## This example is from Hsu and Peruggia
## This is the S-Plus version
## See ?aov.sufficient for R
if.R(r={},
s={
pulmonary <- read.table(hh("datasets/pulmonary.dat"), header=TRUE,
row.names=NULL)
names(pulmonary)[3] <- "FVC"
names(pulmonary)[1] <- "smoker"
pulmonary$smoker <- factor(pulmonary$smoker, levels=pulmonary$smoker)
row.names(pulmonary) <- pulmonary$smoker
pulmonary
pulmonary.aov <- aov.sufficient(FVC ~ smoker,
data=pulmonary)
summary(pulmonary.aov)
## multicomp object
pulmonary.mca <-
multicomp.mean(pulmonary$smoker,
pulmonary$n,
pulmonary$FVC,
pulmonary$s,
ylabel="pulmonary",
focus="smoker")
pulmonary.mca
## lexicographic ordering of contrasts, some positive and some negative
plot(pulmonary.mca)
pulm.lmat <- cbind("npnl-mh"=c( 1, 1, 1, 1,-2,-2), ## not.much vs lots
"n-pnl" =c( 3,-1,-1,-1, 0, 0), ## none vs light
"p-nl" =c( 0, 2,-1,-1, 0, 0), ## {} arbitrary 2 df
"n-l" =c( 0, 0, 1,-1, 0, 0), ## {} for 3 types of light
"m-h" =c( 0, 0, 0, 0, 1,-1)) ## moderate vs heavy
dimnames(pulm.lmat)[[1]] <- row.names(pulmonary)
pulm.lmat
## mmc.multicomp object
pulmonary.mmc <-
multicomp.mmc.mean(pulmonary$smoker,
pulmonary$n,
pulmonary$FVC,
pulmonary$s,
ylabel="pulmonary",
focus="smoker",
lmat=pulm.lmat,
plot=FALSE)
old.par <- par(mar=c(5,4,4,4)+.1)
## pairwise comparisons
plot(pulmonary.mmc, print.mca=TRUE, print.lmat=FALSE)
## tiebreaker plot, with contrasts ordered to match MMC plot,
## with all contrasts forced positive and with names also reversed,
## and with matched x-scale.
plot(pulmonary.mmc$mca, col.signif='red', lty.signif=1, xlabel.print=FALSE,
xaxs="d", plt=par()$plt+c(0,0,-.25,.05), xrange.include=c(-1, 1))
## orthogonal contrasts
plot(pulmonary.mmc, print.lmat=TRUE, col.lmat.signif='blue', col.iso='gray')
## pairwise and orthogonal contrasts on the same plot
plot(pulmonary.mmc, print.mca=TRUE, print.lmat=TRUE,
col.mca.signif='red', col.lmat.signif='blue', col.iso='gray',
lty.lmat.not.signif=2)
par(old.par)
})
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