{"id":1288,"date":"2023-07-27T00:11:30","date_gmt":"2023-07-27T00:11:30","guid":{"rendered":"https:\/\/statorials.org\/ja\/r%e3%81%ae%e4%ba%8c%e4%b9%97%ce%b7\/"},"modified":"2023-07-27T00:11:30","modified_gmt":"2023-07-27T00:11:30","slug":"r%e3%81%ae%e4%ba%8c%e4%b9%97%ce%b7","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/r%e3%81%ae%e4%ba%8c%e4%b9%97%ce%b7\/","title":{"rendered":"R \u306e\u30a4\u30fc\u30bf 2 \u4e57\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/ja\/\u30a4\u30fc\u30bf\u4e8c\u4e57\/\" target=\"_blank\" rel=\"noopener\">\u30a4\u30fc\u30bf\u4e8c\u4e57\u306f\u3001<\/a>\u5206\u6563\u5206\u6790\u30e2\u30c7\u30eb\u3067\u4e00\u822c\u7684\u306b\u4f7f\u7528\u3055\u308c\u308b<a href=\"https:\/\/statorials.org\/ja\/\u52b9\u679c\u306e\u5927\u304d\u3055\/\" target=\"_blank\" rel=\"noopener\">\u52b9\u679c\u91cf<\/a>\u306e\u5c3a\u5ea6\u3067\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u308c\u306f\u3001ANOVA \u30e2\u30c7\u30eb\u306e\u5404\u4e3b\u52b9\u679c\u3068\u4ea4\u4e92\u4f5c\u7528\u52b9\u679c\u306b\u95a2\u9023\u3059\u308b\u5206\u6563\u306e\u5272\u5408\u3092\u6e2c\u5b9a\u3057\u3001<\/span><span style=\"color: #000000;\">\u6b21\u306e\u3088\u3046\u306b\u8a08\u7b97\u3055\u308c\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>Eta \u306e 2 \u4e57 = SS<sub>\u52b9\u679c<\/sub>\/<sub>\u5408\u8a08<\/sub>SS<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u91d1\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>SS <sub>Effect<\/sub> :<\/strong>\u5909\u6570\u306e\u52b9\u679c\u306e\u4e8c\u4e57\u306e\u5408\u8a08\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong><sub>\u5408\u8a08<\/sub>SS:<\/strong> ANOVA \u30e2\u30c7\u30eb\u306e\u5e73\u65b9\u548c\u306e\u5408\u8a08\u3002<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Eta \u4e8c\u4e57\u5024\u306e\u7bc4\u56f2\u306f 0 \u304b\u3089 1 \u3067\u3001\u5024\u304c 1 \u306b\u8fd1\u3044\u307b\u3069\u3001\u30e2\u30c7\u30eb\u5185\u306e\u7279\u5b9a\u306e\u5909\u6570\u306b\u3088\u3063\u3066\u8aac\u660e\u3067\u304d\u308b\u5206\u6563\u306e\u5272\u5408\u304c\u9ad8\u3044\u3053\u3068\u3092\u793a\u3057\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\">Eta \u306e 2 \u4e57\u5024\u3092\u89e3\u91c8\u3059\u308b\u306b\u306f\u3001\u6b21\u306e\u7d4c\u9a13\u5247\u304c\u4f7f\u7528\u3055\u308c\u307e\u3059\u3002<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>.01:<\/strong>\u52b9\u679c\u30b5\u30a4\u30ba\u304c\u5c0f\u3055\u3044<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>.06:<\/strong>\u5e73\u5747\u52b9\u679c\u91cf<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>.14\u4ee5\u4e0a\uff1a<\/strong>\u52b9\u679c\u91cf\u304c\u5927\u304d\u3044<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001R \u306e ANOVA \u30e2\u30c7\u30eb\u306e\u5909\u6570\u306e 2 \u4e57\u30a4\u30fc\u30bf\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5\u3092\u6bb5\u968e\u7684\u306b\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 1: \u30c7\u30fc\u30bf\u3092\u4f5c\u6210\u3059\u308b<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u904b\u52d5\u5f37\u5ea6\u3068\u6027\u5225\u304c\u6e1b\u91cf\u306b\u5f71\u97ff\u3092\u4e0e\u3048\u308b\u304b\u3069\u3046\u304b\u3092\u5224\u65ad\u3057\u305f\u3044\u3068\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u308c\u3092\u30c6\u30b9\u30c8\u3059\u308b\u305f\u3081\u306b\u3001\u7537\u6027 30 \u4eba\u3001\u5973\u6027 30 \u4eba\u3092\u52df\u96c6\u3057\u3001\u305d\u308c\u305e\u308c 10 \u4eba\u3092\u7121\u4f5c\u70ba\u306b\u5272\u308a\u5f53\u3066\u3066\u30011 \u304b\u6708\u9593\u3001\u904b\u52d5\u306a\u3057\u3001\u8efd\u3044\u904b\u52d5\u3001\u307e\u305f\u306f\u6fc0\u3057\u3044\u904b\u52d5\u30d7\u30ed\u30b0\u30e9\u30e0\u306b\u5f93\u3046\u5b9f\u9a13\u3092\u884c\u3044\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001\u4f5c\u696d\u4e2d\u306e\u30c7\u30fc\u30bf\u3092\u4fdd\u6301\u3059\u308b\u30c7\u30fc\u30bf \u30d5\u30ec\u30fc\u30e0\u3092\u4f5c\u6210\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/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>\u30b9\u30c6\u30c3\u30d7 2: ANOVA \u30e2\u30c7\u30eb\u3092\u5f53\u3066\u306f\u3081\u308b<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001\u904b\u52d5\u3068\u6027\u5225\u3092\u56e0\u5b50\u3068\u3057\u3066\u3001\u4f53\u91cd\u6e1b\u5c11\u3092<a