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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