{"id":3040,"date":"2023-07-19T11:54:34","date_gmt":"2023-07-19T11:54:34","guid":{"rendered":"https:\/\/statorials.org\/ja\/%e3%82%b9%e3%83%95%e3%82%9a%e3%83%aa%e3%83%83%e3%83%88r%e8%a9%a6%e9%a8%93%e5%88%97%e8%bb%8a\/"},"modified":"2023-07-19T11:54:34","modified_gmt":"2023-07-19T11:54:34","slug":"%e3%82%b9%e3%83%95%e3%82%9a%e3%83%aa%e3%83%83%e3%83%88r%e8%a9%a6%e9%a8%93%e5%88%97%e8%bb%8a","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/%e3%82%b9%e3%83%95%e3%82%9a%e3%83%aa%e3%83%83%e3%83%88r%e8%a9%a6%e9%a8%93%e5%88%97%e8%bb%8a\/","title":{"rendered":"\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3067\u30c7\u30fc\u30bf\u3092\u5206\u5272\u3059\u308b\u65b9\u6cd5 &amp;#038; r \u306e\u30c6\u30b9\u30c8 \u30bb\u30c3\u30c8 (3 \u3064\u306e\u30e1\u30bd\u30c3\u30c9)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u591a\u304f\u306e\u5834\u5408\u3001<a href=\"https:\/\/statorials.org\/ja\/-10\/\" target=\"_blank\" rel=\"noopener\">\u6a5f\u68b0\u5b66\u7fd2\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092<\/a>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u9069\u5fdc\u3055\u305b\u308b\u3068\u304d\u306f\u3001\u307e\u305a\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30bb\u30c3\u30c8\u3068\u30c6\u30b9\u30c8 \u30bb\u30c3\u30c8\u306b\u5206\u5272\u3057\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\">R \u3067\u30c7\u30fc\u30bf\u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30bb\u30c3\u30c8\u3068\u30c6\u30b9\u30c8 \u30bb\u30c3\u30c8\u306b\u5206\u5272\u3059\u308b\u306b\u306f\u3001\u6b21\u306e 3 \u3064\u306e\u4e00\u822c\u7684\u306a\u65b9\u6cd5\u304c\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u65b9\u6cd5 1: Base R \u3092\u4f7f\u7528\u3059\u308b<\/strong><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#make this example reproducible\n<\/span>set. <span style=\"color: #3366ff;\">seeds<\/span> (1)\n\n<span style=\"color: #008080;\">#use 70% of dataset as training set and 30% as test set\n<\/span>sample &lt;- sample(c( <span style=\"color: #008000;\">TRUE<\/span> , <span style=\"color: #008000;\">FALSE<\/span> ), nrow(df), replace= <span style=\"color: #008000;\">TRUE<\/span> , prob=c( <span style=\"color: #008000;\">0.7<\/span> , <span style=\"color: #008000;\">0.3<\/span> ))\ntrain &lt;- df[sample, ]\ntest &lt;- df[!sample, ]<\/strong><\/pre>\n<p><span style=\"color: #000000;\"><strong>\u65b9\u6cd5 2: caTools \u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u4f7f\u7528\u3059\u308b<\/strong><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008000;\">library<\/span> (caTools)<\/span>\n\n#make this example reproducible\n<\/span>set. <span style=\"color: #3366ff;\">seeds<\/span> (1)\n\n<span style=\"color: #008080;\">#use 70% of dataset as training set and 30% as test set\n<\/span>sample &lt;- sample. <span style=\"color: #3366ff;\">split<\/span> (df$any_column_name, SplitRatio = <span style=\"color: #008000;\">0.7<\/span> )\ntrain &lt;- subset(df, sample == <span style=\"color: #008000;\">TRUE<\/span> )\ntest &lt;- subset(df, sample == <span style=\"color: #008000;\">FALSE<\/span> )<\/strong><\/pre>\n<p><span style=\"color: #000000;\"><strong>\u65b9\u6cd5 3: dplyr \u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u4f7f\u7528\u3059\u308b<\/strong><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008000;\">library<\/span> (dplyr)<\/span>\n\n#make