{"id":485,"date":"2023-07-29T18:02:44","date_gmt":"2023-07-29T18:02:44","guid":{"rendered":"https:\/\/statorials.org\/ja\/%e5%88%86%e6%95%a3%e5%88%86%e6%9e%90%e5%8f%8c%e6%96%b9%e5%90%91\/"},"modified":"2023-07-29T18:02:44","modified_gmt":"2023-07-29T18:02:44","slug":"%e5%88%86%e6%95%a3%e5%88%86%e6%9e%90%e5%8f%8c%e6%96%b9%e5%90%91","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/%e5%88%86%e6%95%a3%e5%88%86%e6%9e%90%e5%8f%8c%e6%96%b9%e5%90%91\/","title":{"rendered":"R \u3067\u4e8c\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"

<\/p>\n

\n

\u4e8c\u5143\u914d\u7f6e ANOVA<\/a> (\u300c\u5206\u6563\u5206\u6790\u300d) \u306f\u30012 \u3064\u306e\u56e0\u5b50\u306b\u5206\u5272\u3055\u308c\u305f 3 \u3064\u4ee5\u4e0a\u306e\u72ec\u7acb\u3057\u305f\u30b0\u30eb\u30fc\u30d7\u306e\u5e73\u5747\u9593\u306b\u7d71\u8a08\u7684\u306b\u6709\u610f\u306a\u5dee\u304c\u3042\u308b\u304b\u3069\u3046\u304b\u3092\u5224\u65ad\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059\u3002<\/span><\/p>\n

\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001R \u3067\u4e8c\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u306b\u3064\u3044\u3066\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\n

\u4f8b: R \u3067\u306e\u4e8c\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790<\/strong><\/span><\/h3>\n

\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\u3053\u306e\u5834\u5408\u3001\u6ce8\u76ee\u3057\u3066\u3044\u308b 2 \u3064\u306e\u8981\u56e0\u306f\u904b\u52d5\u91cf<\/em>\u3068\u6027\u5225<\/em>\u3067\u3042\u308a\u3001\u5fdc\u7b54\u5909\u6570\u306f\u30dd\u30f3\u30c9\u5358\u4f4d\u3067\u6e2c\u5b9a\u3055\u308c\u308b\u4f53\u91cd\u6e1b\u5c11<\/em>\u3067\u3059\u3002<\/span><\/p>\n

\u4e8c\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u3092\u5b9f\u884c\u3057\u3066\u3001\u904b\u52d5\u3068\u6027\u5225\u304c\u4f53\u91cd\u6e1b\u5c11\u306b\u5f71\u97ff\u3092\u4e0e\u3048\u308b\u304b\u3069\u3046\u304b\u3001\u307e\u305f\u904b\u52d5\u3068\u6027\u5225\u306e\u9593\u306b\u4f53\u91cd\u6e1b\u5c11\u306b\u95a2\u3059\u308b\u76f8\u4e92\u4f5c\u7528\u304c\u3042\u308b\u304b\u3069\u3046\u304b\u3092\u5224\u65ad\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n

\u5b9f\u9a13\u306b\u53c2\u52a0\u3059\u308b\u7537\u6027 30 \u4eba\u3001\u5973\u6027 30 \u4eba\u3092\u52df\u96c6\u3057\u307e\u3059\u3002\u3053\u306e\u5b9f\u9a13\u3067\u306f\u3001\u5404 10 \u4eba\u304c\u7121\u4f5c\u70ba\u306b\u5272\u308a\u5f53\u3066\u3089\u308c\u3001\u904b\u52d5\u306a\u3057\u3001\u8efd\u3044\u904b\u52d5\u3001\u307e\u305f\u306f\u6fc0\u3057\u3044\u904b\u52d5\u30d7\u30ed\u30b0\u30e9\u30e0\u3092 1 \u304b\u6708\u9593\u5b9f\u884c\u3057\u307e\u3059\u3002<\/span><\/p>\n

