R help - May 2003 - multcomp and lme

I suppose that multcomp in R and multicomp in S-Plus are related and it 
appears that it is possible to use multicomp with lme in S-Plus given the 
following correspondence on s-news

sally.rodriguez at philips.com	12:57 p.m. 24/04/03 -0400	 7	[S] LME summary 
and multicomp.default()

Is it possible to use multicomp with lme in R and if so what is the syntax 
from a simple readily available example such as the data frame HR in the 
library SASmixed?.

Thank you very much.

Peter B.

> I suppose that multcomp in R and multicomp in S-Plus are related
not really, there are huge differences.
> and it
> appears that it is possible to use multicomp with lme in S-Plus given
> the
> following correspondence on s-news
>
> sally.rodriguez at philips.com     12:57 p.m. 24/04/03 -0400        7
> [S] LME
> summary
> and multicomp.default()
>
> Is it possible to use multicomp with lme in R and if so what is the
> syntax
> from a simple readily available example such as the data frame HR in the
> library SASmixed?.
>
it is possible, yes, but you have to do some extra work. The package
offers two low-level functions `csimint' and `csimtest' which compute
adjusted p-values based on the estimated effects and their covariance
matrix. That means that you have to

1) compute the appropriate contrast matrix (maybe using `contrMat')
2) define the contrasts of the factor of interest (there is a section
   in MASS on how to do it)
3) fit the model
4) and compute the covariance matrix via `vcov'

and you have everything you need to proceed. I attach a short example
using glm, lme should be along the same lines...

best,

Torsten

---------------------------------------------------------------------

library(multcomp)

set.seed(290875)

# a factor at three levels
group <- factor(c(rep(1,10), rep(2, 10), rep(3,10)))

# Williams contrasts
contrasts(group) <- mginv(contrMat(table(group), type="Will"))

# a binary response
z <- factor(rbinom(30, 1, 0.5))

# estimate the model
gmod <- glm( z ~ group, family=binomial(link = "logit"))

# exclude the intercept
summary(csimtest(coef(gmod)[2:3], vcov(gmod)[2:3,2:3],
                 cmatrix=diag(2), df=27))


> Thank you very much.
>
> Peter B.

 _______________________________________________________________________
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|	Dipl.-Stat. Torsten Hothorn					|
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