{"id":3910,"date":"2023-07-14T20:04:35","date_gmt":"2023-07-14T20:04:35","guid":{"rendered":"https:\/\/statorials.org\/cn\/r-%e4%b8%ad%e7%9a%84%e7%a8%b3%e5%81%a5%e6%a0%87%e5%87%86%e8%af%af\/"},"modified":"2023-07-14T20:04:35","modified_gmt":"2023-07-14T20:04:35","slug":"r-%e4%b8%ad%e7%9a%84%e7%a8%b3%e5%81%a5%e6%a0%87%e5%87%86%e8%af%af","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/r-%e4%b8%ad%e7%9a%84%e7%a8%b3%e5%81%a5%e6%a0%87%e5%87%86%e8%af%af\/","title":{"rendered":"\u5982\u4f55\u8ba1\u7b97 r \u4e2d\u7684\u7a33\u5065\u6807\u51c6\u8bef\u5dee"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/cn\/\u7ebf\u6027\u56de\u5f52\u5047\u8bbe\/\" target=\"_blank\" rel=\"noopener\">\u7ebf\u6027\u56de\u5f52\u7684\u5047\u8bbe<\/a>\u4e4b\u4e00\u662f\u6a21\u578b<a href=\"https:\/\/statorials.org\/cn\/\u6b8b\u7559\u7269\/\" target=\"_blank\" rel=\"noopener\">\u6b8b\u5dee<\/a>\u5728\u9884\u6d4b\u53d8\u91cf\u7684\u6bcf\u4e2a\u6c34\u5e73\u4e0a\u5747\u5300\u5206\u5e03\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5f53\u4e0d\u6ee1\u8db3\u6b64\u5047\u8bbe\u65f6\uff0c\u79f0\u56de\u5f52\u6a21\u578b\u4e2d\u5b58\u5728<a href=\"https:\/\/statorials.org\/cn\/\u5f02\u65b9\u5dee\u56de\u5f52\/\" target=\"_blank\" rel=\"noopener\">\u5f02\u65b9\u5dee\u6027<\/a>\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u53d1\u751f\u8fd9\u79cd\u60c5\u51b5\u65f6\uff0c\u6a21\u578b\u56de\u5f52\u7cfb\u6570\u7684\u6807\u51c6\u8bef\u5dee\u53d8\u5f97\u4e0d\u53ef\u9760\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4e3a\u4e86\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\uff0c\u6211\u4eec\u53ef\u4ee5\u8ba1\u7b97<strong>\u7a33\u5065\u7684\u6807\u51c6\u8bef\u5dee<\/strong>\uff0c\u5b83\u5bf9\u4e8e\u5f02\u65b9\u5dee\u6027\u662f\u201c\u7a33\u5065\u7684\u201d\uff0c\u53ef\u4ee5\u8ba9\u6211\u4eec\u66f4\u597d\u5730\u4e86\u89e3\u56de\u5f52\u7cfb\u6570\u7684\u771f\u5b9e\u6807\u51c6\u8bef\u5dee\u503c\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u793a\u4f8b\u6f14\u793a\u5982\u4f55\u8ba1\u7b97 R \u56de\u5f52\u6a21\u578b\u7684\u7a33\u5065\u6807\u51c6\u8bef\u5dee\u3002<\/span><\/p>\n<h2><strong>\u793a\u4f8b\uff1a\u8ba1\u7b97 R \u4e2d\u7684\u7a33\u5065\u6807\u51c6\u8bef\u5dee<\/strong><\/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\u73ed\u7ea7 20 \u540d\u5b66\u751f\u7684\u5b66\u4e60\u65f6\u95f4\u548c\u8003\u8bd5\u6210\u7ee9\u7684\u4fe1\u606f\uff1a<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create data frame\n<\/span>df &lt;- data. <span style=\"color: #3366ff;\">frame<\/span> (hours=c(1, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4,\n                         4, 5, 5, 5, 6, 6, 7, 7, 8),\n                 score=c(67, 68, 74, 70, 71, 75, 80, 70, 84, 72,\n                         88, 75, 95, 75, 99, 78, 99, 65, 96, 70))\n\n<span style=\"color: #008080;\">#view head of