\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001\u4f5c\u696d\u3059\u308b\u30c7\u30fc\u30bf \u30d5\u30ec\u30fc\u30e0\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002<\/span><\/p>\n

 #make this example reproducible\n<\/span>set.seed(10)\n\n#create data frame\n<\/span>data <- data.frame(gender = rep(c(\"Male\", \"Female\"), each = 30),\n                   exercise = rep(c(\"None\", \"Light\", \"Intense\"), each = 10, times = 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#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#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\n<\/strong><\/pre>\n
\u30c7\u30fc\u30bf\u3092\u63a2\u7d22\u3059\u308b<\/strong><\/span><\/h3>\n
\u4e8c\u5143\u914d\u7f6e ANOVA \u30e2\u30c7\u30eb\u3092\u30d5\u30a3\u30c3\u30c6\u30a3\u30f3\u30b0\u3059\u308b\u524d\u306b\u3001 dplyr<\/strong>\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u4f7f\u7528\u3057\u3066 6 \u3064\u306e\u6cbb\u7642\u30b0\u30eb\u30fc\u30d7\u306e\u305d\u308c\u305e\u308c\u306e\u4f53\u91cd\u6e1b\u5c11\u306e\u5e73\u5747\u3068\u6a19\u6e96\u504f\u5dee\u3092\u898b\u3064\u3051\u308b\u3053\u3068\u3067\u3001\u30c7\u30fc\u30bf\u3092\u3088\u308a\u3088\u304f\u7406\u89e3\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 #load dplyr<\/em> package<\/span>\nlibrary(dplyr)\n\n#find mean and standard deviation of weight loss for each treatment group<\/span>\ndata %>%\n  group_by<\/span> (gender, exercise) %>%\n  summarize<\/span> (mean = mean(weight_loss),\n            sd = sd(weight_loss))\n\n# A tibble: 6 x 4\n# Groups: gender [2]\n# gender exercise means sd\n#          \n#1 Female Intense 5.31 1.02 \n#2 Female Light 0.920 0.835\n#3 Female None -0.501 1.77 \n#4 Male Intense 7.37 0.928\n#5 Male Light 2.13 1.22 \n#6 Male None -0.698 1.12 \n<\/strong><\/pre>\n
 #set margins so that axis labels on boxplot don't get cut off<\/span>\nby(mar=c(8, 4.1, 4.1, 2.1))\n\n#create boxplots\n<\/span>boxplot(weight_loss ~ gender:exercise,\ndata = data,\nmain = \"Weight Loss Distribution by Group\",\nxlab = \"Group\",\nylab = \"Weight Loss\",\ncol = \"steelblue\",\nborder = \"black\", \nlas = 2 #make x-axis labels perpendicular<\/span>\n)<\/strong><\/pre>\n
\u6fc0\u3057\u3044<\/em>\u904b\u52d5\u306b\u53c2\u52a0\u3057\u305f 2 \u3064\u306e\u30b0\u30eb\u30fc\u30d7\u306e\u65b9\u304c\u3001\u4f53\u91cd\u6e1b\u5c11\u5024\u304c\u9ad8\u3044\u3053\u3068\u304c\u3059\u3050\u306b\u308f\u304b\u308a\u307e\u3059\u3002\u307e\u305f\u3001\u6fc0\u3057\u3044<\/em>\u904b\u52d5\u30b0\u30eb\u30fc\u30d7\u3068\u8efd\u3044<\/em>\u904b\u52d5\u30b0\u30eb\u30fc\u30d7\u306e\u4e21\u65b9\u3067\u3001\u7537\u6027\u306e\u65b9\u304c\u5973\u6027\u3088\u308a\u3082\u4f53\u91cd\u6e1b\u5c11\u5024\u304c\u9ad8\u3044\u50be\u5411\u304c\u3042\u308b\u3053\u3068\u3082\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n
