[R-sig-ME] issues with weights in glmer (or glmmADMB)

[ONKELINX, Thierry]

Dear Joshua,

The weights in a binomial glmer are used to indicate the sample size when the response is a proportion. Hence the effect on the standard errors.

Best regards,

Thierry

ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
Belgium
+ 32 2 525 02 51
+ 32 54 43 61 85
Thierry.Onkelinx op inbo.be
www.inbo.be

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-----Oorspronkelijk bericht-----
Van: r-sig-mixed-models-bounces op r-project.org [mailto:r-sig-mixed-models-bounces op r-project.org] Namens Joshua Wiley
Verzonden: zondag 2 juni 2013 23:49
Aan: r-sig-mixed-models op r-project.org
Onderwerp: [R-sig-ME] issues with weights in glmer (or glmmADMB)

Hi All,

I have been working with a random effects binomial model (many thousands of observations, although rates of the event are low ~ .1).  I tried using analytic weights, but the results are odd --- the standard errors get much larger in some cases (e.g., 10 fold, while coefficient estimates do not change too much --- weights were scaled to sum to 1).

My other thought was to try the model using glmmADMB, but it looks like it does not support analytic weights.

IIRC, MCMCglmm also does not support weights, so I think the only other option would be glmmPQL from the MASS package.  That's not too bad as a rough approach, but would prefer a true likelihood or Bayesian approach (really, given the dataset size, a likelihood).

Thanks for any suggestions.

Cheers,

Josh


--
Joshua Wiley
Ph.D. Student, Health Psychology
University of California, Los Angeles
http://joshuawiley.com/
Senior Analyst - Elkhart Group Ltd.
http://elkhartgroup.com

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