## continue with the example in glht.mmc in R, or multicomp.mmc in S-Plus
catalystm <- read.table(hh("datasets/catalystm.dat"), header=FALSE,
col.names=c("catalyst","concent"))
catalystm$catalyst <- factor(catalystm$catalyst, labels=c("A","B","C","D"))
catalystm1.aov <- aov(concent ~ catalyst, data=catalystm)
if.R(r={
catalystm.mca <-
glht(catalystm1.aov, linfct = mcp(catalyst = "Tukey"))
print(confint(catalystm.mca))
catalystm.mmc <-
glht.mmc(catalystm1.aov, linfct = mcp(catalyst = "Tukey"))
## the contrasts have been ordered by height (see ?MMC),
## which in this example corresponds to sort.order=c(1,2,4,3,5,6),
## and reversed, to make the contrast Estimates positive.
print(as.glht(catalystm.mmc$mca))
## ## For consistency with the S-Plus example,
## ## we change all factor level "A" to "control".
## as.glht(multicomp.label.change(catalystm.mmc$mca, "A", "control"))
},s={
catalystm.mca <-
multicomp(catalystm1.aov, method="Tukey")
print(catalystm.mca)
catalystm.mmc <-
multicomp.mmc(catalystm1.aov, method="Tukey", plot=FALSE)
## the contrasts have been ordered by height (see ?MMC),
## which in this example corresponds to sort.order=c(1,2,4,3,5,6),
## and reversed, to make the contrast Estimates positive.
print(catalystm.mmc$mca)
## S-Plus multicomp already uses simple names. This function is
## therefore used in more complex two-way ANOVA examples. We illustrate
## here by changing all factor level "A" to "control".
print(multicomp.label.change(catalystm.mmc$mca, "A", "control"))
})
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