href=\"https:\/\/statorials.org\/ja\/\u5909\u6570\u306e\u8aac\u660e\u5fdc\u7b54\/\" target=\"_blank\" rel=\"noopener\">\u5fdc\u7b54\u5909\u6570<\/a>\u3068\u3057\u3066\u4f7f\u7528\u3057\u3066<a href=\"https:\/\/statorials.org\/ja\/\u4e8c\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\/\" target=\"_blank\" rel=\"noopener\">\u3001\u4e8c\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790<\/a>\u3092\u8fd1\u4f3c\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/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>\u30b9\u30c6\u30c3\u30d7 3: \u03b7 2 \u4e57\u3092\u8a08\u7b97\u3059\u308b<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\"><strong>lsr<\/strong>\u30d1\u30c3\u30b1\u30fc\u30b8\u306e<a href=\"https:\/\/www.rdocumentation.org\/packages\/lsr\/versions\/0.5\/topics\/etaSquared\" target=\"_blank\" rel=\"noopener\">etaSquared()<\/a>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u30e2\u30c7\u30eb\u5185\u306e\u5404\u5909\u6570\u306e\u30a4\u30fc\u30bf\u4e8c\u4e57\u52b9\u679c\u30b5\u30a4\u30ba\u3092\u8a08\u7b97\u3067\u304d\u307e\u3059\u3002<\/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;\">\u30bb\u30c3\u30af\u30b9\u3068\u904b\u52d5\u306e\u30a4\u30fc\u30bf\u306e\u4e8c\u4e57\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u6027\u5225\u306e\u30a4\u30fc\u30bf\u306e\u4e8c\u4e57: <strong>0.0258824<\/strong><\/span><\/li>\n<li><span style=\"color: #000000;\">\u6f14\u7fd2\u306e\u03b7 2 \u4e57: <strong>0.8279555<\/strong><\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u904b\u52d5\u306e\u52b9\u679c\u91cf\u306f\u975e\u5e38\u306b\u5927\u304d\u3044\u304c\u3001\u6027\u5225\u306e\u52b9\u679c\u91cf\u306f\u975e\u5e38\u306b\u5c0f\u3055\u3044\u3068\u7d50\u8ad6\u4ed8\u3051\u3089\u308c\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u308c\u3089\u306e\u7d50\u679c\u306f\u3001ANOVA \u8868\u306e\u7d50\u679c\u306b\u8868\u793a\u3055\u308c\u308b p \u5024\u306b\u5bfe\u5fdc\u3057\u307e\u3059\u3002\u904b\u52d5\u306e p \u5024 (&lt;0.000) \u306f\u3001\u6027\u5225\u306e p \u5024 (0.00263) \u3088\u308a\u3082\u306f\u308b\u304b\u306b\u5c0f\u3055\u304f\u3001\u904b\u52d5\u304c\u4f53\u91cd\u6e1b\u5c11\u306e\u4e88\u6e2c\u306b\u304a\u3044\u3066\u306f\u308b\u304b\u306b\u91cd\u8981\u3067\u3042\u308b\u3053\u3068\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001R \u3067\u3055\u307e\u3056\u307e\u306a ANOVA \u30e2\u30c7\u30eb\u3092\u8fd1\u4f3c\u3059\u308b\u65b9\u6cd5\u306b\u3064\u3044\u3066\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/ja\/\u4e00\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\/\" target=\"_blank\" rel=\"noopener\">R \u3067\u4e00\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/\u5206\u6563\u5206\u6790\u53cc\u65b9\u5411\/\" target=\"_blank\" rel=\"noopener\">R \u3067\u4e8c\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/r-\u3066\u3099\u306e\u53cd\u5fa9\u6e2c\u5b9a\u306e\u5206\u6563\u5206\u6790\/\" target=\"_blank\" rel=\"noopener\">R \u3067\u53cd\u5fa9\u6e2c\u5b9a ANOVA \u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u30a4\u30fc\u30bf\u4e8c\u4e57\u306f\u3001\u5206\u6563\u5206\u6790\u30e2\u30c7\u30eb\u3067\u4e00\u822c\u7684\u306b\u4f7f\u7528\u3055\u308c\u308b\u52b9\u679c\u91cf\u306e\u5c3a\u5ea6\u3067\u3059\u3002 \u3053\u308c\u306f\u3001ANOVA \u30e2\u30c7\u30eb\u306e\u5404\u4e3b\u52b9\u679c\u3068\u4ea4\u4e92\u4f5c\u7528\u52b9\u679c\u306b\u95a2\u9023\u3059\u308b\u5206\u6563\u306e\u5272\u5408\u3092\u6e2c\u5b9a\u3057\u3001\u6b21\u306e\u3088\u3046\u306b\u8a08\u7b97\u3055\u308c\u307e\u3059\u3002 Eta \u306e 2 \u4e57 = SS\u52b9\u679c\/\u5408\u8a08SS \u91d1 [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[16],"tags":[],"class_list":["post-1288","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>R \u3067\u30a4\u30fc\u30bf 2 \u4e57\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5 - \u7d71\u8a08\u5b66<\/title>\n<meta name=\"description\" content=\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001R \u306e\u30a4\u30fc\u30bf 2 \u4e57\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5\u3092\u30b9\u30c6\u30c3\u30d7\u30d0\u30a4\u30b9\u30c6\u30c3\u30d7\u306e\u4f8b\u3067\u8aac\u660e\u3057\u307e\u3059\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link 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