[R] mixed effect-models

[Jim Lindsey]

> 
> 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

gnlmix will fit one arbitrarily selected random parameter (usually not
the intercept) in a linear or nonlinear regression function with
arbitrary conditional and mixing distributions. Jim

> 
>  - 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
> good.
> 
> > 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).
> 
> 	-thomas
> 
> 
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