{"id":2548,"date":"2023-07-21T19:20:33","date_gmt":"2023-07-21T19:20:33","guid":{"rendered":"https:\/\/statorials.org\/cn\/r%e4%b8%ad%e7%9a%84%e4%b8%89%e5%9b%a0%e7%b4%a0%e6%96%b9%e5%b7%ae%e5%88%86%e6%9e%90\/"},"modified":"2023-07-21T19:20:33","modified_gmt":"2023-07-21T19:20:33","slug":"r%e4%b8%ad%e7%9a%84%e4%b8%89%e5%9b%a0%e7%b4%a0%e6%96%b9%e5%b7%ae%e5%88%86%e6%9e%90","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/r%e4%b8%ad%e7%9a%84%e4%b8%89%e5%9b%a0%e7%b4%a0%e6%96%b9%e5%b7%ae%e5%88%86%e6%9e%90\/","title":{"rendered":"\u5982\u4f55\u5728 r \u4e2d\u6267\u884c\u4e09\u5411\u65b9\u5dee\u5206\u6790"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><strong>\u4e09\u5411\u65b9\u5dee\u5206\u6790<\/strong>\u7528\u4e8e\u786e\u5b9a\u5206\u5e03\u5728\u4e09\u4e2a\u56e0\u7d20\u4e2d\u7684\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;\">\u4ee5\u4e0b\u793a\u4f8b\u663e\u793a\u5982\u4f55\u5728 R \u4e2d\u6267\u884c\u4e09\u5411\u65b9\u5dee\u5206\u6790\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u793a\u4f8b\uff1aR \u4e2d\u7684\u4e09\u5411\u65b9\u5dee\u5206\u6790<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u5047\u8bbe\u7814\u7a76\u4eba\u5458\u60f3\u8981\u786e\u5b9a\u4e24\u79cd\u8bad\u7ec3\u8ba1\u5212\u662f\u5426\u4f1a\u5bfc\u81f4\u5927\u5b66\u7bee\u7403\u8fd0\u52a8\u5458\u7684\u8df3\u8dc3\u9ad8\u5ea6\u5e73\u5747\u63d0\u9ad8\u4e0d\u540c\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u7814\u7a76\u4eba\u5458\u6000\u7591\u6027\u522b\u548c\u5206\u533a\uff08\u5206\u533a I \u6216\u5206\u533a II\uff09\u4e5f\u53ef\u80fd\u5f71\u54cd\u8df3\u8dc3\u9ad8\u5ea6\uff0c\u8fd9\u5c31\u662f\u4ed6\u4e5f\u6536\u96c6\u8fd9\u4e9b\u56e0\u7d20\u6570\u636e\u7684\u539f\u56e0\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4ed6\u7684\u76ee\u6807\u662f\u8fdb\u884c\u4e09\u5411\u65b9\u5dee\u5206\u6790\uff0c\u4ee5\u786e\u5b9a\u8bad\u7ec3\u8ba1\u5212\u3001\u6027\u522b\u548c\u5212\u5206\u5982\u4f55\u5f71\u54cd\u8df3\u8dc3\u9ad8\u5ea6\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8bf7\u6309\u7167\u4ee5\u4e0b\u6b65\u9aa4\u5728 R \u4e2d\u6267\u884c\u6b64\u4e09\u5411\u65b9\u5dee\u5206\u6790\uff1a<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u7b2c 1 \u6b65\uff1a\u521b\u5efa\u6570\u636e<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u9996\u5148\uff0c\u8ba9\u6211\u4eec\u521b\u5efa\u4e00\u4e2a\u6570\u636e\u6846\u6765\u4fdd\u5b58\u6570\u636e\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create dataset\n<\/span>df &lt;- data. <span style=\"color: #3366ff;\">frame<\/span> (program=rep(c(1, 2), each= <span style=\"color: #008000;\">20<\/span> ),\n                 gender=rep(c(' <span style=\"color: #ff0000;\">M<\/span> ', ' <span style=\"color: #ff0000;\">F<\/span> '), each= <span style=\"color: #008000;\">10<\/span> , times= <span style=\"color: #008000;\">2<\/span> ),\n                 division=rep(c(1, 2), each= <span style=\"color: #008000;\">5<\/span> , times= <span style=\"color: #008000;\">4<\/span> ),\n                 height=c(7, 7, 8, 8, 7, 6, 6, 5, 6, 5,\n                          5, 5, 4, 5, 4, 3, 3, 4, 3, 3,\n                          6, 6, 5, 4, 5, 4, 5, 4, 4, 3,\n                          2, 2, 1, 4, 4, 2, 1, 1, 2, 1)) \n\n<span style=\"color: #008080;\">#view first six rows of dataset\n<\/span>head(df)\n\n  program gender division height\n1 1 M 1 7\n2 1 M 1 7\n3 1 M 1 8\n4 1 M 1 8\n5 1 M 1 7\n6 1 M 2 6<\/strong><\/pre>\n<p><span style=\"color: #000000;\"><strong>\u7b2c 2 \u6b65\uff1a\u67e5\u770b\u63cf\u8ff0\u6027\u7edf\u8ba1\u6570\u636e<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u5728\u8fdb\u884c\u4e09\u5411\u65b9\u5dee\u5206\u6790\u4e4b\u524d\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528<a href=\"https:\/\/dplyr.tidyverse.org\/\" target=\"_blank\" rel=\"noopener\">dplyr<\/a>\u5feb\u901f\u603b\u7ed3\u6309\u8bad\u7ec3\u8ba1\u5212\u3001\u6027\u522b\u548c\u7ec4\u522b\u5206\u7ec4\u7684\u5e73\u5747\u8df3\u8dc3\u9ad8\u5ea6\u589e\u52a0\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">library<\/span> (dplyr)\n\n<span style=\"color: #008080;\">#calculate mean jumping height increase grouped by program, gender, and division\n<\/span>df %&gt;%\n  group_by(program, gender, division) %&gt;%\n  summarize(mean_height = mean(height))\n\n# A tibble: 8 x 4\n# Groups: program, gender [4]\n  program gender division mean_height\n                 \n1 1 F 1 4.6\n2 1 F 2 3.2\n3 1 M 1 7.4\n4 1 M 2 5.6\n5 2 F 1 2.6\n6 2 F 2 1.4\n7 2 M 1 5.2\n8 2 M 2 4  \n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u662f\u5982\u4f55\u89e3\u91ca\u7ed3\u679c\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u4f7f\u7528\u8bad\u7ec3\u8ba1\u5212 1 \u7684\u4e00\u7ea7\u5973\u5b50\u8fd0\u52a8\u5458\u7684\u8df3\u8dc3\u9ad8\u5ea6\u5e73\u5747\u589e\u52a0\u4e86<strong>4.6 \u82f1\u5bf8<\/strong>\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u4f7f\u7528\u8bad\u7ec3\u8ba1\u5212 1 \u7684 II \u7ea7\u5973\u5b50\u8df3\u8dc3\u9ad8\u5ea6\u5e73\u5747\u589e\u52a0<strong>3.2 \u82f1\u5bf8<\/strong>\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u4f7f\u7528\u8bad\u7ec3\u8ba1\u5212 1 \u7684\u4e00\u7ea7\u7537\u5b50\u8df3\u8dc3\u9ad8\u5ea6\u5e73\u5747\u589e\u52a0<strong>7.4 \u82f1\u5bf8<\/strong>\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u7b49\u7b49\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u6b65\u9aa4 3\uff1a\u6267\u884c\u4e09\u56e0\u7d20\u65b9\u5dee\u5206\u6790<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528<strong>aov()<\/strong>\u51fd\u6570\u6765\u6267\u884c\u4e09\u5411\u65b9\u5dee\u5206\u6790\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#perform three-way ANOVA<\/span>\nmodel &lt;- aov(height ~ program * gender * division, data=df)\n\n<span style=\"color: #008080;\">#view summary of three-way ANOVA\n<\/span>summary(model)\n\n                        Df Sum Sq Mean Sq F value Pr(&gt;F)    \nprogram 1 36.1 36.10 65.636 2.98e-09 ***\ngender 1 67.6 67.60 122.909 1.71e-12 ***\ndivision 1 19.6 19.60 35.636 1.19e-06 ***\nprogram:gender 1 0.0 0.00 0.000 1.000    \nprogram:division 1 0.4 0.40 0.727 0.400    \ngender:division 1 0.1 0.10 0.182 0.673    \nprogram:gender:division 1 0.1 0.10 0.182 0.673    \nResiduals 32 17.6 0.55                     \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><strong>Pr(&gt;F)<\/strong>\u5217\u663e\u793a\u6bcf\u4e2a\u5355\u72ec\u56e0\u5b50\u7684 p \u503c\u4ee5\u53ca\u56e0\u5b50\u4e4b\u95f4\u7684\u4ea4\u4e92\u4f5c\u7528\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4ece\u7ed3\u679c\u4e2d\u6211\u4eec\u53ef\u4ee5\u770b\u51fa\uff0c\u4e09\u4e2a\u56e0\u7d20\u4e4b\u95f4\u7684\u4ea4\u4e92\u4f5c\u7528\u5747\u4e0d\u5177\u6709\u7edf\u8ba1\u663e\u7740\u6027\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u8fd8\u53ef\u4ee5\u770b\u5230\uff0c\u9879\u76ee\u3001\u6027\u522b\u548c\u90e8\u95e8\u8fd9\u4e09\u4e2a\u56e0\u7d20\u5747\u5177\u6709\u7edf\u8ba1\u663e\u7740\u6027\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u73b0\u5728\uff0c\u6211\u4eec\u53ef\u4ee5\u518d\u6b21\u4f7f\u7528 dplyr \u5206\u522b\u67e5\u627e\u9879\u76ee\u3001\u6027\u522b\u548c\u7ec4\u522b\u7684\u5e73\u5747\u8df3\u8dc3\u9ad8\u5ea6\u589e\u52a0\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> (dplyr)\n<\/span>\n#find