Moore1 <- read.table("c:/temp/moore.txt",header=F,skip=1)
names(Moore1) <- c("partner","status","conformity","fcategory","fscore")


 # defining deviation-coded variables
Moore1$statuslow <- as.numeric(Moore1$status == "low") - as.numeric(Moore1$status == "high")
Moore1$fcatlow <- as.numeric(Moore1$fcategory == "low") - as.numeric(Moore1$fcategory == "high")
Moore1$fcatmed <- as.numeric(Moore1$fcategory == "medium") - as.numeric(Moore1$fcategory == "high")


 # defining crossproduct variables used to test for interaction
Moore1$statLcatL <- Moore1$statuslow*Moore1$fcatlow
Moore1$statLcatM <- Moore1$statuslow*Moore1$fcatmed


 # individual regression models 

anova(lm(conformity ~ statuslow +fcatlow +fcatmed +statLcatL +statLcatM, data=Moore1 ) )


anova(lm(conformity ~ statuslow +fcatlow +fcatmed, data=Moore1 ) )


anova(lm(conformity ~ statuslow +statLcatL +statLcatM, data=Moore1 ) )


anova(lm(conformity ~ fcatlow +fcatmed +statLcatL +statLcatM, data=Moore1 ) )


anova(lm(conformity ~ statuslow, data=Moore1 ) )


anova(lm(conformity ~ fcatlow +fcatmed, data=Moore1 ) )




 # the car package contains a function 'Anova' that can calculate Type II or Type III SS
 # it calculates Type II SS by default

library(car)

 # this produces the same results as in Table 8.4 for Type II tests
Anova(lm(conformity ~ status +fcategory +status:fcategory, data=Moore1 ),type="II")

 # this output does not match the Type III tests from Table 8.4, but the regression models
 # above can be used to match the output in the text
Anova(lm(conformity ~ status +fcategory +status:fcategory, data=Moore1 ),type="III")