Hi I updated my R version to 2.6.1, now glht() worked with lme() from nlmn package. thanks for responding.
Now I have a questions: can I use the contrast() from contrast package to do the comparison that I am interested in? The comparison that I am intersted is to test the linear relationship among the 3 levels ('b','m','t') of the fixed effect "type"? how to specify that comparison? Using glht(), it is easy to specify by: batch<-as.factor(rep(1:4,3,each=20)) type<-as.factor(rep(c('b','m','t'),each=80)) y<-2*(type=='m')+4*(type=='t')+rnorm(240,100,20) dat<-cbind(as.data.frame(y),type=type,batch=batch) rm(batch,type,y) library(nlme) dat.lme<-lme(y~type, random=~1|batch/type, data=dat) library(multcomp) summary(glht(dat.lme, linfct = mcp(type =c("b+t-2*m=0"))),test=Chisqtest()) However, after looking at the examples from the help page of contrast(), I still couldn't figure out how to specify that comparison. Thanks for any suggestions. --- Dieter Menne <[EMAIL PROTECTED]> wrote: > array chip <arrayprofile <at> yahoo.com> writes: > > (Real names are prefered here) > > > I am trying to test some contrasts, using glht() > in > > multcomp package on fixed effects in a linear > mixed > > model fitted with lme() in nlme package. The > command I > > used is: > > data=dat) > > glht(dat.lme, linfct = mcp(type =c("b+t-2*m=0"))) > > > > The lme model fit is ok, but I got an error > message > > with glht(): > > Error in eval(expr, envir, enclos) : object > > "info.index" not found > > Error in factor_contrasts(model) : no > 'model.matrix' > > method for 'model' found! > > > > according to help page of glht(), it should work > with > > linear mixed model, what is the problem here? > > > Here an example from Torsten Hothorn (author of > multcomp), both for lme > and lmer : > To get a better answer, please provide the > (simulated?) data for your example > as required by the posting guide, > > # multcomplme.r > nlmeOK <- require("nlme") > lme4OK <- require("lme4") > library("multcomp") > K <- rbind(c(0,1,-1,0),c(0,1,0,-1),c(0,0,1,-1)) > data("ergoStool", package = "nlme") > stool.lmer <- lmer(effort ~ Type + (1 | Subject), > data = ergoStool) > glme4 <- glht(stool.lmer,K) > summary(glme4) > > > #Linear Hypotheses: > # Estimate Std. Error z value p value > #1 == 0 1.6667 0.5187 3.213 0.00376 ** > #2 == 0 3.2222 0.5187 6.212 < 0.001 *** > #3 == 0 1.5556 0.5187 2.999 0.00761 ** > stool.lme <- lme(effort ~ Type, data = ergoStool, > random = ~ 1 | Subject) > gnlme <- glht(stool.lme,K) > summary(gnlme) > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, > reproducible code. > ____________________________________________________________________________________ Looking for last minute shopping deals? ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.