{"id":1291,"date":"2023-07-27T00:11:30","date_gmt":"2023-07-27T00:11:30","guid":{"rendered":"https:\/\/statorials.org\/cn\/eta-%e7%9a%84-r-%e5%b9%b3%e6%96%b9\/"},"modified":"2023-07-27T00:11:30","modified_gmt":"2023-07-27T00:11:30","slug":"eta-%e7%9a%84-r-%e5%b9%b3%e6%96%b9","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/eta-%e7%9a%84-r-%e5%b9%b3%e6%96%b9\/","title":{"rendered":"\u5982\u4f55\u5728 r \u4e2d\u8ba1\u7b97 eta \u5e73\u65b9"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/cn\/\u57c3\u5854\u5e73\u65b9\/\" target=\"_blank\" rel=\"noopener\">Eta \u5e73\u65b9<\/a>\u662f\u65b9\u5dee\u5206\u6790\u6a21\u578b\u4e2d\u5e38\u7528\u7684<a href=\"https:\/\/statorials.org\/cn\/\u89c4\u6a21\u6548\u5e94\/\" target=\"_blank\" rel=\"noopener\">\u6548\u5e94\u5927\u5c0f<\/a>\u5ea6\u91cf\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5b83\u8861\u91cf\u65b9\u5dee\u5206\u6790\u6a21\u578b\u4e2d\u4e0e\u6bcf\u4e2a\u4e3b\u6548\u5e94\u548c\u4ea4\u4e92\u6548\u5e94\u76f8\u5173\u7684\u65b9\u5dee\u6bd4\u4f8b\uff0c\u8ba1\u7b97<\/span><span style=\"color: #000000;\">\u516c\u5f0f\u5982\u4e0b\uff1a<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>Eta \u5e73\u65b9 = SS<sub>\u6548\u679c<\/sub>\/<sub>\u603b<\/sub>SS<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u91d1\u5b50\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>SS<sub>\u6548\u5e94<\/sub>\uff1a<\/strong>\u53d8\u91cf\u6548\u5e94\u7684\u5e73\u65b9\u548c\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong><sub>\u603b<\/sub>SS\uff1a<\/strong> ANOVA \u6a21\u578b\u4e2d\u7684\u603b\u5e73\u65b9\u548c\u3002<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Eta \u5e73\u65b9\u7684\u503c\u8303\u56f4\u4ece 0 \u5230 1\uff0c\u5176\u4e2d\u503c\u8d8a\u63a5\u8fd1 1 \u8868\u793a\u6a21\u578b\u4e2d\u7ed9\u5b9a\u53d8\u91cf\u53ef\u4ee5\u89e3\u91ca\u7684\u65b9\u5dee\u6bd4\u4f8b\u8d8a\u9ad8\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u7ecf\u9a8c\u6cd5\u5219\u7528\u4e8e\u89e3\u91ca Eta \u5e73\u65b9\u503c\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>.01\uff1a<\/strong>\u6548\u5e94\u91cf\u5c0f<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>.06\uff1a<\/strong>\u5e73\u5747\u6548\u5e94\u5927\u5c0f<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>.14\u6216\u66f4\u5927\uff1a<\/strong>\u6548\u5e94\u91cf\u5927<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u672c\u6559\u7a0b\u63d0\u4f9b\u4e86\u5982\u4f55\u8ba1\u7b97 R \u65b9\u5dee\u5206\u6790\u6a21\u578b\u4e2d\u53d8\u91cf\u7684 Eta \u5e73\u65b9\u7684\u5206\u6b65\u793a\u4f8b\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u7b2c 1 \u6b65\uff1a\u521b\u5efa\u6570\u636e<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u5047\u8bbe\u6211\u4eec\u60f3\u8981\u786e\u5b9a\u8fd0\u52a8\u5f3a\u5ea6\u548c\u6027\u522b\u662f\u5426\u4f1a\u5f71\u54cd\u51cf\u80a5\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4e3a\u4e86\u6d4b\u8bd5\u8fd9\u4e00\u70b9\uff0c\u6211\u4eec\u62db\u52df\u4e86 30 \u540d\u7537\u6027\u548c 30 \u540d\u5973\u6027\u53c2\u52a0\u4e00\u9879\u5b9e\u9a8c\uff0c\u5176\u4e2d\u6211\u4eec\u968f\u673a\u5206\u914d\u6bcf\u4eba 10 \u4eba\u9075\u5faa\u4e00\u4e2a\u6708\u7684\u65e0\u8fd0\u52a8\u3001\u8f7b\u5ea6\u8fd0\u52a8\u6216\u5267\u70c8\u8fd0\u52a8\u8ba1\u5212\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u521b\u5efa\u4e00\u4e2a\u6570\u636e\u6846\u6765\u4fdd\u5b58\u6211\u4eec\u6b63\u5728\u4f7f\u7528\u7684\u6570\u636e\uff1a<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#make this example reproducible\n<\/span>set.seed(10)\n\n<span style=\"color: #008080;\">#create data frame\n<\/span>data &lt;- data.frame(gender= <span style=\"color: #3366ff;\">rep<\/span> (c(\" <span style=\"color: #008000;\">Male<\/span> \", \" <span style=\"color: #008000;\">Female<\/span> \"), <span style=\"color: #3366ff;\">each<\/span> = 30),\n                   exercise= <span style=\"color: #3366ff;\">rep<\/span> (c(\" <span style=\"color: #008000;\">None<\/span> \", \" <span style=\"color: #008000;\">Light<\/span> \", \"Intense\"), <span style=\"color: #3366ff;\">each<\/span> = 10, <span style=\"color: #3366ff;\">times<\/span> =2),\n                   weight_loss=c(runif(10, -3, 3), runif(10, 0, 5), runif(10, 5, 9),\n                                 