{"id":1160,"date":"2023-07-27T11:00:03","date_gmt":"2023-07-27T11:00:03","guid":{"rendered":"https:\/\/statorials.org\/ja\/r-%e3%81%a6%e3%82%99%e3%81%ae%e7%b7%9a%e5%bd%a2%e5%88%a4%e5%88%a5%e5%88%86%e6%9e%90\/"},"modified":"2023-07-27T11:00:03","modified_gmt":"2023-07-27T11:00:03","slug":"r-%e3%81%a6%e3%82%99%e3%81%ae%e7%b7%9a%e5%bd%a2%e5%88%a4%e5%88%a5%e5%88%86%e6%9e%90","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/r-%e3%81%a6%e3%82%99%e3%81%ae%e7%b7%9a%e5%bd%a2%e5%88%a4%e5%88%a5%e5%88%86%e6%9e%90\/","title":{"rendered":"R \u3067\u306e\u7dda\u5f62\u5224\u5225\u5206\u6790 (\u30b9\u30c6\u30c3\u30d7\u30d0\u30a4\u30b9\u30c6\u30c3\u30d7)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/ja\/\u7dda\u5f62\u5224\u5225\u5206\u6790\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u7dda\u5f62\u5224\u5225\u5206\u6790\u306f\u3001<\/a>\u4e00\u9023\u306e\u4e88\u6e2c\u5b50\u5909\u6570\u304c\u3042\u308a\u3001 <a href=\"https:\/\/statorials.org\/ja\/\u5909\u6570\u306e\u8aac\u660e\u5fdc\u7b54\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u5fdc\u7b54\u5909\u6570\u3092<\/a>2 \u3064\u4ee5\u4e0a\u306e\u30af\u30e9\u30b9\u306b\u5206\u985e\u3059\u308b\u5834\u5408\u306b\u4f7f\u7528\u3067\u304d\u308b\u65b9\u6cd5\u3067\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001R \u3067\u7dda\u5f62\u5224\u5225\u5206\u6790\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u306e\u4f8b\u3092\u6bb5\u968e\u7684\u306b\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 1: \u5fc5\u8981\u306a\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u30ed\u30fc\u30c9\u3059\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u307e\u305a\u3001\u3053\u306e\u4f8b\u306b\u5fc5\u8981\u306a\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u30ed\u30fc\u30c9\u3057\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><b><span style=\"color: #993300;\">library<\/span> (MASS)\n<span style=\"color: #993300;\">library<\/span> (ggplot2)<\/b><\/span><\/pre>\n<h3><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 2: \u30c7\u30fc\u30bf\u3092\u30ed\u30fc\u30c9\u3059\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u3053\u306e\u4f8b\u3067\u306f\u3001R \u306b\u7d44\u307f\u8fbc\u307e\u308c\u3066\u3044\u308b<strong>iris<\/strong>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001\u3053\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u30ed\u30fc\u30c9\u3057\u3066\u8868\u793a\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;\">#attach <em>iris<\/em> dataset to make it easy to work with<\/span>\nattach(iris)\n\n<span style=\"color: #008080;\">#view structure of dataset\n<\/span>str(iris)\n\n'data.frame': 150 obs. of 5 variables:\n $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...\n $ Sepal.Width: num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...\n $Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...\n $Petal.Width: num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...\n $ Species: Factor w\/ 3 levels \"setosa\",\"versicolor\",..: 1 1 1 1 1 1 1 ...