{"id":1178,"date":"2023-07-27T09:26:41","date_gmt":"2023-07-27T09:26:41","guid":{"rendered":"https:\/\/statorials.org\/ja\/r-%e3%81%a6%e3%82%99%e3%81%ae%e3%83%95%e3%82%99%e3%83%bc%e3%83%88%e3%82%b9%e3%83%88%e3%83%a9%e3%83%83%e3%83%95%e3%82%9a\/"},"modified":"2023-07-27T09:26:41","modified_gmt":"2023-07-27T09:26:41","slug":"r-%e3%81%a6%e3%82%99%e3%81%ae%e3%83%95%e3%82%99%e3%83%bc%e3%83%88%e3%82%b9%e3%83%88%e3%83%a9%e3%83%83%e3%83%95%e3%82%9a","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/r-%e3%81%a6%e3%82%99%e3%81%ae%e3%83%95%e3%82%99%e3%83%bc%e3%83%88%e3%82%b9%e3%83%88%e3%83%a9%e3%83%83%e3%83%95%e3%82%9a\/","title":{"rendered":"R \u3067\u30d6\u30fc\u30c8\u30b9\u30c8\u30e9\u30c3\u30d7\u3059\u308b\u65b9\u6cd5 (\u4f8b\u4ed8\u304d)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><strong>\u30d6\u30fc\u30c8\u30b9\u30c8\u30e9\u30c3\u30d7\u306f<\/strong>\u3001 <a href=\"https:\/\/statorials.org\/ja\/\u7d71\u8a08\u3068\u30cf\u309a\u30e9\u30e1\u30fc\u30bf\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u7d71\u8a08<\/a>\u306e\u6a19\u6e96\u8aa4\u5dee\u3092\u63a8\u5b9a\u3057\u3001\u7d71\u8a08\u306e<a href=\"https:\/\/statorials.org\/ja\/\u4fe1\u983c\u533a\u9593-1\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u4fe1\u983c\u533a\u9593<\/a>\u3092\u751f\u6210\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3067\u304d\u308b\u65b9\u6cd5\u3067\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u30d6\u30fc\u30c8\u30b9\u30c8\u30e9\u30c3\u30d7\u306e\u57fa\u672c\u7684\u306a\u30d7\u30ed\u30bb\u30b9\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u6307\u5b9a\u3055\u308c\u305f\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u304b\u3089\u7f6e\u63db\u3055\u308c\u305f<em>k \u500b\u306e<\/em>\u53cd\u5fa9\u30b5\u30f3\u30d7\u30eb\u3092\u53d6\u5f97\u3057\u307e\u3059\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u30b5\u30f3\u30d7\u30eb\u3054\u3068\u306b\u3001\u76ee\u7684\u306e\u7d71\u8a08\u3092\u8a08\u7b97\u3057\u307e\u3059\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u3053\u308c\u306b\u3088\u308a\u3001\u7279\u5b9a\u306e\u7d71\u8a08\u91cf\u306b\u5bfe\u3057\u3066<em>k \u500b<\/em>\u306e\u7570\u306a\u308b\u63a8\u5b9a\u5024\u304c\u5f97\u3089\u308c\u3001\u3053\u308c\u3092\u4f7f\u7528\u3057\u3066\u7d71\u8a08\u91cf\u306e\u6a19\u6e96\u8aa4\u5dee\u3092\u8a08\u7b97\u3057\u3001\u7d71\u8a08\u91cf\u306e\u4fe1\u983c\u533a\u9593\u3092\u4f5c\u6210\u3067\u304d\u307e\u3059\u3002<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\"><a href=\"https:\/\/cran.r-project.org\/web\/packages\/boot\/boot.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">\u30d6\u30fc\u30c8\u30b9\u30c8\u30e9\u30c3\u30d7 \u30e9\u30a4\u30d6\u30e9\u30ea<\/a>\u306e\u6b21\u306e\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001R \u3067\u30d6\u30fc\u30c8\u30b9\u30c8\u30e9\u30c3\u30d7\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\">1. \u30d6\u30fc\u30c8\u30b9\u30c8\u30e9\u30c3\u30d7 \u30b5\u30f3\u30d7\u30eb\u3092\u751f\u6210\u3057\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>boot(\u30c7\u30fc\u30bf\u3001\u7d71\u8a08\u3001R\u3001\u2026)<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u91d1\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\"><strong>\u30c7\u30fc\u30bf:<\/strong>\u30c7\u30fc\u30bf\u306e\u30d9\u30af\u30c8\u30eb\u3001\u884c\u5217\u3001\u307e\u305f\u306f\u30d6\u30ed\u30c3\u30af<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>statistic:<\/strong>\u958b\u59cb\u3055\u308c\u308b\u7d71\u8a08\u3092\u751f\u6210\u3059\u308b\u95a2\u6570<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