Rで分析：一元配置分散分析と二元配置分散分析

#一元配置分散分析
hsb2 <- read.table("https://stats.oarc.ucla.edu/stat/data/hsb2.csv", sep = ",", header = T)

library(car)
Anova(lm(write ~ factor(ses), data = hsb2), type = 2)
#Anova Table (Type II tests)
#Response: write
#Sum Sq  Df F value   Pr(>F)   
#factor(ses)   858.7   2  4.9696 0.007842 **
#  Residuals   17020.2 197                    
#---
#  Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

summary(aov(lm(write ~ factor(ses), data = hsb2)))
#Df Sum Sq Mean Sq F value  Pr(>F)   
#factor(ses)   2    859   429.4    4.97 0.00784 **
#  Residuals   197  17020    86.4                   
#---
#  Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

########################################
#二元配置分散分析

exer <- read.csv("https://stats.oarc.ucla.edu/stat/data/exer.csv")

exer <- within(exer, {
  diet <- factor(diet)
  exertype <- factor(exertype)
  time <- factor(time)
  id <- factor(id)
})

exertype.aov <- aov(pulse ~ exertype * time + Error(id), data = exer)
summary(exertype.aov)
#Error: id
#Df Sum Sq Mean Sq F value   Pr(>F)    
#exertype   2   8326    4163      27 3.62e-07 ***
#  Residuals 27   4163     154                     
#---
#  Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

#Error: Within
#Df Sum Sq Mean Sq F value   Pr(>F)    
#time           2   2067  1033.3   23.54 4.45e-08 ***
#  exertype:time  4   2723   680.8   15.51 1.65e-08 ***
#  Residuals     54   2370    43.9                     
#---
#  Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1