{"id":1242,"date":"2023-07-27T04:19:56","date_gmt":"2023-07-27T04:19:56","guid":{"rendered":"https:\/\/statorials.org\/cn\/r-%e4%b8%ad%e7%9a%84-bonferroni-%e6%a0%a1%e6%ad%a3\/"},"modified":"2023-07-27T04:19:56","modified_gmt":"2023-07-27T04:19:56","slug":"r-%e4%b8%ad%e7%9a%84-bonferroni-%e6%a0%a1%e6%ad%a3","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/r-%e4%b8%ad%e7%9a%84-bonferroni-%e6%a0%a1%e6%ad%a3\/","title":{"rendered":"\u5982\u4f55\u5728 r \u4e2d\u6267\u884c bonferroni \u6821\u6b63"},"content":{"rendered":"

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\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

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\u672c\u6559\u7a0b\u4ecb\u7ecd\u5982\u4f55\u5728 R \u4e2d\u6267\u884c Bonferroni \u6821\u6b63\u3002<\/span><\/p>\n

\u793a\u4f8b\uff1aR \u4e2d\u7684 Bonferroni \u6821\u6b63<\/strong><\/span><\/h3>\n

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\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528 R \u4e2d\u7684\u4ee5\u4e0b\u6b65\u9aa4\u6765\u62df\u5408\u5355\u5411\u65b9\u5dee\u5206\u6790\uff0c\u5e76\u4f7f\u7528 Bonferroni \u6821\u6b63\u6765\u8ba1\u7b97\u6bcf\u7ec4\u8003\u8bd5\u7ed3\u679c\u4e4b\u95f4\u7684\u6210\u5bf9\u5dee\u5f02\u3002<\/span><\/p>\n

\u6b65\u9aa4 1\uff1a\u521b\u5efa\u6570\u636e\u96c6\u3002<\/span><\/strong><\/p>\n

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 #create data frame<\/span>\ndata <- data.frame(technique = rep<\/span> (c(\"tech1\", \"tech2\", \"tech3\"), each<\/span> = 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#view first six rows of data frame<\/span>\nhead(data)\n\n  technical score\n1 tech1 76\n2 tech1 77\n3 tech1 77\n4 tech1 81\n5 tech1 82\n6 tech1 82\n<\/strong><\/pre>\n
\u7b2c 2 \u6b65\uff1a\u67e5\u770b\u6bcf\u7ec4\u7684\u8003\u8bd5\u7ed3\u679c\u3002<\/span><\/strong><\/p>\n
\u4ee5\u4e0b\u4ee3\u7801\u663e\u793a\u5982\u4f55\u751f\u6210\u7bb1\u7ebf\u56fe\u4ee5\u53ef\u89c6\u5316\u6bcf\u7ec4\u8003\u8bd5\u7ed3\u679c\u7684\u5206\u5e03\uff1a<\/span> <\/p>\n
 boxplot(score ~ technique,\n        data = data,\n        main = \"Exam Scores by Studying Technique\",\n        xlab = \"Studying Technique\",\n        ylab = \"Exam Scores\",\n        col = \"steelblue\",\n        border = \"black\")\n<\/strong><\/pre>\n
\"R<\/p>\n
\u6b65\u9aa4 3\uff1a\u6267\u884c\u5355\u5411\u65b9\u5dee\u5206\u6790\u3002<\/span><\/strong><\/p>\n
\u4ee5\u4e0b\u4ee3\u7801\u663e\u793a\u5982\u4f55\u6267\u884c\u5355\u5411\u65b9\u5dee\u5206\u6790\u6765\u6d4b\u8bd5\u6bcf\u7ec4\u5e73\u5747\u8003\u8bd5\u6210\u7ee9\u4e4b\u95f4\u7684\u5dee\u5f02\uff1a<\/span><\/p>\n
 #fit the one-way ANOVA model<\/span>\nmodel <- aov(score ~ technique, data = data)\n\n#view model output\n<\/span>summary(model)\n\n            Df Sum Sq Mean Sq F value Pr(>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
\u7531\u4e8e\u603b\u4f53 p \u503c ( 0.0476<\/strong> ) \u5c0f\u4e8e 0.05\uff0c\u8fd9\u8868\u660e\u6bcf\u4e2a\u7ec4\u7684\u5e73\u5747\u8003\u8bd5\u6210\u7ee9\u4e0d\u540c\u3002<\/span><\/p>\n
\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u4f7f\u7528 p \u503c\u7684 Bonferroni \u6821\u6b63\u8fdb\u884c\u6210\u5bf9 t \u68c0\u9a8c\uff0c\u4ee5\u8ba1\u7b97\u6bcf\u7ec4\u8003\u8bd5\u7ed3\u679c\u4e4b\u95f4\u7684\u6210\u5bf9\u5dee\u5f02\u3002<\/span><\/p>\n
\u6b65\u9aa4 4\uff1a\u6267\u884c\u914d\u5bf9 t \u68c0\u9a8c\u3002<\/strong><\/span><\/p>\n
\u8981\u5728 R \u4e2d\u4f7f\u7528 Bonferroni \u6821\u6b63\u6267\u884c\u6210\u5bf9 t \u68c0\u9a8c\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528pairwise.t.test()<\/a>\u51fd\u6570\uff0c\u8be5\u51fd\u6570\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\uff1a<\/span><\/p>\n
 pairwise.t.test(x, g, p.adjust.method=\u201dbonferroni\u201d)<\/strong><\/span><\/p>\n
\u91d1\u5b50\uff1a<\/span><\/p>\n