{"id":1323,"date":"2023-07-26T21:09:28","date_gmt":"2023-07-26T21:09:28","guid":{"rendered":"https:\/\/statorials.org\/ja\/r-%e3%81%a6%e3%82%99%e3%81%ae%e5%88%86%e4%bd%8d%e5%9b%9e%e5%b8%b0\/"},"modified":"2023-07-26T21:09:28","modified_gmt":"2023-07-26T21:09:28","slug":"r-%e3%81%a6%e3%82%99%e3%81%ae%e5%88%86%e4%bd%8d%e5%9b%9e%e5%b8%b0","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/r-%e3%81%a6%e3%82%99%e3%81%ae%e5%88%86%e4%bd%8d%e5%9b%9e%e5%b8%b0\/","title":{"rendered":"R \u3067\u5206\u4f4d\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u7dda\u5f62\u56de\u5e30\u306f\u30011 \u3064\u4ee5\u4e0a\u306e\u4e88\u6e2c\u5909\u6570\u3068<a href=\"https:\/\/statorials.org\/ja\/\u5909\u6570\u306e\u8aac\u660e\u5fdc\u7b54\/\" target=\"_blank\" rel=\"noopener\">\u5fdc\u7b54\u5909\u6570<\/a>\u306e\u9593\u306e\u95a2\u4fc2\u3092\u7406\u89e3\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3067\u304d\u308b\u65b9\u6cd5\u3067\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u901a\u5e38\u3001\u7dda\u5f62\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u3068\u304d\u306f\u3001\u5fdc\u7b54\u5909\u6570\u306e\u5e73\u5747\u5024\u3092\u63a8\u5b9a\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u305f\u3060\u3057\u3001\u4ee3\u308f\u308a\u306b<strong>\u5206\u4f4d\u70b9\u56de\u5e30<\/strong>\u3068\u3057\u3066\u77e5\u3089\u308c\u308b\u65b9\u6cd5\u3092\u4f7f\u7528\u3057\u3066\u300170 \u30d1\u30fc\u30bb\u30f3\u30bf\u30a4\u30eb\u300190 \u30d1\u30fc\u30bb\u30f3\u30bf\u30a4\u30eb\u300198 \u30d1\u30fc\u30bb\u30f3\u30bf\u30a4\u30eb\u306a\u3069\u306e\u5fdc\u7b54\u5024\u306e<em>\u4efb\u610f\u306e<\/em>\u5206\u4f4d\u5024\u307e\u305f\u306f\u30d1\u30fc\u30bb\u30f3\u30bf\u30a4\u30eb\u5024\u3092\u63a8\u5b9a\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\">R \u3067\u5206\u4f4d\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u306b\u306f\u3001 <a href=\"https:\/\/cran.r-project.org\/web\/packages\/quantreg\/quantreg.pdf\" target=\"_blank\" rel=\"noopener\">quantreg<\/a>\u30d1\u30c3\u30b1\u30fc\u30b8\u306e<strong>rq()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3067\u304d\u307e\u3059\u3002\u3053\u306e\u95a2\u6570\u306f\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #993300;\">library<\/span> (quantreg)\n\nmodel &lt;- rq(y ~ x, data = dataset, tau = <span style=\"color: #008000;\">0.5<\/span> )\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u91d1\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>y:<\/strong>\u5fdc\u7b54\u5909\u6570<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>x:<\/strong>\u4e88\u6e2c\u5909\u6570<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>\u30c7\u30fc\u30bf:<\/strong>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u540d\u524d<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>tau:<\/strong>\u691c\u7d22\u3059\u308b\u30d1\u30fc\u30bb\u30f3\u30bf\u30a4\u30eb\u3002\u30c7\u30d5\u30a9\u30eb\u30c8\u306f\u4e2d\u592e\u5024 (tau = 0.5) \u3067\u3059\u304c\u30010 \u304b\u3089 1 \u307e\u3067\u306e\u4efb\u610f\u306e\u6570\u5024\u306b\u8a2d\u5b9a\u3067\u304d\u307e\u3059\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001\u3053\u306e\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066 R \u3067\u5206\u4f4d\u70b9\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u306e\u6bb5\u968e\u7684\u306a\u4f8b\u3092\u793a\u3057\u307e\u3059\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 1: \u30c7\u30fc\u30bf\u3092\u5165\u529b\u3059\u308b<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u3053\u306e\u4f8b\u3067\u306f\u3001\u5927\u5b66\u306e 