how to estimate overdispersion in glmer models?

<lcayuela <at> ugr.es> writes:

[snip]

model1 <- glmer(fruitset ~ Dist*wire + (1|Site), data, binomial)
summary(model1)

Generalized linear mixed model fit by the Laplace approximation
Formula: fruitset ~ Dist * wire + (1 | Site)
   Data: data
   AIC   BIC logLik deviance
 68.23 70.65 -29.11    58.23
Random effects:
 Groups Name        Variance   Std.Dev.
 Lugar  (Intercept) 3.5155e-14 1.8750e-07
Number of obs: 12, groups: Lugar, 2

[snip]

My question is, how can I check for overdispersion? In glm models you can
check this by comparing the residual deviance with the residual degrees of
freedom, but in glmer you don't get this information.

(Ubuntu Intrepid Ibex / R 2.7.1)

a few thoughts --

(1) probably better to ask this question on the R-sig-mixed-models
list, which specializes in these problems
(2) try lme4:::sigma
(3) do you really have just 12 observations in 2 groups?  In that case
I would strongly recommend just treating group as a fixed
factor -- you have no power to estimate variance (note your
random effect has a standard deviation of 2 x 10^-7), and you
will avoid lots of heartache if you just fit
glm(fruitset ~ Dist*wire + Site, data, binomial)
[not everyone will agree with me about this ...]
(4) I'm a little puzzled that your formula has "Site"
as a random effect but your summary lists "Lugar" as a
random effect.  Did you edit the summary?