{"id":1273,"date":"2023-07-27T01:26:47","date_gmt":"2023-07-27T01:26:47","guid":{"rendered":"https:\/\/statorials.org\/ja\/r%e3%81%ae%e5%95%8f%e9%a1%8c\/"},"modified":"2023-07-27T01:26:47","modified_gmt":"2023-07-27T01:26:47","slug":"r%e3%81%ae%e5%95%8f%e9%a1%8c","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/r%e3%81%ae%e5%95%8f%e9%a1%8c\/","title":{"rendered":"R \u3067 dffits \u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u7d71\u8a08\u3067\u306f\u3001\u3055\u307e\u3056\u307e\u306a<a href=\"https:\/\/statorials.org\/ja\/\u7d71\u8a08\u306b\u304a\u3051\u308b\u89b3\u5bdf\/\" target=\"_blank\" rel=\"noopener\">\u89b3\u6e2c\u5024\u304c<\/a>\u56de\u5e30\u30e2\u30c7\u30eb\u306b\u3069\u306e\u3088\u3046\u306a\u5f71\u97ff\u3092\u4e0e\u3048\u308b\u304b\u3092\u77e5\u308a\u305f\u3044\u3053\u3068\u304c\u3088\u304f\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u89b3\u6e2c\u5024\u306e\u5f71\u97ff\u3092\u8a08\u7b97\u3059\u308b 1 \u3064\u306e\u65b9\u6cd5\u306f\u3001\u300c\u9069\u5408\u5ea6\u306e\u5dee\u300d\u3092\u8868\u3059<strong>DFFITS<\/strong>\u3068\u3057\u3066\u77e5\u3089\u308c\u308b\u6307\u6a19\u3092\u4f7f\u7528\u3059\u308b\u3053\u3068\u3067\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u306e\u30e1\u30c8\u30ea\u30af\u30b9\u306f\u3001\u500b\u3005\u306e\u89b3\u6e2c\u5024\u3092\u7701\u7565\u3057\u305f\u3068\u304d\u306b\u56de\u5e30\u30e2\u30c7\u30eb\u306b\u3088\u308b\u4e88\u6e2c\u304c\u3069\u306e\u7a0b\u5ea6\u5909\u5316\u3059\u308b\u304b\u3092\u793a\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001R \u306e\u30e2\u30c7\u30eb\u5185\u306e\u5404\u89b3\u6e2c\u5024\u306e DFFITS \u3092\u8a08\u7b97\u3057\u3066\u8996\u899a\u5316\u3059\u308b\u65b9\u6cd5\u306e\u6bb5\u968e\u7684\u306a\u4f8b\u3092\u793a\u3057\u307e\u3059\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 1: \u56de\u5e30\u30e2\u30c7\u30eb\u3092\u4f5c\u6210\u3059\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u307e\u305a\u3001R \u306b\u7d44\u307f\u8fbc\u307e\u308c\u3066\u3044\u308b<strong>mtcars<\/strong>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f7f\u7528\u3057\u3066<a href=\"https:\/\/statorials.org\/ja\/\u91cd\u7dda\u5f62\u56de\u5e30r\/\" target=\"_blank\" rel=\"noopener\">\u91cd\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb<\/a>\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#load the dataset<\/span>\ndata(mtcars)\n\n<span style=\"color: #008080;\">#fit a regression model<\/span>\nmodel &lt;- lm(mpg~disp+hp, data=mtcars)\n\n<span style=\"color: #008080;\">#view model summary\n<\/span>summary(model)\n\nCoefficients:\n             Estimate Std. Error t value Pr(&gt;|t|)    \n(Intercept) 30.735904 1.331566 23.083 &lt; 2nd-16 ***\navailable -0.030346 0.007405 -4.098 0.000306 ***\nhp -0.024840 0.013385 -1.856 0.073679 .  \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 3.127 on 29 degrees of freedom\nMultiple R-squared: 0.7482, Adjusted R-squared: 0.7309 \nF-statistic: 43.09 on 2 and 29 DF, p-value: 2.062e-09\n<\/strong><\/pre>\n<h3><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 2: \u5404\u89b3\u6e2c\u5024\u306e DFFITS \u3092\u8a08\u7b97\u3059\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6b21\u306b\u3001\u7d44\u307f\u8fbc\u307f\u306e<strong>dffits()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u30e2\u30c7\u30eb\u5185\u306e\u5404\u89b3\u6e2c\u5024\u306e DFFITS \u5024\u3092\u8a08\u7b97\u3057\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#calculate DFFITS for each observation in the