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@statkclee
Created November 30, 2015 11:58
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Think Stats 2, 13장 연습문제 해답
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"통계적 사고 (2판) 연습문제 ([thinkstats2.com](thinkstats2.com), [think-stat.xwmooc.org](http://think-stat.xwmooc.org))<br>\n",
"Allen Downey / 이광춘(xwMOOC)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 연습문제 13.1\n",
"\n",
"NSFG 주기 6과 7에서, cmdivorcx 변수는 응답자의 만약 적용되다면 년도-월(century-month)로 부호화된 첫 혼인에 대한 이혼 날짜 정보를 담고 있다.\n",
"이혼으로 끝나는 결혼기간과 지금까지 지속되는 혼인 기간을 계산하시오. 결혼기간에 대한 위험함수와 생존함수를 추정하시오.\n",
"\n",
"표집 가중치를 고려해서 재표집을 사용하고, 표집오차를 시각화하는데 재표집으로 나온 표본에서 데이터를 도식화하시오.\n",
"\n",
"응답자를 10년주기 출생으로 집단화하고, 가능하면 첫번째 혼인 연령으로 나누는 것을 고려한다."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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{
"data": {
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LjPFVQogrgc8KIZ4fYxw/fMMbb7xx7/YNN9zADTfc8AQO9cIhUZq1dpcF3yL3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\ngzR4K6XUGfTvAJTSkzhjcJpXAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xa7eb528c>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0xa7f4e22c>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"from __future__ import print_function\n",
"\n",
"import pandas\n",
"import numpy as np\n",
"\n",
"import thinkplot\n",
"import thinkstats2\n",
"import survival\n",
"\n",
"\n",
"def CleanData(resp):\n",
" \"\"\"Cleans respondent data.\n",
"\n",
" resp: DataFrame\n",
" \"\"\"\n",
" resp.cmdivorcx.replace([9998, 9999], np.nan, inplace=True)\n",
"\n",
" resp['notdivorced'] = resp.cmdivorcx.isnull().astype(int)\n",
" resp['duration'] = (resp.cmdivorcx - resp.cmmarrhx) / 12.0\n",
" resp['durationsofar'] = (resp.cmintvw - resp.cmmarrhx) / 12.0\n",
"\n",
" month0 = pandas.to_datetime('1899-12-15')\n",
" dates = [month0 + pandas.DateOffset(months=cm) \n",
" for cm in resp.cmbirth]\n",
" resp['decade'] = (pandas.DatetimeIndex(dates).year - 1900) // 10\n",
"\n",
"\n",
"def ResampleDivorceCurve(resps):\n",
" \"\"\"Plots divorce curves based on resampled data.\n",
"\n",
" resps: list of respondent DataFrames\n",
" \"\"\"\n",
" for _ in range(41):\n",
" samples = [thinkstats2.ResampleRowsWeighted(resp) \n",
" for resp in resps]\n",
" sample = pandas.concat(samples, ignore_index=True)\n",
" PlotDivorceCurveByDecade(sample, color='#225EA8', alpha=0.1)\n",
"\n",
" thinkplot.Show(xlabel='years',\n",
" axis=[0, 28, 0, 1])\n",
"\n",
"\n",
"def ResampleDivorceCurveByDecade(resps):\n",
" \"\"\"Plots divorce curves for each birth cohort.\n",
"\n",
" resps: list of respondent DataFrames \n",
" \"\"\"\n",
" for i in range(41):\n",
" samples = [thinkstats2.ResampleRowsWeighted(resp) \n",
" for resp in resps]\n",
" sample = pandas.concat(samples, ignore_index=True)\n",
" groups = sample.groupby('decade')\n",
" if i == 0:\n",
" survival.AddLabelsByDecade(groups, alpha=0.7)\n",
"\n",
" EstimateSurvivalByDecade(groups, alpha=0.1)\n",
"\n",
" thinkplot.Show(xlabel='years',\n",
" axis=[0, 28, 0, 1])\n",
"\n",
"\n",
"def EstimateSurvivalByDecade(groups, **options):\n",
" \"\"\"Groups respondents by decade and plots survival curves.\n",
"\n",
" groups: GroupBy object\n",
" \"\"\"\n",
" thinkplot.PrePlot(len(groups))\n",
" for name, group in groups:\n",
" print(name, len(group))\n",
" _, sf = EstimateSurvival(group)\n",
" thinkplot.Plot(sf, **options)\n",
"\n",
"\n",
"def EstimateSurvival(resp):\n",
" \"\"\"Estimates the survival curve.\n",
"\n",
" resp: DataFrame of respondents\n",
"\n",
" returns: pair of HazardFunction, SurvivalFunction\n",
" \"\"\"\n",
" complete = resp[resp.notdivorced == 0].duration\n",
" ongoing = resp[resp.notdivorced == 1].durationsofar\n",
"\n",
" hf = survival.EstimateHazardFunction(complete, ongoing)\n",
" sf = hf.MakeSurvival()\n",
"\n",
" return hf, sf\n",
"\n",
"\n",
"def main():\n",
" resp6 = survival.ReadFemResp2002()\n",
" CleanData(resp6)\n",
" married6 = resp6[resp6.evrmarry==1]\n",
"\n",
" resp7 = survival.ReadFemResp2010()\n",
" CleanData(resp7)\n",
" married7 = resp7[resp7.evrmarry==1]\n",
"\n",
" ResampleDivorceCurveByDecade([married6, married7])\n",
"\n",
"\n",
"if __name__ == '__main__':\n",
" main()"
]
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