[R] mixed effect-models

Thomas Lumley tlumley at u.washington.edu
Mon Oct 21 16:03:03 CEST 2002

On Mon, 21 Oct 2002, Xavi wrote:

> Hello,
> I believe that in R, it is not possible to analyze mixed effect-models
> when the distribucion is not gaussian (p.e. binomial or poisson), isn't?

It depends on exactly what you mean.

 - Jim Lindsey's packages will fit (at least) random intercept models

 - For binomial or Poisson models with reasonably large means (perhaps 4
or so) the PQL approximation used by glmmPQL in the MASS package is pretty

> Somebody can suggest me alternative?

Again, it depends on why you want to fit mixed-effects models. You may be
able to fit marginal models (GEE) instead.

If you really want to fit mixed models with multiple random effects to
binary data you probably need SAS PROC NLMIXED or a Bayesian solution
(or HLM or MLWiN might be able to do it by now).


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