{"id":4267,"date":"2023-07-12T10:22:19","date_gmt":"2023-07-12T10:22:19","guid":{"rendered":"https:\/\/statorials.org\/cn\/r-lm-%e9%a2%84%e6%b5%8b\/"},"modified":"2023-07-12T10:22:19","modified_gmt":"2023-07-12T10:22:19","slug":"r-lm-%e9%a2%84%e6%b5%8b","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/r-lm-%e9%a2%84%e6%b5%8b\/","title":{"rendered":"\u5982\u4f55\u5728 r \u4e2d\u5c06 predict() \u51fd\u6570\u4e0e lm() \u4e00\u8d77\u4f7f\u7528"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">R \u4e2d\u7684<strong>lm()<\/strong>\u51fd\u6570\u53ef\u7528\u4e8e\u62df\u5408\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4e00\u65e6\u6211\u4eec\u62df\u5408\u4e86\u6a21\u578b\uff0c\u6211\u4eec\u5c31\u53ef\u4ee5\u4f7f\u7528<strong>predict()<\/strong>\u51fd\u6570\u6765\u9884\u6d4b\u65b0<a href=\"https:\/\/statorials.org\/cn\/\u7edf\u8ba1\u89c2\u5bdf\/\" target=\"_blank\" rel=\"noopener\">\u89c2\u5bdf<\/a>\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>\u4f7f\u7528glm()\u51fd\u6570\u8fdb\u884c\u6a21\u578b\u8c03\u6574\u7684\u540d\u79f0<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>newdata\uff1a<\/strong>\u8981\u8fdb\u884c\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\u7684\u7c7b\u578b\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u793a\u4f8b\u6f14\u793a\u5982\u4f55\u4f7f\u7528<strong>lm()<\/strong>\u51fd\u6570\u5728 R \u4e2d\u62df\u5408\u7ebf\u6027\u56de\u5f52\u6a21\u578b\uff0c\u7136\u540e\u5982\u4f55\u4f7f\u7528<strong>Predict()<\/strong>\u51fd\u6570\u6765\u9884\u6d4b\u6a21\u578b\u4ee5\u524d\u4ece\u672a\u89c1\u8fc7\u7684\u65b0\u89c2\u6d4b\u503c\u7684\u54cd\u5e94\u503c\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u793a\u4f8b\uff1a\u5728 R \u4e2d\u4f7f\u7528 Predict() \u51fd\u6570\u548c lm()<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u5047\u8bbe\u6211\u4eec\u5728 R \u4e2d\u6709\u4ee5\u4e0b\u6570\u636e\u6846\uff0c\u5176\u4e2d\u5305\u542b\u6709\u5173\u5404\u79cd\u7bee\u7403\u8fd0\u52a8\u5458\u7684\u4fe1\u606f\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create data frame\n<\/span>df &lt;- data. <span style=\"color: #3366ff;\">frame<\/span> (minutes=c(5, 10, 13, 14, 20, 22, 26, 34, 38, 40),\n                 fouls=c(5, 5, 3, 4, 2, 1, 3, 2, 1, 1),\n                 points=c(6, 8, 8, 7, 14, 10, 22, 24, 28, 30))\n\n<span style=\"color: #008080;\">#view data frame\n<\/span>df\n\n   minutes fouls points\n1 5 5 6\n2 10 5 8\n3 13 3 8\n4 14 4 7\n5 20 2 14\n6 22 1 10\n7 26 3 22\n8 34 2 24\n9 38 1 28\n10 40 1 30\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u5047\u8bbe\u6211\u4eec\u8981\u5e94\u7528\u4ee5\u4e0b<a href=\"https:\/\/statorials.org\/cn\/\u591a\u5143\u7ebf\u6027\u56de\u5f52-1\/\" target=\"_blank\" rel=\"noopener\">\u591a\u5143\u7ebf\u6027\u56de\u5f52\u6a21\u578b\uff0c<\/a>\u4f7f\u7528\u4e0a\u573a\u65f6\u95f4\u548c\u72af\u89c4\u603b\u6570\u6765\u9884\u6d4b\u6bcf\u4e2a\u7403\u5458\u7684\u5f97\u5206\uff1a<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u79ef\u5206 = \u03b2 <sub>0<\/sub> + \u03b2 <sub>1<\/sub> \uff08\u5206\u949f\uff09+ \u03b2 <sub>2<\/sub> \uff08\u72af\u89c4\uff09<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\"><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528<strong>lm()<\/strong>\u51fd\u6570\u6765\u9002\u5e94\u8fd9\u4e2a\u6a21\u578b\uff1a<\/span><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#fit multiple