[R] Help to conduct a random factor analysis with binomial response
Prof Brian Ripley
ripley at stats.ox.ac.uk
Tue Nov 20 18:58:21 CET 2001
On 20 Nov 2001, Douglas Bates wrote:
> Leif Egil Loe <l.e.loe at bio.uio.no> writes:
>
> > I am a ph.d. student in biology working on red deer in Norway, who
> > would like to conduct an analysis with random factor where the
> > response is binomially distributed. This cannot be conducted in
> > S-plus, and I was told by others that it may be possible in
> > R. However, I soon got into trouble which I hope you can help me to
> > solve.
>
> This type of model is a generalized linear mixed model.
>
> I think you would have to use the GLMMGibbs package in R to analyze
> it. You can't fit this type of model with the nlme package in R or in
> S-PLUS.
Since there was just a single additive random factor, another alternative
is another glmm() in one of Jim Lindsey's packages (gnlr, I think).
I had a student exploring some of these over the summer, and there were
lots of problem getting GLMMGibbs' glmm to keep running with binary
responses (it got internal errors), and a couple of times Lindsey's glmm
gave (practically) different answers to all other methods.
There will soon be some alternatives. I have a wrapper around lme that
fits GLMMs by PQL, and that is working in R and S-PLUS. It will make the
MASS library soon, and I can make beta versions available. (For S-PLUS
Jose' Pinheiro has at alpha/beta a more sophisticated wrapper called GLME,
but that depends on the very latest S-PLUS version of NLME). James
McBroom and I have at alpha stage an adaptively tilted numerical
quadrature method that seems to do ML fitting fast and accurately thus
far.
There are some alternatives for S code for PQL about, too, although
those I have seen are none too flexible.
We (James and I) would be happy to try out what we have on the deer data
for you.
--
Brian D. Ripley, ripley at stats.ox.ac.uk
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272860 (secr)
Oxford OX1 3TG, UK Fax: +44 1865 272595
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