{"id":2479,"date":"2023-07-22T02:13:43","date_gmt":"2023-07-22T02:13:43","guid":{"rendered":"https:\/\/statorials.org\/ja\/r-%e3%81%ae%e5%8c%ba%e5%88%86%e7%9a%84%e5%9b%9e%e5%b8%b0\/"},"modified":"2023-07-22T02:13:43","modified_gmt":"2023-07-22T02:13:43","slug":"r-%e3%81%ae%e5%8c%ba%e5%88%86%e7%9a%84%e5%9b%9e%e5%b8%b0","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/r-%e3%81%ae%e5%8c%ba%e5%88%86%e7%9a%84%e5%9b%9e%e5%b8%b0\/","title":{"rendered":"R \u3067\u533a\u5206\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5 (\u30b9\u30c6\u30c3\u30d7\u30d0\u30a4\u30b9\u30c6\u30c3\u30d7)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><strong>\u533a\u5206\u56de\u5e30\u306f\u3001<\/strong>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u660e\u78ba\u306a\u300c\u30d6\u30ec\u30fc\u30af\u30dd\u30a4\u30f3\u30c8\u300d\u304c\u3042\u308b\u5834\u5408\u306b\u3088\u304f\u4f7f\u7528\u3055\u308c\u308b\u56de\u5e30\u624b\u6cd5\u3067\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u6bb5\u968e\u7684\u306a\u4f8b\u306f\u3001R \u3067\u533a\u5206\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 1: \u30c7\u30fc\u30bf\u3092\u4f5c\u6210\u3059\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u307e\u305a\u3001\u6b21\u306e\u30c7\u30fc\u30bf \u30d5\u30ec\u30fc\u30e0\u3092\u4f5c\u6210\u3057\u307e\u3057\u3087\u3046\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#view DataFrame\n<\/span>df &lt;- data. <span style=\"color: #3366ff;\">frame<\/span> (x=c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16),\n                 y=c(2, 4, 5, 6, 8, 10, 12, 13, 15, 19, 24, 28, 31, 34, 39, 44))\n\n<span style=\"color: #008080;\">#view first six rows of data frame<\/span>\nhead(df)\n\n  xy\n1 1 2\n2 2 4\n3 3 5\n4 4 6\n5 5 8\n6 6 10\n<\/strong><\/pre>\n<h3><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 2: \u30c7\u30fc\u30bf\u3092\u8996\u899a\u5316\u3059\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6b21\u306b\u3001\u6563\u5e03\u56f3\u3092\u4f5c\u6210\u3057\u3066\u30c7\u30fc\u30bf\u3092\u8996\u899a\u5316\u3057\u307e\u3057\u3087\u3046\u3002<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create scatterplot of x vs. y\n<\/span>plot(df$x, df$y, pch= <span style=\"color: #008000;\">16<\/span> , col=' <span style=\"color: #3366ff;\">steelblue<\/span> ')\n<\/strong><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-22180 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/seg1.png\" alt=\"\" width=\"452\" height=\"409\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p> <span style=\"color: #000000;\">x \u3068 y \u306e\u95a2\u4fc2\u304c<strong>x = 9<\/strong>\u4ed8\u8fd1\u3067\u6025\u6fc0\u306b\u5909\u5316\u3057\u3066\u3044\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 3: \u533a\u5206\u7684\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u5f53\u3066\u306f\u3081\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">R \u306e<a href=\"https:\/\/cran.r-project.org\/web\/packages\/segmented\/segmented.pdf\" target=\"_blank\" rel=\"noopener\">\u30bb\u30b0\u30e1\u30f3\u30c8\u5316<\/a>\u30d1\u30c3\u30b1\u30fc\u30b8\u306e<strong>segmented()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u533a\u5206\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u9069\u5408\u3055\u305b\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">library<\/span> (segmented)\n\n<span style=\"color: #008080;\">#fit simple linear regression model<\/span>\nfit &lt;- lm(y ~ x, data=df)\n\n<span style=\"color: #008080;\">#fit piecewise regression model to original model, estimating a breakpoint at x=9\n<\/span>segmented. <span style=\"color: #3366ff;\">fit<\/span> &lt;- segmented(fit, seg.Z = ~x, psi= <span style=\"color: #008000;\">9<\/span> )\n\n<span style=\"color: #008080;\">#view summary of segmented model\n<\/span>summary( <span style=\"color: #3366ff;\">segmented.fit<\/span> )\n\nCall: \nsegmented.lm(obj = fit, seg.Z = ~x, psi = 9)\n\nEstimated Break-Point(s):\n         East. St.Err\npsi1.x 8.762 0.26\n\nMeaningful coefficients of the linear terms:\n            Estimate Std. Error t value Pr(&gt;|t|)    \n(Intercept) 0.32143 0.48343 0.665 0.519    \nx 1.59524 0.09573 16.663 1.16e-09 ***\nU1.x 2.40476 0.13539 17.762 NA    \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 0.6204 on 12 degrees of freedom\nMultiple R-Squared: 0.9983, Adjusted R-squared: 0.9978 \n\nConvergence