{"id":3658,"date":"2023-07-16T06:46:06","date_gmt":"2023-07-16T06:46:06","guid":{"rendered":"https:\/\/statorials.org\/ja\/r%e3%81%aelm%e3%81%8b%e3%82%89rmse%e3%82%92%e6%8a%bd%e5%87%ba\/"},"modified":"2023-07-16T06:46:06","modified_gmt":"2023-07-16T06:46:06","slug":"r%e3%81%aelm%e3%81%8b%e3%82%89rmse%e3%82%92%e6%8a%bd%e5%87%ba","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/r%e3%81%aelm%e3%81%8b%e3%82%89rmse%e3%82%92%e6%8a%bd%e5%87%ba\/","title":{"rendered":"R\u306elm()\u95a2\u6570\u304b\u3089rmse\u3092\u62bd\u51fa\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"

<\/p>\n

\n

\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3057\u3066\u3001R \u306elm()<\/a>\u95a2\u6570\u306e\u4e8c\u4e57\u5e73\u5747\u5e73\u65b9\u6839\u8aa4\u5dee (RMSE)<\/strong>\u3092\u62bd\u51fa\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n

 sqrt(mean(model$residuals^2))\n<\/strong><\/pre>\n
\u6b21\u306e\u4f8b\u306f\u3001\u3053\u306e\u69cb\u6587\u3092\u5b9f\u969b\u306b\u4f7f\u7528\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n
\u95a2\u9023:<\/strong>\u4e8c\u4e57\u5e73\u5747\u5e73\u65b9\u6839\u8aa4\u5dee (RMSE) \u3092\u89e3\u91c8\u3059\u308b\u65b9\u6cd5<\/a><\/span><\/p>\n
\u4f8b: R \u306e lm() \u304b\u3089 RMSE \u3092\u62bd\u51fa\u3059\u308b<\/strong><\/span><\/h2>\n
\u6b21\u306e\u91cd\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u3092 R \u306b\u5f53\u3066\u306f\u3081\u308b\u3068\u3057\u307e\u3059\u3002<\/span><\/p>\n
 #create data frame\n<\/span>df <- data. frame<\/span> (rating=c(67, 75, 79, 85, 90, 96, 97),\n                 points=c(8, 12, 16, 15, 22, 28, 24),\n                 assists=c(4, 6, 6, 5, 3, 8, 7),\n                 rebounds=c(1, 4, 3, 3, 2, 6, 7))\n\n#fit multiple linear regression model\n<\/span>model <- lm(rating ~ points + assists + rebounds, data=df)\n<\/strong><\/pre>\n
 summary()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3059\u308b\u3068\u3001\u56de\u5e30\u30e2\u30c7\u30eb\u306e\u5b8c\u5168\u306a\u6982\u8981\u3092\u8868\u793a\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 #view model summary\n<\/span>summary(model)\n\nCall:\nlm(formula = rating ~ points + assists + rebounds, data = df)\n\nResiduals:\n      1 2 3 4 5 6 7 \n-1.5902 -1.7181 0.2413 4.8597 -1.0201 -0.6082 -0.1644 \n\nCoefficients:\n            Estimate Std. Error t value Pr(>|t|)   \n(Intercept) 66.4355 6.6932 9.926 0.00218 **\npoints 1.2152 0.2788 4.359 0.02232 * \nassists -2.5968 1.6263 -1.597 0.20860   \nrebounds 2.8202 1.6118 1.750 0.17847   \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 3.193 on 3 degrees of freedom\nMultiple R-squared: 0.9589, Adjusted R-squared: 0.9179 \nF-statistic: 23.35 on 3 and 3 DF, p-value: 0.01396\n<\/strong><\/pre>\n
\u30e2\u30c7\u30eb\u306e\u4e8c\u4e57\u5e73\u5747\u5e73\u65b9\u6839\u8aa4\u5dee (RMSE) \u306e\u307f\u3092\u62bd\u51fa\u3059\u308b\u306b\u306f\u3001\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 #extract RMSE of regression model\nsqrt(mean(model$residuals^2))\n\n[1] 2.090564\n<\/span><\/span><\/strong><\/pre>\n
\u30e2\u30c7\u30eb\u306e RMSE \u306f2.090564<\/strong>\u3067\u3059\u3002<\/span><\/p>\n
\u3053\u308c\u306f\u3001\u30e2\u30c7\u30eb\u306e\u4e88\u6e2c\u5024\u3068\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u5b9f\u969b\u306e\u5024\u306e\u9593\u306e\u5e73\u5747\u8ddd\u96e2\u3092\u8868\u3057\u307e\u3059\u3002<\/span><\/p>\n
 RMSE \u304c\u4f4e\u3044\u307b\u3069\u3001\u7279\u5b9a\u306e\u30e2\u30c7\u30eb\u304c\u30c7\u30fc\u30bf \u30bb\u30c3\u30c8\u306b\u300c\u9069\u5408\u300d\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u308b\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/span><\/p>\n
\u3044\u304f\u3064\u304b\u306e\u7570\u306a\u308b\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u6bd4\u8f03\u3059\u308b\u5834\u5408\u3001RMSE \u304c\u6700\u3082\u4f4e\u3044\u30e2\u30c7\u30eb\u304c\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u6700\u3082\u3088\u304f\u300c\u9069\u5408\u300d\u3059\u308b\u3068\u8003\u3048\u3089\u308c\u307e\u3059\u3002<\/span><\/p>\n
\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h2>\n
\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
 R \u3067\u5358\u7d14\u306a\u7dda\u5f62\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5<\/a>
R \u3067\u91cd\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5<\/a>
R \u3067\u6b8b\u5dee\u30d7\u30ed\u30c3\u30c8\u3092\u4f5c\u6210\u3059\u308b\u65b9\u6cd5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3057\u3066\u3001R \u306elm()\u95a2\u6570\u306e\u4e8c\u4e57\u5e73\u5747\u5e73\u65b9\u6839\u8aa4\u5dee (RMSE)\u3092\u62bd\u51fa\u3067\u304d\u307e\u3059\u3002 sqrt(mean(model$residuals^2)) \u6b21\u306e\u4f8b\u306f\u3001\u3053\u306e\u69cb\u6587\u3092\u5b9f\u969b\u306b\u4f7f\u7528\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002 \u95a2\u9023:\u4e8c\u4e57\u5e73\u5747 […]<\/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-3658","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"\nR \u306e lm() \u95a2\u6570\u304b\u3089 RMSE \u3092\u62bd\u51fa\u3059\u308b\u65b9\u6cd5 - Statorials<\/title>\n<meta name=\"description\" content=\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001R \u306e\u56de\u5e30\u30e2\u30c7\u30eb\u306e lm() \u95a2\u6570\u304b\u3089 RMSE \u5024\u3092\u62bd\u51fa\u3059\u308b\u65b9\u6cd5\u3092\u4f8b\u3092\u6319\u3052\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\" href=\"https:\/\/statorials.org\/ja\/r\u306elm\u304b\u3089rmse\u3092\u62bd\u51fa\/\" \/>\n<meta property=\"og:locale\" content=\"ja_JP\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"R \u306e lm() \u95a2\u6570\u304b\u3089 RMSE \u3092\u62bd\u51fa\u3059\u308b\u65b9\u6cd5 - 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