\u6b21\u306b\u3001\u4e8c\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u30e2\u30c7\u30eb\u3092\u30c7\u30fc\u30bf\u306b\u5f53\u3066\u306f\u3081\u3066\u3001\u3053\u308c\u3089\u306e\u8996\u899a\u7684\u306a\u9055\u3044\u304c\u5b9f\u969b\u306b\u7d71\u8a08\u7684\u306b\u6709\u610f\u3067\u3042\u308b\u304b\u3069\u3046\u304b\u3092\u78ba\u8a8d\u3057\u307e\u3059\u3002<\/span><\/p>\n
\u4e8c\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u30e2\u30c7\u30eb\u306e\u30d5\u30a3\u30c3\u30c6\u30a3\u30f3\u30b0<\/strong><\/span><\/h3>\n
R \u3067\u4e8c\u5143\u914d\u7f6e ANOVA \u30e2\u30c7\u30eb\u3092\u8fd1\u4f3c\u3059\u308b\u305f\u3081\u306e\u4e00\u822c\u7684\u306a\u69cb\u6587\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/span><\/p>\n
 aov(\u5fdc\u7b54\u5909\u6570 ~predictor_variable1 *predictor_variable2, data = \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8)<\/span><\/strong><\/p>\n
 2 \u3064\u306e\u4e88\u6e2c\u5b50\u5909\u6570\u9593\u306e*<\/strong>\u306f\u30012 \u3064\u306e\u4e88\u6e2c\u5b50\u5909\u6570\u9593\u306e\u4ea4\u4e92\u4f5c\u7528\u52b9\u679c\u3082\u30c6\u30b9\u30c8\u3059\u308b\u3053\u3068\u3092\u793a\u3057\u3066\u3044\u308b\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/span><\/p>\n
\u3053\u306e\u4f8b\u3067\u306f\u3001\u6b21\u306e\u30b3\u30fc\u30c9\u3092\u4f7f\u7528\u3057\u3066\u3001 weight_loss \u3092<\/em>\u5fdc\u7b54\u5909\u6570\u3068\u3057\u3066\u3001\u6027\u5225<\/em>\u3068\u904b\u52d5\u91cf<\/em>\u3092 2 \u3064\u306e\u4e88\u6e2c\u5909\u6570\u3068\u3057\u3066\u4f7f\u7528\u3057\u3066\u3001\u4e8c\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u30e2\u30c7\u30eb\u3092\u8fd1\u4f3c\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
\u6b21\u306b\u3001 summary()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u30e2\u30c7\u30eb\u306e\u7d50\u679c\u3092\u8868\u793a\u3057\u307e\u3059\u3002<\/span><\/p>\n
 #fit the two-way ANOVA model<\/span>\nmodel <- aov(weight_loss ~ gender * exercise, data = data)\n\n#view the model output<\/span>\nsummary(model)\n\n# Df Sum Sq Mean Sq F value Pr(>F)    \n#gender 1 15.8 15.80 11.197 0.0015 ** \n#exercise 2 505.6 252.78 179.087 <2e-16 ***\n#gender:exercise 2 13.0 6.51 4.615 0.0141 *  \n#Residuals 54 76.2 1.41                   \n#---\n#Significant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n<\/strong><\/pre>\n
\u30e2\u30c7\u30eb\u306e\u7d50\u679c\u304b\u3089\u3001\u6027\u5225<\/em>\u3001\u904b\u52d5<\/em>\u3001\u304a\u3088\u3073 2 \u3064\u306e\u5909\u6570\u9593\u306e\u76f8\u4e92\u4f5c\u7528\u306f\u3059\u3079\u3066\u3001\u6709\u610f\u6c34\u6e96 0.05 \u3067\u7d71\u8a08\u7684\u306b\u6709\u610f\u3067\u3042\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n
\u30e2\u30c7\u30eb\u306e\u4eee\u5b9a\u3092\u78ba\u8a8d\u3059\u308b<\/strong><\/span><\/h3>\n
