{"id":1258,"date":"2023-07-27T02:53:55","date_gmt":"2023-07-27T02:53:55","guid":{"rendered":"https:\/\/statorials.org\/cn\/r%e4%b8%ad%e7%9a%84%e4%bc%bc%e7%84%b6%e6%af%94%e6%a3%80%e9%aa%8c\/"},"modified":"2023-07-27T02:53:55","modified_gmt":"2023-07-27T02:53:55","slug":"r%e4%b8%ad%e7%9a%84%e4%bc%bc%e7%84%b6%e6%af%94%e6%a3%80%e9%aa%8c","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/r%e4%b8%ad%e7%9a%84%e4%bc%bc%e7%84%b6%e6%af%94%e6%a3%80%e9%aa%8c\/","title":{"rendered":"\u5982\u4f55\u5728 r \u4e2d\u6267\u884c\u4f3c\u7136\u6bd4\u68c0\u9a8c"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><strong>\u4f3c\u7136\u6bd4\u68c0\u9a8c<\/strong>\u6bd4\u8f83\u4e24\u4e2a\u5d4c\u5957\u56de\u5f52\u6a21\u578b\u7684\u62df\u5408\u4f18\u5ea6\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/cn\/\u5d4c\u5957\u6a21\u578b\/\" target=\"_blank\" rel=\"noopener\">\u5d4c\u5957\u6a21\u578b<\/a>\u53ea\u662f\u5728\u6574\u4f53\u56de\u5f52\u6a21\u578b\u4e2d\u5305\u542b\u9884\u6d4b\u53d8\u91cf\u5b50\u96c6\u7684\u6a21\u578b\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4f8b\u5982\uff0c\u5047\u8bbe\u6211\u4eec\u6709\u4ee5\u4e0b\u5177\u6709\u56db\u4e2a\u9884\u6d4b\u53d8\u91cf\u7684\u56de\u5f52\u6a21\u578b\uff1a<\/span><\/p>\n<p> <span style=\"color: #000000;\">Y = \u03b2 <sub>0<\/sub> + \u03b2 <sub>1<\/sub> x <sub>1<\/sub> + \u03b2 <sub>2<\/sub> x <sub>2<\/sub> + \u03b2 <sub>3<\/sub> x <sub>3<\/sub> + \u03b2 <sub>4<\/sub> x <sub>4<\/sub> + \u03b5<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5d4c\u5957\u6a21\u578b\u7684\u4e00\u4e2a\u793a\u4f8b\u662f\u4ee5\u4e0b\u4ec5\u5305\u542b\u4e24\u4e2a\u539f\u59cb\u9884\u6d4b\u53d8\u91cf\u7684\u6a21\u578b\uff1a<\/span><\/p>\n<p> <span style=\"color: #000000;\">Y = \u03b2 <sub>0<\/sub> + \u03b2 <sub>1<\/sub> x <sub>1<\/sub> + \u03b2 <sub>2<\/sub> x <sub>2<\/sub> + \u03b5<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4e3a\u4e86\u786e\u5b9a\u8fd9\u4e24\u4e2a\u6a21\u578b\u662f\u5426\u663e\u7740\u4e0d\u540c\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u539f\u5047\u8bbe\u548c\u5907\u62e9\u5047\u8bbe\u6267\u884c\u4f3c\u7136\u6bd4\u68c0\u9a8c\uff1a<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>H <sub>0<\/sub> \uff1a<\/strong>\u5b8c\u6574\u6a21\u578b\u548c\u5d4c\u5957\u6a21\u578b\u540c\u6837\u9002\u5408\u6570\u636e\u3002\u6240\u4ee5\uff0c\u4f60\u5e94\u8be5<strong>\u4f7f\u7528\u5d4c\u5957\u6a21\u578b<\/strong>\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>H <sub>A<\/sub> \uff1a<\/strong>\u5b8c\u6574\u6a21\u578b\u6bd4\u5d4c\u5957\u6a21\u578b\u66f4\u9002\u5408\u6570\u636e\u3002\u6240\u4ee5\u4f60\u5fc5\u987b<strong>\u4f7f\u7528\u5b8c\u6574\u7684\u6a21\u677f<\/strong>\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5982\u679c\u68c0\u9a8c\u7684 p \u503c\u4f4e\u4e8e\u4e00\u5b9a\u7684\u663e\u7740\u6027\u6c34\u5e73\uff08\u4f8b\u5982 0.05\uff09\uff0c\u90a3\u4e48\u6211\u4eec\u53ef\u4ee5\u62d2\u7edd\u96f6\u5047\u8bbe\u5e76\u5f97\u51fa\u7ed3\u8bba\uff1a\u5b8c\u6574\u6a21\u578b\u63d0\u4f9b\u4e86\u660e\u663e\u66f4\u597d\u7684\u62df\u5408\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u5728 