[R] Mixed-effects model for overdispersed count data?

[Ben Bolker]

dave fournier <otter <at> otter-rsch.com> writes:

> 
> According to the documentation for glmmADMB if you fit
> your model with a statment like
> 
> fit =glmm.admb(y~Base*trt+Age+Visit, ...  data=epil2,family="nbinom")
> 
> and that the parameter estimates are in
> 
>     fit$b  while their estimated standard deviations are
> in
> 
>      fit$stdbeta
> 
> so presumably  p values can be constructed from the
> quotient
> 
>       fit$b/fit$stdbeta
> 
> by assuming a t distribution with (somehow) the correct
> degrees of freedom.


  As I commented elsewhere (for the record in this group),
you would do that in R via

2*pnorm(-abs(fit$b/fit$stdbeta))

for a 2-tailed test, but these values should be taken as
order-of-magnitude estimates of the 'true' (???) p-value at
best, because they are Wald tests (not score or likelihood,
both of which are more reliable) and because they assume
infinite 'denominator degrees of freedom' (i.e. Z/chi-squared
test rather than t/F test equivalent).
   Probably reliable only for a large, well-behaved data set
(e.g., >40 random-effects levels (species or nests)) ...