{"id":2942,"date":"2023-07-19T23:21:19","date_gmt":"2023-07-19T23:21:19","guid":{"rendered":"https:\/\/statorials.org\/ja\/r-%e3%81%a6%e3%82%99%e9%87%8d%e5%9b%9e%e5%b8%b0%e3%82%92%e4%ba%88%e6%b8%ac%e3%81%99%e3%82%8b\/"},"modified":"2023-07-19T23:21:19","modified_gmt":"2023-07-19T23:21:19","slug":"r-%e3%81%a6%e3%82%99%e9%87%8d%e5%9b%9e%e5%b8%b0%e3%82%92%e4%ba%88%e6%b8%ac%e3%81%99%e3%82%8b","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/r-%e3%81%a6%e3%82%99%e9%87%8d%e5%9b%9e%e5%b8%b0%e3%82%92%e4%ba%88%e6%b8%ac%e3%81%99%e3%82%8b\/","title":{"rendered":"\u91cd\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3057\u3066 r \u306e\u5024\u3092\u4e88\u6e2c\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"

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\u6b21\u306e\u57fa\u672c\u69cb\u6587\u3092\u4f7f\u7528\u3059\u308b\u3068\u3001\u8fd1\u4f3c\u91cd\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3057\u3066 R \u306e\u5024\u3092\u4e88\u6e2c\u3067\u304d\u307e\u3059\u3002<\/span><\/span><\/p>\n

 #define new observation\n<\/span>new <- data. frame<\/span> (x1=c(5), x2=c(10), x3=c(12.5))\n\n#use fitted model to predict the response value for the new observation\n<\/span>predict(model, newdata=new)<\/strong><\/pre>\n
\u6b21\u306e\u4f8b\u306f\u3001\u3053\u306e\u95a2\u6570\u3092\u5b9f\u969b\u306b\u4f7f\u7528\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n
\u4f8b: \u8fd1\u4f3c\u91cd\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3057\u305f\u5024\u306e\u4e88\u6e2c<\/strong><\/span><\/h3>\n
R \u306b\u30d0\u30b9\u30b1\u30c3\u30c8\u30dc\u30fc\u30eb\u9078\u624b\u306b\u95a2\u3059\u308b\u60c5\u5831\u3092\u542b\u3080\u6b21\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u304c\u3042\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#view data frame\n<\/span>df\n\n  rating points assists rebounds\n1 67 8 4 1\n2 75 12 6 4\n3 79 16 6 3\n4 85 15 5 3\n5 90 22 3 2\n6 96 28 8 6\n7 97 24 7 7\n<\/strong><\/pre>\n
\u3053\u3053\u3067\u3001\u30dd\u30a4\u30f3\u30c8<\/strong>\u3001\u30a2\u30b7\u30b9\u30c8<\/strong>\u3001\u304a\u3088\u3073\u30ea\u30d0\u30a6\u30f3\u30c9\u3092<\/strong>\u4e88\u6e2c\u5909\u6570\u3068\u3057\u3066\u3001\u8a55\u4fa1\u3092<\/strong>\u5fdc\u7b54\u5909\u6570<\/a>\u3068\u3057\u3066\u4f7f\u7528\u3057\u3066\u3001\u91cd\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u8fd1\u4f3c\u3059\u308b\u3068\u3057\u307e\u3059\u3002<\/span><\/p>\n
 #fit multiple linear regression model\n<\/span>model <- lm(rating ~ points + assists + rebounds, data=df)\n\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
 \u300c\u63a8\u5b9a\u300d<\/strong>\u5217\u306e\u5024\u304b\u3089\u3001\u9069\u5408\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u66f8\u304f\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
\u30b9\u30b3\u30a2 = 66.4355 + 1.2151 (\u30dd\u30a4\u30f3\u30c8) \u2013 2.5968 (\u30a2\u30b7\u30b9\u30c8) + 2.8202 (\u30ea\u30d0\u30a6\u30f3\u30c9)<\/span><\/p>\n
\u6b21\u306e\u30b3\u30fc\u30c9\u3092\u4f7f\u7528\u3057\u3066\u300120 \u5f97\u70b9\u30015 \u30a2\u30b7\u30b9\u30c8\u30012 \u30ea\u30d0\u30a6\u30f3\u30c9\u3092\u6301\u3064\u65b0\u3057\u3044\u30d7\u30ec\u30fc\u30e4\u30fc\u306e\u8a55\u4fa1\u3092\u4e88\u6e2c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 #define new player\n<\/span>new <- data. frame<\/span> (points=c(20), assists=c(5), rebounds=c(2))\n\n#use the fitted model to predict the rating for the new player\n<\/span>predict(model, newdata=new)\n\n       1 \n83.39607 \n<\/strong><\/pre>\n
\u3053\u306e\u30e2\u30c7\u30eb\u306f\u3001\u3053\u306e\u65b0\u3057\u3044\u30d7\u30ec\u30fc\u30e4\u30fc\u306e\u30ec\u30fc\u30c6\u30a3\u30f3\u30b0\u304c83.39607<\/strong>\u306b\u306a\u308b\u3068\u4e88\u6e2c\u3057\u307e\u3059\u3002<\/span><\/p>\n
\u65b0\u3057\u3044\u30d7\u30ec\u30a4\u30e4\u30fc\u306e\u5024\u3092\u5f53\u3066\u306f\u3081\u305f\u56de\u5e30\u5f0f\u306b\u633f\u5165\u3059\u308b\u3053\u3068\u3067\u3001\u3053\u308c\u304c\u6b63\u3057\u3044\u3053\u3068\u3092\u78ba\u8a8d\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n