[R] mixed effect-models
Grathwohl,Dominik,LAUSANNE,NRC/NT
dominik.grathwohl at rdls.nestle.com
Tue Oct 22 08:34:47 CEST 2002
Do I understand right, there is no multinom (nnet), with random effects
available in R!
Do I need to switch to SAS, PROC NLMIXED applying Agresti's example:
http://stat2.uibk.ac.at/SMIJ/hartzel_abs.html
Dominik
> On Mon, 21 Oct 2002, Xavi wrote:
>
> > Hello,
> > =A0
> > 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 prett=
> y
> 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
Dominik Grathwohl
Biostatistician
Nestlé Research Center
PO Box 44, CH-1000 Lausanne 26
Phone: + 41 21 785 8034
Fax: + 41 21 785 8556
e-mail: dominik.grathwohl at rdls.nestle.com
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