[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

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

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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