Dear Thierry,

Thanks, that explains what is going on.  As far as I can see how the
weights are implemented was not mentioned under ?glmer, so I was thinking
they were weights on contribution of each case to the likelihood.  Ah well.

Thanks,

Josh



On Mon, Jun 3, 2013 at 4:28 AM, ONKELINX, Thierry
<Thierry.ONKELINX@inbo.be>wrote:

> 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@inbo.be
> www.inbo.be
>
> To call in the statistician after the experiment is done may be no more
> than asking him to perform a post-mortem examination: he may be able to say
> what the experiment died of.
> ~ Sir Ronald Aylmer Fisher
>
> The plural of anecdote is not data.
> ~ Roger Brinner
>
> The combination of some data and an aching desire for an answer does not
> ensure that a reasonable answer can be extracted from a given body of data.
> ~ John Tukey
>
> -----Oorspronkelijk bericht-----
> Van: r-sig-mixed-models-bounces@r-project.org [mailto:
> r-sig-mixed-models-bounces@r-project.org] Namens Joshua Wiley
> Verzonden: zondag 2 juni 2013 23:49
> Aan: r-sig-mixed-models@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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-- 
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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