{"id":1181,"date":"2023-07-27T09:26:41","date_gmt":"2023-07-27T09:26:41","guid":{"rendered":"https:\/\/statorials.org\/cn\/r-%e4%b8%ad%e7%9a%84%e5%bc%95%e5%af%bc%e7%a8%8b%e5%ba%8f\/"},"modified":"2023-07-27T09:26:41","modified_gmt":"2023-07-27T09:26:41","slug":"r-%e4%b8%ad%e7%9a%84%e5%bc%95%e5%af%bc%e7%a8%8b%e5%ba%8f","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/r-%e4%b8%ad%e7%9a%84%e5%bc%95%e5%af%bc%e7%a8%8b%e5%ba%8f\/","title":{"rendered":"\u5982\u4f55\u5728 r \u4e2d\u5f15\u5bfc\uff08\u5e26\u6709\u793a\u4f8b\uff09"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><strong>Bootstrapping<\/strong>\u662f\u4e00\u79cd\u53ef\u7528\u4e8e\u4f30\u8ba1\u4efb\u4f55<a href=\"https:\/\/statorials.org\/cn\/\u7edf\u8ba1\u4e0e\u53c2\u6570\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u7edf\u8ba1\u91cf<\/a>\u7684\u6807\u51c6\u8bef\u5dee\u5e76\u751f\u6210\u7edf\u8ba1\u91cf\u7684<a href=\"https:\/\/statorials.org\/cn\/\u7f6e\u4fe1\u533a\u95f4-1\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u7f6e\u4fe1\u533a\u95f4<\/a>\u7684\u65b9\u6cd5\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\">bootstrapping\u7684\u57fa\u672c\u6d41\u7a0b\u5982\u4e0b\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u4ece\u7ed9\u5b9a\u7684\u6570\u636e\u96c6\u4e2d\u53d6\u51fa<em>k \u4e2a<\/em>\u5177\u6709\u66ff\u6362\u7684\u91cd\u590d\u6837\u672c\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u5bf9\u4e8e\u6bcf\u4e2a\u6837\u672c\uff0c\u8ba1\u7b97\u611f\u5174\u8da3\u7684\u7edf\u8ba1\u91cf\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u8fd9\u4e3a\u7ed9\u5b9a\u7edf\u8ba1\u91cf\u63d0\u4f9b\u4e86<em>k \u4e2a<\/em>\u4e0d\u540c\u7684\u4f30\u8ba1\u503c\uff0c\u7136\u540e\u60a8\u53ef\u4ee5\u4f7f\u7528\u8fd9\u4e9b\u4f30\u8ba1\u503c\u6765\u8ba1\u7b97\u7edf\u8ba1\u91cf\u7684\u6807\u51c6\u8bef\u5dee\u5e76\u4e3a\u7edf\u8ba1\u91cf\u521b\u5efa\u7f6e\u4fe1\u533a\u95f4\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528<a href=\"https:\/\/cran.r-project.org\/web\/packages\/boot\/boot.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">bootstrap \u5e93<\/a>\u4e2d\u7684\u4ee5\u4e0b\u51fd\u6570\u5728 R \u4e2d\u8fdb\u884c\u5f15\u5bfc\uff1a<\/span><\/p>\n<p> <span style=\"color: #000000;\">1. \u751f\u6210\u5f15\u5bfc\u6837\u672c\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u542f\u52a8\uff08\u6570\u636e\u3001\u7edf\u8ba1\u3001R\u3001&#8230;\uff09<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u91d1\u5b50\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\"><strong>\u6570\u636e\uff1a<\/strong>\u5411\u91cf\u3001\u77e9\u9635\u6216\u6570\u636e\u5757<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>\u7edf\u8ba1\uff1a<\/strong>\u751f\u6210\u8981\u542f\u52a8\u7684\u7edf\u8ba1\u6570\u636e\u7684\u51fd\u6570<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>A\uff1a<\/strong>\u5f15\u5bfc\u91cd\u590d\u6b21\u6570<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">2. \u751f\u6210\u5f15\u5bfc\u7f6e\u4fe1\u533a\u95f4\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>boot.ci\uff08\u542f\u52a8\u5bf9\u8c61\u3001conf\u3001\u7c7b\u578b\uff09<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u91d1\u5b50\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>bootobject\uff1a<\/strong> boot()\u51fd\u6570\u8fd4\u56de\u7684\u5bf9\u8c61<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>conf\uff1a<\/strong>\u8981\u8ba1\u7b97\u7684\u7f6e\u4fe1\u533a\u95f4\u3002\u9ed8\u8ba4\u503c\u4e3a 0.95<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>type\uff1a<\/strong>\u8981\u8ba1\u7b97\u7684\u7f6e\u4fe1\u533a\u95f4\u7c7b\u578b\u3002\u9009\u9879\u5305\u62ec\u201c\u6807\u51c6\u201d\u3001\u201c\u57fa\u672c\u201d\u3001\u201c\u87ba\u67f1\u201d\u3001\u201cperc\u201d\u3001\u201cbca\u201d\u548c\u201c\u5168\u90e8\u201d &#8211; \u9ed8\u8ba4\u4e3a\u201c\u5168\u90e8\u201d<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u5728\u5b9e\u8df5\u4e2d\u4f7f\u7528\u8fd9\u4e9b\u529f\u80fd\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u793a\u4f8b 