this example reproducible\n<\/span>set. <span style=\"color: #3366ff;\">seeds<\/span> (1)\n\n<span style=\"color: #008080;\">#create ID column\n<\/span>df$id &lt;- 1:nrow(df)\n\n<span style=\"color: #008080;\">#use 70% of dataset as training set and 30% as test set<\/span>\ntrain &lt;- df %&gt;% dplyr::sample_frac( <span style=\"color: #008000;\">0.70<\/span> )\ntest &lt;- dplyr::anti_join(df, train, by = ' <span style=\"color: #ff0000;\">id<\/span> ')<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u4f8b\u306f\u3001R \u306e\u7d44\u307f\u8fbc\u307f<a href=\"https:\/\/statorials.org\/ja\/\u30a2\u30a4\u30ea\u30b9r\u30c6\u3099\u30fc\u30bf\u30bb\u30c3\u30c8\/\" target=\"_blank\" rel=\"noopener\">iris \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8<\/a>\u3092\u4f7f\u7528\u3057\u3066\u5404\u30e1\u30bd\u30c3\u30c9\u3092\u5b9f\u969b\u306b\u4f7f\u7528\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u4f8b 1: Base R \u3092\u4f7f\u7528\u3057\u3066\u30c7\u30fc\u30bf\u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30bb\u30c3\u30c8\u3068\u30c6\u30b9\u30c8 \u30bb\u30c3\u30c8\u306b\u5206\u5272\u3059\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\"><span style=\"color: #000000;\">\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001R \u30d9\u30fc\u30b9\u3092\u4f7f\u7528\u3057\u3066\u3001\u884c\u306e 70% \u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30bb\u30c3\u30c8\u3068\u3057\u3066\u4f7f\u7528\u3057\u3001\u6b8b\u308a\u306e 30% \u3092\u30c6\u30b9\u30c8 \u30bb\u30c3\u30c8\u3068\u3057\u3066\u4f7f\u7528\u3057\u3066\u3001iris \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30bb\u30c3\u30c8\u3068\u30c6\u30b9\u30c8 \u30bb\u30c3\u30c8\u306b\u5206\u5272\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#load iris dataset\n<\/span>data(iris)\n\n<span style=\"color: #008080;\">#make this example reproducible\n<\/span>set. <span style=\"color: #3366ff;\">seeds<\/span> (1)\n\n<span style=\"color: #008080;\">#Use 70% of dataset as training set and remaining 30% as testing set\n<\/span>sample &lt;- sample(c( <span style=\"color: #008000;\">TRUE<\/span> , <span style=\"color: #008000;\">FALSE<\/span> ), nrow(iris), replace= <span style=\"color: #008000;\">TRUE<\/span> , prob=c( <span style=\"color: #008000;\">0.7<\/span> , <span style=\"color: #008000;\">0.3<\/span> ))\ntrain &lt;- iris[sample, ]\ntest &lt;- iris[!sample, ]\n\n<span style=\"color: #008080;\">#view dimensions of training set\n<\/span>sun(train)\n\n[1] 106 5\n\n<span style=\"color: #008080;\">#view dimensions of test set\n<\/span>dim(test)\n\n[1] 44 5<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u7d50\u679c\u304b\u3089\u6b21\u306e\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30bb\u30c3\u30c8\u306f 106 \u884c 5 \u5217\u306e\u30c7\u30fc\u30bf \u30d5\u30ec\u30fc\u30e0\u3067\u3059\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u30c6\u30b9\u30c8\u306f 44 \u884c 5 \u5217\u306e\u30c7\u30fc\u30bf \u30d6\u30ed\u30c3\u30af\u3067\u3059\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u5143\u306e\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306b\u306f\u5408\u8a08 150 \u884c\u304c\u3042\u3063\u305f\u305f\u3081\u3001\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30bb\u30c3\u30c8\u306b\u306f\u5143\u306e\u884c\u306e\u7d04 106\/150 = 70.6% \u304c\u542b\u307e\u308c\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5fc5\u8981\u306b\u5fdc\u3058\u3066\u3001\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30bb\u30c3\u30c8\u306e\u6700\u521d\u306e\u6570\u884c\u3092\u8868\u793a\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#view first few rows of training set\n<\/span>head(train)\n\n  Sepal.Length Sepal.Width Petal.Length Petal.Width Species\n1 5.1 3.5 1.4 0.2 setosa\n2 4.9 3.0 1.4 0.2 setosa\n3 4.7 3.2 1.3 0.2 setosa\n5 5.0 3.6 1.4 0.2 setosa\n8 5.0 3.4 1.5 0.2 setosa\n9 4.4 2.9 1.4 0.2 setosa\n<\/strong><\/pre>\n<h3><span style=\"color: #000000;\"><strong>\u4f8b 2: caTools \u3092\u4f7f\u7528\u3057\u3066\u30c7\u30fc\u30bf\u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30bb\u30c3\u30c8\u3068\u30c6\u30b9\u30c8 \u30bb\u30c3\u30c8\u306b\u5206\u5272\u3059\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001R \u3067<strong>caTools<\/strong>\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u4f7f\u7528\u3057\u3066\u3001\u884c\u306e 70% \u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30bb\u30c3\u30c8\u3068\u3057\u3066\u4f7f\u7528\u3057\u3001\u6b8b\u308a\u306e 30% \u3092\u30c6\u30b9\u30c8 \u30bb\u30c3\u30c8\u3068\u3057\u3066\u4f7f\u7528\u3057\u3066\u3001iris \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30bb\u30c3\u30c8\u3068\u30c6\u30b9\u30c8 \u30bb\u30c3\u30c8\u306b\u5206\u5272\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\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: #008000;\">library<\/span> (caTools)<\/span>\n\n#load iris dataset\n<\/span>data(iris)\n\n<span style=\"color: #008080;\">#make this example reproducible\n<\/span>set. <span style=\"color: #3366ff;\">seeds<\/span> (1)\n\n<span style=\"color: #008080;\">#Use 70% of dataset as training set and remaining 30% as testing set\n<\/span>sample &lt;- sample. <span style=\"color: #3366ff;\">split<\/span> (iris$Species, SplitRatio = <span style=\"color: #008000;\">0.7<\/span> )\ntrain &lt;- subset(iris, sample == <span style=\"color: #008000;\">TRUE<\/span> )\ntest &lt;- subset(iris, sample == <span style=\"color: #008000;\">FALSE<\/span> )\n\n<span style=\"color: #008080;\">#view dimensions of training set\n<\/span>sun(train)\n\n[1] 105 5\n\n<span style=\"color: #008080;\">#view dimensions of test set\n<\/span>dim(test)\n\n[1] 45 5<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u7d50\u679c\u304b\u3089\u6b21\u306e\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30bb\u30c3\u30c8\u306f 105 \u884c 5 \u5217\u306e\u30c7\u30fc\u30bf \u30d5\u30ec\u30fc\u30e0\u3067\u3059\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u30c6\u30b9\u30c8\u306f 45 \u884c 5 \u5217\u306e\u30c7\u30fc\u30bf \u30d6\u30ed\u30c3\u30af\u3067\u3059\u3002<\/span><\/li>\n<\/ul>\n<h3><span style=\"color: #000000;\"><strong>\u4f8b 3: dplyr \u3092\u4f7f\u7528\u3057\u3066\u30c7\u30fc\u30bf\u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30bb\u30c3\u30c8\u3068\u30c6\u30b9\u30c8 \u30bb\u30c3\u30c8\u306b\u5206\u5272\u3059\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001R \u3067<strong>caTools<\/strong>\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u4f7f\u7528\u3057\u3066\u3001\u884c\u306e 