runif(10, -4, 2), runif(10, 0, 3), runif(10, 3, 8)))\n\n<span style=\"color: #008080;\">#view first six rows of data frame\n<\/span>head(data)\n\n# gender exercise weight_loss\n#1 Male None 0.04486922\n#2 Male None -1.15938896\n#3 Male None -0.43855400\n#4 Male None 1.15861249\n#5 Male None -2.48918419\n#6 Male None -1.64738030\n\n<span style=\"color: #008080;\">#see how many participants are in each group<\/span>\ntable(data$gender, data$exercise)\n\n# Intense Light None\n# Female 10 10 10\n# Male 10 10 10<\/strong><\/pre>\n<h2><span style=\"color: #000000;\"><strong>\u7b2c 2 \u6b65\uff1a\u62df\u5408\u65b9\u5dee\u5206\u6790\u6a21\u578b<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u4ee3\u7801\u663e\u793a\u4e86\u5982\u4f55\u4f7f\u7528\u8fd0\u52a8\u548c\u6027\u522b\u4f5c\u4e3a\u56e0\u7d20\u4ee5\u53ca\u4f53\u91cd\u51cf\u8f7b\u4f5c\u4e3a<a href=\"https:\/\/statorials.org\/cn\/\u53d8\u91cf\u89e3\u91ca\u6027\u53cd\u5e94\/\" target=\"_blank\" rel=\"noopener\">\u54cd\u5e94\u53d8\u91cf<\/a>\u6765\u62df\u5408<a href=\"https:\/\/statorials.org\/cn\/\u53cc\u5411\u65b9\u5dee\u5206\u6790\/\" target=\"_blank\" rel=\"noopener\">\u53cc\u5411\u65b9\u5dee\u5206\u6790<\/a>\uff1a<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#fit the two-way ANOVA model<\/span>\nmodel &lt;- aov(weight_loss ~ gender + exercise, data = data)\n\n<span style=\"color: #008080;\">#view the model output<\/span>\nsummary(model)\n\n            Df Sum Sq Mean Sq F value Pr(&gt;F)    \ngender 1 15.8 15.80 9.916 0.00263 ** \nexercise 2 505.6 252.78 158.610 &lt; 2nd-16 ***\nResiduals 56 89.2 1.59       \n<\/strong><\/pre>\n<h2><span style=\"color: #000000;\"><strong>\u7b2c 3 \u6b65\uff1a\u8ba1\u7b97 Eta \u5e73\u65b9<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528<strong>lsr<\/strong>\u5305\u4e2d\u7684<a href=\"https:\/\/www.rdocumentation.org\/packages\/lsr\/versions\/0.5\/topics\/etaSquared\" target=\"_blank\" rel=\"noopener\">etaSquared()<\/a>\u51fd\u6570\u8ba1\u7b97\u6a21\u578b\u4e2d\u6bcf\u4e2a\u53d8\u91cf\u7684 Eta \u5e73\u65b9\u6548\u5e94\u5927\u5c0f\uff1a<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#load lsr package<\/span>\n<span style=\"color: #993300;\">library<\/span> (lsr)\n\n<span style=\"color: #008080;\">#calculate Eta Squared<\/span>\netaSquared(model)\n\n            eta.sq eta.sq.part\ngender 0.0258824 0.1504401\nexercise 0.8279555 0.8499543\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6027\u548c\u8fd0\u52a8\u7684 Eta \u5e73\u65b9\u5982\u4e0b\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u6027\u522b\u7684 Eta \u5e73\u65b9\uff1a <strong>0.0258824<\/strong><\/span><\/li>\n<li><span style=\"color: #000000;\">\u7ec3\u4e60\u7684 Eta \u5e73\u65b9\uff1a <strong>0.8279555<\/strong><\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u5f97\u51fa\u7ed3\u8bba\uff0c\u8fd0\u52a8\u7684\u5f71\u54cd\u5927\u5c0f\u975e\u5e38\u5927\uff0c\u800c\u6027\u522b\u7684\u5f71\u54cd\u5927\u5c0f\u76f8\u5f53\u5c0f\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8fd9\u4e9b\u7ed3\u679c\u5bf9\u5e94\u4e8e ANOVA \u8868\u7ed3\u679c\u4e2d\u663e\u793a\u7684 p \u503c\u3002\u8fd0\u52a8\u7684 p \u503c (&lt;0.000) \u8fdc\u5c0f\u4e8e\u6027\u522b\u7684 p \u503c (0.00263)\uff0c\u8868\u660e\u8fd0\u52a8\u5728\u9884\u6d4b\u4f53\u91cd\u51cf\u8f7b\u65b9\u9762\u66f4\u4e3a\u91cd\u8981\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\u4e0d\u540c\u7684\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><br \/><a href=\"https:\/\/statorials.org\/cn\/r\u4e2d\u7684\u91cd\u590d\u6d4b\u91cf\u65b9\u5dee\u5206\u6790\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u5728 R \u4e2d\u6267\u884c\u91cd\u590d\u6d4b\u91cf\u65b9\u5dee\u5206\u6790<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Eta \u5e73\u65b9\u662f\u65b9\u5dee\u5206\u6790\u6a21\u578b\u4e2d\u5e38\u7528\u7684\u6548\u5e94\u5927\u5c0f\u5ea6\u91cf\u3002 \u5b83\u8861\u91cf\u65b9\u5dee\u5206\u6790\u6a21\u578b\u4e2d\u4e0e\u6bcf\u4e2a\u4e3b\u6548\u5e94\u548c\u4ea4\u4e92\u6548\u5e94\u76f8\u5173\u7684\u65b9\u5dee\u6bd4\u4f8b\uff0c\u8ba1 [&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-1291","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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