multcomp.wrapper: Simultaneous confidence intervals for arbitrary parametric...
In mutoss: Unified multiple testing procedures

Description Usage Arguments Details Value Author(s) Examples

Simultaneous confidence intervals for arbitrary parametric contrasts in unbalanced one-way layouts. The procedure controls the FWER in the strong sense.

1	multcomp.wrapper(model, hypotheses, alternative, rhs=0, alpha, factorC)

`model`	a fitted model, for example an object returned by lm, glm, or aov etc. It is assumed that coef and vcov methods are available for model.
`hypotheses`	a specification of the linear hypotheses to be tested.
`alternative`	a character string specifying the alternative hypothesis, must be one of 'two.sided' (default), 'greater' or 'less'.
`rhs`	an optional numeric vector specifying the right hand side of the hypothesis.
`alpha`	the significance level
`factorC`	character string, specifing the factor variable of interest

this function, it is possible to compute simultaneous confidence for arbitrary parametric contrasts in the unbalanced one way layout. Moreover, it computes p-values. The simultaneous confidence intervals are computed using multivariate t-distribution.

A list containing:

`adjPValues`	A numeric vector containing the adjusted pValues
`rejected`	A logical vector indicating which hypotheses are rejected
`confIntervals`	A matrix containing the estimates and the lower and upper confidence bound
`errorControl`	A Mutoss S4 class of type `errorControl`, containing the type of error controlled by the function.

MuToss-Coding Team

data(warpbreaks)
# Tukey contrast on the levels of the factor 'Tension'

multcomp.wrapper(aov(breaks ~ tension, data = warpbreaks), hypotheses = "Tukey", alternative="two.sided", factorC="tension",alpha=0.05)

# Williams contrast on 'Tension'
multcomp.wrapper(aov(breaks ~ tension, data = warpbreaks), hypotheses= "Williams", alternative="two.sided",alpha=0.05,factorC="tension")

# Userdefined contrast matrix
K <-matrix(c(-1,0,1,-1,1,0, -1,0.5,0.5),ncol=3,nrow=3,byrow=TRUE)
multcomp.wrapper(aov(breaks ~ tension, data = warpbreaks), hypotheses=K, alternative="two.sided",alpha=0.05,factorC="tension")

# Two-way anova
multcomp.wrapper(aov(breaks ~ tension*wool, data = warpbreaks), hypotheses="Tukey", alternative="two.sided",alpha=0.05,factorC="wool")
multcomp.wrapper(aov(breaks ~ tension*wool, data = warpbreaks), hypotheses="Tukey", alternative="two.sided",alpha=0.05,factorC="tension")
multcomp.wrapper(aov(breaks ~ tension*wool, data = warpbreaks), hypotheses=K, alternative="two.sided",alpha=0.05, factorC="tension")
data(iris)
multcomp.wrapper(model=lm(Sepal.Length ~ Species, data=iris),hypotheses="Tukey","two.sided",alpha=0.05, factorC="Species")
K <-matrix(c(-1,0,1,-1,1,0, -1,0.5,0.5),ncol=3,nrow=3,byrow=TRUE)
multcomp.wrapper(model=lm(Sepal.Length ~ Species, data=iris),hypotheses=K,"two.sided",alpha=0.05, factorC="Species")