\u6b21\u306b\u9032\u3080\u524d\u306b\u3001\u30e2\u30c7\u30eb\u306e\u7d50\u679c\u304c\u4fe1\u983c\u3067\u304d\u308b\u3088\u3046\u306b\u3001\u30e2\u30c7\u30eb\u306e\u4eee\u5b9a\u304c\u6e80\u305f\u3055\u308c\u3066\u3044\u308b\u3053\u3068\u3092\u78ba\u8a8d\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\u7279\u306b\u3001\u4e8c\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u3067\u306f\u6b21\u306e\u3053\u3068\u3092\u524d\u63d0\u3068\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n
 1. \u72ec\u7acb\u6027<\/strong>\u2013 \u5404\u30b0\u30eb\u30fc\u30d7\u306e\u89b3\u5bdf\u306f\u4e92\u3044\u306b\u72ec\u7acb\u3057\u3066\u3044\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/span>\u30e9\u30f3\u30c0\u30e0\u5316\u3055\u308c\u305f\u8a2d\u8a08\u3092\u4f7f\u7528\u3057\u305f\u305f\u3081<\/span>\u3001\u3053\u306e\u4eee\u5b9a\u306f\u6e80\u305f\u3055\u308c\u308b\u306f\u305a\u306a\u306e\u3067\u3001\u3042\u307e\u308a\u5fc3\u914d\u3059\u308b\u5fc5\u8981\u306f\u3042\u308a\u307e\u305b\u3093\u3002<\/span><\/p>\n
 2. \u6b63\u898f\u6027<\/strong>\u2013 \u5f93\u5c5e\u5909\u6570\u306f\u30012 \u3064\u306e\u56e0\u5b50\u306e\u30b0\u30eb\u30fc\u30d7\u306e\u7d44\u307f\u5408\u308f\u305b\u3054\u3068\u306b\u307b\u307c\u6b63\u898f\u5206\u5e03\u3092\u6301\u3064\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n
\u3053\u306e\u4eee\u5b9a\u3092\u30c6\u30b9\u30c8\u3059\u308b 1 \u3064\u306e\u65b9\u6cd5\u306f\u3001\u30e2\u30c7\u30eb\u6b8b\u5dee\u306e\u30d2\u30b9\u30c8\u30b0\u30e9\u30e0\u3092\u4f5c\u6210\u3059\u308b\u3053\u3068\u3067\u3059\u3002\u6b8b\u5dee\u304c\u307b\u307c\u6b63\u898f\u5206\u5e03\u3057\u3066\u3044\u308b\u5834\u5408\u3001\u3053\u306e\u4eee\u5b9a\u306f\u6e80\u305f\u3055\u308c\u308b\u306f\u305a\u3067\u3059\u3002<\/span><\/p>\n
 #define model residuals\n<\/b><\/span>reside <- model$residuals<\/strong>\n\n#create histogram of residuals<\/strong><\/span>\nhist(resid, main = \"Histogram of Residuals\", xlab = \"Residuals\", col = \"steelblue\")<\/strong><\/pre>\n
\u6b8b\u5dee\u306f\u307b\u307c\u6b63\u898f\u5206\u5e03\u3057\u3066\u3044\u308b\u305f\u3081\u3001\u6b63\u898f\u6027\u306e\u4eee\u5b9a\u304c\u6e80\u305f\u3055\u308c\u3066\u3044\u308b\u3068\u60f3\u5b9a\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 3. \u7b49\u5206\u6563<\/strong>\u2013 \u5404\u30b0\u30eb\u30fc\u30d7\u306e\u5206\u6563\u306f\u7b49\u3057\u3044\u304b\u307b\u307c\u7b49\u3057\u3044\u3002<\/span><\/p>\n
\u3053\u306e\u4eee\u5b9a\u3092\u78ba\u8a8d\u3059\u308b 1 \u3064\u306e\u65b9\u6cd5\u306f\u3001 car<\/strong>\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u4f7f\u7528\u3057\u3066\u5206\u6563\u306e\u7b49\u4fa1\u6027\u306b\u3064\u3044\u3066 Levene \u691c\u5b9a\u3092\u5b9f\u884c\u3059\u308b\u3053\u3068\u3067\u3059\u3002<\/span><\/p>\n
