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\name{litter}
\alias{litter}
\docType{data}
\title{ Litter Weights Data Set }
\usage{data("litter")}
\description{
Dose response of litter weights in rats.
}
\format{
This data frame contains the following variables
\describe{
\item{dose}{dosages at four levels: \code{0}, \code{5}, \code{50},
\code{500}.}
\item{gesttime}{gestation time as covariate.}
\item{number}{number of animals in litter as covariate.}
\item{weight}{response variable: average post-birth weights
in the entire litter.}
}
}
\details{
Pregnant mice were divided into four groups and the compound in four
different doses was administered during pregnancy. Their litters
were evaluated for birth weights.
}
\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 109.
P. H. Westfall (1997). Multiple Testing of General Contrasts Using
Logical Constraints and Correlations. \emph{Journal of the American
Statistical Association}, \bold{92}(437), 299--306.
}
\examples{
### fit ANCOVA model to data
amod <- aov(weight ~ dose + gesttime + number, data = litter)
### define matrix of linear hypotheses for `dose'
doselev <- as.integer(levels(litter$dose))
K <- rbind(contrMat(table(litter$dose), "Tukey"),
otrend = c(-1.5, -0.5, 0.5, 1.5),
atrend = doselev - mean(doselev),
ltrend = log(1:4) - mean(log(1:4)))
### set up multiple comparison object
Kht <- glht(amod, linfct = mcp(dose = K), alternative = "less")
### cf. Westfall (1997, Table 2)
summary(Kht, test = univariate())
summary(Kht, test = adjusted("bonferroni"))
summary(Kht, test = adjusted("Shaffer"))
summary(Kht, test = adjusted("Westfall"))
summary(Kht, test = adjusted("single-step"))
}
\keyword{datasets}
|
