{"id":4442,"date":"2023-07-11T02:36:29","date_gmt":"2023-07-11T02:36:29","guid":{"rendered":"https:\/\/statorials.org\/cn\/r-%e9%80%bb%e8%be%91%e5%9b%9e%e5%bd%92%e9%a2%84%e6%b5%8b\/"},"modified":"2023-07-11T02:36:29","modified_gmt":"2023-07-11T02:36:29","slug":"r-%e9%80%bb%e8%be%91%e5%9b%9e%e5%bd%92%e9%a2%84%e6%b5%8b","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/r-%e9%80%bb%e8%be%91%e5%9b%9e%e5%bd%92%e9%a2%84%e6%b5%8b\/","title":{"rendered":"\u5982\u4f55\u5728 r \u4e2d\u5c06 predict() \u4e0e\u903b\u8f91\u56de\u5f52\u6a21\u578b\u7ed3\u5408\u4f7f\u7528"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u4e00\u65e6\u6211\u4eec\u5728 R \u4e2d\u62df\u5408\u4e86<a href=\"https:\/\/statorials.org\/cn\/\u903b\u8f91\u56de\u5f521\/\" target=\"_blank\" rel=\"noopener\">\u903b\u8f91\u56de\u5f52\u6a21\u578b<\/a>\uff0c\u6211\u4eec\u5c31\u53ef\u4ee5\u4f7f\u7528<strong>Predict()<\/strong>\u51fd\u6570\u6765\u9884\u6d4b\u6a21\u578b\u4ee5\u524d\u4ece\u672a\u89c1\u8fc7\u7684\u65b0\u89c2\u5bdf\u503c\u7684\u54cd\u5e94\u503c\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8be5\u51fd\u6570\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\uff1a<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u9884\u6d4b\uff08\u5bf9\u8c61\uff0c\u65b0\u6570\u636e\uff0c\u7c7b\u578b=\u201c\u54cd\u5e94\u201d\uff09<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u91d1\u5b50\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>object\uff1a<\/strong>\u903b\u8f91\u56de\u5f52\u6a21\u578b\u7684\u540d\u79f0<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>newdata\uff1a<\/strong>\u7528\u4e8e\u9884\u6d4b\u7684\u65b0\u6570\u636e\u6846\u7684\u540d\u79f0<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>type\uff1a<\/strong>\u8981\u8fdb\u884c\u7684\u9884\u6d4b\u7c7b\u578b<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u4e0b\u9762\u7684\u4f8b\u5b50\u5c55\u793a\u4e86\u5982\u4f55\u5728\u5b9e\u9645\u4e2d\u4f7f\u7528\u8fd9\u4e2a\u529f\u80fd\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u793a\u4f8b\uff1a\u5728 R \u4e2d\u5c06 Predict() \u4e0e\u903b\u8f91\u56de\u5f52\u6a21\u578b\u7ed3\u5408\u4f7f\u7528<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u5bf9\u4e8e\u6b64\u793a\u4f8b\uff0c\u6211\u4eec\u5c06\u4f7f\u7528\u540d\u4e3a<a href=\"https:\/\/statorials.org\/cn\/mtcars-r-\u6570\u636e\u96c6\/\" target=\"_blank\" rel=\"noopener\">mtcars<\/a>\u7684\u5185\u7f6e R \u6570\u636e\u96c6\uff1a<\/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;\">\u6211\u4eec\u5c06\u62df\u5408\u4ee5\u4e0b\u903b\u8f91\u56de\u5f52\u6a21\u578b\uff0c\u5176\u4e2d\u4f7f\u7528\u53d8\u91cf<strong>disp<\/strong>\u548c<strong>hp<\/strong>\u6765\u9884\u6d4b\u54cd\u5e94\u53d8\u91cf<strong>am<\/strong> \uff08\u6c7d\u8f66\u7684\u53d8\u901f\u7bb1\u7c7b\u578b\uff1a0 = \u81ea\u52a8\uff0c1 = \u624b\u52a8\uff09\uff1a<\/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;\">\u7136\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u521b\u5efa\u4e00\u4e2a\u65b0\u7684\u6570\u636e\u6846\uff0c\u5176\u4e2d\u5305\u542b\u6a21\u578b\u4ee5\u524d\u4ece\u672a\u89c1\u8fc7\u7684\u516b\u8f86\u6c7d\u8f66\u7684\u4fe1\u606f\uff0c\u5e76\u4f7f\u7528<strong>Predict()<\/strong>\u51fd\u6570\u6765\u9884\u6d4b\u65b0\u8f66\u5c06\u5177\u6709\u81ea\u52a8\u53d8\u901f\u7bb1 (am=0) \u6216\u624b\u52a8\u53d8\u901f\u7bb1 (am=0) \u7684\u6982\u7387\uff08\u4e0a\u5348=1\uff09\uff1a<\/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$am_prob &lt;- predict(model, newdata, type=\" <span style=\"color: #ff0000;\">response<\/span> \")\n\n<span style=\"color: #008080;\">#view updated data frame\n<\/span>newdata\n\n  disp hp am am_prob\n1 200 100 0 0.004225640\n2 180 90 0 