data frame\n<\/span>head(df)\n\n  hours score\n1 1 67\n2 1 68\n3 1 74\n4 1 70\n5 2 71\n6 2 75\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528<a href=\"https:\/\/statorials.org\/cn\/r\u4e2d\u7684lm\u51fd\u6570\/\" target=\"_blank\" rel=\"noopener\">lm()<\/a>\u51fd\u6570\u6765\u62df\u5408 R \u4e2d\u7684\u56de\u5f52\u6a21\u578b\uff0c\u8be5\u6a21\u578b\u4f7f\u7528<strong>\u5c0f\u65f6<\/strong>\u4f5c\u4e3a\u9884\u6d4b\u53d8\u91cf\uff0c<strong>\u5206\u6570<\/strong>\u4f5c\u4e3a\u54cd\u5e94\u53d8\u91cf\uff1a<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#fit regression model\n<\/span>fit &lt;- lm(score ~ hours, data=df)\n\n<span style=\"color: #008080;\">#view summary of model\n<\/span>summary(fit)\n\nCall:\nlm(formula = score ~ hours, data = df)\n\nResiduals:\n    Min 1Q Median 3Q Max \n-19,775 -5,298 -3,521 7,520 18,116 \n\nCoefficients:\n            Estimate Std. Error t value Pr(&gt;|t|)    \n(Intercept) 71.158 4.708 15.11 1.14e-11 ***\nhours 1.945 1.075 1.81 0.087 .  \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 10.48 on 18 degrees of freedom\nMultiple R-squared: 0.154, Adjusted R-squared: 0.107 \nF-statistic: 3.278 on 1 and 18 DF, p-value: 0.08696<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u76ee\u89c6\u68c0\u67e5\u5f02\u65b9\u5dee\u662f\u5426\u662f\u56de\u5f52\u6a21\u578b\u4e2d\u7684\u95ee\u9898\u7684\u6700\u7b80\u5355\u65b9\u6cd5\u662f\u521b\u5efa\u6b8b\u5dee\u56fe\uff1a<\/span> <\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create residual vs. fitted plot\n<\/span>plot(fitted(fit), reside(fit))\n\n<span style=\"color: #008080;\">#add a horizontal line at y=0 \n<\/span>abline(0,0)<\/strong> <\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-31443 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/voler1.jpg\" alt=\"\" width=\"440\" height=\"403\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p> <span style=\"color: #000000;\">x\u8f74\u663e\u793a\u54cd\u5e94\u53d8\u91cf\u7684\u62df\u5408\u503c\uff0cy\u8f74\u663e\u793a\u76f8\u5e94\u7684\u6b8b\u5dee\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4ece\u56fe\u4e2d\u6211\u4eec\u53ef\u4ee5\u770b\u5230\uff0c\u6b8b\u5dee\u7684\u65b9\u5dee\u968f\u7740\u62df\u5408\u503c\u7684\u589e\u5927\u800c\u589e\u5927\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8fd9\u8868\u660e\u5f02\u65b9\u5dee\u6027\u53ef\u80fd\u662f\u56de\u5f52\u6a21\u578b\u4e2d\u7684\u95ee\u9898\uff0c\u5e76\u4e14\u6a21\u578b\u6458\u8981\u7684\u6807\u51c6\u8bef\u5dee\u4e0d\u53ef\u9760\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4e3a\u4e86\u8ba1\u7b97\u7a33\u5065\u7684\u6807\u51c6\u8bef\u5dee\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528<strong>lmtest<\/strong>\u5305\u4e2d\u7684<strong>coeftest()<\/strong>\u51fd\u6570\u548c<strong>\u4e09\u660e\u6cbb<\/strong>\u5305\u4e2d\u7684<strong>vcovHC()<\/strong>\u51fd\u6570\uff0c\u5982\u4e0b\u6240\u793a\uff1a<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008000;\">library<\/span> (lmtest)\n<span