R \u4e2d\u6267\u884c\u4f3c\u7136\u6bd4\u68c0\u9a8c\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u793a\u4f8b\uff1aR \u4e2d\u7684\u4f3c\u7136\u6bd4\u6d4b\u8bd5<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528\u5185\u7f6e<strong>mtcars<\/strong>\u6570\u636e\u96c6\u4e2d\u7684\u6570\u636e\u5728 R \u4e2d\u62df\u5408\u4ee5\u4e0b\u4e24\u4e2a\u56de\u5f52\u6a21\u578b\uff1a<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u5b8c\u6574\u6a21\u578b\uff1a<\/strong> mpg = \u03b2 <sub>0<\/sub> + \u03b2 <sub>1<\/sub>\u53ef\u7528 + \u03b2 <sub>2<\/sub>\u78b3\u6c34\u5316\u5408\u7269 + \u03b2 <sub>3<\/sub> hp + \u03b2 <sub>4<\/sub>\u6c7d\u7f38<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u6a21\u578b\uff1a<\/strong> mpg = \u03b2 <sub>0<\/sub> + \u03b2 <sub>1<\/sub>\u53ef\u7528 + \u03b2 <sub>2<\/sub>\u78b3\u6c34\u5316\u5408\u7269<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u5c06\u4f7f\u7528<strong>lmtest<\/strong>\u5305\u7684<strong>lrtest()<\/strong>\u51fd\u6570\u5bf9\u8fd9\u4e24\u4e2a\u6a21\u578b\u8fdb\u884c\u4f3c\u7136\u6bd4\u68c0\u9a8c\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #993300;\">library<\/span> (lmtest)<\/span>\n\n#fit full model<\/span>\nmodel_full &lt;- lm(mpg ~ disp + carb + hp + cyl, data = mtcars)\n\n<span style=\"color: #008080;\">#fit reduced model\n<\/span>model_reduced &lt;- lm(mpg ~ disp + carb, data = mtcars)\n\n<span style=\"color: #008080;\">#perform likelihood ratio test for differences in models\n<\/span>lrtest(model_full, model_reduced)\n\nLikelihood ratio test\n\nModel 1: mpg ~ disp + carb + hp + cyl\nModel 2: mpg ~ available + carb\n  #Df LogLik Df Chisq Pr(&gt;Chisq)\n1 6 -77.558                     \n2 4 -78.603 -2 2.0902 0.3517<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u4ece\u7ed3\u679c\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u5361\u65b9\u68c0\u9a8c\u7edf\u8ba1\u91cf\u4e3a<strong>2.0902<\/strong> \uff0c\u76f8\u5e94\u7684 p \u503c\u4e3a<strong>0.3517<\/strong> \u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u7531\u4e8e\u8be5 p \u503c\u4e0d\u5c0f\u4e8e 0.05\uff0c\u56e0\u6b64\u6211\u4eec\u5c06\u65e0\u6cd5\u62d2\u7edd\u539f\u5047\u8bbe\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8fd9\u610f\u5473\u7740\u5b8c\u6574\u6a21\u578b\u548c\u5d4c\u5957\u6a21\u578b\u540c\u6837\u9002\u5408\u6570\u636e\u3002\u56e0\u6b64\uff0c\u6211\u4eec\u5fc5\u987b\u4f7f\u7528\u5d4c\u5957\u6a21\u578b\uff0c\u56e0\u4e3a\u5b8c\u6574\u6a21\u578b\u4e2d\u7684\u9644\u52a0\u9884\u6d4b\u53d8\u91cf\u4e0d\u4f1a\u663e\u7740\u6539\u5584\u62df\u5408\u6548\u679c\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u7136\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u6267\u884c\u53e6\u4e00\u4e2a\u4f3c\u7136\u6bd4\u68c0\u9a8c\uff0c\u4ee5\u786e\u5b9a\u5177\u6709\u5355\u4e2a\u9884\u6d4b\u53d8\u91cf\u7684\u6a21\u578b\u662f\u5426\u4e0e\u5177\u6709\u4e24\u4e2a\u9884\u6d4b\u53d8\u91cf\u7684\u6a21\u578b\u663e\u7740\u4e0d\u540c\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #993300;\">library<\/span> (lmtest)<\/span>\n\n#fit