model\n<\/span>dffits &lt;- <span style=\"color: #3366ff;\">as<\/span> . <span style=\"color: #3366ff;\">data<\/span> . <span style=\"color: #3366ff;\">frame<\/span> (dffits(model))\n\n<span style=\"color: #008080;\">#display DFFITS for each observation\n<\/span>challenges\n\n                    dffits(model)\nMazda RX4 -0.14633456\nMazda RX4 Wag -0.14633456\nDatsun 710 -0.19956440\nHornet 4 Drive 0.11540062\nHornet Sportabout 0.32140303\nValiant -0.26586716\nDuster 360 0.06282342\nMerc 240D -0.03521572\nMerc 230 -0.09780612\nMerc 280 -0.22680622\nMerc 280C -0.32763355\nMerc 450SE -0.09682952\nMerc 450SL -0.03841129\nMerc 450SLC -0.17618948\nCadillac Fleetwood -0.15860270\nLincoln Continental -0.15567627\nChrysler Imperial 0.39098449\nFiat 128 0.60265798\nHonda Civic 0.35544919\nToyota Corolla 0.78230167\nToyota Corona -0.25804885\nDodge Challenger -0.16674639\nAMC Javelin -0.20965432\nCamaro Z28 -0.08062828\nPontiac Firebird 0.67858692\nFiat X1-9 0.05951528\nPorsche 914-2 0.09453310\nLotus Europa 0.55650363\nFord Pantera L 0.31169050\nFerrari Dino -0.29539098\nMaserati Bora 0.76464932\nVolvo 142E -0.24266054\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u901a\u5e38\u3001\u95be\u5024 2\u221a <span style=\"text-decoration: overline;\">p\/n<\/span>\u3092\u8d85\u3048\u308b DFFITS \u5024\u306e\u89b3\u6e2c\u5024\u3092\u8a73\u3057\u304f\u8abf\u3079\u307e\u3059\u3002<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>p:<\/strong>\u30e2\u30c7\u30eb\u3067\u4f7f\u7528\u3055\u308c\u308b\u4e88\u6e2c\u5909\u6570\u306e\u6570<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>n:<\/strong>\u30e2\u30c7\u30eb\u3067\u4f7f\u7528\u3055\u308c\u308b\u89b3\u6e2c\u5024\u306e\u6570<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u3053\u306e\u4f8b\u3067\u306f\u3001\u3057\u304d\u3044\u5024\u306f<strong>0.5<\/strong>\u306b\u306a\u308a\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#find number of predictors in model\n<\/span>p &lt;- <span style=\"color: #3366ff;\">length<\/span> (model$coefficients)-1\n\n<span style=\"color: #008080;\">#find number of observations<\/span>\nn &lt;- <span style=\"color: #3366ff;\">nrow<\/span> (mtcars)\n\n<span style=\"color: #008080;\">#calculate DFFITS threshold value<\/span>\nthresh &lt;- 2* <span style=\"color: #3366ff;\">sqrt<\/span> (p\/n)\n\nthresh\n\n[1] 0.5\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">DFFITS \u5024\u306b\u57fa\u3065\u3044\u3066\u89b3\u6e2c\u5024\u3092\u4e26\u3079\u66ff\u3048\u3066\u3001\u305d\u308c\u3089\u306e\u3044\u305a\u308c\u304b\u304c\u3057\u304d\u3044\u5024\u3092\u8d85\u3048\u3066\u3044\u308b\u304b\u3069\u3046\u304b\u3092\u78ba\u8a8d\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#sort observations by DFFITS, descending<\/span>\ndffits[ <span style=\"color: #3366ff;\">order<\/span> (-dffits[' <span style=\"color: #008000;\">dffits(model)<\/span> ']), ]\n\n [1] 0.78230167 0.76464932 0.67858692 0.60265798 0.55650363 0.39098449\n [7] 0.35544919 0.32140303 0.31169050 0.11540062 0.09453310 0.06282342\n[13] 0.05951528 -0.03521572 -0.03841129 -0.08062828 -0.09682952 -0.09780612\n[19] -0.14633456 -0.14633456 -0.15567627 -0.15860270 -0.16674639 -0.17618948\n[25] -0.19956440 -0.20965432 -0.22680622 -0.24266054 -0.25804885 -0.26586716\n[31] -0.29539098 -0.32763355\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6700\u521d\u306e 5 \u3064\u306e\u89b3\u6e2c\u5024\u306e DFFITS \u5024\u304c 0.5 \u3088\u308a\u5927\u304d\u3044\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002\u3053\u308c\u306f\u3001\u3053\u308c\u3089\u306e\u89b3\u6e2c\u5024\u304c\u30e2\u30c7\u30eb\u306b\u5927\u304d\u306a\u5f71\u97ff\u3092\u4e0e\u3048\u3066\u3044\u308b\u304b\u3069\u3046\u304b\u3092\u5224\u65ad\u3059\u308b\u305f\u3081\u306b\u3001\u3053\u308c\u3089\u306e\u89b3\u6e2c\u5024\u3092\u3088\u308a\u8a73\u3057\u304f\u8abf\u67fb\u3059\u308b\u5fc5\u8981\u304c\u3042\u308b\u3053\u3068\u3092\u610f\u5473\u3057\u307e\u3059\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 3: \u5404\u89b3\u6e2c\u5024\u306e DFFITS \u3092\u8996\u899a\u5316\u3059\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6700\u5f8c\u306b\u3001\u5404\u89b3\u6e2c\u5024\u306e DFFITS \u3092\u8996\u899a\u5316\u3059\u308b\u7c21\u5358\u306a\u30b0\u30e9\u30d5\u3092\u4f5c\u6210\u3067\u304d\u307e\u3059\u3002<\/span> <\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#plot DFFITS values for each observation<\/span>\nplot(dffits(model), type = ' <span style=\"color: #008000;\">h<\/span> ')\n\n<span style=\"color: #008080;\">#add horizontal lines at absolute values for threshold<\/span>\nabline(h = thresh, lty = 2)\nabline(h = -thresh, lty = 2)\n<\/strong><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-12542 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/dffits1.png\" alt=\"R\u306eDFFITS\" width=\"451\" height=\"405\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p> <span style=\"color: #000000;\">X \u8ef8\u306f\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u5185\u306e\u5404\u89b3\u6e2c\u5024\u306e\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u3092\u8868\u793a\u3057\u3001Y \u5024\u306f\u5404\u89b3\u6e2c\u5024\u306b\u5bfe\u5fdc\u3059\u308b DFFITS \u5024\u3092\u8868\u793a\u3057\u307e\u3059\u3002<\/span><\/p>\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\">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\/r\u306e\u30ec\u30cf\u3099\u30ec\u30c3\u30b7\u3099\/\" target=\"_blank\" rel=\"noopener\">R \u3067\u30ec\u30d0\u30ec\u30c3\u30b8\u7d71\u8a08\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/\u6b8b\u7559\u75d5\u8de1\/\" target=\"_blank\" rel=\"noopener\">R \u3067\u6b8b\u5dee\u30d7\u30ed\u30c3\u30c8\u3092\u4f5c\u6210\u3059\u308b\u65b9\u6cd5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u7d71\u8a08\u3067\u306f\u3001\u3055\u307e\u3056\u307e\u306a\u89b3\u6e2c\u5024\u304c\u56de\u5e30\u30e2\u30c7\u30eb\u306b\u3069\u306e\u3088\u3046\u306a\u5f71\u97ff\u3092\u4e0e\u3048\u308b\u304b\u3092\u77e5\u308a\u305f\u3044\u3053\u3068\u304c\u3088\u304f\u3042\u308a\u307e\u3059\u3002 \u89b3\u6e2c\u5024\u306e\u5f71\u97ff\u3092\u8a08\u7b97\u3059\u308b 1 \u3064\u306e\u65b9\u6cd5\u306f\u3001\u300c\u9069\u5408\u5ea6\u306e\u5dee\u300d\u3092\u8868\u3059DFFITS\u3068\u3057\u3066\u77e5\u3089\u308c\u308b\u6307\u6a19\u3092\u4f7f\u7528\u3059\u308b\u3053\u3068\u3067\u3059\u3002 \u3053\u306e\u30e1\u30c8\u30ea\u30af [&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-1273","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 DFFITS \u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5 - \u7d71\u8a08<\/title>\n<meta name=\"description\" content=\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001R \u3067 DFFITS \u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5\u3092\u3001\u30b9\u30c6\u30c3\u30d7\u30d0\u30a4\u30b9\u30c6\u30c3\u30d7\u306e\u4f8b\u3092\u542b\u3081\u3066\u8aac\u660e\u3057\u307e\u3059\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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