{"id":2494,"date":"2023-07-22T01:04:49","date_gmt":"2023-07-22T01:04:49","guid":{"rendered":"https:\/\/statorials.org\/cn\/r%e4%b8%ad%e7%9a%84%e6%88%90%e5%af%b9%e6%af%94%e8%be%83\/"},"modified":"2023-07-22T01:04:49","modified_gmt":"2023-07-22T01:04:49","slug":"r%e4%b8%ad%e7%9a%84%e6%88%90%e5%af%b9%e6%af%94%e8%be%83","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/r%e4%b8%ad%e7%9a%84%e6%88%90%e5%af%b9%e6%af%94%e8%be%83\/","title":{"rendered":"\u5982\u4f55\u5728 r \u4e2d\u6267\u884c\u4e8b\u540e\u6210\u5bf9\u6bd4\u8f83"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/cn\/\u5355\u5411\u65b9\u5dee\u5206\u6790\/\" target=\"_blank\" rel=\"noopener\">\u5355\u5411\u65b9\u5dee\u5206\u6790<\/a>\u7528\u4e8e\u786e\u5b9a\u4e09\u4e2a\u6216\u66f4\u591a\u72ec\u7acb\u7ec4\u7684\u5e73\u5747\u503c\u4e4b\u95f4\u662f\u5426\u5b58\u5728\u7edf\u8ba1\u663e\u7740\u5dee\u5f02\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5355\u5411\u65b9\u5dee\u5206\u6790\u4f7f\u7528\u4ee5\u4e0b\u539f\u5047\u8bbe\u548c\u5907\u62e9\u5047\u8bbe\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>H <sub>0<\/sub><\/strong> \uff1a\u6240\u6709\u7ec4\u5e73\u5747\u503c\u76f8\u7b49\u3002<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>H <sub>A<\/sub><\/strong> \uff1a\u5e76\u975e\u6240\u6709\u7ec4\u7684\u5e73\u5747\u503c\u90fd\u76f8\u540c\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u5982\u679c\u65b9\u5dee\u5206\u6790\u7684\u603b\u4f53<a href=\"https:\/\/statorials.org\/cn\/\u65b9\u5dee\u5206\u6790-f-\u503c-p-\u503c\/\" target=\"_blank\" rel=\"noopener\">p \u503c<\/a>\u4f4e\u4e8e\u4e00\u5b9a\u7684\u663e\u7740\u6027\u6c34\u5e73\uff08\u4f8b\u5982 \u03b1 = 0.05\uff09\uff0c\u5219\u6211\u4eec\u62d2\u7edd\u96f6\u5047\u8bbe\u5e76\u5f97\u51fa\u7ed3\u8bba\uff1a\u6240\u6709\u7ec4\u5747\u503c\u4e0d\u76f8\u7b49\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4e3a\u4e86\u627e\u51fa\u54ea\u7ec4\u5747\u503c\u4e0d\u540c\uff0c\u6211\u4eec\u53ef\u4ee5\u8fdb\u884c<strong>\u4e8b\u540e\u6210\u5bf9\u6bd4\u8f83<\/strong>\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u5728 R \u4e2d\u6267\u884c\u4ee5\u4e0b\u4e8b\u540e\u6210\u5bf9\u6bd4\u8f83\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u56fe\u57fa\u6cd5<\/span><\/li>\n<li><span style=\"color: #000000;\">\u8c22\u592b\u65b9\u6cd5<\/span><\/li>\n<li><span style=\"color: #000000;\">\u90a6\u8d39\u7f57\u5c3c\u65b9\u6cd5<\/span><\/li>\n<li><span style=\"color: #000000;\">\u970d\u5c14\u59c6\u6cd5<\/span><\/li>\n<\/ul>\n<h3><span style=\"color: #000000;\"><strong>\u793a\u4f8b\uff1aR \u4e2d\u7684\u5355\u5411\u65b9\u5dee\u5206\u6790<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u5047\u8bbe\u8001\u5e08\u60f3\u77e5\u9053\u4e09\u79cd\u4e0d\u540c\u7684\u5b66\u4e60\u6280\u5de7\u662f\u5426\u4f1a\u5bfc\u81f4\u5b66\u751f\u7684\u8003\u8bd5\u6210\u7ee9\u4e0d\u540c\u3002\u4e3a\u4e86\u6d4b\u8bd5\u8fd9\u4e00\u70b9\uff0c\u5979<a href=\"https:\/\/statorials.org\/cn\/\u968f\u673a\u9009\u62e9\u4e0e\u968f\u673a\u5206\u914d\/\" target=\"_blank\" rel=\"noopener\">\u968f\u673a\u5206\u914d<\/a>10 \u540d\u5b66\u751f\u4f7f\u7528\u6bcf\u79cd\u5b66\u4e60\u6280\u5de7\u5e76\u8bb0\u5f55\u4ed6\u4eec\u7684\u8003\u8bd5\u7ed3\u679c\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u5728 R \u4e2d\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u6267\u884c\u5355\u5411\u65b9\u5dee\u5206\u6790\u6765\u6d4b\u8bd5\u4e09\u7ec4\u4e4b\u95f4\u5e73\u5747\u8003\u8bd5\u6210\u7ee9\u7684\u5dee\u5f02\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create data frame<\/span>\ndf &lt;- data.frame(technique = rep(c(\" <span style=\"color: #ff0000;\">tech1<\/span> \", \" <span style=\"color: #ff0000;\">tech2<\/span> \", \" <span style=\"color: #ff0000;\">tech3<\/span> \"), each= <span style=\"color: #008000;\">10<\/span> ),\n                 score = c(76, 77, 77, 81, 82, 82, 83, 84, 85, 89,\n                           81, 82, 83, 83, 83, 84, 87, 90, 92, 93,\n                           77, 78, 79, 88, 89, 90, 91, 95, 95, 98))\n\n<span style=\"color: #008080;\">#perform one-way ANOVA\n<\/span>model &lt;- aov(score ~ technique, data = df)\n\n<span style=\"color: #008080;\">#view output of ANOVA\n<\/span>summary(model)\n\n            Df Sum Sq Mean Sq F value Pr(&gt;F)  \ntechnical 2 211.5 105.73 3.415 0.0476 *\nResiduals 27 836.0 30.96                 \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u65b9\u5dee\u5206\u6790\u7684\u603b\u4f53 p \u503c (0.0476) \u5c0f\u4e8e \u03b1 = 0.05\uff0c\u56e0\u6b64\u6211\u4eec\u5c06\u62d2\u7edd\u6bcf\u79cd\u7814\u7a76\u6280\u672f\u7684\u5e73\u5747\u8003\u8bd5\u6210\u7ee9\u76f8\u540c\u7684\u539f\u5047\u8bbe\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u8fdb\u884c\u4e8b\u540e\u914d\u5bf9\u6bd4\u8f83\uff0c\u4ee5\u786e\u5b9a\u54ea\u4e9b\u7ec4\u5177\u6709\u4e0d\u540c\u7684\u5747\u503c\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u56fe\u57fa\u6cd5<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u5f53\u6bcf\u7ec4\u6837\u672c\u91cf\u76f8\u7b49\u65f6\uff0c\u6700\u597d\u4f7f\u7528 Tukey \u4e8b\u540e\u65b9\u6cd5\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u5185\u7f6e\u7684<strong>TukeyHSD()<\/strong>\u51fd\u6570\u6765\u6267\u884c R \u4e2d\u7684 Tukey post-hoc \u65b9\u6cd5\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#perform the Tukey post-hoc method<\/span>\nTukeyHSD(model, conf. <span style=\"color: #3366ff;\">level<\/span> = <span style=\"color: #008000;\">.95<\/span> )\n\n  Tukey multiple comparisons of means\n    95% family-wise confidence level\n\nFit: aov(formula = score ~ technique, data = df)\n\n$technical\n            diff lwr upr p adj\ntech2-tech1 4.2 -1.9700112 10.370011 0.2281369\ntech3-tech1 6.4 0.2299888 12.570011 0.0409017\ntech3-tech2 2.2 -3.9700112 8.370011 0.6547756<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u4ece\u7ed3\u679c\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u552f\u4e00\u5c0f\u4e8e 0.05 \u7684 p \u503c\uff08\u201c <strong>p adj<\/strong> \u201d\uff09\u662f\u6280\u672f\u4e0e\u6280\u672f 3 \u4e4b\u95f4\u7684\u5dee\u5f02\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u56e0\u6b64\uff0c\u6211\u4eec\u53ef\u4ee5\u5f97\u51fa\u7ed3\u8bba\uff0c\u4f7f\u7528\u6280\u672f 1 \u548c\u6280\u672f 3 \u7684\u5b66\u751f\u4e4b\u95f4\u7684\u5e73\u5747\u8003\u8bd5\u6210\u7ee9\u4ec5\u5b58\u5728\u7edf\u8ba1\u5b66\u4e0a\u7684\u663e\u7740\u5dee\u5f02\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u8c22\u592b\u65b9\u6cd5<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">Scheffe \u65b9\u6cd5\u662f\u6700\u4fdd\u5b88\u7684\u4e8b\u540e\u6210\u5bf9\u6bd4\u8f83\u65b9\u6cd5\uff0c\u5728\u6bd4\u8f83\u7ec4\u5747\u503c\u65f6\u4f1a\u4ea7\u751f\u6700\u5bbd\u7684\u7f6e\u4fe1\u533a\u95f4\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528<a href=\"https:\/\/cran.r-project.org\/web\/packages\/DescTools\/DescTools.pdf\" target=\"_blank\" rel=\"noopener\">DescTools<\/a>\u5305\u4e2d\u7684<strong>ScheffeTest()<\/strong>\u51fd\u6570\u5728 R \u4e2d\u8fd0\u884c Scheffe \u4e8b\u540e\u65b9\u6cd5\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008000;\">library<\/span> (DescTools)<\/span>\n\n#perform the Scheffe post-hoc method<\/span>\nScheffeTest(model)\n\n  Posthoc multiple comparisons of means: Scheffe Test \n    95% family-wise confidence level\n\n$technical\n            diff lwr.ci upr.ci pval    \ntech2-tech1 4.2 -2.24527202 10.645272 0.2582    \ntech3-tech1 6.4 -0.04527202 12.845272 0.0519 .  \ntech3-tech2 2.2 -4.24527202 8.645272 0.6803    \n\n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1'''156<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u4ece\u7ed3\u679c\u4e2d\u6211\u4eec\u53ef\u4ee5\u770b\u5230\uff0c\u6ca1\u6709p\u503c\u4f4e\u4e8e0.05\uff0c\u56e0\u6b64\u6211\u4eec\u53ef\u4ee5\u5f97\u51fa\u7ed3\u8bba\uff0c\u5404\u7ec4\u4e4b\u95f4\u7684\u5e73\u5747\u8003\u8bd5\u6210\u7ee9\u6ca1\u6709\u7edf\u8ba1\u5b66\u4e0a\u7684\u663e\u7740\u5dee\u5f02\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u90a6\u8d39\u7f57\u5c3c\u65b9\u6cd5<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u5f53\u60a8\u60f3\u8981\u6267\u884c\u4e00\u7ec4\u8ba1\u5212\u7684\u6210\u5bf9\u6bd4\u8f83\u65f6\uff0c\u6700\u597d\u4f7f\u7528 Bonferroni \u65b9\u6cd5\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u5728 R \u4e2d\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\u6765\u6267\u884c Bonferroni \u4e8b\u540e\u65b9\u6cd5\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#perform the Bonferroni post-hoc method\n<span style=\"color: #000000;\">pairwise. <span style=\"color: #3366ff;\">t<\/span> . <span style=\"color: #3366ff;\">test<\/span> (df$score, df$technique, p. <span style=\"color: #3366ff;\">adj<\/span> = ' <span style=\"color: #ff0000;\">bonferroni<\/span> ')<\/span>\n<\/span>\n\tPairwise comparisons using t tests with pooled SD \n\ndata: df$score and df$technique \n\n      tech1 tech2\ntech2 0.309 -    \ntech3 0.048 1.000\n\nP value adjustment method: bonferroni<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u4ece\u7ed3\u679c\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u552f\u4e00\u5c0f\u4e8e 0.05 \u7684 p \u503c\u662f\u6280\u672f\u4e0e\u6280\u672f 3 \u4e4b\u95f4\u7684\u5dee\u5f02\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u56e0\u6b64\uff0c\u6211\u4eec\u53ef\u4ee5\u5f97\u51fa\u7ed3\u8bba\uff0c\u4f7f\u7528\u6280\u672f 1 \u548c\u6280\u672f 3 \u7684\u5b66\u751f\u4e4b\u95f4\u7684\u5e73\u5747\u8003\u8bd5\u6210\u7ee9\u4ec5\u5b58\u5728\u7edf\u8ba1\u5b66\u4e0a\u7684\u663e\u7740\u5dee\u5f02\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u970d\u5c14\u59c6\u6cd5<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u5f53\u60a8\u60f3\u8981\u9884\u5148\u6267\u884c\u4e00\u7ec4\u8ba1\u5212\u7684\u6210\u5bf9\u6bd4\u8f83\u65f6\uff0c\u4e5f\u4f1a\u4f7f\u7528 Holm \u65b9\u6cd5\uff0c\u5e76\u4e14\u5b83\u5f80\u5f80\u6bd4 Bonferroni \u65b9\u6cd5\u5177\u6709\u66f4\u9ad8\u7684\u529f\u6548\uff0c\u56e0\u6b64\u901a\u5e38\u9996\u9009\u5b83\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u5728 R \u4e2d\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\u6765\u8fd0\u884c Holm \u4e8b\u540e\u65b9\u6cd5\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#perform the Holm post-hoc method\n<span style=\"color: #000000;\">pairwise. <span style=\"color: #3366ff;\">t<\/span> . <span style=\"color: #3366ff;\">test<\/span> (df$score, df$technique, p. <span style=\"color: #3366ff;\">adj<\/span> = ' <span style=\"color: #ff0000;\">holm<\/span> ')<\/span>\n<\/span>\n\tPairwise comparisons using t tests with pooled SD \n\ndata: df$score and df$technique \n\n      tech1 tech2\ntech2 0.206 -    \ntech3 0.048 0.384\n\nP value adjustment method: holm<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u4ece\u7ed3\u679c\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u552f\u4e00\u5c0f\u4e8e 0.05 \u7684 p \u503c\u662f\u6280\u672f\u4e0e\u6280\u672f 3 \u4e4b\u95f4\u7684\u5dee\u5f02\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u56e0\u6b64\uff0c\u6211\u4eec\u518d\u6b21\u5f97\u51fa\u7ed3\u8bba\uff0c\u4f7f\u7528\u6280\u672f 1 \u548c\u6280\u672f 3 \u7684\u5b66\u751f\u4e4b\u95f4\u7684\u5e73\u5747\u8003\u8bd5\u6210\u7ee9\u4ec5\u5b58\u5728\u7edf\u8ba1\u5b66\u4e0a\u7684\u663e\u7740\u5dee\u5f02\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u6559\u7a0b\u63d0\u4f9b\u6709\u5173\u65b9\u5dee\u5206\u6790\u548c\u4e8b\u540e\u6d4b\u8bd5\u7684\u5176\u4ed6\u4fe1\u606f\uff1a<\/span><\/p>\n<p><a href=\"https:\/\/statorials.org\/cn\/\u65b9\u5dee\u5206\u6790-f-\u503c-p-\u503c\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u89e3\u91ca\u65b9\u5dee\u5206\u6790\u4e2d\u7684F\u503c\u548cP\u503c<\/a><br \/> <a href=\"https:\/\/statorials.org\/cn\/\u5982\u4f55\u62a5\u544a\u65b9\u5dee\u5206\u6790\u7ed3\u679c\/\" target=\"_blank\" rel=\"noopener\">\u5b8c\u6574\u6307\u5357\uff1a\u5982\u4f55\u62a5\u544a\u65b9\u5dee\u5206\u6790\u7ed3\u679c<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/\u56fe\u57fa-vs-\u90a6\u8d39\u7f57\u5c3c-vs-\u8c22\u592b\/\" target=\"_blank\" rel=\"noopener\">\u56fe\u57fa vs.\u90a6\u8d39\u7f57\u5c3c VS \u90a6\u8d39\u7f57\u5c3cScheffe\uff1a\u60a8\u5e94\u8be5\u4f7f\u7528\u54ea\u79cd\u6d4b\u8bd5\uff1f<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u5355\u5411\u65b9\u5dee\u5206\u6790\u7528\u4e8e\u786e\u5b9a\u4e09\u4e2a\u6216\u66f4\u591a\u72ec\u7acb\u7ec4\u7684\u5e73\u5747\u503c\u4e4b\u95f4\u662f\u5426\u5b58\u5728\u7edf\u8ba1\u663e\u7740\u5dee\u5f02\u3002 \u5355\u5411\u65b9\u5dee\u5206\u6790\u4f7f\u7528\u4ee5\u4e0b\u539f\u5047\u8bbe\u548c\u5907\u62e9\u5047\u8bbe\uff1a [&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-2494","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\u4e8b\u540e\u6210\u5bf9\u6bd4\u8f83 - Statorials<\/title>\n<meta name=\"description\" content=\"\u672c\u6559\u7a0b\u89e3\u91ca\u4e86\u5982\u4f55\u5728 R \u4e2d\u6267\u884c\u4e8b\u540e\u6210\u5bf9\u6bd4\u8f83\uff0c\u5305\u62ec\u4e00\u4e2a\u5b8c\u6574\u7684\u793a\u4f8b\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" 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