 #load car<\/em> package<\/span>\nlibrary(car)\n\n#conduct Levene's Test for equality of variances<\/span>\nleveneTest(weight_loss ~ gender * exercise, data = data)\n\n#Levene's Test for Homogeneity of Variance (center = median)\n# Df F value Pr(>F)\n#group 5 1.8547 0.1177\n#54  \n<\/strong><\/pre>\n
\u691c\u5b9a\u306e p \u5024\u306f\u6709\u610f\u6c34\u6e96 0.05 \u3088\u308a\u5927\u304d\u3044\u305f\u3081\u3001\u30b0\u30eb\u30fc\u30d7\u9593\u306e\u5206\u6563\u304c\u7b49\u3057\u3044\u3068\u3044\u3046\u4eee\u5b9a\u304c\u6e80\u305f\u3055\u308c\u3066\u3044\u308b\u3068\u8003\u3048\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
\u6cbb\u7642\u6cd5\u306e\u9055\u3044\u3092\u5206\u6790\u3059\u308b<\/strong><\/span><\/h3>\n
\u30e2\u30c7\u30eb\u306e\u4eee\u5b9a\u304c\u6e80\u305f\u3055\u308c\u3066\u3044\u308b\u3053\u3068\u3092\u78ba\u8a8d\u3057\u305f\u3089\u3001\u4e8b\u5f8c\u30c6\u30b9\u30c8<\/a>\u3092\u5b9f\u884c\u3057\u3066\u3001\u3069\u306e\u6cbb\u7642\u30b0\u30eb\u30fc\u30d7\u304c\u4e92\u3044\u306b\u7570\u306a\u308b\u304b\u3092\u6b63\u78ba\u306b\u5224\u65ad\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
\u4e8b\u5f8c\u30c6\u30b9\u30c8\u3067\u306f\u3001 TukeyHSD()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u591a\u91cd\u6bd4\u8f03\u306e Tukey \u30c6\u30b9\u30c8\u3092\u5b9f\u884c\u3057\u307e\u3059\u3002<\/span><\/p>\n
 #perform Tukey's Test for multiple comparisons\n<\/span>TukeyHSD(model, conf.level=.95) \n\n#Tukey multiple comparisons of means\n# 95% family-wise confidence level\n#\n#Fit: aov(formula = weight_loss ~ gender * exercise, data = data)\n#\n#$gender\n# diff lwr upr p adj\n#Male-Female 1.026456 0.4114451 1.641467 0.0014967\n#\n#$exercise\n# diff lwr upr p adj\n#Light-Intense -4.813064 -5.718493 -3.907635 0.0e+00\n#None-Intense -6.938966 -7.844395 -6.033537 0.0e+00\n#None-Light -2.125902 -3.031331 -1.220473 1.8e-06\n#\n#$`gender:exercise`\n# diff lwr upr p adj\n#Male:Intense-Female:Intense 2.0628297 0.4930588 3.63260067 0.0036746\n#Female:Light-Female:Intense -4.3883563 -5.9581272 -2.81858535 0.0000000\n#Male:Light-Female:Intense -3.1749419 -4.7447128 -1.60517092 0.0000027\n#Female:None-Female:Intense -5.8091131 -7.3788841 -4.23934219 0.0000000\n#Male:None-Female:Intense -6.0059891 -7.5757600 -4.43621813 0.0000000\n#Female:Light-Male:Intense -6.4511860 -8.0209570 -4.88141508 0.0000000\n#Male:Light-Male:Intense -5.2377716 -6.8075425 -3.66800066 0.0000000\n#Female:None-Male:Intense -7.8719429 -9.4417138 -6.30217192 0.0000000\n#Male:None-Male:Intense -8.0688188 -9.6385897 -6.49904786 0.0000000\n#Male:Light-Female:Light 1.2134144 -0.3563565 2.78318536 0.2185439\n#Female:None-Female:Light -1.4207568 -2.9905278 0.14901410 0.0974193\n#Male:None-Female:Light -1.6176328 -3.1874037 -0.04786184 0.0398106\n#Female:None-Male:Light -2.6341713 -4.2039422 -1.06440032 0.0001050\n#Male:None-Male:Light -2.8310472 -4.4008181 -1.26127627 0.0000284\n#Male:None-Female:None -0.1968759 -1.7666469 1.37289500 0.9990364<\/strong><\/pre>\n
