[R] how to estimate overdispersion in glmer models?

[Ben Bolker]

 <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?