{"id":1309,"date":"2023-07-26T22:17:32","date_gmt":"2023-07-26T22:17:32","guid":{"rendered":"https:\/\/statorials.org\/ja\/r-%e3%81%a6%e3%82%99%e6%a8%99%e6%ba%96%e5%8c%96%e3%81%95%e3%82%8c%e3%81%9f%e6%ae%8b%e5%9f%ba\/"},"modified":"2023-07-26T22:17:32","modified_gmt":"2023-07-26T22:17:32","slug":"r-%e3%81%a6%e3%82%99%e6%a8%99%e6%ba%96%e5%8c%96%e3%81%95%e3%82%8c%e3%81%9f%e6%ae%8b%e5%9f%ba","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/r-%e3%81%a6%e3%82%99%e6%a8%99%e6%ba%96%e5%8c%96%e3%81%95%e3%82%8c%e3%81%9f%e6%ae%8b%e5%9f%ba\/","title":{"rendered":"R \u3067\u6a19\u6e96\u5316\u6b8b\u5dee\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><strong>\u6b8b\u5dee<\/strong>\u306f\u3001<a href=\"https:\/\/statorials.org\/ja\/\u7dda\u5f62\u56de\u5e30-1\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u56de\u5e30\u30e2\u30c7\u30eb<\/a>\u306b\u304a\u3051\u308b\u89b3\u6e2c\u5024\u3068\u4e88\u6e2c\u5024\u306e\u5dee\u3067\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u3088\u3046\u306b\u8a08\u7b97\u3055\u308c\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u6b8b\u5dee = \u89b3\u6e2c\u5024 \u2013 \u4e88\u6e2c\u5024<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u89b3\u6e2c\u5024\u3092\u30d7\u30ed\u30c3\u30c8\u3057\u3001\u8fd1\u4f3c\u3055\u308c\u305f\u56de\u5e30\u76f4\u7dda\u3092\u91cd\u306d\u5408\u308f\u305b\u308b\u3068\u3001\u5404<a href=\"https:\/\/statorials.org\/ja\/\u7d71\u8a08\u306b\u304a\u3051\u308b\u89b3\u5bdf\/\" target=\"_blank\" rel=\"noopener\">\u89b3\u6e2c\u5024<\/a>\u306e\u6b8b\u5dee\u306f\u89b3\u6e2c\u5024\u3068\u56de\u5e30\u76f4\u7dda\u306e\u9593\u306e\u5782\u76f4\u8ddd\u96e2\u306b\u306a\u308a\u307e\u3059\u3002<\/span> <\/p>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-12422 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/residus1-1.png\" alt=\"\u7d71\u8a08\u306b\u304a\u3051\u308b\u6b8b\u5dee\u306e\u4f8b\" width=\"487\" height=\"382\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p><span style=\"color: #000000;\">\u56de\u5e30\u30e2\u30c7\u30eb\u3067\u5916\u308c\u5024\u3092\u7279\u5b9a\u3059\u308b\u305f\u3081\u306b\u3088\u304f\u4f7f\u7528\u3055\u308c\u308b\u6b8b\u5dee\u306e 1 \u3064\u306e\u30bf\u30a4\u30d7\u306f\u3001<strong>\u6a19\u6e96\u5316\u6b8b\u5dee<\/strong>\u3068\u547c\u3070\u308c\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u3088\u3046\u306b\u8a08\u7b97\u3055\u308c\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>r <sub>i<\/sub> = e <sub>i<\/sub> \/ s(e <sub>i<\/sub> )<\/strong> = <strong>e <sub>i<\/sub> \/ RSE\u221a <span style=\"border-top: 1px solid black;\">1-h <sub>ii<\/sub><\/span><\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u91d1\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>e <sub>i<\/sub> :<\/strong> i \u756a\u76ee<sup>\u306e<\/sup>\u5270\u4f59<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>RSE:<\/strong>\u30e2\u30c7\u30eb\u306e\u6b8b\u5dee\u6a19\u6e96\u8aa4\u5dee<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>h <sub>ii<\/sub><\/strong> : <sup>i \u756a\u76ee<\/sup>\u306e\u89b3\u6e2c\u306e\u7acb\u3061\u4e0a\u304c\u308a<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u5b9f\u969b\u306b\u306f\u3001\u7d76\u5bfe\u5024\u304c 3 \u3088\u308a\u5927\u304d\u3044\u6a19\u6e96\u5316\u6b8b\u5dee\u3092\u5916\u308c\u5024\u3068\u898b\u306a\u3059\u3053\u3068\u304c\u3088\u304f\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001R \u3067\u6a19\u6e96\u5316\u6b8b\u5dee\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5\u306e\u30b9\u30c6\u30c3\u30d7\u30d0\u30a4\u30b9\u30c6\u30c3\u30d7\u306e\u4f8b\u3092\u63d0\u4f9b\u3057\u307e\u3059\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 