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\name{cholesterol}
\alias{cholesterol}
\docType{data}
\title{ Cholesterol Reduction Data Set }
\usage{data("cholesterol")}
\description{
Cholesterol reduction for five treatments.
}
\format{
This data frame contains the following variables
\describe{
\item{trt}{treatment groups, a factor at levels \code{1time}, \code{2times},
\code{4times}, \code{drugD} and \code{drugE}.}
\item{response}{cholesterol reduction.}
}
}
\details{
A clinical study was conducted to assess the effect of three formulations
of the same drug on reducing cholesterol. The formulations were
20mg at once (\code{1time}), 10mg twice a day (\code{2times}), and 5mg
four times a day (\code{4times}). In addition, two competing drugs
were used as control group (\code{drugD} and \code{drugE}). The purpose of
the study was to find which of the formulations, if any, is efficacious and how
these formulations compare with the existing drugs.
}
\source{
P. H. Westfall, R. D. Tobias, D. Rom, R. D. Wolfinger, Y. Hochberg (1999).
\emph{Multiple Comparisons and Multiple Tests Using the SAS System}.
Cary, NC: SAS Institute Inc., page 153.
}
\examples{
### adjusted p-values for all-pairwise comparisons in a one-way layout
### set up ANOVA model
amod <- aov(response ~ trt, data = cholesterol)
### set up multiple comparisons object for all-pair comparisons
cht <- glht(amod, linfct = mcp(trt = "Tukey"))
### cf. Westfall et al. (1999, page 171)
summary(cht, test = univariate())
summary(cht, test = adjusted("Shaffer"))
summary(cht, test = adjusted("Westfall"))
### use only a subset of all pairwise hypotheses
K <- contrMat(table(cholesterol$trt), type="Tukey")
Ksub <- rbind(K[c(1,2,5),],
"D - test" = c(-1, -1, -1, 3, 0),
"E - test" = c(-1, -1, -1, 0, 3))
### reproduce results in Westfall et al. (1999, page 172)
### note: the ordering of our estimates here is different
amod <- aov(response ~ trt - 1, data = cholesterol)
summary(glht(amod, linfct = mcp(trt = Ksub[,5:1])),
test = adjusted("Westfall"))
}
\keyword{datasets}
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