0.008361069\n3 160 108 0 0.335916069\n4 140 90 1 0.275162866\n5 120 80 0 0.429961894\n6 120 90 1 0.718090728\n7 100 80 1 0.835013994\n8 160 90 1 0.053546152<\/span><\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u662f\u5982\u4f55\u89e3\u91ca\u7ed3\u679c\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u6c7d\u8f66 1 \u914d\u5907\u624b\u52a8\u53d8\u901f\u7bb1\u7684\u6982\u7387\u4e3a<strong>0.004<\/strong> \u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u6c7d\u8f66 2 \u914d\u5907\u624b\u52a8\u53d8\u901f\u7bb1\u7684\u6982\u7387\u4e3a<strong>0.008<\/strong> \u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u6c7d\u8f66 3 \u914d\u5907\u624b\u52a8\u53d8\u901f\u7bb1\u7684\u6982\u7387\u4e3a<strong>0.336<\/strong> \u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u7b49\u7b49\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u8fd8\u53ef\u4ee5\u4f7f\u7528<strong>table()<\/strong>\u51fd\u6570\u521b\u5efa\u4e00\u4e2a\u6df7\u6dc6\u77e9\u9635\uff0c\u663e\u793a\u5b9e\u9645\u7684 am \u503c\u4e0e\u6a21\u578b\u9884\u6d4b\u7684\u503c\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create vector that contains 0 or 1 depending on predicted value of am\n<span style=\"color: #000000;\">am_pred = rep(0, dim(newdata)[1])\nam_pred[newdata$am_prob &gt; .5] = 1\n\n<span style=\"color: #008080;\">#create confusion matrix\n<\/span>table(am_pred, newdata$am)\n\nam_pred 0 1\n      0 4 2\n      1 0 2\n<\/span><\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6700\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528<strong>Mean()<\/strong>\u51fd\u6570\u6765\u8ba1\u7b97\u65b0\u6570\u636e\u5e93\u4e2d\u6a21\u578b\u6b63\u786e\u9884\u6d4b<strong>am<\/strong>\u503c\u7684\u89c2\u6d4b\u503c\u7684\u767e\u5206\u6bd4\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#calculate percentage of observations the model correctly predicted response value for\n<span style=\"color: #000000;\">mean(am_pred == newdata$am)\n\n[1] 0.75\n<\/span><\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u8be5\u6a21\u578b\u6b63\u786e\u9884\u6d4b\u4e86\u65b0\u6570\u636e\u5e93\u4e2d<strong>75%<\/strong>\u7684\u6c7d\u8f66\u7684<strong>am<\/strong>\u503c\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u6559\u7a0b\u89e3\u91ca\u4e86\u5982\u4f55\u5728 R \u4e2d\u6267\u884c\u5176\u4ed6\u5e38\u89c1\u4efb\u52a1\uff1a<\/span><\/p>\n<p><a href=\"https:\/\/statorials.org\/cn\/r-\u4e2d\u7684\u7b80\u5355\u7ebf\u6027\u56de\u5f52\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u5728 R \u4e2d\u6267\u884c\u7b80\u5355\u7ebf\u6027\u56de\u5f52<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/\u591a\u5143\u7ebf\u6027\u56de\u5f52\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u5728 R \u4e2d\u6267\u884c\u591a\u5143\u7ebf\u6027\u56de\u5f52<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/\u591a\u9879\u5f0f\u56de\u5f52-r\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u5728 R \u4e2d\u6267\u884c\u591a\u9879\u5f0f\u56de\u5f52<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/\u9884\u6d4b\u533a\u95f4r\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u5728 R \u4e2d\u521b\u5efa\u9884\u6d4b\u533a\u95f4<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u4e00\u65e6\u6211\u4eec\u5728 R \u4e2d\u62df\u5408\u4e86\u903b\u8f91\u56de\u5f52\u6a21\u578b\uff0c\u6211\u4eec\u5c31\u53ef\u4ee5\u4f7f\u7528Predict()\u51fd\u6570\u6765\u9884\u6d4b\u6a21\u578b\u4ee5\u524d\u4ece\u672a\u89c1\u8fc7\u7684\u65b0\u89c2\u5bdf\u503c\u7684\u54cd [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"class_list":["post-4442","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin 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