{"id":3903,"date":"2023-07-14T21:02:11","date_gmt":"2023-07-14T21:02:11","guid":{"rendered":"https:\/\/statorials.org\/cn\/%e8%9c%9c%e9%a5%afr\/"},"modified":"2023-07-14T21:02:11","modified_gmt":"2023-07-14T21:02:11","slug":"%e8%9c%9c%e9%a5%afr","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/%e8%9c%9c%e9%a5%afr\/","title":{"rendered":"\u5982\u4f55\u5728 r \u4e2d\u4f7f\u7528 confit() \u51fd\u6570"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u60a8\u53ef\u4ee5\u4f7f\u7528 R \u4e2d\u7684<strong>confint()<\/strong>\u51fd\u6570\u6765\u8ba1\u7b97\u62df\u5408\u56de\u5f52\u6a21\u578b\u4e2d\u4e00\u4e2a\u6216\u591a\u4e2a\u53c2\u6570\u7684\u7f6e\u4fe1\u533a\u95f4\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> <strong><span style=\"color: #000000;\">confint\uff08\u5bf9\u8c61\uff0c\u53c2\u6570\uff0c\u7ea7\u522b=0.95\uff09<\/span><\/strong><\/p>\n<p><span style=\"color: #000000;\">\u91d1\u5b50\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>object<\/strong> : \u62df\u5408\u56de\u5f52\u6a21\u578b\u7684\u540d\u79f0<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>parm<\/strong> \uff1a\u8ba1\u7b97\u7f6e\u4fe1\u533a\u95f4\u7684\u53c2\u6570\uff08\u9ed8\u8ba4\u4e3a\u5168\u90e8\uff09<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>level<\/strong> \uff1a\u8981\u4f7f\u7528\u7684\u7f6e\u4fe1\u5ea6\uff08\u9ed8\u8ba4\u503c\u4e3a 0.95\uff09<\/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\u5982\u4f55\u5728 R \u4e2d\u4f7f\u7528 confit() \u51fd\u6570<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u5047\u8bbe\u6211\u4eec\u5728 R \u4e2d\u6709\u4ee5\u4e0b\u6570\u636e\u6846\uff0c\u663e\u793a\u4e86\u73ed\u7ea7 10 \u540d\u5b66\u751f\u7684\u5b66\u4e60\u5c0f\u65f6\u6570\u3001\u53c2\u52a0\u7684\u6a21\u62df\u8003\u8bd5\u6b21\u6570\u4ee5\u53ca\u671f\u672b\u8003\u8bd5\u6210\u7ee9\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> (score=c(77, 79, 84, 85, 88, 99, 95, 90, 92, 94),\n                 hours=c(1, 1, 2, 3, 2, 4, 4, 2, 3, 3),\n                 prac_exams=c(2, 3, 3, 2, 4, 5, 4, 3, 5, 4))\n\n<span style=\"color: #008080;\">#view data frame\n<\/span>df\n\n   score hours prac_exams\n1 77 1 2\n2 79 1 3\n3 84 2 3\n4 85 3 2\n5 88 2 4\n6 99 4 5\n7 95 4 4\n8 90 2 3\n9 92 3 5\n10 94 3 4\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u73b0\u5728\u5047\u8bbe\u6211\u4eec\u60f3\u8981\u5728 R \u4e2d\u62df\u5408\u4ee5\u4e0b\u591a\u5143\u7ebf\u6027\u56de\u5f52\u6a21\u578b\uff1a<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8003\u8bd5\u6210\u7ee9 = \u03b2 <sub>0<\/sub> + \u03b2 <sub>1<\/sub> \uff08\u5c0f\u65f6\uff09+ \u03b2 <sub>2<\/sub> \uff08\u5b9e\u8df5\u8003\u8bd5\uff09<\/span><\/p>\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\u9002\u5e94\u8fd9\u4e2a\u6a21\u578b\uff1a<\/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(score ~ hours + prac_exams, data=df)\n\n<span style=\"color: #008080;\">#view summary of model\n<\/span>summary(fit)\n\nCall:\nlm(formula = score ~ hours + prac_exams, data = df)\n\nResiduals:\n    Min 1Q Median 3Q Max \n-2.4324 -1.2632 -0.8956 0.4316 5.1412 \n\nCoefficients:\n            Estimate Std. Error t value Pr(&gt;|t|)    \n(Intercept) 68.4029 2.8723 23.815 5.85e-08 ***\nhours 4.1912 0.9961 4.207 0.0040 ** \nprac_exams 2.6912 0.9961 2.702 0.0306 *  \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 2.535 on 7 degrees of freedom\nMultiple R-squared: 