>A:<\/strong>\u30d6\u30fc\u30c8\u30b9\u30c8\u30e9\u30c3\u30d7\u306e\u7e70\u308a\u8fd4\u3057\u56de\u6570<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">2. \u30d6\u30fc\u30c8\u30b9\u30c8\u30e9\u30c3\u30d7\u4fe1\u983c\u533a\u9593\u3092\u751f\u6210\u3057\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>boot.ci(\u30d6\u30fc\u30c8\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3001\u8a2d\u5b9a\u3001\u30bf\u30a4\u30d7)<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u91d1\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>bootobject:<\/strong> boot() \u95a2\u6570\u306b\u3088\u3063\u3066\u8fd4\u3055\u308c\u308b\u30aa\u30d6\u30b8\u30a7\u30af\u30c8<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>conf:<\/strong>\u8a08\u7b97\u3059\u308b\u4fe1\u983c\u533a\u9593\u3002\u30c7\u30d5\u30a9\u30eb\u30c8\u5024\u306f0.95\u3067\u3059<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>type:<\/strong>\u8a08\u7b97\u3059\u308b\u4fe1\u983c\u533a\u9593\u306e\u30bf\u30a4\u30d7\u3002\u30aa\u30d7\u30b7\u30e7\u30f3\u306b\u306f\u300cstandard\u300d\u3001\u300cbasic\u300d\u3001\u300cstud\u300d\u3001\u300cperc\u300d\u3001\u300cbca\u300d\u3001\u300call\u300d\u304c\u542b\u307e\u308c\u307e\u3059\u3002\u30c7\u30d5\u30a9\u30eb\u30c8\u306f\u300call\u300d\u3067\u3059\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u4f8b\u306f\u3001\u3053\u308c\u3089\u306e\u95a2\u6570\u3092\u5b9f\u969b\u306b\u4f7f\u7528\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u4f8b 1: \u5358\u4e00\u306e\u7d71\u8a08\u3092\u30d6\u30fc\u30c8\u30b9\u30c8\u30e9\u30c3\u30d7\u3059\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001\u5358\u7d14\u306a\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u306e<a href=\"https:\/\/statorials.org\/ja\/\u826f\u597d\u306ar\u4e8c\u4e57\u5024\/\" target=\"_blank\" rel=\"noopener noreferrer\">R \u4e8c\u4e57<\/a>\u306e\u6a19\u6e96\u8aa4\u5dee\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>set.seed(0)\n<span style=\"color: #993300;\">library<\/span> (boot)\n\n<span style=\"color: #008080;\">#define function to calculate R-squared\n<\/span>rsq_function &lt;- <span style=\"color: #008000;\">function<\/span> (formula, data, indices) {\n  d &lt;- data[indices,] <span style=\"color: #008080;\">#allows boot to select sample<\/span>\n  fit &lt;- lm(formula, data=d) <span style=\"color: #008080;\">#fit regression model<\/span>\n  <span style=\"color: #008000;\">return<\/span> (summary(fit)$r.square) <span style=\"color: #008080;\">#return R-squared of model<\/span>\n}\n<span style=\"color: #008080;\">#perform bootstrapping with 2000 replications\n<\/span>reps &lt;- boot(data=mtcars, statistic=rsq_function, R=2000, formula=mpg~disp)\n\n<span style=\"color: #008080;\">#view results of boostrapping\n<\/span>reps\n\nORDINARY NONPARAMETRIC BOOTSTRAP\n\n\nCall:\nboot(data = mtcars, statistic = rsq_function, R = 2000, formula = mpg ~ \n    available)\n\n\nBootstrap Statistics:\n     original bias std. error\nt1* 0.7183433 0.002164339 0.06513426<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u7d50\u679c\u304b\u3089\u6b21\u306e\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u3053\u306e\u56de\u5e30\u30e2\u30c7\u30eb\u306e\u63a8\u5b9a R \u4e8c\u4e57\u306f<strong>0.7183433<\/strong>\u3067\u3059\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u3053\u306e\u63a8\u5b9a\u5024\u306e\u6a19\u6e96\u8aa4\u5dee\u306f<strong>0.06513426<\/strong>\u3067\u3059\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u30d6\u30fc\u30c8\u30b9\u30c8\u30e9\u30c3\u30d7\u3055\u308c\u305f\u30b5\u30f3\u30d7\u30eb\u306e\u5206\u5e03\u3092\u3059\u3070\u3084\u304f\u8996\u899a\u5316\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>plot(reps)\n<\/strong><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-11750 