{"id":4436,"date":"2023-07-11T02:36:29","date_gmt":"2023-07-11T02:36:29","guid":{"rendered":"https:\/\/statorials.org\/ja\/r-%e3%83%ad%e3%82%b7%e3%82%99%e3%82%b9%e3%83%86%e3%82%a3%e3%83%83%e3%82%af%e5%9b%9e%e5%b8%b0%e4%ba%88%e6%b8%ac\/"},"modified":"2023-07-11T02:36:29","modified_gmt":"2023-07-11T02:36:29","slug":"r-%e3%83%ad%e3%82%b7%e3%82%99%e3%82%b9%e3%83%86%e3%82%a3%e3%83%83%e3%82%af%e5%9b%9e%e5%b8%b0%e4%ba%88%e6%b8%ac","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/r-%e3%83%ad%e3%82%b7%e3%82%99%e3%82%b9%e3%83%86%e3%82%a3%e3%83%83%e3%82%af%e5%9b%9e%e5%b8%b0%e4%ba%88%e6%b8%ac\/","title":{"rendered":"R \u306e\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u30e2\u30c7\u30eb\u3067 predict() \u3092\u4f7f\u7528\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">R \u3067<a href=\"https:\/\/statorials.org\/ja\/\u30ed\u30b7\u3099\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30-1\/\" target=\"_blank\" rel=\"noopener\">\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u30e2\u30c7\u30eb<\/a>\u3092\u9069\u5408\u3055\u305b\u305f\u3089\u3001 <strong>predict()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u30e2\u30c7\u30eb\u304c\u3053\u308c\u307e\u3067\u306b\u898b\u305f\u3053\u3068\u306e\u306a\u3044\u65b0\u3057\u3044\u89b3\u6e2c\u5024\u306e\u5fdc\u7b54\u5024\u3092\u4e88\u6e2c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u306e\u95a2\u6570\u306f\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u4e88\u6e2c(\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3001\u65b0\u3057\u3044\u30c7\u30fc\u30bf\u3001\u30bf\u30a4\u30d7 = &#8220;\u5fdc\u7b54&#8221;)<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u91d1\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\"><strong>\u30aa\u30d6\u30b8\u30a7\u30af\u30c8:<\/strong>\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u30e2\u30c7\u30eb\u306e\u540d\u524d<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>newdata:<\/strong>\u4e88\u6e2c\u3092\u884c\u3046\u65b0\u3057\u3044\u30c7\u30fc\u30bf \u30d5\u30ec\u30fc\u30e0\u306e\u540d\u524d<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>type:<\/strong>\u884c\u3046\u4e88\u6e2c\u306e\u30bf\u30a4\u30d7<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\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<h2><span style=\"color: #000000;\"><strong>\u4f8b: R \u306e\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u30e2\u30c7\u30eb\u3067\u306e Predict() \u306e\u4f7f\u7528<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u3053\u306e\u4f8b\u3067\u306f\u3001 <a href=\"https:\/\/statorials.org\/ja\/mtcars-r-\u30c6\u3099\u30fc\u30bf\u30bb\u30c3\u30c8\/\" target=\"_blank\" rel=\"noopener\">mtcars<\/a>\u3068\u3044\u3046\u7d44\u307f\u8fbc\u307f\u306e R \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#view first six rows of <em>mtcars<\/em> dataset<\/span>\nhead(mtcars)\n\n                   mpg cyl disp hp drat wt qsec vs am gear carb\nMazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4\nMazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4\nDatsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1\nHornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1\nHornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2\nValiant 18.1 6 225 105 2.76 3,460 20.22 1 0 3 