0.9005, Adjusted R-squared: 0.8721 \nF-statistic: 31.68 on 2 and 7 DF, p-value: 0.0003107\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u8bf7\u6ce8\u610f\uff0c\u6a21\u578b\u6458\u8981\u663e\u793a\u62df\u5408\u56de\u5f52\u7cfb\u6570\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u622a\u8ddd = 68.4029<\/span><\/li>\n<li><span style=\"color: #000000;\">\u5c0f\u65f6 = 4.1912<\/span><\/li>\n<li> <span style=\"color: #000000;\">prac_exams = 2.6912<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u8981\u83b7\u5f97\u6bcf\u4e2a\u7cfb\u6570\u7684 95% \u7f6e\u4fe1\u533a\u95f4\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528<strong>confint()<\/strong>\u51fd\u6570\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#calculate 95% confidence interval for each coefficient in model<\/span>\nconfined(fit)\n\n                 2.5% 97.5%\n(Intercept) 61.6111102 75.194772\nhours 1.8357237 6.546629\nprac_exams 0.3357237 5.046629\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6bcf\u4e2a\u53c2\u6570\u7684 95% \u7f6e\u4fe1\u533a\u95f4\u5982\u4e0b\u6240\u793a\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u622a\u8ddd 95% CI = [61.61, 75.19]<\/span><\/li>\n<li><span style=\"color: #000000;\">\u5c0f\u65f6 95% CI = [1.84, 6.55]<\/span><\/li>\n<li> <span style=\"color: #000000;\">prac_exams \u7684 95% CI = [0.34, 5.05]<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u8981\u8ba1\u7b97 99% \u7f6e\u4fe1\u533a\u95f4\uff0c\u53ea\u9700\u66f4\u6539<strong>level<\/strong>\u53c2\u6570\u7684\u503c\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#calculate 99% confidence interval for each coefficient in model\n<\/span>confint(fit, level= <span style=\"color: #008000;\">0.99<\/span> )\n\n                 0.5% 99.5%\n(Intercept) 58.3514926 78.454390\nhours 0.7052664 7.677087\nprac_exams -0.7947336 6.177087\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u8981\u4ec5\u8ba1\u7b97\u7279\u5b9a\u53c2\u6570\u7684\u7f6e\u4fe1\u533a\u95f4\uff0c\u53ea\u9700\u4f7f\u7528<strong>parm<\/strong>\u53c2\u6570\u6307\u5b9a\u7cfb\u6570\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#calculate 99% confidence interval for hours\n<\/span>confint(fit, parm=' <span style=\"color: #ff0000;\">hours<\/span> ', level= <span style=\"color: #008000;\">0.99<\/span> )\n\n          0.5% 99.5%\nhours 0.7052664 7.677087\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u8bf7\u6ce8\u610f\uff0c\u4ec5\u663e\u793a\u5c0f\u65f6\u53d8\u91cf\u7684 99% \u7f6e\u4fe1\u533a\u95f4\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\u63d0\u4f9b\u4e86\u6709\u5173 R \u4e2d\u7ebf\u6027\u56de\u5f52\u7684\u66f4\u591a\u4fe1\u606f\uff1a<\/span><\/p>\n<p><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\u56de\u5f52\u8f93\u51fa<\/a><br \/><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 \/>\u5982\u4f55\u5728 R \u4e2d\u6267\u884c\u903b\u8f91\u56de\u5f52<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u60a8\u53ef\u4ee5\u4f7f\u7528 R \u4e2d\u7684confint()\u51fd\u6570\u6765\u8ba1\u7b97\u62df\u5408\u56de\u5f52\u6a21\u578b\u4e2d\u4e00\u4e2a\u6216\u591a\u4e2a\u53c2\u6570\u7684\u7f6e\u4fe1\u533a\u95f4\u3002 \u8be5\u51fd\u6570\u4f7f\u7528\u4ee5\u4e0b\u57fa\u672c\u8bed [&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-3903","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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