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).
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. Journal of the American
Statistical Association, 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"))