mutoss (version 0.1-12)

multcomp.wrapper: Simultaneous confidence intervals for arbitrary parametric contrasts in unbalanced one-way layouts.

Description

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

Usage

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

Value

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.

Author

MuToss-Coding Team

Arguments

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

Details

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.

Examples

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")