style=\"color: #008000;\">library<\/span> (sandwich)\n\n<span style=\"color: #008080;\">#calculate robust standard errors for model coefficients\n<\/span>coeftest(fit, vcov = vcovHC(fit, type = ' <span style=\"color: #ff0000;\">HC0<\/span> '))\n\nt test of coefficients:\n\n            Estimate Std. Error t value Pr(&gt;|t|)    \n(Intercept) 71.1576 3.3072 21.5160 2.719e-14 ***\nhours 1.9454 1.2072 1.6115 0.1245    \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u8bf7\u6ce8\u610f\uff0c<strong>\u5c0f\u65f6<\/strong>\u9884\u6d4b\u53d8\u91cf\u7684\u6807\u51c6\u8bef\u5dee\u4ece\u5148\u524d\u6a21\u578b\u6458\u8981\u4e2d\u7684 1.075 \u589e\u52a0\u5230\u6b64\u6a21\u578b\u6458\u8981\u4e2d\u7684 1.2072\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u7531\u4e8e\u539f\u59cb\u56de\u5f52\u6a21\u578b\u4e2d\u5b58\u5728\u5f02\u65b9\u5dee\u6027\uff0c\u56e0\u6b64\u6b64\u6807\u51c6\u8bef\u5dee\u4f30\u8ba1\u66f4\u53ef\u9760\uff0c\u5e94\u5728\u8ba1\u7b97<strong>\u5c0f\u65f6<\/strong>\u9884\u6d4b\u53d8\u91cf\u7684\u7f6e\u4fe1\u533a\u95f4\u65f6\u4f7f\u7528\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u6ce8\u610f<\/strong>\uff1a\u5728<strong>vcovHC()<\/strong>\u51fd\u6570\u4e2d\u8ba1\u7b97\u7684\u6700\u5e38\u89c1\u4f30\u8ba1\u7c7b\u578b\u662f\u201cHC0\u201d\uff0c\u4f46\u60a8\u53ef\u4ee5\u53c2\u8003<a href=\"https:\/\/www.rdocumentation.org\/packages\/sandwich\/versions\/3.0-2\/topics\/vcovHC\" target=\"_blank\" rel=\"noopener\">\u6587\u6863<\/a>\u6765\u67e5\u627e\u5176\u4ed6\u7c7b\u578b\u7684\u4f30\u8ba1\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\u767d\u8272\u6d4b\u8bd5\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u5728 R \u4e2d\u6267\u884c White \u5f02\u65b9\u5dee\u68c0\u9a8c<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/\u89e3\u91ca-r-\u4e2d\u7684\u56de\u5f52\u8f93\u51fa\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u89e3\u91ca R \u4e2d\u7684\u7ebf\u6027\u56de\u5f52\u8f93\u51fa<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/\u6b8b\u7559\u75d5\u91cf\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u5728 R \u4e2d\u521b\u5efa\u6b8b\u5dee\u56fe<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u7ebf\u6027\u56de\u5f52\u7684\u5047\u8bbe\u4e4b\u4e00\u662f\u6a21\u578b\u6b8b\u5dee\u5728\u9884\u6d4b\u53d8\u91cf\u7684\u6bcf\u4e2a\u6c34\u5e73\u4e0a\u5747\u5300\u5206\u5e03\u3002 \u5f53\u4e0d\u6ee1\u8db3\u6b64\u5047\u8bbe\u65f6\uff0c\u79f0\u56de\u5f52\u6a21\u578b\u4e2d\u5b58\u5728\u5f02\u65b9\u5dee\u6027\u3002 \u53d1 [&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-3910","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - 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