{"id":1977,"date":"2023-07-24T05:44:05","date_gmt":"2023-07-24T05:44:05","guid":{"rendered":"https:\/\/statorials.org\/cn\/r%e4%b8%ad%e7%9a%84lm%e5%87%bd%e6%95%b0\/"},"modified":"2023-07-24T05:44:05","modified_gmt":"2023-07-24T05:44:05","slug":"r%e4%b8%ad%e7%9a%84lm%e5%87%bd%e6%95%b0","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/r%e4%b8%ad%e7%9a%84lm%e5%87%bd%e6%95%b0\/","title":{"rendered":"\u5982\u4f55\u4f7f\u7528r\u4e2d\u7684lm()\u51fd\u6570\u6765\u62df\u5408\u7ebf\u6027\u6a21\u578b"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">R \u4e2d\u7684<strong>lm()<\/strong>\u51fd\u6570\u7528\u4e8e\u62df\u5408\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8be5\u51fd\u6570\u4f7f\u7528\u4ee5\u4e0b\u57fa\u672c\u8bed\u6cd5\uff1a<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>lm(\u516c\u5f0f\u3001\u6570\u636e\u3001&#8230;)<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u91d1\u5b50\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>Formula\uff1a<\/strong>\u7ebf\u6027\u6a21\u578b\u516c\u5f0f\uff08\u4f8b\u5982y ~ x1 + x2\uff09<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>data\uff1a<\/strong>\u5305\u542b\u6570\u636e\u7684\u6570\u636e\u5757\u7684\u540d\u79f0<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u5728 R \u4e2d\u4f7f\u7528\u6b64\u51fd\u6570\u6765\u6267\u884c\u4ee5\u4e0b\u64cd\u4f5c\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u62df\u5408\u56de\u5f52\u6a21\u578b<\/span><\/li>\n<li><span style=\"color: #000000;\">\u67e5\u770b\u56de\u5f52\u6a21\u578b\u62df\u5408\u6458\u8981<\/span><\/li>\n<li><span style=\"color: #000000;\">\u67e5\u770b\u6a21\u578b\u8bca\u65ad\u56fe<\/span><\/li>\n<li><span style=\"color: #000000;\">\u7ed8\u5236\u62df\u5408\u56de\u5f52\u6a21\u578b<\/span><\/li>\n<li><span style=\"color: #000000;\">\u4f7f\u7528\u56de\u5f52\u6a21\u578b\u8fdb\u884c\u9884\u6d4b<\/span><\/li>\n<\/ul>\n<h3><span style=\"color: #000000;\"><strong>\u62df\u5408\u56de\u5f52\u6a21\u578b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528<strong>lm()<\/strong>\u51fd\u6570\u5728 R \u4e2d\u62df\u5408\u7ebf\u6027\u56de\u5f52\u6a21\u578b\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#define data<\/span>\ndf = data. <span style=\"color: #3366ff;\">frame<\/span> (x=c(1, 3, 3, 4, 5, 5, 6, 8, 9, 12),\n                y=c(12, 14, 14, 13, 17, 19, 22, 26, 24, 22))\n\n<span style=\"color: #008080;\">#fit linear regression model using 'x' as predictor and 'y' as response variable<\/span>\nmodel &lt;- lm(y ~ x, data=df)\n<\/strong><\/pre>\n<h3><span style=\"color: #000000;\"><strong>\u663e\u793a\u56de\u5f52\u6a21\u578b\u6458\u8981<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u7136\u540e\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528<strong>summary()<\/strong>\u51fd\u6570\u6765\u663e\u793a\u56de\u5f52\u6a21\u578b\u62df\u5408\u7684\u6458\u8981\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#view summary of regression model<\/span>\nsummary(model)\n\nCall:\nlm(formula = y ~ x, data = df)\n\nResiduals:\n    Min 1Q Median 3Q Max \n-4.4793 -0.9772 -0.4772 1.4388 4.6328 \n\nCoefficients:\n            Estimate Std. Error t value Pr(&gt;|t|)    \n(Intercept) 11.1432 1.9104 5.833 0.00039 ***\nx 1.2780 0.2984 4.284 0.00267 ** \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 2.929 on 8 degrees of freedom\nMultiple R-squared: 0.6964, Adjusted R-squared: 0.6584 \nF-statistic: 18.35 on 1 and 8 DF, p-value: 0.002675\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u662f\u5982\u4f55\u89e3\u91ca\u6a21\u578b\u4e2d\u6700\u91cd\u8981\u7684\u503c\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>F \u7edf\u8ba1\u91cf<\/strong>= 18.35\uff0c\u76f8\u5e94\u7684<strong>p \u503c<\/strong>= 0.002675\u3002\u7531\u4e8e\u8be5 p \u503c\u5c0f\u4e8e 0.05\uff0c\u56e0\u6b64\u6574\u4e2a\u6a21\u578b\u5177\u6709\u7edf\u8ba1\u663e\u7740\u6027\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>\u500d\u6570 R \u5e73\u65b9<\/strong>= 0.6964\u3002\u8fd9\u544a\u8bc9\u6211\u4eec\uff0c\u54cd\u5e94\u53d8\u91cf y \u4e2d 69.64% \u7684\u53d8\u5f02\u53ef\u4ee5\u7531\u9884\u6d4b\u53d8\u91cf x \u6765\u89e3\u91ca\u3002<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>x \u7684\u4f30\u8ba1\u7cfb\u6570<\/strong>\uff1a1.2780\u3002\u8fd9\u544a\u8bc9\u6211\u4eec\uff0cx \u6bcf\u589e\u52a0\u4e00\u4e2a\u5355\u4f4d\uff0cy \u5c31\u4f1a\u5e73\u5747\u589e\u52a0 