achieved in 2 iter. (rel. changes 0)\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><strong>segmented()<\/strong>\u95a2\u6570\u306f\u3001x = 8.762 \u3067\u30d6\u30ec\u30fc\u30af\u30dd\u30a4\u30f3\u30c8\u3092\u691c\u51fa\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8fd1\u4f3c\u3055\u308c\u305f\u533a\u5206\u7684\u56de\u5e30\u30e2\u30c7\u30eb\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\">x \u2264 8.762 \u306e\u5834\u5408: y = 0.32143 + 1.59524*(x)<\/span><\/p>\n<p> <span style=\"color: #000000;\">x &gt; 8.762 \u306e\u5834\u5408: y = 0.32143 + 1.59524*(8.762) + (1.59524+2.40476)*(x-8.762)<\/span><\/p>\n<p><span style=\"color: #000000;\">\u305f\u3068\u3048\u3070\u3001\u5024<strong>x = 5<\/strong>\u304c\u3042\u308b\u3068\u3057\u307e\u3059\u3002\u63a8\u5b9a\u5024\u306f\u6b21\u306e\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">y = 0.32143 + 1.59524*(x)<\/span><\/li>\n<li> <span style=\"color: #000000;\">y = 0.32143 + 1.59524*(5)<\/span><\/li>\n<li> <span style=\"color: #000000;\">y = <strong>8.297<\/strong><\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u307e\u305f\u306f\u3001\u5024<strong>x = 12<\/strong>\u304c\u3042\u308b\u3068\u3057\u307e\u3059\u3002\u63a8\u5b9a\u5024\u306f\u6b21\u306e\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">y = 0.32143 + 1.59524*(8.762) + (1.59524+2.40476)*(12-8.762)<\/span><\/li>\n<li> <span style=\"color: #000000;\">y = <strong>27.25<\/strong><\/span><\/li>\n<\/ul>\n<h3><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 4: \u6700\u7d42\u7684\u306a\u533a\u5206\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u8996\u899a\u5316\u3059\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b3\u30fc\u30c9\u3092\u4f7f\u7528\u3057\u3066\u3001\u5143\u306e\u30c7\u30fc\u30bf\u306b\u57fa\u3065\u3044\u3066\u6700\u7d42\u7684\u306a\u533a\u5206\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u8996\u899a\u5316\u3067\u304d\u307e\u3059\u3002<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#plot original data<\/span>\nplot(df$x, df$y, pch= <span style=\"color: #008000;\">16<\/span> , col=' <span style=\"color: #ff0000;\">steelblue<\/span> ')\n\n<span style=\"color: #008080;\">#add segmented regression model<\/span>\nplot(segmented. <span style=\"color: #3366ff;\">fit<\/span> , add= <span style=\"color: #008000;\">T<\/span> )\n<\/strong><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-22181 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/seg2.png\" alt=\"\" width=\"455\" height=\"414\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p><span style=\"color: #000000;\">\u533a\u5206\u7684\u56de\u5e30\u30e2\u30c7\u30eb\u306f\u30c7\u30fc\u30bf\u306b\u975e\u5e38\u306b\u3088\u304f\u9069\u5408\u3057\u3066\u3044\u308b\u3088\u3046\u3067\u3059\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001R \u306e\u56de\u5e30\u30e2\u30c7\u30eb\u306b\u95a2\u3059\u308b\u8ffd\u52a0\u60c5\u5831\u3092\u63d0\u4f9b\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 \/>R \u3067\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5<br \/><a href=\"https:\/\/statorials.org\/ja\/r-\u3066\u3099\u306e\u5206\u4f4d\u56de\u5e30\/\" target=\"_blank\" rel=\"noopener\">R \u3067\u5206\u4f4d\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/r-\u306e\u52a0\u91cd\u6700\u5c0f\u4e8c\u4e57\/\" target=\"_blank\" rel=\"noopener\">R \u3067\u91cd\u307f\u4ed8\u304d\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u533a\u5206\u56de\u5e30\u306f\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u660e\u78ba\u306a\u300c\u30d6\u30ec\u30fc\u30af\u30dd\u30a4\u30f3\u30c8\u300d\u304c\u3042\u308b\u5834\u5408\u306b\u3088\u304f\u4f7f\u7528\u3055\u308c\u308b\u56de\u5e30\u624b\u6cd5\u3067\u3059\u3002 \u6b21\u306e\u6bb5\u968e\u7684\u306a\u4f8b\u306f\u3001R \u3067\u533a\u5206\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002 \u30b9\u30c6\u30c3\u30d7 1: \u30c7\u30fc\u30bf\u3092\u4f5c\u6210\u3059\u308b \u307e\u305a\u3001\u6b21\u306e\u30c7\u30fc\u30bf \u30d5\u30ec\u30fc\u30e0\u3092 [&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-2479","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>R \u3067\u533a\u5206\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5 (\u30b9\u30c6\u30c3\u30d7\u30d0\u30a4\u30b9\u30c6\u30c3\u30d7) - Statorials<\/title>\n<meta name=\"description\" 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