[R] generalized linear mixed model by ML

[Ben Bolker]

Abderrahim Oulhaj <abderrahim.oulhaj <at> pharmacology.oxford.ac.uk> writes:

> 
> Dear All,
> 
> I wonder if there is a way to fit a generalized linear mixed models (for
repeated  binomial data)  via a direct
> Maximum Likelihood Approach. The "glmm" in the "repeated" package (Lindsey),
the "glmmPQL" in the 
> "MASS" package (Ripley) and "glmmGIBBS"  (Myle and Calyton) are not using the
full maximum likelihood as I
> understand. The "glmmML" of Brostrom uses the "full maximum likelihood" by
approximating the integral
> via  Gauss- Hermite  quadrature. However, glmmML is only valid for the random
intercept model and the
> binomial family must be represented only as  binary data. Does the lmer do the
work?
> 

  Hmmm.  I will be interested to hear what others have to say on
this topic.  

* lmer() in the lme4 package (new version of nlme) can in
fact do GLMMs with a choice of different
integration methods (PQL is the default but not the only choice).

* GLMMGibbs [sic] was actually
using a full likelihood rather than an approximation, but was
a Bayesian rather than a ML approach [GLMMGibbs is now in the
"Devel" section of CRAN, apparently because of various unresolved
compilation/installation problems.]

  If you want to fit temporal correlation, as well as 
individual random effects, you may be out of luck: a GEE
model will probably be your best bet in that case.  When I asked
about the possibility of incorporating temporal/spatial correlation
structures like those in nlme into lme4, Doug Bates said that he
wanted to work first on getting the basic framework of the package
really solid [can't blame him at all, and of course honor and
glory to him for putting so much work into these tools in the first place]

   good luck,
    Ben Bolker