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Simultaneous confidence intervals for arbitrary parametric contrasts in unbalanced one-way layouts. The procedure controls the FWER in the strong sense.
multcomp.wrapper(model, hypotheses, alternative, rhs=0, alpha, factorC)A list containing:
A numeric vector containing the adjusted pValues
A logical vector indicating which hypotheses are rejected
A matrix containing the estimates and the lower and upper confidence bound
A Mutoss S4 class of type errorControl, containing the type of error controlled by the function.
MuToss-Coding Team
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.
a specification of the linear hypotheses to be tested.
a character string specifying the alternative hypothesis, must be one of 'two.sided' (default), 'greater' or 'less'.
an optional numeric vector specifying the right hand side of the hypothesis.
the significance level
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.
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")Run the code above in your browser using DataLab