VCA (version 1.3.1)

test.lsmeans: Perform t-Tests for Linear Contrasts on LS Means.

Description

Perform custom hypothesis tests on Least Squares Means (LS Means) of fixed effect.

Usage

test.lsmeans(obj, L, ddfm = c("contain", "residual", "satterthwaite"), quiet = FALSE)

Arguments

obj

(VCA) object

L

(matrix) specifying one or multiple custom hypothesis tests as linear contrasts of LS Means. Which LS Means have to be used is inferred from the column names of matrix $L$, which need to be in line with the naming of LS Means in function lsmeans.

ddfm

(character) string specifying the method used for computing the denominator degrees of freedom of t-tests of LS Means. Available methods are "contain", "residual", and "satterthwaite".

quiet

(logical) TRUE = will suppress any warning, which will be issued otherwise

Details

This function is similar to function test.fixef and represents a convenient way of specifying linear contrasts of LS Means.

See Also

test.fixef, lsmeans

Examples

## Not run: 
# data(dataEP05A2_2)
# ub.dat <- dataEP05A2_2[-c(11,12,23,32,40,41,42),]
# fit1 <- anovaMM(y~day/(run), ub.dat)
# fit2 <- remlMM(y~day/(run), ub.dat)
# lsm1 <- lsmeans(fit1)
# lsm2 <- lsmeans(fit2)
# lsm1
# lsm2
# 
# lc.mat <- getL(fit1, c("day1-day2", "day3-day6"), "lsm")
# lc.mat[1,c(1,2)] <- c(1,-1)
# lc.mat[2,c(3,6)] <- c(1,-1)
# lc.mat
# test.lsmeans(fit1, lc.mat)
# test.lsmeans(fit2, lc.mat)
# 
# # fit mixed model from the 'nlme' package
# 
# library(nlme)
# data(Orthodont)
# fit.lme <- lme(distance~Sex*I(age-11), random=~I(age-11)|Subject, Orthodont)
# 
# # re-organize data for using 'anovaMM'
# Ortho <- Orthodont
# Ortho$age2 <- Ortho$age - 11
# Ortho$Subject <- factor(as.character(Ortho$Subject))
# 
# # model without intercept
# fit.anovaMM <- anovaMM(distance~Sex+Sex:age2+(Subject)+(Subject):age2-1, Ortho)
# fit.remlMM1 <- remlMM( distance~Sex+Sex:age2+(Subject)+(Subject):age2-1, Ortho)
# fit.remlMM2 <- remlMM( distance~Sex+Sex:age2+(Subject)+(Subject):age2-1, Ortho, cov=FALSE)
# lsm0 <- lsmeans(fit.anovaMM)
# lsm1 <- lsmeans(fit.remlMM1)
# lsm2 <- lsmeans(fit.remlMM2)
# lsm0
# lsm1
# lsm2
# 
# lc.mat <- matrix(c(1,-1), nrow=1, dimnames=list("int.Male-int.Female", c("SexMale", "SexFemale")))
# lc.mat
# test.lsmeans(fit.anovaMM, lc.mat)
# test.lsmeans(fit.remlMM1, lc.mat)
# test.lsmeans(fit.remlMM2, lc.mat)
# ## End(Not run)