mean jumping increase by program<\/span>\ndf %&gt;%\n  group_by(program) %&gt;%\n  summarize(mean_height = mean(height))\n\n# A tibble: 2 x 2\n  program mean_height\n           \n1 1 5.2\n2 2 3.3\n\n<span style=\"color: #008080;\">#find mean jumping increase by gender\n<\/span>df %&gt;%\n  group_by(gender) %&gt;%\n  summarize(mean_height = mean(height))\n\n# A tibble: 2 x 2\n  gender mean_height\n          \n1 F 2.95\n2M 5.55\n\n<span style=\"color: #008080;\">#find mean jumping increase by division\n<\/span>df %&gt;%\ngroup_by(division) %&gt;%\nsummarize(mean_height = mean(height))\n\n# A tibble: 2 x 2\n  division mean_height\n            \n1 1 4.95\n2 2 3.55<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u4ece\u7ed3\u679c\u4e2d\u6211\u4eec\u53ef\u4ee5\u89c2\u5bdf\u5230\u4ee5\u4e0b\u51e0\u70b9\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u4f7f\u7528\u8bad\u7ec3\u8ba1\u5212 1 \u7684\u4eba\u7684\u8df3\u8dc3\u9ad8\u5ea6\u7684\u5e73\u5747\u589e\u5e45\uff08 <strong>5.2 \u82f1\u5bf8<\/strong>\uff09\u5927\u4e8e\u4f7f\u7528\u8bad\u7ec3\u8ba1\u5212 2 \u7684\u4eba\u7684\u5e73\u5747\u589e\u5e45<strong>\uff083.3 \u82f1\u5bf8<\/strong>\uff09\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u7537\u6027\u8df3\u8dc3\u9ad8\u5ea6\u7684\u5e73\u5747\u589e\u5e45\uff08 <strong>5.55 \u82f1\u5bf8<\/strong>\uff09\u5927\u4e8e\u5973\u6027\u7684\u5e73\u5747\u589e\u5e45<strong>\uff082.95 \u82f1\u5bf8<\/strong>\uff09\u3002<\/span><\/li>\n<li> <span style=\"color: #000000;\">1 \u7ea7\u7403\u5458\u7684\u8df3\u8dc3\u9ad8\u5ea6\u5e73\u5747\u589e\u5e45\uff08 <strong>4.95 \u82f1\u5bf8<\/strong>\uff09\u5927\u4e8e 2 \u7ea7\u7403\u5458\u7684\u5e73\u5747\u8df3\u8dc3\u9ad8\u5ea6\u589e\u5e45<strong>\uff083.55 \u82f1\u5bf8<\/strong>\uff09\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u603b\u4e4b\uff0c\u6211\u4eec\u53ef\u4ee5\u8bf4\uff0c\u8bad\u7ec3\u8ba1\u5212\u3001\u6027\u522b\u548c\u7ea7\u522b\u90fd\u662f\u63d0\u9ad8\u8fd0\u52a8\u5458\u5f39\u8df3\u9ad8\u5ea6\u7684\u91cd\u8981\u6307\u6807\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u8fd8\u53ef\u4ee5\u8bf4\uff0c\u8fd9\u4e09\u4e2a\u56e0\u7d20\u4e4b\u95f4\u4e0d\u5b58\u5728\u663e\u7740\u7684\u4ea4\u4e92\u4f5c\u7528\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\u62df\u5408\u5176\u4ed6\u65b9\u5dee\u5206\u6790\u6a21\u578b\uff1a<\/span><\/p>\n<p><a href=\"https:\/\/statorials.org\/cn\/\u5355\u56e0\u7d20\u65b9\u5dee\u5206\u6790\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u5728 R \u4e2d\u6267\u884c\u5355\u5411\u65b9\u5dee\u5206\u6790<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/\u4e24\u56e0\u7d20\u65b9\u5dee\u5206\u6790\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u5728 R \u4e2d\u6267\u884c\u53cc\u5411\u65b9\u5dee\u5206\u6790<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u4e09\u5411\u65b9\u5dee\u5206\u6790\u7528\u4e8e\u786e\u5b9a\u5206\u5e03\u5728\u4e09\u4e2a\u56e0\u7d20\u4e2d\u7684\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 \u4ee5\u4e0b\u793a\u4f8b\u663e\u793a\u5982\u4f55\u5728  [&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-2548","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\u4e09\u5411\u65b9\u5dee\u5206\u6790 - Statorials<\/title>\n<meta name=\"description\" content=\"\u672c\u6559\u7a0b\u4ecb\u7ecd\u4e86\u5982\u4f55\u5728 R \u4e2d\u6267\u884c\u4e09\u5411\u65b9\u5dee\u5206\u6790\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\" \/>\n<link rel=\"canonical\" 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