full model<\/span>\nmodel_full &lt;- lm(mpg ~ disp + carb, data = mtcars)\n\n<span style=\"color: #008080;\">#fit reduced model\n<\/span>model_reduced &lt;- lm(mpg ~ disp, data = mtcars)\n\n<span style=\"color: #008080;\">#perform likelihood ratio test for differences in models\n<\/span>lrtest(model_full, model_reduced)\n\nLikelihood ratio test\n\nModel 1: mpg ~ available + carb\nModel 2: mpg ~ available\n  #Df LogLik Df Chisq Pr(&gt;Chisq)   \n1 4 -78.603                        \n2 3 -82.105 -1 7.0034 0.008136 **\n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u4ece\u7ed3\u679c\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u4f3c\u7136\u6bd4\u68c0\u9a8c\u7684 p \u503c\u4e3a<strong>0.008136<\/strong> \u3002\u7531\u4e8e\u8fd9\u4e2a\u6570\u5b57\u5c0f\u4e8e 0.05\uff0c\u6211\u4eec\u5c06\u62d2\u7edd\u539f\u5047\u8bbe\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u56e0\u6b64\uff0c\u6211\u4eec\u5f97\u51fa\u7684\u7ed3\u8bba\u662f\uff0c\u4e0e\u5355\u9884\u6d4b\u5668\u6a21\u578b\u76f8\u6bd4\uff0c\u53cc\u9884\u6d4b\u5668\u6a21\u578b\u5728\u62df\u5408\u65b9\u9762\u63d0\u4f9b\u4e86\u663e\u7740\u7684\u6539\u8fdb\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6240\u4ee5\uff0c\u6211\u4eec\u7684\u6700\u7ec8\u6a21\u578b\u662f\uff1a<\/span><\/p>\n<p> <span style=\"color: #000000;\">mpg = \u03b2 <sub>0<\/sub> + \u03b2 <sub>1<\/sub>\u53ef\u7528 + \u03b2 <sub>2<\/sub>\u78b3\u6c34\u5316\u5408\u7269<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h3>\n<p><a href=\"https:\/\/statorials.org\/cn\/r-\u4e2d\u7684\u7b80\u5355\u7ebf\u6027\u56de\u5f52\/\" target=\"_blank\" rel=\"noopener noreferrer\">\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 noreferrer\">\u5982\u4f55\u5728 R \u4e2d\u6267\u884c\u591a\u5143\u7ebf\u6027\u56de\u5f52<\/a><br \/> <a href=\"https:\/\/statorials.org\/cn\/r\u4e2d\u7684\u4ee3\u7801\u542b\u4e49\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u5982\u4f55\u89e3\u91ca R \u4e2d\u7684\u542b\u4e49\u4ee3\u7801<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u4f3c\u7136\u6bd4\u68c0\u9a8c\u6bd4\u8f83\u4e24\u4e2a\u5d4c\u5957\u56de\u5f52\u6a21\u578b\u7684\u62df\u5408\u4f18\u5ea6\u3002 \u5d4c\u5957\u6a21\u578b\u53ea\u662f\u5728\u6574\u4f53\u56de\u5f52\u6a21\u578b\u4e2d\u5305\u542b\u9884\u6d4b\u53d8\u91cf\u5b50\u96c6\u7684\u6a21\u578b\u3002 \u4f8b\u5982\uff0c\u5047\u8bbe\u6211 [&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-1258","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\u6267\u884c\u4f3c\u7136\u6bd4\u68c0\u9a8c - Statorials<\/title>\n<meta name=\"description\" content=\"\u672c\u6559\u7a0b\u901a\u8fc7\u4e00\u4e2a\u793a\u4f8b\u89e3\u91ca\u4e86\u5982\u4f55\u5728 R \u4e2d\u6267\u884c\u4f3c\u7136\u6bd4\u68c0\u9a8c\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\u4e2d\u7684\u4f3c\u7136\u6bd4\u68c0\u9a8c\/\" \/>\n<meta property=\"og:locale\" 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