1: \u30c7\u30fc\u30bf\u3092\u5165\u529b\u3059\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u307e\u305a\u3001R \u3067\u64cd\u4f5c\u3059\u308b\u305f\u3081\u306e\u5c0f\u3055\u306a\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;\">#create data<\/span>\ndata &lt;- data.frame(x=c(8, 12, 12, 13, 14, 16, 17, 22, 24, 26, 29, 30),\n                   y=c(41, 42, 39, 37, 35, 39, 45, 46, 39, 49, 55, 57))\n\n<span style=\"color: #008080;\">#viewdata\n<\/span>data\n\n    xy\n1 8 41\n2 12 42\n3 12 39\n4 13 37\n5 14 35\n6 16 39\n7 17 45\n8 22 46\n9 24 39\n10 26 49\n11 29 55\n12 30 57<\/strong><\/pre>\n<h3><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 2: \u56de\u5e30\u30e2\u30c7\u30eb\u3092\u5f53\u3066\u306f\u3081\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6b21\u306b\u3001 <strong>lm()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066<a href=\"https:\/\/statorials.org\/ja\/r-\u3066\u3099\u306e\u5358\u7d14\u306a\u7dda\u5f62\u56de\u5e30\/\" target=\"_blank\" rel=\"noopener\">\u3001\u5358\u7d14\u306a\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb<\/a>\u3092\u8fd1\u4f3c\u3057\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#fit model\n<\/span>model &lt;- lm(y ~ x, data=data)\n\n<span style=\"color: #008080;\">#view model summary\n<span style=\"color: #000000;\">summary(model)<\/span> \n\n<span style=\"color: #000000;\">Call:\nlm(formula = y ~ x, data = data)\n\nResiduals:\n    Min 1Q Median 3Q Max \n-8.7578 -2.5161 0.0292 3.3457 5.3268 \n\nCoefficients:\n            Estimate Std. Error t value Pr(&gt;|t|)    \n(Intercept) 29.6309 3.6189 8.188 9.6e-06 ***\nx 0.7553 0.1821 4.148 0.00199 ** \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 4.442 on 10 degrees of freedom\nMultiple R-squared: 0.6324, Adjusted R-squared: 0.5956 \nF-statistic: 17.2 on 1 and 10 DF, p-value: 0.001988<\/span><\/span><\/strong><\/pre>\n<h3><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 3: \u6a19\u6e96\u5316\u6b8b\u5dee\u3092\u8a08\u7b97\u3059\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\"><span style=\"color: #000000;\">\u6b21\u306b\u3001\u7d44\u307f\u8fbc\u307f\u95a2\u6570<strong>rstandard()<\/strong>\u3092\u4f7f\u7528\u3057\u3066\u3001\u30e2\u30c7\u30eb\u306e\u6a19\u6e96\u5316\u6b8b\u5dee\u3092\u8a08\u7b97\u3057\u307e\u3059\u3002<\/span><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#calculate the standardized residuals\n<\/span>standard_res &lt;- rstandard(model)\n\n<span style=\"color: #008080;\">#view the standardized residuals<\/span>\nstandard_res\n\n          1 2 3 4 5 6 \n 1.40517322 0.81017562 0.07491009 -0.59323342 -1.24820530 -0.64248883 \n          7 8 9 10 11 12 \n 0.59610905 -0.05876884 -2.11711982 -0.06655600 0.91057211 1.26973888\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u5fc5\u8981\u306b\u5fdc\u3058\u3066\u3001\u6a19\u6e96\u5316\u3055\u308c\u305f\u6b8b\u5dee\u3092\u5143\u306e\u30c7\u30fc\u30bf \u30d5\u30ec\u30fc\u30e0\u306b\u8ffd\u52a0\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#column bind standardized residuals back to original data frame\n<\/span>final_data &lt;- cbind(data, standard_res)\n\n<span style=\"color: #008080;\">#view data frame<\/span>\n    xy standard_res\n1 8 41 1.40517322\n2 12 42 0.81017562\n3 12 39 0.07491009\n4 13 37 -0.59323342\n5 14 35 -1.24820530\n6 16 39 -0.64248883\n7 17 45 0.59610905\n8 22 46 -0.05876884\n9 24 39 -2.11711982\n10 26 49 -0.06655600\n11 29 55 0.91057211\n12 30 57 