1<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u5f53\u3066\u306f\u3081\u307e\u3059\u3002\u3053\u306e\u30e2\u30c7\u30eb\u3067\u306f\u3001\u5909\u6570<strong>disp<\/strong>\u3068<strong>hp<\/strong>\u3092\u4f7f\u7528\u3057\u3066\u5fdc\u7b54\u5909\u6570<strong>am<\/strong> (\u8eca\u306e\u30c8\u30e9\u30f3\u30b9\u30df\u30c3\u30b7\u30e7\u30f3 \u30bf\u30a4\u30d7: 0 = \u30aa\u30fc\u30c8\u30de\u30c1\u30c3\u30af\u30011 = \u30de\u30cb\u30e5\u30a2\u30eb) \u3092\u4e88\u6e2c\u3057\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#fit logistic regression model<\/span>\nmodel &lt;- glm(am ~ disp + hp, data=mtcars, family=binomial)\n\n<span style=\"color: #008080;\">#view model summary\n<\/span>summary(model)\n\nCall:\nglm(formula = am ~ disp + hp, family = binomial, data = mtcars)\n\nDeviance Residuals: \n    Min 1Q Median 3Q Max  \n-1.9665 -0.3090 -0.0017 0.3934 1.3682  \n\nCoefficients:\n            Estimate Std. Error z value Pr(&gt;|z|)  \n(Intercept) 1.40342 1.36757 1.026 0.3048  \navailable -0.09518 0.04800 -1.983 0.0474 *\nhp 0.12170 0.06777 1.796 0.0725 .\n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\n(Dispersion parameter for binomial family taken to be 1)\n\n    Null deviance: 43,230 on 31 degrees of freedom\nResidual deviance: 16,713 on 29 degrees of freedom\nAIC: 22,713\n\nNumber of Fisher Scoring iterations: 8\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6b21\u306b\u3001\u30e2\u30c7\u30eb\u304c\u3053\u308c\u307e\u3067\u898b\u305f\u3053\u3068\u306e\u306a\u3044 8 \u53f0\u306e\u8eca\u306b\u95a2\u3059\u308b\u60c5\u5831\u3092\u542b\u3080\u65b0\u3057\u3044\u30c7\u30fc\u30bf \u30d5\u30ec\u30fc\u30e0\u3092\u4f5c\u6210\u3057\u3001 <strong>predict()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u65b0\u3057\u3044\u8eca\u304c\u30aa\u30fc\u30c8\u30de\u30c1\u30c3\u30af \u30c8\u30e9\u30f3\u30b9\u30df\u30c3\u30b7\u30e7\u30f3 (am=0) \u307e\u305f\u306f\u30de\u30cb\u30e5\u30a2\u30eb \u30c8\u30e9\u30f3\u30b9\u30df\u30c3\u30b7\u30e7\u30f3\u3092\u642d\u8f09\u3059\u308b\u78ba\u7387\u3092\u4e88\u6e2c\u3057\u307e\u3059 (\u5348\u524d =1):<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#define new data frame\n<span style=\"color: #000000;\">newdata = data. <span style=\"color: #3366ff;\">frame<\/span> (disp=c(200, 180, 160, 140, 120, 120, 100, 160),\n                     hp=c(100, 90, 108, 90, 80, 90, 80, 90),\n                     am=c(0, 0, 0, 1, 0, 1, 1, 1))\n\n<span style=\"color: #008080;\">#view data frame\n<\/span>newdata\n\n<span style=\"color: #008080;\">#use model to predict value of am for all new cars\n<\/span>newdata$am_prob &lt;- predict(model, newdata, type=\" <span style=\"color: #ff0000;\">response<\/span> \")\n\n<span style=\"color: #008080;\">#view updated data frame\n<\/span>newdata\n\n  disp hp am am_prob\n1 200 100 0 0.004225640\n2 180 90 0 0.008361069\n3 160 108 0 0.335916069\n4 140 90 1 0.275162866\n5 120 80 0 0.429961894\n6 120 90 1 0.718090728\n7 100 80 1 0.835013994\n8 160 90 1 0.053546152<\/span><\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u7d50\u679c\u3092\u89e3\u91c8\u3059\u308b\u65b9\u6cd5\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u8eca 1 \u304c\u30de\u30cb\u30e5\u30a2\u30eb \u30c8\u30e9\u30f3\u30b9\u30df\u30c3\u30b7\u30e7\u30f3\u3092\u5099\u3048\u3066\u3044\u308b\u78ba\u7387\u306f<strong>0.004<\/strong>\u3067\u3059\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u8eca 2 \u304c\u30de\u30cb\u30e5\u30a2\u30eb \u30c8\u30e9\u30f3\u30b9\u30df\u30c3\u30b7\u30e7\u30f3\u3092\u5099\u3048\u3066\u3044\u308b\u78ba\u7387\u306f<strong>0.008<\/strong>\u3067\u3059\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u8eca 3 \u304c\u30de\u30cb\u30e5\u30a2\u30eb \u30c8\u30e9\u30f3\u30b9\u30df\u30c3\u30b7\u30e7\u30f3\u3092\u5099\u3048\u3066\u3044\u308b\u78ba\u7387\u306f<strong>0.336<\/strong>\u3067\u3059\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u7b49\u3005\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>table()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u5b9f\u969b\u306e am \u5024\u3068\u30e2\u30c7\u30eb\u306b\u3088\u3063\u3066\u4e88\u6e2c\u3055\u308c\u305f\u5024\u3092\u8868\u793a\u3059\u308b\u6df7\u540c\u884c\u5217\u3092\u4f5c\u6210\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;\">#create vector that contains 0 or 1 depending on predicted value of am\n<span style=\"color: #000000;\">am_pred = rep(0, dim(newdata)[1])\nam_pred[newdata$am_prob &gt; .5] = 1\n\n<span style=\"color: #008080;\">#create confusion matrix\n<\/span>table(am_pred, newdata$am)\n\nam_pred 0 1\n      0 4 2\n      1 0 2\n<\/span><\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6700\u5f8c\u306b\u3001 <strong>Mean()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u30e2\u30c7\u30eb\u304c<strong>am<\/strong>\u306e\u5024\u3092\u6b63\u3057\u304f\u4e88\u6e2c\u3057\u305f\u65b0\u3057\u3044\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u5185\u306e\u89b3\u6e2c\u5024\u306e\u5272\u5408\u3092\u8a08\u7b97\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#calculate percentage of observations the model correctly predicted response value for\n<span style=\"color: #000000;\">mean(am_pred == newdata$am)\n\n[1] 0.75\n<\/span><\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u30e2\u30c7\u30eb\u304c\u65b0\u3057\u3044\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u5185\u306e<strong>75%<\/strong>\u306e\u81ea\u52d5\u8eca\u306e<strong>am<\/strong>\u5024\u3092\u6b63\u78ba\u306b\u4e88\u6e2c\u3057\u305f\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h2>\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\u30bf\u30b9\u30af\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\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\/\u591a\u9805\u5f0f\u56de\u5e30-r\/\" target=\"_blank\" rel=\"noopener\">R \u3067\u591a\u9805\u5f0f\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/\u4e88\u6e2c\u533a\u9593-r\/\" target=\"_blank\" rel=\"noopener\">R \u3067\u4e88\u6e2c\u533a\u9593\u3092\u4f5c\u6210\u3059\u308b\u65b9\u6cd5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>R \u3067\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u9069\u5408\u3055\u305b\u305f\u3089\u3001 predict()\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u30e2\u30c7\u30eb\u304c\u3053\u308c\u307e\u3067\u306b\u898b\u305f\u3053\u3068\u306e\u306a\u3044\u65b0\u3057\u3044\u89b3\u6e2c\u5024\u306e\u5fdc\u7b54\u5024\u3092\u4e88\u6e2c\u3067\u304d\u307e\u3059\u3002 \u3053\u306e\u95a2\u6570\u306f\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002 \u4e88\u6e2c(\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3001\u65b0\u3057\u3044\u30c7\u30fc\u30bf\u3001 [&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-4436","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 \u306e\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u30e2\u30c7\u30eb\u3067 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