70% \u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30bb\u30c3\u30c8\u3068\u3057\u3066\u4f7f\u7528\u3057\u3001\u6b8b\u308a\u306e 30% \u3092\u30c6\u30b9\u30c8 \u30bb\u30c3\u30c8\u3068\u3057\u3066\u4f7f\u7528\u3057\u3066\u3001iris \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30bb\u30c3\u30c8\u3068\u30c6\u30b9\u30c8 \u30bb\u30c3\u30c8\u306b\u5206\u5272\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\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: #008000;\">library<\/span> (dplyr)<\/span>\n\n#load iris dataset\n<\/span>data(iris)\n\n<span style=\"color: #008080;\">#make this example reproducible\n<\/span>set. <span style=\"color: #3366ff;\">seeds<\/span> (1)\n\n<span style=\"color: #008080;\">#create variable ID\n<\/span>iris$id &lt;- 1:nrow(iris)\n\n<span style=\"color: #008080;\">#Use 70% of dataset as training set and remaining 30% as testing set<\/span> \ntrain &lt;- iris %&gt;% dplyr::sample_frac( <span style=\"color: #008000;\">0.7<\/span> )\ntest &lt;- dplyr::anti_join(iris, train, by = ' <span style=\"color: #ff0000;\">id<\/span> ')\n\n<span style=\"color: #008080;\">#view dimensions of training set\n<\/span>sun(train)\n\n[1] 105 6\n\n<span style=\"color: #008080;\">#view dimensions of test set\n<\/span>dim(test)\n\n[1] 45 6\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u7d50\u679c\u304b\u3089\u6b21\u306e\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30bb\u30c3\u30c8\u306f 105 \u884c 6 \u5217\u306e\u30c7\u30fc\u30bf \u30d5\u30ec\u30fc\u30e0\u3067\u3059\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u30c6\u30b9\u30c8\u306f 45 \u884c 6 \u5217\u306e\u30c7\u30fc\u30bf \u30d6\u30ed\u30c3\u30af\u3067\u3059\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u3053\u308c\u3089\u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30bb\u30c3\u30c8\u3068\u30c6\u30b9\u30c8 \u30bb\u30c3\u30c8\u306b\u306f\u3001\u4f5c\u6210\u3057\u305f\u8ffd\u52a0\u306e\u300cid\u300d\u5217\u304c\u542b\u307e\u308c\u3066\u3044\u308b\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6a5f\u68b0\u5b66\u7fd2\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u8abf\u6574\u3059\u308b\u3068\u304d\u306f\u3001\u3053\u306e\u5217\u3092\u4f7f\u7528\u3057\u306a\u3044\u3088\u3046\u306b\u3057\u3066\u304f\u3060\u3055\u3044 (\u307e\u305f\u306f\u30c7\u30fc\u30bf \u30d5\u30ec\u30fc\u30e0\u304b\u3089\u5b8c\u5168\u306b\u524a\u9664\u3057\u3066\u304f\u3060\u3055\u3044)\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 \u3067\u4ed6\u306e\u4e00\u822c\u7684\u306a\u64cd\u4f5c\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u306b\u3064\u3044\u3066\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><a href=\"https:\/\/statorials.org\/ja\/r\u3066\u3099mse\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5\/\" target=\"_blank\" rel=\"noopener\">R \u3067 MSE \u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/r\u3066\u3099rmse\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5\/\" target=\"_blank\" rel=\"noopener\">R \u3067 RMSE \u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/r-\u5e73\u65b9\u306e-r-\u30d5\u30a3\u30c3\u30c8\/\" target=\"_blank\" rel=\"noopener\">R \u306e\u8abf\u6574\u6e08\u307f R 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