1\uff1a\u5f15\u5bfc\u5355\u4e2a\u7edf\u8ba1\u6570\u636e<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u4ee3\u7801\u663e\u793a\u5982\u4f55\u8ba1\u7b97\u7b80\u5355\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u7684<a href=\"https:\/\/statorials.org\/cn\/\u826f\u597d\u7684-r-\u5e73\u65b9\u503c\/\" target=\"_blank\" rel=\"noopener noreferrer\">R \u5e73\u65b9<\/a>\u7684\u6807\u51c6\u8bef\u5dee\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>set.seed(0)\n<span style=\"color: #993300;\">library<\/span> (boot)\n\n<span style=\"color: #008080;\">#define function to calculate R-squared\n<\/span>rsq_function &lt;- <span style=\"color: #008000;\">function<\/span> (formula, data, indices) {\n  d &lt;- data[indices,] <span style=\"color: #008080;\">#allows boot to select sample<\/span>\n  fit &lt;- lm(formula, data=d) <span style=\"color: #008080;\">#fit regression model<\/span>\n  <span style=\"color: #008000;\">return<\/span> (summary(fit)$r.square) <span style=\"color: #008080;\">#return R-squared of model<\/span>\n}\n<span style=\"color: #008080;\">#perform bootstrapping with 2000 replications\n<\/span>reps &lt;- boot(data=mtcars, statistic=rsq_function, R=2000, formula=mpg~disp)\n\n<span style=\"color: #008080;\">#view results of boostrapping\n<\/span>reps\n\nORDINARY NONPARAMETRIC BOOTSTRAP\n\n\nCall:\nboot(data = mtcars, statistic = rsq_function, R = 2000, formula = mpg ~ \n    available)\n\n\nBootstrap Statistics:\n     original bias std. error\nt1* 0.7183433 0.002164339 0.06513426<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u4ece\u7ed3\u679c\u6211\u4eec\u53ef\u4ee5\u770b\u51fa\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u8be5\u56de\u5f52\u6a21\u578b\u7684\u4f30\u8ba1 R \u5e73\u65b9\u4e3a<strong>0.7183433<\/strong> \u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u6b64\u4f30\u8ba1\u7684\u6807\u51c6\u8bef\u5dee\u4e3a<strong>0.06513426<\/strong> \u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u8fd8\u53ef\u4ee5\u5feb\u901f\u53ef\u89c6\u5316\u5f15\u5bfc\u6837\u672c\u7684\u5206\u5e03\uff1a<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>plot(reps)\n<\/strong><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-11750 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/demarrage1.png\" alt=\"R \u4e2d\u5f15\u5bfc\u6837\u672c\u7684\u76f4\u65b9\u56fe\" width=\"456\" height=\"437\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u8fd8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u6765\u8ba1\u7b97\u6a21\u578b\u4f30\u8ba1 R \u5e73\u65b9\u7684 95% \u7f6e\u4fe1\u533a\u95f4\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#calculate adjusted bootstrap percentile (BCa) interval\n<\/span>boot.ci(reps, type=\" <span style=\"color: #008000;\">bca<\/span> \")\n\nCALL: \nboot.ci(boot.out = reps, type = \"bca\")\n\nIntervals: \nLevel BCa          \n95% (0.5350, 0.8188)  \nCalculations and Intervals on Original Scale<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u4ece\u7ed3\u679c\u4e2d\u6211\u4eec\u53ef\u4ee5\u770b\u51fa\uff0c\u771f\u5b9e R \u5e73\u65b9\u503c\u7684\u81ea\u4e3e 95% \u7f6e\u4fe1\u533a\u95f4\u4e3a (0.5350, 0.8188)\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u793a\u4f8b2\uff1a\u5f15\u5bfc\u591a\u91cd\u7edf\u8ba1<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u4ee3\u7801\u663e\u793a\u5982\u4f55\u8ba1\u7b97\u591a\u5143\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u4e2d\u6bcf\u4e2a\u7cfb\u6570\u7684\u6807\u51c6\u8bef\u5dee\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>set.seed(0)\n<span style=\"color: #993300;\">library<\/span> (boot)\n\n<span style=\"color: #008080;\">#define function to calculate fitted regression coefficients\n<\/span>coef_function &lt;- <span style=\"color: #008000;\">function<\/span> (formula, data, indices) {\n  d &lt;- data[indices,] <span style=\"color: #008080;\">#allows boot to select sample<\/span>\n  fit &lt;- lm(formula, data=d) <span style=\"color: #008080;\">#fit regression model<\/span>\n  <span style=\"color: #008000;\">return<\/span> (coef(fit)) <span style=\"color: #008080;\">#return coefficient estimates of model<\/span>\n}\n\n<span style=\"color: #008080;\">#perform bootstrapping with 2000 replications\n<\/span>reps &lt;- boot(data=mtcars, statistic=coef_function, R=2000, formula=mpg~disp)\n\n<span style=\"color: #008080;\">#view results of boostrapping\n<\/span>reps\n\nORDINARY NONPARAMETRIC BOOTSTRAP\n\n\nCall:\nboot(data = mtcars, statistic = coef_function, R = 2000, formula = mpg ~ \n    available)\n\n\nBootstrap Statistics:\n       original bias std. error\nt1* 29.59985476 -5.058601e-02 1.49354577\nt2* -0.04121512 6.549384e-05 0.00527082<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u4ece\u7ed3\u679c\u6211\u4eec\u53ef\u4ee5\u770b\u51fa\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u6a21\u578b\u622a\u8ddd\u7684\u4f30\u8ba1\u7cfb\u6570\u4e3a<strong>29.59985476<\/strong> \uff0c\u8be5\u4f30\u8ba1\u7684\u6807\u51c6\u8bef\u5dee\u4e3a<strong>1.49354577<\/strong> \u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u6a21\u578b\u4e2d\u9884\u6d4b\u53d8\u91cf<em>disp<\/em>\u7684\u4f30\u8ba1\u7cfb\u6570\u4e3a<strong>-0.04121512<\/strong> \uff0c\u8be5\u4f30\u8ba1\u503c\u7684\u6807\u51c6\u8bef\u5dee\u4e3a<strong>0.00527082<\/strong> \u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u8fd8\u53ef\u4ee5\u5feb\u901f\u53ef\u89c6\u5316\u5f15\u5bfc\u6837\u672c\u7684\u5206\u5e03\uff1a<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>plot(reps, index=1) <span style=\"color: #008080;\">#intercept of model<\/span>\nplot(reps, index=2) <span style=\"color: #008080;\">#disp predictor variable\n<\/span><\/strong><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-11752 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/demarrage2.png\" alt=\"R \u4e2d\u7684\u5f15\u5bfc\" width=\"669\" height=\"325\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u8fd8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u6765\u8ba1\u7b97\u6bcf\u4e2a\u7cfb\u6570\u7684 95% \u7f6e\u4fe1\u533a\u95f4\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#calculate adjusted bootstrap percentile (BCa) intervals\n<\/span>boot.ci(reps, type=\" <span style=\"color: #008000;\">bca<\/span> \", index=1) <span style=\"color: #008080;\">#intercept of model<\/span>\nboot.ci(reps, type=\" <span style=\"color: #008000;\">bca<\/span> \", index=2) <span style=\"color: #008080;\">#disp predictor variable\n<\/span>\nCALL: \nboot.ci(boot.out = reps, type = \"bca\", index = 1)\n\nIntervals: \nLevel BCa          \n95% (26.78, 32.66)  \nCalculations and Intervals on Original Scale\nBOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS\nBased on 2000 bootstrap replicates\n\nCALL: \nboot.ci(boot.out = reps, type = \"bca\", index = 2)\n\nIntervals: \nLevel BCa          \n95% (-0.0520, -0.0312)  \nCalculations and Intervals on Original Scale<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u4ece\u7ed3\u679c\u4e2d\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u6a21\u578b\u7cfb\u6570\u7684 bootstrap 95% \u7f6e\u4fe1\u533a\u95f4\u5982\u4e0b\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u62e6\u622aIC\uff1a(26.78, 32.66)<\/span><\/li>\n<li><span style=\"color: #000000;\"><em>\u663e\u793a<\/em>CI\uff1a(-.0520\uff0c-.0312)<\/span><\/li>\n<\/ul>\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\/\u7f6e\u4fe1\u533a\u95f4-1\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u7f6e\u4fe1\u533a\u95f4\u7b80\u4ecb<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Bootstrapping\u662f\u4e00\u79cd\u53ef\u7528\u4e8e\u4f30\u8ba1\u4efb\u4f55\u7edf\u8ba1\u91cf\u7684\u6807\u51c6\u8bef\u5dee\u5e76\u751f\u6210\u7edf\u8ba1\u91cf\u7684\u7f6e\u4fe1\u533a\u95f4\u7684\u65b9\u6cd5\u3002 bootstra [&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-1181","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"<!-- This site is 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