 p \u5024\u306f\u3001\u5404\u30b0\u30eb\u30fc\u30d7\u9593\u306b\u7d71\u8a08\u7684\u306b\u6709\u610f\u306a\u5dee\u304c\u3042\u308b\u304b\u3069\u3046\u304b\u3092\u793a\u3057\u307e\u3059\u3002<\/span><\/p>\n
\u305f\u3068\u3048\u3070\u3001\u4e0a\u306e\u6700\u5f8c\u306e\u884c\u3067\u306f\u3001\u7537\u6027\u306e\u904b\u52d5\u306a\u3057\u30b0\u30eb\u30fc\u30d7\u306f\u3001\u5973\u6027\u306e\u904b\u52d5\u306a\u3057\u30b0\u30eb\u30fc\u30d7\u3068\u6bd4\u8f03\u3057\u3066\u3001\u4f53\u91cd\u6e1b\u5c11\u306b\u304a\u3044\u3066\u7d71\u8a08\u7684\u306b\u6709\u610f\u306a\u5dee\u304c\u898b\u3089\u308c\u306a\u304b\u3063\u305f\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059 (p \u5024: 0.990364)\u3002<\/span><\/p>\n
 R \u306eLot()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001Tukey \u691c\u5b9a\u306e\u7d50\u679c\u3068\u3057\u3066\u5f97\u3089\u308c\u308b 95% \u4fe1\u983c\u533a\u9593\u3092\u8996\u899a\u5316\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 #set axis margins so labels don't get cut off\n<\/span>by(mar=c(4.1, 13, 4.1, 2.1))\n\n#create confidence interval for each comparison\n<\/span>plot(TukeyHSD(model, conf.level=.95), las = 2)\n<\/strong><\/pre>\n
\u4e8c\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u7d50\u679c\u306e\u30ec\u30dd\u30fc\u30c8<\/span><\/strong><\/h3>\n
\u6700\u5f8c\u306b\u3001\u7d50\u679c\u3092\u8981\u7d04\u3057\u305f\u65b9\u6cd5\u3067\u4e8c\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u306e\u7d50\u679c\u3092\u30ec\u30dd\u30fc\u30c8\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
\u4e8c\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u3092\u5b9f\u884c\u3057\u3066\u3001\u6027\u5225 (\u7537\u6027\u3001\u5973\u6027)<\/em>\u3068\u904b\u52d5\u30d7\u30ed\u30b0\u30e9\u30e0(\u306a\u3057\u3001\u8efd\u3044\u3001\u6fc0\u3057\u3044)<\/em>\u304c\u4f53\u91cd\u6e1b\u5c11(\u30dd\u30f3\u30c9\u3067\u6e2c\u5b9a) \u306b\u53ca\u307c\u3059\u5f71\u97ff\u3092\u8abf\u3079\u307e\u3057\u305f\u3002<\/em>\u4f53\u91cd\u6e1b\u5c11\u306b\u5bfe\u3059\u308b\u6027\u5225\u3068\u904b\u52d5\u306e\u52b9\u679c\u306e\u9593\u306b\u306f\u3001\u7d71\u8a08\u7684\u306b\u6709\u610f\u306a\u76f8\u4e92\u4f5c\u7528\u304c\u3042\u308a\u307e\u3057\u305f (F(2, 54) = 4.615\u3001p = 0.0141)\u3002<\/span>\u4e8b\u5f8c Tukey \u306e HSD \u30c6\u30b9\u30c8\u3092\u5b9f\u884c\u3057\u307e\u3057\u305f\u3002<\/span><\/p>\n
