[R] glmm

[Thomas Lumley]

On Thu, 2 Sep 2004, Dimitris Rizopoulos wrote:

> Hi Niko,
>
> look at functions `GLMM' (package: lme4) and `glmmPQL' (package:
> MASS).

Yes, but they don't take sampling weights.

We had this discussion a while back for linear mixed models and no-one had
a really satisfactory solution. In contrast to most simple regression
models, mixed models don't even give the right point estimates when you
use sampling weights and pretend they are precision weights.

I think the best solution that was suggested is to put the weights in the
model as a predictor (unless they depend on variables that shouldn't be in
the model).  As the weights completely describe the biased sampling, this
will give a valid model-based analysis.

For a design-based analysis you are probably out of luck.

	-thomas


>
> Best,
> Dimitris
>
> ----
> Dimitris Rizopoulos
> Doctoral Student
> Biostatistical Centre
> School of Public Health
> Catholic University of Leuven
>
> Address: Kapucijnenvoer 35, Leuven, Belgium
> Tel: +32/16/396887
> Fax: +32/16/337015
> Web: http://www.med.kuleuven.ac.be/biostat/
>      http://www.student.kuleuven.ac.be/~m0390867/dimitris.htm
>
>
> ----- Original Message -----
> From: "Niko Speybroeck" <NSpeybroeck at itg.be>
> To: <R-help at stat.math.ethz.ch>
> Sent: Thursday, September 02, 2004 10:42 AM
> Subject: [R] glmm
>
>
> >
> > I am trying to use R. My question is if R can calculate a random
> effect
> > probit model {e.g. glmm} but including sampling weights. I am
> desperately
> > looking for a random effect model but wanted to use it on survey
> data.
> >
> > Thanks for an answer: Niko Speybroeck.
> >
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Thomas Lumley			Assoc. Professor, Biostatistics
tlumley at u.washington.edu	University of Washington, Seattle