linear regression model\n<\/span>fit &lt;- lm(points ~ minutes + fouls, data=df)\n\n<span style=\"color: #008080;\">#view summary of model\n<\/span>summary(fit)\n\nCall:\nlm(formula = points ~ minutes + fouls, data = df)\n\nResiduals:\n    Min 1Q Median 3Q Max \n-3.5241 -1.4782 0.5918 1.6073 2.0889 \n\nCoefficients:\n            Estimate Std. Error t value Pr(&gt;|t|)    \n(Intercept) -11.8949 4.5375 -2.621 0.0343 *  \nminutes 0.9774 0.1086 9.000 4.26e-05 ***\nfouls 2.1838 0.8398 2.600 0.0354 *  \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 2.148 on 7 degrees of freedom\nMultiple R-squared: 0.959, Adjusted R-squared: 0.9473 \nF-statistic: 81.93 on 2 and 7 DF, p-value: 1.392e-05\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u4f7f\u7528\u6a21\u578b\u7ed3\u679c\u4e2d\u7684\u7cfb\u6570\uff0c\u6211\u4eec\u53ef\u4ee5\u5199\u51fa\u62df\u5408\u56de\u5f52\u65b9\u7a0b\uff1a<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u5206 = -11.8949 + 0.9774\uff08\u5206\u949f\uff09+ 2.1838\uff08\u72af\u89c4\uff09<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u7136\u540e\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528<strong>predict()<\/strong>\u51fd\u6570\u6765\u9884\u6d4b\u4e00\u4e2a\u4e0a\u573a15\u5206\u949f\u5e76\u4e14\u603b\u5171\u72af\u89c43\u6b21\u7684\u7403\u5458\u4f1a\u5f97\u5230\u591a\u5c11\u5206\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#define new observation\n<span style=\"color: #000000;\">newdata = data. <span style=\"color: #3366ff;\">frame<\/span> (minutes=15, fouls=3)\n<\/span>\n#use model to predict points value\n<span style=\"color: #000000;\">predict(fit, newdata)\n\n       1 \n9.317731\n<\/span><\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u8be5\u6a21\u578b\u9884\u6d4b\u8be5\u73a9\u5bb6\u5c06\u83b7\u5f97<strong>9.317731<\/strong>\u5206\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8bf7\u6ce8\u610f\uff0c\u5982\u679c\u6211\u4eec\u6709\u4e00\u4e2a\u5305\u542b\u591a\u4e2a\u65b0\u89c2\u5bdf\u503c\u7684\u6570\u636e\u6846\uff0c\u6211\u4eec\u4e5f\u53ef\u4ee5\u4e00\u6b21\u8fdb\u884c\u591a\u4e2a\u9884\u6d4b\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4f8b\u5982\uff0c\u4ee5\u4e0b\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528\u62df\u5408\u56de\u5f52\u6a21\u578b\u6765\u9884\u6d4b\u4e09\u4e2a\u7403\u5458\u7684\u5206\u503c\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#define new data frame of three cars\n<span style=\"color: #000000;\">newdata = data. <span style=\"color: #3366ff;\">frame<\/span> (minutes=c(15, 20, 25),\n                     fouls=c(3, 2, 1))\n\n<span style=\"color: #008080;\">#view data frame\n<\/span>newdata\n\n  minutes fouls\n1 15 3\n2 20 2\n3 25 1\n<\/span>\n#use model to predict points for all three players\n<span style=\"color: #000000;\">predict(model, newdata)\n\n        1 2 3 \n 9.317731 12.021032 14.724334 \n<\/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;\">\u51fa\u573a 15 \u5206\u949f\u4e14\u72af\u89c4 3 \u6b21\u7684\u7403\u5458\u7684\u9884\u671f\u5f97\u5206\u4e3a<strong>9.32 \u5206<\/strong>\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u51fa\u573a 20 \u5206\u949f\u4e14\u72af\u89c4 2 \u6b21\u7684\u7403\u5458\u7684\u9884\u671f\u5f97\u5206\u4e3a<strong>12.02 \u5206<\/strong>\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u51fa\u573a 25 \u5206\u949f\u4e14\u72af\u89c4 1 \u6b21\u7684\u7403\u5458\u7684\u9884\u671f\u5f97\u5206\u4e3a<strong>14.72<\/strong> \u3002<\/span><\/li>\n<\/ul>\n<h2><span style=\"color: #000000;\"><strong>\u4f7f\u7528predict()\u7684\u6ce8\u610f\u4e8b\u9879<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u65b0\u6570\u636e\u6846\u4e2d\u7684\u5217\u540d\u79f0\u5fc5\u987b\u4e0e\u7528\u4e8e\u521b\u5efa\u6a21\u578b\u7684\u6570\u636e\u6846\u4e2d\u7684\u5217\u540d\u79f0\u5b8c\u5168\u5339\u914d\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8bf7\u6ce8\u610f\uff0c\u5728\u524d\u9762\u7684\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u7528\u4e8e\u521b\u5efa\u6a21\u578b\u7684\u6570\u636e\u6846\u5305\u542b\u9884\u6d4b\u53d8\u91cf\u7684\u4ee5\u4e0b\u5217\u540d\u79f0\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\"><strong>\u5206\u949f<\/strong><\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>\u9519\u8bef<\/strong><\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u56e0\u6b64\uff0c\u5f53\u6211\u4eec\u521b\u5efa\u540d\u4e3a<strong>newdata \u7684\u65b0\u6570\u636e\u6846\u65f6\uff0c<\/strong>\u6211\u4eec\u4e5f\u8981\u786e\u4fdd\u4e3a\u5217\u547d\u540d\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\"><strong>\u5206\u949f<\/strong><\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>\u9519\u8bef<\/strong><\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u5982\u679c\u5217\u540d\u79f0\u4e0d\u5339\u914d\uff0c\u60a8\u5c06\u6536\u5230\u4ee5\u4e0b<a href=\"https:\/\/statorials.org\/cn\/\u672a\u627e\u5230-evalpredvars-\u6570\u636e\u73af\u5883\u5bf9\u8c61\u4e2d\u7684\u9519\u8bef\/\" target=\"_blank\" rel=\"noopener\">\u9519\u8bef\u6d88\u606f<\/a>\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\">Error in eval(predvars, data, env)<\/span><\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u4f7f\u7528<strong>Predict()<\/strong>\u51fd\u6570\u65f6\u8bf7\u8bb0\u4f4f\u8fd9\u4e00\u70b9\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>R \u4e2d\u7684lm()\u51fd\u6570\u53ef\u7528\u4e8e\u62df\u5408\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u3002 \u4e00\u65e6\u6211\u4eec\u62df\u5408\u4e86\u6a21\u578b\uff0c\u6211\u4eec\u5c31\u53ef\u4ee5\u4f7f\u7528predict()\u51fd\u6570\u6765\u9884\u6d4b\u65b0 [&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-4267","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\u5982\u4f55\u5728 R \u4e2d\u5c06 Predict() \u51fd\u6570\u4e0e lm() \u4e00\u8d77\u4f7f\u7528 - Statorials<\/title>\n<meta name=\"description\" content=\"\u672c\u6559\u7a0b\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528 R \u4e2d\u7684 Predict() \u51fd\u6570\u4f7f\u7528\u62df\u5408\u56de\u5f52\u6a21\u578b\u6765\u9884\u6d4b\u65b0\u89c2\u6d4b\u503c\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/cn\/r-lm-\u9884\u6d4b\/\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u5982\u4f55\u5728 R \u4e2d\u5c06 Predict() \u51fd\u6570\u4e0e lm() \u4e00\u8d77\u4f7f\u7528 - Statorials\" \/>\n<meta property=\"og:description\" content=\"\u672c\u6559\u7a0b\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528 R \u4e2d\u7684 Predict() \u51fd\u6570\u4f7f\u7528\u62df\u5408\u56de\u5f52\u6a21\u578b\u6765\u9884\u6d4b\u65b0\u89c2\u6d4b\u503c\u3002\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/cn\/r-lm-\u9884\u6d4b\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta 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