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/demarrage1.png\" alt=\"R \u3067\u30d6\u30fc\u30c8\u30b9\u30c8\u30e9\u30c3\u30d7\u3055\u308c\u305f\u30b5\u30f3\u30d7\u30eb\u306e\u30d2\u30b9\u30c8\u30b0\u30e9\u30e0\" width=\"456\" height=\"437\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b3\u30fc\u30c9\u3092\u4f7f\u7528\u3057\u3066\u3001\u30e2\u30c7\u30eb\u306e\u63a8\u5b9a R \u4e8c\u4e57\u306e 95% \u4fe1\u983c\u533a\u9593\u3092\u8a08\u7b97\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#calculate adjusted bootstrap percentile (BCa) interval\n<\/span>boot.ci(reps, type=\" <span style=\"color: #008000;\">bca<\/span> \")\n\nCALL: \nboot.ci(boot.out = reps, type = \"bca\")\n\nIntervals: \nLevel BCa          \n95% (0.5350, 0.8188)  \nCalculations and Intervals on Original Scale<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u7d50\u679c\u304b\u3089\u3001\u771f\u306e R \u4e8c\u4e57\u5024\u306e\u30d6\u30fc\u30c8\u30b9\u30c8\u30e9\u30c3\u30d7\u3055\u308c\u305f 95% \u4fe1\u983c\u533a\u9593\u306f (0.5350, 0.8188) \u3067\u3042\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u4f8b 2: \u8907\u6570\u306e\u7d71\u8a08\u3092\u30d6\u30fc\u30c8\u30b9\u30c8\u30e9\u30c3\u30d7\u3059\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001\u91cd\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u306e\u5404\u4fc2\u6570\u306e\u6a19\u6e96\u8aa4\u5dee\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>set.seed(0)\n<span style=\"color: #993300;\">library<\/span> (boot)\n\n<span style=\"color: #008080;\">#define function to calculate fitted regression coefficients\n<\/span>coef_function &lt;- <span style=\"color: #008000;\">function<\/span> (formula, data, indices) {\n  d &lt;- data[indices,] <span style=\"color: #008080;\">#allows boot to select sample<\/span>\n  fit &lt;- lm(formula, data=d) <span style=\"color: #008080;\">#fit regression model<\/span>\n  <span style=\"color: #008000;\">return<\/span> (coef(fit)) <span style=\"color: #008080;\">#return coefficient estimates of model<\/span>\n}\n\n<span style=\"color: #008080;\">#perform bootstrapping with 2000 replications\n<\/span>reps &lt;- boot(data=mtcars, statistic=coef_function, R=2000, formula=mpg~disp)\n\n<span style=\"color: #008080;\">#view results of boostrapping\n<\/span>reps\n\nORDINARY NONPARAMETRIC BOOTSTRAP\n\n\nCall:\nboot(data = mtcars, statistic = coef_function, R = 2000, formula = mpg ~ \n    available)\n\n\nBootstrap Statistics:\n       original bias std. error\nt1* 29.59985476 -5.058601e-02 1.49354577\nt2* -0.04121512 6.549384e-05 0.00527082<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u7d50\u679c\u304b\u3089\u6b21\u306e\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u30e2\u30c7\u30eb\u5207\u7247\u306e\u63a8\u5b9a\u4fc2\u6570\u306f<strong>29.59985476<\/strong>\u3067\u3001\u3053\u306e\u63a8\u5b9a\u306e\u6a19\u6e96\u8aa4\u5dee\u306f<strong>1.49354577<\/strong>\u3067\u3059\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u30e2\u30c7\u30eb\u5185\u306e\u4e88\u6e2c\u5909\u6570<em>disp<\/em>\u306e\u63a8\u5b9a\u4fc2\u6570\u306f<strong>-0.04121512<\/strong>\u3067\u3001\u3053\u306e\u63a8\u5b9a\u306e\u6a19\u6e96\u8aa4\u5dee\u306f<strong>0.00527082<\/strong>\u3067\u3059\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u30d6\u30fc\u30c8\u30b9\u30c8\u30e9\u30c3\u30d7\u3055\u308c\u305f\u30b5\u30f3\u30d7\u30eb\u306e\u5206\u5e03\u3092\u3059\u3070\u3084\u304f\u8996\u899a\u5316\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>plot(reps, index=1) <span