\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u306f 5 \u3064\u306e\u5909\u6570\u3068\u5408\u8a08 150 \u306e\u89b3\u6e2c\u5024\u304c\u542b\u307e\u308c\u3066\u3044\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u306e\u4f8b\u3067\u306f\u3001\u7279\u5b9a\u306e\u82b1\u304c\u3069\u306e\u7a2e\u306b\u5c5e\u3059\u308b\u304b\u3092\u5206\u985e\u3059\u308b\u305f\u3081\u306e\u7dda\u5f62\u5224\u5225\u5206\u6790\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u30e2\u30c7\u30eb\u3067\u306f\u6b21\u306e\u4e88\u6e2c\u5b50\u5909\u6570\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u304c\u304f\u7247\u306e\u9577\u3055<\/span><\/li>\n<li><span style=\"color: #000000;\">\u304c\u304f\u7247\u306e\u5e45<\/span><\/li>\n<li><span style=\"color: #000000;\">\u82b1\u3073\u3089\u306e\u9577\u3055<\/span><\/li>\n<li><span style=\"color: #000000;\">\u82b1\u3073\u3089\u306e\u5e45<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u305d\u3057\u3066\u3001\u305d\u308c\u3089\u3092\u4f7f\u7528\u3057\u3066\u3001\u6b21\u306e 3 \u3064\u306e\u6f5c\u5728\u7684\u306a\u30af\u30e9\u30b9\u3092\u30b5\u30dd\u30fc\u30c8\u3059\u308b<em>\u7a2e\u306e<\/em>\u5fdc\u7b54\u5909\u6570\u3092\u4e88\u6e2c\u3057\u307e\u3059\u3002<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u30bb\u30c8\u30b5<\/span><\/li>\n<li><span style=\"color: #000000;\">\u765c\u98a8<\/span><\/li>\n<li><span style=\"color: #000000;\">\u30d0\u30fc\u30b8\u30cb\u30a2\u5dde<\/span><\/li>\n<\/ul>\n<h3><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 3: \u30c7\u30fc\u30bf\u3092\u30b9\u30b1\u30fc\u30eb\u3059\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u7dda\u5f62\u5224\u5225\u5206\u6790\u306e\u91cd\u8981\u306a\u524d\u63d0\u306e 1 \u3064\u306f\u3001\u5404\u4e88\u6e2c\u5909\u6570\u304c\u540c\u3058\u5206\u6563\u3092\u6301\u3064\u3068\u3044\u3046\u3053\u3068\u3067\u3059\u3002\u3053\u306e\u4eee\u5b9a\u304c\u6e80\u305f\u3055\u308c\u3066\u3044\u308b\u3053\u3068\u3092\u78ba\u8a8d\u3059\u308b\u7c21\u5358\u306a\u65b9\u6cd5\u306f\u3001\u5e73\u5747\u304c 0\u3001\u6a19\u6e96\u504f\u5dee\u304c 1 \u306b\u306a\u308b\u3088\u3046\u306b\u5404\u5909\u6570\u3092\u30b9\u30b1\u30fc\u30ea\u30f3\u30b0\u3059\u308b\u3053\u3068\u3067\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\">R \u3067\u306f\u3001 <strong>scale()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3053\u308c\u3092\u3059\u3070\u3084\u304f\u5b9f\u884c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#scale each predictor variable (ie first 4 columns)\n<\/span>iris[1:4] &lt;- scale(iris[1:4])\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/ja\/rapply-sapply\u3068tapply\u3092\u9069\u7528\u3059\u308b\u305f\u3081\u306e\u30ab\u3099\u30a4\u30c8\u3099\/\" target=\"_blank\" rel=\"noopener noreferrer\">apply() \u95a2\u6570<\/a>\u3092\u4f7f\u7528\u3057\u3066\u3001\u5404\u4e88\u6e2c\u5b50\u5909\u6570\u306e\u5e73\u5747\u304c 0 \u3067<a href=\"https:\/\/statorials.org\/ja\/r\u306e\u6a19\u6e96\u504f\u5dee\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u6a19\u6e96\u504f\u5dee<\/a>\u304c 1 \u3067\u3042\u308b\u3053\u3068\u3092\u78ba\u8a8d\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#find mean of each predictor variable\n<\/span>apply(iris[1:4], 2, mean)\n\n Sepal.Length Sepal.Width Petal.Length Petal.Width \n-4.484318e-16 2.034094e-16 -2.895326e-17 -3.663049e-17 \n\n<span style=\"color: #008080;\">#find standard deviation of each predictor variable\n<\/span>apply(iris[1:4], 2, sd) \n\nSepal.Length Sepal.Width Petal.Length Petal.Width \n           1 1 1 1\n<\/strong><\/pre>\n<h3><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 4: \u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u304a\u3088\u3073\u30c6\u30b9\u30c8\u306e\u30b5\u30f3\u30d7\u30eb\u3092\u4f5c\u6210\u3059\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6b21\u306b\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u3001\u30e2\u30c7\u30eb\u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3059\u308b\u305f\u3081\u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30bb\u30c3\u30c8\u3068\u3001\u30e2\u30c7\u30eb\u3092\u30c6\u30b9\u30c8\u3059\u308b\u305f\u3081\u306e\u30c6\u30b9\u30c8 \u30bb\u30c3\u30c8\u306b\u5206\u5272\u3057\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#make this example reproducible\n<\/span>set.seed(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> ), <span style=\"color: #3366ff;\">nrow<\/span> (iris), <span style=\"color: #3366ff;\">replace<\/span> = <span style=\"color: #008000;\">TRUE<\/span> , <span style=\"color: #3366ff;\">prob<\/span> =c(0.7,0.3))\ntrain &lt;- iris[sample, ]\ntest &lt;- iris[!sample, ] \n<\/strong><\/pre>\n<h3><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 5: LDA \u30e2\u30c7\u30eb\u3092\u8abf\u6574\u3059\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6b21\u306b\u3001 <strong>MASS<\/strong>\u30d1\u30c3\u30b1\u30fc\u30b8\u306e<a href=\"https:\/\/www.rdocumentation.org\/packages\/MASS\/versions\/7.3-53\/topics\/lda\" target=\"_blank\" rel=\"noopener noreferrer\">lda() \u95a2\u6570<\/a>\u3092\u4f7f\u7528\u3057\u3066\u3001LDA \u30e2\u30c7\u30eb\u3092\u30c7\u30fc\u30bf\u306b\u9069\u5408\u3055\u305b\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#fit LDA model\n<\/span>model &lt;- lda(Species~., data=train)\n\n<span style=\"color: #008080;\">#view model output<\/span>\nmodel\n\nCall:\nlda(Species ~ ., data = train)\n\nPrior probabilities of groups:\n    setosa versicolor virginica \n 0.3207547 0.3207547 0.3584906 \n\nGroup means:\n           Sepal.Length Sepal.Width Petal.Length Petal.Width\nsetosa -1.0397484 0.8131654 -1.2891006 -1.2570316\nversicolor 0.1820921 -0.6038909 0.3403524 0.2208153\nvirginica 0.9582674 -0.1919146 1.0389776 1.1229172\n\nCoefficients of linear discriminants:\n                    LD1 LD2\nSepal.Length 0.7922820 0.5294210\nSepal.Width 0.5710586 0.7130743\nPetal.Length -4.0762061 -2.7305131\nPetal.Width -2.0602181 2.6326229\n\nProportion of traces:\n   LD1 LD2 \n0.9921 0.0079 \n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u30e2\u30c7\u30eb\u306e\u7d50\u679c\u3092\u89e3\u91c8\u3059\u308b\u65b9\u6cd5\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u30b0\u30eb\u30fc\u30d7\u4e8b\u524d\u78ba\u7387:<\/strong>\u3053\u308c\u3089\u306f\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30bb\u30c3\u30c8\u5185\u306e\u5404\u7a2e\u306e\u5272\u5408\u3092\u8868\u3057\u307e\u3059\u3002\u305f\u3068\u3048\u3070\u3001\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30bb\u30c3\u30c8\u5185\u306e\u3059\u3079\u3066\u306e\u89b3\u6e2c\u5024\u306e 35.8% \u306f\u3001 <em>virginica<\/em>\u7a2e\u306b\u95a2\u3059\u308b\u3082\u306e\u3067\u3057\u305f\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u30b0\u30eb\u30fc\u30d7\u5e73\u5747:<\/strong>\u3053\u308c\u3089\u306f\u3001\u7a2e\u3054\u3068\u306e\u5404\u4e88\u6e2c\u5909\u6570\u306e\u5e73\u5747\u5024\u3092\u8868\u793a\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u7dda\u5f62\u5224\u5225\u4fc2\u6570:<\/strong> LDA \u30e2\u30c7\u30eb\u306e\u6c7a\u5b9a\u30eb\u30fc\u30eb\u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3055\u308c\u308b\u4e88\u6e2c\u5b50\u5909\u6570\u306e\u7dda\u5f62\u7d50\u5408\u3092\u8868\u793a\u3057\u307e\u3059\u3002\u4f8b\u3048\u3070\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>LD1:<\/strong> 0.792 * \u304c\u304f\u7247\u306e\u9577\u3055 + 0.571 * \u304c\u304f\u7247\u306e\u5e45 \u2013 4.076 * \u82b1\u3073\u3089\u306e\u9577\u3055 \u2013 2.06 * \u82b1\u3073\u3089\u306e\u5e45<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>LD2:<\/strong> 0.529 * \u304c\u304f\u7247\u306e\u9577\u3055 + 0.713 * \u304c\u304f\u7247\u306e\u5e45 \u2013 2.731 * \u82b1\u3073\u3089\u306e\u9577\u3055 + 2.63 * \u82b1\u3073\u3089\u306e\u5e45<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\"><strong>\u30c8\u30ec\u30fc\u30b9\u6bd4\u7387:<\/strong>\u5404\u7dda\u5f62\u5224\u5225\u95a2\u6570\u306b\u3088\u3063\u3066\u9054\u6210\u3055\u308c\u308b\u5206\u96e2\u306e\u30d1\u30fc\u30bb\u30f3\u30c6\u30fc\u30b8\u304c\u8868\u793a\u3055\u308c\u307e\u3059\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 6: \u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3057\u3066\u4e88\u6e2c\u3092\u884c\u3046<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30c7\u30fc\u30bf\u3092\u4f7f\u7528\u3057\u3066\u30e2\u30c7\u30eb\u3092\u9069\u5408\u3055\u305b\u305f\u3089\u3001\u305d\u308c\u3092\u4f7f\u7528\u3057\u3066\u30c6\u30b9\u30c8 \u30c7\u30fc\u30bf\u306e\u4e88\u6e2c\u3092\u884c\u3046\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#use LDA model to make predictions on test data\n<\/span>predicted &lt;- <span style=\"color: #3366ff;\">predict<\/span> (model, test)\n\nnames(predicted)\n\n[1] \"class\" \"posterior\" \"x\"   \n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u3053\u308c\u306b\u3088\u308a\u30013 \u3064\u306e\u5909\u6570\u3092\u542b\u3080\u30ea\u30b9\u30c8\u304c\u8fd4\u3055\u308c\u307e\u3059\u3002<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>class:<\/strong>\u4e88\u6e2c\u3055\u308c\u305f\u30af\u30e9\u30b9<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>\u4e8b\u5f8c\u78ba\u7387:<\/strong>\u89b3\u6e2c\u5024\u304c\u5404\u30af\u30e9\u30b9\u306b\u5c5e\u3059\u308b<a href=\"https:\/\/statorials.org\/ja\/\u4e8b\u5f8c\u78ba\u7387\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u4e8b\u5f8c\u78ba\u7387<\/a><\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>x:<\/strong>\u7dda\u5f62\u5224\u5225\u5f0f<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u30c6\u30b9\u30c8 \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u6700\u521d\u306e 6 \u3064\u306e\u89b3\u6e2c\u5024\u306b\u5bfe\u3059\u308b\u3053\u308c\u3089\u306e\u5404\u7d50\u679c\u3092\u3059\u3050\u306b\u8996\u899a\u5316\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#view predicted class for first six observations in test set\n<\/span>head(predicted$class)\n\n[1] setosa setosa setosa setosa setosa setosa\nLevels: setosa versicolor virginica\n\n<span style=\"color: #008080;\">#view posterior probabilities for first six observations in test set<\/span>\nhead(predicted$posterior)\n\n   setosa versicolor virginica\n4 1 2.425563e-17 1.341984e-35\n6 1 1.400976e-21 4.482684e-40\n7 1 3.345770e-19 1.511748e-37\n15 1 6.389105e-31 7.361660e-53\n17 1 1.193282e-25 2.238696e-45\n18 1 6.445594e-22 4.894053e-41\n\n<span style=\"color: #008080;\">#view linear discriminants for first six observations in test set\n<\/span>head(predicted$x)\n\n         LD1 LD2\n4 7.150360 -0.7177382\n6 7.961538 1.4839408\n7 7.504033 0.2731178\n15 10.170378 1.9859027\n17 8.885168 2.1026494\n18 8.113443 0.7563902\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b3\u30fc\u30c9\u3092\u4f7f\u7528\u3059\u308b\u3068\u3001LDA \u30e2\u30c7\u30eb\u304c\u7a2e\u3092\u6b63\u3057\u304f\u4e88\u6e2c\u3057\u305f\u89b3\u6e2c\u5024\u306e\u5272\u5408\u3092\u78ba\u8a8d\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#find accuracy of model\n<\/span>mean(predicted$class==test$Species)\n\n[1] 1<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u3053\u306e\u30e2\u30c7\u30eb\u306f\u3001\u30c6\u30b9\u30c8 \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u5185\u306e\u89b3\u6e2c\u5024\u306e<strong>100%<\/strong>\u306b\u3064\u3044\u3066\u7a2e\u3092\u6b63\u3057\u304f\u4e88\u6e2c\u3057\u305f\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3057\u305f\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u73fe\u5b9f\u306e\u4e16\u754c\u3067\u306f\u3001LDA \u30e2\u30c7\u30eb\u304c\u5404\u30af\u30e9\u30b9\u306e\u7d50\u679c\u3092\u6b63\u78ba\u306b\u4e88\u6e2c\u3059\u308b\u3053\u3068\u306f\u307b\u3068\u3093\u3069\u3042\u308a\u307e\u305b\u3093\u304c\u3001\u3053\u306e\u8679\u5f69\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306f\u3001\u6a5f\u68b0\u5b66\u7fd2\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u304c\u975e\u5e38\u306b\u3046\u307e\u304f\u6a5f\u80fd\u3059\u308b\u50be\u5411\u306b\u3042\u308b\u65b9\u6cd5\u3067\u5358\u7d14\u306b\u69cb\u7bc9\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 7: \u7d50\u679c\u3092\u8996\u899a\u5316\u3059\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6700\u5f8c\u306b\u3001LDA \u30d7\u30ed\u30c3\u30c8\u3092\u4f5c\u6210\u3057\u3066\u30e2\u30c7\u30eb\u306e\u7dda\u5f62\u5224\u5225\u5f0f\u3092\u8996\u899a\u5316\u3057\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u5185\u306e 3 \u3064\u306e\u7570\u306a\u308b\u7a2e\u3092\u3069\u306e\u7a0b\u5ea6\u5206\u96e2\u3057\u3066\u3044\u308b\u304b\u3092\u8996\u899a\u5316\u3067\u304d\u307e\u3059\u3002<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#define data to plot\n<\/span>lda_plot &lt;- cbind(train, predict(model)$x)\n\n<span style=\"color: #008080;\">#createplot\n<\/span>ggplot(lda_plot, <span style=\"color: #3366ff;\">aes<\/span> (LD1, LD2)) +\n  geom_point( <span style=\"color: #3366ff;\">aes<\/span> (color=Species))\n<\/strong><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-11639 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/lda_r1.png\" alt=\"R \u3067\u306e\u7dda\u5f62\u5224\u5225\u5206\u6790\" width=\"431\" height=\"427\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p><span style=\"color: #000000;\">\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u4f7f\u7528\u3055\u308c\u308b\u5b8c\u5168\u306a R \u30b3\u30fc\u30c9\u306f<a href=\"https:\/\/github.com\/Statorials\/R-Guides\/blob\/main\/linear_discriminant_analysis\" target=\"_blank\" rel=\"noopener noreferrer\">\u3001\u3053\u3053\u3067<\/a>\u898b\u3064\u3051\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u7dda\u5f62\u5224\u5225\u5206\u6790\u306f\u3001\u4e00\u9023\u306e\u4e88\u6e2c\u5b50\u5909\u6570\u304c\u3042\u308a\u3001 \u5fdc\u7b54\u5909\u6570\u30922 \u3064\u4ee5\u4e0a\u306e\u30af\u30e9\u30b9\u306b\u5206\u985e\u3059\u308b\u5834\u5408\u306b\u4f7f\u7528\u3067\u304d\u308b\u65b9\u6cd5\u3067\u3059\u3002 \u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001R \u3067\u7dda\u5f62\u5224\u5225\u5206\u6790\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u306e\u4f8b\u3092\u6bb5\u968e\u7684\u306b\u8aac\u660e\u3057\u307e\u3059\u3002 \u30b9\u30c6\u30c3\u30d7 1: \u5fc5\u8981\u306a\u30e9\u30a4\u30d6 [&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-1160","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\u306e\u7dda\u5f62\u5224\u5225\u5206\u6790 (\u30b9\u30c6\u30c3\u30d7\u30d0\u30a4\u30b9\u30c6\u30c3\u30d7)<\/title>\n<meta name=\"description\" content=\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001R \u3067\u7dda\u5f62\u5224\u5225\u5206\u6790\u3092\u5b9f\u884c\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 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