\u7537\u6027\u306e\u5834\u5408\u3001\u6fc0\u3057\u3044<\/em>\u904b\u52d5\u30d7\u30ed\u30b0\u30e9\u30e0\u306f\u3001\u8efd\u3044<\/em>\u30d7\u30ed\u30b0\u30e9\u30e0 (p < 0.0001) \u307e\u305f\u306f\u904b\u52d5\u30d7\u30ed\u30b0\u30e9\u30e0\u306a\u3057<\/em>(p < 0.0001) \u3088\u308a\u3082\u5927\u5e45\u306b\u5927\u304d\u306a\u4f53\u91cd\u6e1b\u5c11\u3092\u3082\u305f\u3089\u3057\u307e\u3057\u305f\u3002\u3055\u3089\u306b\u3001\u7537\u6027\u3067\u306f\u3001\u8efd\u3044<\/em>\u98df\u4e8b\u7642\u6cd5\u306f\u3001\u904b\u52d5\u7642\u6cd5\u3092\u884c\u308f\u306a\u3044<\/em>\u5834\u5408\u3088\u308a\u3082\u6709\u610f\u306b\u5927\u304d\u306a\u4f53\u91cd\u6e1b\u5c11\u3092\u3082\u305f\u3089\u3057\u307e\u3057\u305f\uff08p < 0.0001\uff09\u3002<\/span><\/p>\n
\u5973\u6027\u306e\u5834\u5408\u3001\u6fc0\u3057\u3044<\/em>\u904b\u52d5\u30d7\u30ed\u30b0\u30e9\u30e0\u306f\u3001\u8efd\u3044<\/em>\u30d7\u30ed\u30b0\u30e9\u30e0 (p < 0.0001) \u307e\u305f\u306f\u904b\u52d5\u30d7\u30ed\u30b0\u30e9\u30e0\u306a\u3057<\/em>(p < 0.0001) \u3088\u308a\u3082\u5927\u5e45\u306b\u5927\u304d\u306a\u4f53\u91cd\u6e1b\u5c11\u3092\u3082\u305f\u3089\u3057\u307e\u3057\u305f\u3002<\/span><\/p>\n
\u6b63\u898f\u6027\u30c1\u30a7\u30c3\u30af\u3068 Levene \u691c\u5b9a\u3092\u5b9f\u884c\u3057\u3066\u3001ANOVA \u306e\u4eee\u5b9a\u304c\u6e80\u305f\u3055\u308c\u3066\u3044\u308b\u3053\u3068\u3092\u78ba\u8a8d\u3057\u307e\u3057\u305f\u3002<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"
\u4e8c\u5143\u914d\u7f6e ANOVA (\u300c\u5206\u6563\u5206\u6790\u300d) \u306f\u30012 \u3064\u306e\u56e0\u5b50\u306b\u5206\u5272\u3055\u308c\u305f 3 \u3064\u4ee5\u4e0a\u306e\u72ec\u7acb\u3057\u305f\u30b0\u30eb\u30fc\u30d7\u306e\u5e73\u5747\u9593\u306b\u7d71\u8a08\u7684\u306b\u6709\u610f\u306a\u5dee\u304c\u3042\u308b\u304b\u3069\u3046\u304b\u3092\u5224\u65ad\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059\u3002 \u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001R \u3067\u4e8c\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u3092\u5b9f […]<\/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-485","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"\nR \u3067\u4e8c\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5 - \u7d71\u8a08<\/title>\n<meta name=\"description\" content=\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001R \u3067\u4e8c\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u3092\u7c21\u5358\u306b\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u306b\u3064\u3044\u3066\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 rel=\"canonical\" href=\"https:\/\/statorials.org\/ja\/\u5206\u6563\u5206\u6790\u53cc\u65b9\u5411\/\" \/>\n<meta property=\"og:locale\" content=\"ja_JP\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"R \u3067\u4e8c\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5 - 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