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.
>
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