100 \u4eba\u306e\u7570\u306a\u308b\u5b66\u751f\u306e\u5b66\u7fd2\u6642\u9593\u3068\u5f97\u3089\u308c\u305f\u8a66\u9a13\u7d50\u679c\u3092\u542b\u3080\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#make this example reproducible\n<\/span>set.seed(0)\n\n<span style=\"color: #008080;\">#create data frame<\/span> \nhours &lt;- runif(100, 1, 10)\nscore &lt;- 60 + 2*hours + rnorm(100, mean=0, sd=.45*hours)\ndf &lt;- data.frame(hours, score)\n\n<span style=\"color: #008080;\">#view first six rows<\/span>\nhead(df)\n\n     hours score\n1 9.070275 79.22682\n2 3.389578 66.20457\n3 4.349115 73.47623\n4 6.155680 70.10823\n5 9.173870 78.12119\n6 2.815137 65.94716\n<\/strong><\/pre>\n<h2><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 2: \u5206\u4f4d\u70b9\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u6b21\u306b\u3001\u5b66\u7fd2\u6642\u9593\u3092\u4e88\u6e2c\u5909\u6570\u3068\u3057\u3066\u3001\u8a66\u9a13\u306e\u30b9\u30b3\u30a2\u3092\u5fdc\u7b54\u5909\u6570\u3068\u3057\u3066\u4f7f\u7528\u3057\u3066\u3001\u5206\u4f4d\u70b9\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u8fd1\u4f3c\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u306e\u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3057\u3066\u3001\u5b66\u7fd2\u6642\u9593\u6570\u306b\u57fa\u3065\u3044\u3066\u8a66\u9a13\u30b9\u30b3\u30a2\u306e\u4e88\u60f3\u3055\u308c\u308b 90 \u30d1\u30fc\u30bb\u30f3\u30bf\u30a4\u30eb\u3092\u4e88\u6e2c\u3057\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #993300;\">library<\/span> (quantreg)\n\n<span style=\"color: #008080;\">#fit model\n<\/span>model &lt;- rq(score ~ hours, data = df, tau = <span style=\"color: #008000;\">0.9<\/span> )\n\n<span style=\"color: #008080;\">#view summary of model\n<\/span>summary(model)\n\nCall: rq(formula = score ~ hours, tau = 0.9, data = df)\n\ntau: [1] 0.9\n\nCoefficients:\n            coefficients lower bd upper bd\n(Intercept) 60.25185 59.27193 62.56459\nhours 2.43746 1.98094 2.76989\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u7d50\u679c\u304b\u3089\u3001\u63a8\u5b9a\u3055\u308c\u305f\u56de\u5e30\u5f0f\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\">90 \u30d1\u30fc\u30bb\u30f3\u30bf\u30a4\u30eb\u306e\u8a66\u9a13\u30b9\u30b3\u30a2 = 60.25 + 2.437*(\u6642\u9593)<\/span><\/p>\n<p><span style=\"color: #000000;\">\u305f\u3068\u3048\u3070\u30018 \u6642\u9593\u52c9\u5f37\u3057\u305f\u3059\u3079\u3066\u306e\u751f\u5f92\u306e 90 \u30d1\u30fc\u30bb\u30f3\u30bf\u30a4\u30eb \u30b9\u30b3\u30a2\u306f 79.75 \u306b\u306a\u308b\u306f\u305a\u3067\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8a66\u9a13\u30b9\u30b3\u30a2\u306e 90 \u30d1\u30fc\u30bb\u30f3\u30bf\u30a4\u30eb = 60.25 + 2.437*(8) = <strong>79.75<\/strong> \u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u51fa\u529b\u306b\u306f\u3001\u4e88\u6e2c\u5b50\u5909\u6570\u306e\u5207\u7247\u3068\u6642\u9593\u306e\u4fe1\u983c\u9650\u754c\u306e\u4e0a\u9650\u3068\u4e0b\u9650\u3082\u8868\u793a\u3055\u308c\u307e\u3059\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 3: \u7d50\u679c\u3092\u8996\u899a\u5316\u3059\u308b<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u8fd1\u4f3c\u3055\u308c\u305f\u5206\u4f4d\u70b9\u56de\u5e30\u5f0f\u3092\u30b0\u30e9\u30d5\u306b\u91cd\u306d\u305f\u6563\u5e03\u56f3\u3092\u4f5c\u6210\u3057\u3066\u3001\u56de\u5e30\u7d50\u679c\u3092\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><span style=\"color: #993300;\">library<\/span> (ggplot2)\n\n<span style=\"color: #008080;\">#create scatterplot with quantile regression