17.755 20.375 21.985 23.589 26.631\n",
"\t18.649 20.975 22.566 24.288 27.44\n",
"\t18.321 21.454 23.114 24.72 28.298\n",
"\t14.484 17.066 18.596 20.209 22.781\n",
"\t18.951 21.325 22.706 24.229 27.308\n",
"\t\n"
]
}
],
"source": [
"M_pneumononPCV.rate_west_over64.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### PneumononPCV Tennessee-wide"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 50000 of 50000 complete in 28.9 sec"
]
}
],
"source": [
"M_pneumononPCV_tenn = pm.MCMC(rate_model('pneumononPCV', regional=False))\n",
"M_pneumononPCV_tenn.sample(50000, 40000)"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n"
]
},
{
"data": {
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