Dear all
I'm trying to figure out how to assess overdispersion in a binomial glmm
using lmer, and have encountered the problem that no estimated scale
parameter is given in the summary. I noticed that this problem has
already been raised on the list, and suggested solutions were to use the
sigma slot, which I'd identified as a possibility from the lmer help
file. Looking at responses on the list, I noted that there are different
methods/syntaxes to access this slot, the ones I've figured out being:
summary(model)@sigma
slot(summary(model),"sigma")
lmelme4:::sigma(model)
(I also noted the caution from Douglas Bates on using the latter).
My problem is that all of these just give me the assumed scale parameter
(1), which is not helpful.
So my questions are:
1.	Is it actually possible to extract a reliable estimated (as
opposed to assumed) scale parameter?
2.	If so, how?
3.	If not, or if you can but it's a dubious thing to do, how should
one go about assessing overdispersion in an lmer model?
Many thanks for any insights
Marcus
(PS I'm not at all sure that "estimated" and "assumed" are the correct
terms here, but I hope you understand what I mean)



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