1.26973888\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6b21\u306b\u3001\u6a19\u6e96\u5316\u3055\u308c\u305f\u6b8b\u5dee\u306b\u57fa\u3065\u3044\u3066\u5404\u89b3\u6e2c\u5024\u3092\u6700\u5927\u304b\u3089\u6700\u5c0f\u306e\u9806\u306b\u4e26\u3079\u66ff\u3048\u3066\u3001\u3069\u306e\u89b3\u6e2c\u5024\u304c\u5916\u308c\u5024\u306b\u6700\u3082\u8fd1\u3044\u304b\u3092\u628a\u63e1\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#sort standardized residuals descending\n<span style=\"color: #000000;\">final_data[ <span style=\"color: #3366ff;\">order<\/span> (-standard_res),]\n\n    xy standard_res\n1 8 41 1.40517322\n12 30 57 1.26973888\n11 29 55 0.91057211\n2 12 42 0.81017562\n7 17 45 0.59610905\n3 12 39 0.07491009\n8 22 46 -0.05876884\n10 26 49 -0.06655600\n4 13 37 -0.59323342\n6 16 39 -0.64248883\n5 14 35 -1.24820530\n9 24 39 -2.11711982<\/span><\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u7d50\u679c\u304b\u3089\u3001\u6a19\u6e96\u5316\u3055\u308c\u305f\u6b8b\u5dee\u306f\u3044\u305a\u308c\u3082\u7d76\u5bfe\u5024 3 \u3092\u8d85\u3048\u3066\u3044\u306a\u3044\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002\u3057\u305f\u304c\u3063\u3066\u3001\u3069\u306e\u89b3\u6e2c\u5024\u3082\u5916\u308c\u5024\u3067\u3042\u308b\u3088\u3046\u306b\u898b\u3048\u307e\u305b\u3093\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 4: \u6a19\u6e96\u5316\u3055\u308c\u305f\u6b8b\u5dee\u3092\u8996\u899a\u5316\u3059\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6700\u5f8c\u306b\u3001\u6563\u5e03\u56f3\u3092\u4f5c\u6210\u3057\u3066\u3001\u6a19\u6e96\u5316\u6b8b\u5dee\u306b\u5bfe\u3059\u308b\u4e88\u6e2c\u5b50\u5909\u6570\u306e\u5024\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;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#plot predictor variable vs. standardized residuals\n<\/span>plot(final_data$x, standard_res, ylab=' <span style=\"color: #008000;\">Standardized Residuals<\/span> ', xlab=' <span style=\"color: #008000;\">x<\/span> ') \n\n<span style=\"color: #008080;\">#add horizontal line at 0\n<\/span>abline(0, 0)<\/span><\/span><\/strong><\/pre>\n<h3><span style=\"color: #000000;\"><strong>\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h3>\n<p><a href=\"https:\/\/statorials.org\/ja\/\u6b8b\u57fa\/\" target=\"_blank\" rel=\"noopener\">\u6b8b\u7559\u7269\u3068\u306f\u4f55\u3067\u3059\u304b?<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/\u6a19\u6e96\u5316\u3055\u308c\u305f\u6b8b\u57fa\/\">\u6a19\u6e96\u5316\u6b8b\u5dee\u3068\u306f\u4f55\u3067\u3059\u304b?<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/\u91cd\u7dda\u5f62\u56de\u5e30\/\" target=\"_blank\" rel=\"noopener\">\u91cd\u7dda\u5f62\u56de\u5e30\u306e\u6982\u8981<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u6b8b\u5dee\u306f\u3001\u56de\u5e30\u30e2\u30c7\u30eb\u306b\u304a\u3051\u308b\u89b3\u6e2c\u5024\u3068\u4e88\u6e2c\u5024\u306e\u5dee\u3067\u3059\u3002 \u6b21\u306e\u3088\u3046\u306b\u8a08\u7b97\u3055\u308c\u307e\u3059\u3002 \u6b8b\u5dee = \u89b3\u6e2c\u5024 \u2013 \u4e88\u6e2c\u5024 \u89b3\u6e2c\u5024\u3092\u30d7\u30ed\u30c3\u30c8\u3057\u3001\u8fd1\u4f3c\u3055\u308c\u305f\u56de\u5e30\u76f4\u7dda\u3092\u91cd\u306d\u5408\u308f\u305b\u308b\u3068\u3001\u5404\u89b3\u6e2c\u5024\u306e\u6b8b\u5dee\u306f\u89b3\u6e2c\u5024\u3068\u56de\u5e30\u76f4\u7dda\u306e\u9593\u306e\u5782\u76f4\u8ddd\u96e2\u306b\u306a\u308a\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-1309","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\u6a19\u6e96\u5316\u6b8b\u5dee\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5<\/title>\n<meta name=\"description\" content=\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001R 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