[R-sig-ME] glmer z values binomial data

[Ben Bolker]

Clelia Gasparini <clelia.gasparini at ...> writes:

> 
> 
> Hi
> 
> I'm using glmer (lme4) to analyse binomial data. I have 
> used observation level to account for overdispersion.
> My problem is that a referee asked to get wald t or F 
> instead of wald Z mentioning Bolker's review in TREE 2008.
> 
> Can you suggest a way to get t or F instead of z in the output?
> 
> this is the code I'm using now:
> 
> y=cbind(data$Success,data$Failure) 
> glmer.r<-glmer(y~ treat  + (1|obs), data=data, 
>  family=binomial(logit), weights =total.size) 
> summary(glmer.r)
> 
> Many thanks in advance, 
> Clelia 
> 

  The Bolker 2008 t/F suggestion is based on using penalized 
quasi-likelihood (PQL) to account for overdispersion; lme4 no longer does 
PQL, because we decided that we don't really understand/aren't comfortable
with the properties of PQL for GLMMs.

* if you can decide on the degrees of freedom (see 
http://glmm.wikidot.com/faq#df , and scroll down to "DF alternatives"),
then you can take the Z scores, reinterpret them as t scores, and
use 2*pt(abs(scores),df=df,lower.tail=FALSE) to get p-values.
* including a per-observation random effect should account for
overdispersion anyway (see the "overdispersion" section of the FAQ)
* I'm a little puzzled that you have specified the response as
a two-column matrix *and* have separately specified the weights:
normally you would do one or the other ...
* the model specification you have above has no grouping variable/
random effect term other than the observation-level RE, which makes
it look just like a regular binomial GLM + overdispersion.  Therefore
you might be better off/have an easier time with

  glm(y~ treat , data=data, family=quasibinomial)

or a beta-binomial model.