style=\"color: #008080;\">#intercept of model<\/span>\nplot(reps, index=2) <span style=\"color: #008080;\">#disp predictor variable\n<\/span><\/strong><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-11752 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/demarrage2.png\" alt=\"R \u3067\u306e\u30d6\u30fc\u30c8\u30b9\u30c8\u30e9\u30c3\u30d7\" width=\"669\" height=\"325\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b3\u30fc\u30c9\u3092\u4f7f\u7528\u3057\u3066\u3001\u5404\u4fc2\u6570\u306e 95% \u4fe1\u983c\u533a\u9593\u3092\u8a08\u7b97\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#calculate adjusted bootstrap percentile (BCa) intervals\n<\/span>boot.ci(reps, type=\" <span style=\"color: #008000;\">bca<\/span> \", index=1) <span style=\"color: #008080;\">#intercept of model<\/span>\nboot.ci(reps, type=\" <span style=\"color: #008000;\">bca<\/span> \", index=2) <span style=\"color: #008080;\">#disp predictor variable\n<\/span>\nCALL: \nboot.ci(boot.out = reps, type = \"bca\", index = 1)\n\nIntervals: \nLevel BCa          \n95% (26.78, 32.66)  \nCalculations and Intervals on Original Scale\nBOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS\nBased on 2000 bootstrap replicates\n\nCALL: \nboot.ci(boot.out = reps, type = \"bca\", index = 2)\n\nIntervals: \nLevel BCa          \n95% (-0.0520, -0.0312)  \nCalculations and Intervals on Original Scale<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u7d50\u679c\u304b\u3089\u3001\u30e2\u30c7\u30eb\u4fc2\u6570\u306e\u30d6\u30fc\u30c8\u30b9\u30c8\u30e9\u30c3\u30d7\u3055\u308c\u305f 95% \u4fe1\u983c\u533a\u9593\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3042\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u508d\u53d7\u7528IC\uff1a\uff0826.78\u300132.66\uff09<\/span><\/li>\n<li><span style=\"color: #000000;\"><em>\u8868\u793a<\/em>\u306e CI : (-.0520\u3001-.0312)<\/span><\/li>\n<\/ul>\n<h3><span style=\"color: #000000;\"><strong>\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h3>\n<p><a href=\"https:\/\/statorials.org\/ja\/r-\u3066\u3099\u306e\u5358\u7d14\u306a\u7dda\u5f62\u56de\u5e30\/\" target=\"_blank\" rel=\"noopener noreferrer\">R \u3067\u5358\u7d14\u306a\u7dda\u5f62\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/\u91cd\u7dda\u5f62\u56de\u5e30r\/\" target=\"_blank\" rel=\"noopener noreferrer\">R \u3067\u91cd\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/\u4fe1\u983c\u533a\u9593-1\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u4fe1\u983c\u533a\u9593\u306e\u6982\u8981<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u30d6\u30fc\u30c8\u30b9\u30c8\u30e9\u30c3\u30d7\u306f\u3001 \u7d71\u8a08\u306e\u6a19\u6e96\u8aa4\u5dee\u3092\u63a8\u5b9a\u3057\u3001\u7d71\u8a08\u306e\u4fe1\u983c\u533a\u9593\u3092\u751f\u6210\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3067\u304d\u308b\u65b9\u6cd5\u3067\u3059\u3002 \u30d6\u30fc\u30c8\u30b9\u30c8\u30e9\u30c3\u30d7\u306e\u57fa\u672c\u7684\u306a\u30d7\u30ed\u30bb\u30b9\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002 \u6307\u5b9a\u3055\u308c\u305f\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u304b\u3089\u7f6e\u63db\u3055\u308c\u305fk \u500b\u306e\u53cd\u5fa9\u30b5\u30f3\u30d7\u30eb\u3092\u53d6\u5f97\u3057\u307e\u3059 [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[16],"tags":[],"class_list":["post-1178","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - 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