line\n<\/span>ggplot(df, aes(hours,score)) +\n  geom_point() + \n  geom_abline(intercept= <span style=\"color: #3366ff;\">coef<\/span> (model)[1], slope= <span style=\"color: #3366ff;\">coef<\/span> (model)[2])<\/strong> <\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-12951 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/quantileregr1.png\" alt=\"R \u3067\u306e\u5206\u4f4d\u70b9\u56de\u5e30\u306e\u4f8b\" width=\"444\" height=\"449\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p><span style=\"color: #000000;\">\u5f93\u6765\u306e\u7dda\u5f62\u56de\u5e30\u76f4\u7dda\u3068\u306f\u7570\u306a\u308a\u3001\u3053\u306e\u8fd1\u4f3c\u76f4\u7dda\u306f\u30c7\u30fc\u30bf\u306e\u4e2d\u5fc3\u3092\u901a\u904e\u3057\u3066\u3044\u306a\u3044\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u4ee3\u308f\u308a\u306b\u3001\u4e88\u6e2c\u5b50\u5909\u6570\u306e\u5404\u30ec\u30d9\u30eb\u3067\u63a8\u5b9a\u3055\u308c\u305f 90 \u30d1\u30fc\u30bb\u30f3\u30bf\u30a4\u30eb\u3092\u901a\u904e\u3057\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>geom_smooth()<\/strong>\u5f15\u6570\u3092\u8ffd\u52a0\u3059\u308b\u3068\u3001\u8fd1\u4f3c\u3055\u308c\u305f\u5206\u4f4d\u6570\u56de\u5e30\u5f0f\u3068\u5358\u7d14\u306a\u7dda\u5f62\u56de\u5e30\u5f0f\u306e\u9055\u3044\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #993300;\">library<\/span> (ggplot2)\n\n<span style=\"color: #008080;\">#create scatterplot with quantile regression line <em>and<\/em> simple linear regression line\n<\/span>ggplot(df, aes(hours,score)) +\n  geom_point() + \n  geom_abline(intercept= <span style=\"color: #3366ff;\">coef<\/span> (model)[1], slope= <span style=\"color: #3366ff;\">coef<\/span> (model)[2]) +\n  geom_smooth(method=\" <span style=\"color: #008000;\">lm<\/span> \", se= <span style=\"color: #008000;\">F<\/span> )<\/strong> <\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-12952 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/quantileregr2.png\" alt=\"\u5206\u4f4d\u70b9\u56de\u5e30\u3068\u5358\u7d14\u306a\u7dda\u5f62\u56de\u5e30\u30d7\u30ed\u30c3\u30c8\" width=\"457\" height=\"439\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p><span style=\"color: #000000;\">\u9ed2\u7dda\u306f 90 \u30d1\u30fc\u30bb\u30f3\u30bf\u30a4\u30eb\u3067\u8abf\u6574\u3055\u308c\u305f\u5206\u4f4d\u70b9\u56de\u5e30\u76f4\u7dda\u3092\u8868\u793a\u3057\u3001\u9752\u7dda\u306f\u5fdc\u7b54\u5909\u6570\u306e\u5e73\u5747\u5024\u3092\u63a8\u5b9a\u3059\u308b\u5358\u7d14\u7dda\u5f62\u56de\u5e30\u7dda\u3092\u8868\u793a\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4e88\u60f3\u3069\u304a\u308a\u3001\u5358\u7d14\u306a\u7dda\u5f62\u56de\u5e30\u76f4\u7dda\u304c\u30c7\u30fc\u30bf\u5168\u4f53\u3092\u8cab\u3044\u3066\u304a\u308a\u3001\u5404\u6642\u9593\u30ec\u30d9\u30eb\u3067\u306e\u8a66\u9a13\u30b9\u30b3\u30a2\u306e\u63a8\u5b9a\u5e73\u5747\u5024\u304c\u793a\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001R \u3067\u4ed6\u306e\u4e00\u822c\u7684\u306a\u30bf\u30b9\u30af\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u306b\u3064\u3044\u3066\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/ja\/r-\u3066\u3099\u306e\u5358\u7d14\u306a\u7dda\u5f62\u56de\u5e30\/\" target=\"_blank\" rel=\"noopener\">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\">R \u3067\u91cd\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/\u4e8c\u6b21\u56de\u5e30-r\/\" target=\"_blank\" rel=\"noopener\">R \u3067\u4e8c\u6b21\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u7dda\u5f62\u56de\u5e30\u306f\u30011 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