Hi all,

I'm having difficulty estimating the dispersion of the random effects.  For
example, in the *Dyestuff* data that comes with the package there are
several examples in which "batch" is the grouping variable, and so in the
examples provided the models all have "batch-specific" intercepts:

http://lme4.r-forge.r-project.org/book/Ch1.pdf

My model is:

*model <- glmer(CVD_hospitalizations ~ offset(log(as.integer(denom))) + 1 +
(1|aport1) + c90.noise[-exclude] + (0 + c90.noise[-exclude]|aport1) +
as.numeric(Migration_within_5_year_rate), data = agg.data.strat[-exclude,],
family=poisson)*
Ideally, I'd like to make plots similar to Fig 1.11 in the PDF above.  My
understanding is that the best way to get dispersion estimates would be
using the *ranef()* command with *postVar=TRUE*.  I'm fitting a model using
*glmer()* with a Poisson link.  I'm able to use the *ranef()* command with *
postVar=FALSE*, but with *postVar=TRUE* I get the following error:

*Error in .local(object, ...) : Code not written yet*

Is this because there isn't any code for families besides Gaussian?
 Alternatively, it makes sense that I could sample from the posterior
distribution using *mcmcsamp() *and then take the mean/sd of that sample*, *but
I haven't had any success using that either, and I've seen a number of
forums that say the *mcmcsamp()* function doesn't work.  Here's what I get:

*> samp0 <- mcmcsamp(model, n=10000)*

*Error in .local(object, n, verbose, ...) : Update not yet written*

I'm wondering if you have any suggestions on how I might get those
dispersion estimates or if I'm doing something wrong, etc.

Thanks,

Andrew

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