1.2780\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u7136\u540e\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u8f93\u51fa\u7684\u7cfb\u6570\u4f30\u8ba1\u6765\u7f16\u5199\u4f30\u8ba1\u7684\u56de\u5f52\u65b9\u7a0b\uff1a<\/span><\/p>\n<p> <span style=\"color: #000000;\">y = 11.1432 + 1.2780*(x)<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u989d\u5916\u5956\u52b1<\/strong>\uff1a\u60a8\u53ef\u4ee5<a href=\"https:\/\/statorials.org\/cn\/\u89e3\u91ca-r-\u4e2d\u7684\u56de\u5f52\u8f93\u51fa\/\" target=\"_blank\" rel=\"noopener\">\u5728\u6b64\u5904<\/a>\u627e\u5230\u89e3\u91ca R \u4e2d\u56de\u5f52\u8f93\u51fa\u7684\u6bcf\u4e2a\u503c\u7684\u5b8c\u6574\u6307\u5357\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u67e5\u770b\u6a21\u578b\u8bca\u65ad\u56fe<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u7136\u540e\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528<strong>plot()<\/strong>\u51fd\u6570\u7ed8\u5236\u56de\u5f52\u6a21\u578b\u7684\u8bca\u65ad\u56fe\uff1a<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create diagnostic plots\n<span style=\"color: #000000;\">plot(model)<\/span><\/span><\/strong> <\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-18677 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/lm2.png\" alt=\"\" width=\"653\" height=\"649\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p><span style=\"color: #000000;\">\u8fd9\u4e9b\u56fe\u8868\u4f7f\u6211\u4eec\u80fd\u591f\u5206\u6790\u56de\u5f52\u6a21\u578b\u7684<a href=\"https:\/\/statorials.org\/cn\/\u6b8b\u7559\u7269\/\" target=\"_blank\" rel=\"noopener\">\u6b8b\u5dee<\/a>\uff0c\u4ee5\u786e\u5b9a\u8be5\u6a21\u578b\u662f\u5426\u9002\u5408\u7528\u4e8e\u6570\u636e\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8bf7\u53c2\u9605<a href=\"https:\/\/statorials.org\/cn\/r-\u4e2d\u7684\u8bca\u65ad\u56fe\/\" target=\"_blank\" rel=\"noopener\">\u672c\u6559\u7a0b\uff0c<\/a>\u4e86\u89e3\u5982\u4f55\u5728 R \u4e2d\u89e3\u91ca\u6a21\u578b\u8bca\u65ad\u56fe\u7684\u5b8c\u6574\u8bf4\u660e\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u7ed8\u5236\u62df\u5408\u56de\u5f52\u6a21\u578b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528<strong>abline()<\/strong>\u51fd\u6570\u6765\u7ed8\u5236\u62df\u5408\u56de\u5f52\u6a21\u578b\uff1a<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create scatterplot of raw data<\/span>\nplot(df$x, df$y, col=' <span style=\"color: #ff0000;\">red<\/span> ', main=' <span style=\"color: #ff0000;\">Summary of Regression Model<\/span> ', xlab=' <span style=\"color: #ff0000;\">x<\/span> ', ylab=' <span style=\"color: #ff0000;\">y<\/span> ')\n\n<span style=\"color: #008080;\">#add fitted regression line\n<span style=\"color: #000000;\">abline(model)\n<\/span><\/span><\/strong><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-18678\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/lm3.png\" alt=\"\u5728 R \u4e2d\u7ed8\u5236 lm()\" width=\"448\" height=\"442\" srcset=\"\" sizes=\"auto, \"><\/p>\n<h3><strong>\u4f7f\u7528\u56de\u5f52\u6a21\u578b\u8fdb\u884c\u9884\u6d4b<\/strong><\/h3>\n<p><span style=\"color: #000000;\"><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528<strong>Predict()<\/strong>\u51fd\u6570\u6765\u9884\u6d4b\u65b0\u89c2\u5bdf\u7684\u54cd\u5e94\u503c\uff1a<\/span><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#define new observation\n<\/span>new &lt;- data. <span style=\"color: #3366ff;\">frame<\/span> (x=c(5))\n\n<span style=\"color: #008080;\">#use the fitted model to predict the value for the new observation\n<\/span>predict(model, newdata = new)\n\n      1 \n17.5332<\/span>\n<\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u8be5\u6a21\u578b\u9884\u6d4b\u8fd9\u4e2a\u65b0\u89c2\u6d4b\u503c\u7684\u54cd\u5e94\u503c\u4e3a<strong>17.5332<\/strong> \u3002<\/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\">\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\/\u9010\u6b65\u56de\u5f52-r\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u5728 R \u4e2d\u6267\u884c\u9010\u6b65\u56de\u5f52<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>R \u4e2d\u7684lm()\u51fd\u6570\u7528\u4e8e\u62df\u5408\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u3002 \u8be5\u51fd\u6570\u4f7f\u7528\u4ee5\u4e0b\u57fa\u672c\u8bed\u6cd5\uff1a lm(\u516c\u5f0f\u3001\u6570\u636e\u3001&#8230;) \u91d1\u5b50 [&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-1977","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"<!-- This 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