[R] glmm
Baskin, Robert
RBaskin at ahrq.gov
Thu Sep 2 17:08:35 CEST 2004
I believe in the earlier discussion it was Spencer Graves that pointed out
that there is earlier work by DuMouchel using design information but not
weights as predictors.
The reference for the use of design weights as predictors is:
<<<Start insert from earlier email<<<
< 9. Pfeffermann, D. , Skinner, C. J. , Holmes, D. J. , Goldstein, H. , and
Rasbash, J. (1998), ``Weighting for unequal selection probabilities in
multilevel models (Disc: p41-56)'', Journal of the Royal Statistical
Society, Series B, Methodological, 60 , 23-40 >
which refers back to:
<29. Pfeffermann, D. , and LaVange, L. (1989), ``Regression models for
stratified multi-stage cluster samples'', Analysis of Complex Surveys,
237-260 >
If you don't like statistical papers, then see section 4.5 of <8. Korn,
Edward Lee , and Graubard, Barry I. (1999), ``Analysis of health surveys'',
John Wiley & Sons (New York; Chichester) > They explain the idea of using
weights in a model fairly simply.
>>>End insert>>>
In the earlier discussion Thomas Lumley pointed out that this means your
resulting estimates are conditional on the weights - so it's not a good
solution - just the only one published using weights.
I believe there is a Bayesian solution in the vein of Ghosh & Meeden
(1997-Chapman Hall) but it hasn't been published.
And my personal opinion is that before anyone uses design weights they
should read:
http://www-unix.oit.umass.edu/~cluster/ed/outline/c00ed72.PDF
bob
-----Original Message-----
From: Niko Speybroeck [mailto:NSpeybroeck at itg.be]
Sent: Thursday, September 02, 2004 10:28 AM
To: Thomas Lumley; Dimitris Rizopoulos
Cc: r-help at stat.math.ethz.ch
Subject: RE: [R] glmm
Thanks a lot for you answer Thomas. Do you have a reference which supports
this solution? Can you give an example of a weight that depends on
variables that shouldn't be in the model?
________________________________
Van: Thomas Lumley [mailto:tlumley at u.washington.edu]
Verzonden: do 2/09/2004 16:15
Aan: Dimitris Rizopoulos
CC: Niko Speybroeck; r-help at stat.math.ethz.ch
Onderwerp: Re: [R] glmm
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
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