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?

The reason for asking that is:

McCulloch (1997): "Unfortunately, PQL methods can perform poorly relative to Maximum likelihood" 
Agresti (2002) p. 524 "........but where possible we recommend using ML rather than PQL".


Thanks a lot,

Abderrahim 

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