[R-sig-ME] lme4 - GLMM dispersion parameter?

[Ben Bolker]

Liam Crowther (BIO <L.Crowther at ...> writes:

>  Dear list users, I'm using lme4 to model the densities of several
> bee species in response to landscape gradients. For some species
> I've used just a random intercept and for others I've allowed a
> random effect of forage quality, this is determined by comparing
> maximal models with the different random components before refining
> the fixed effects. The dependent variable is a count at a
> transect of which there are repeated measures so I'm using a GLMM
> with Poisson errors, examples of final models below:

> hy25<-glmer(Bh~+DATE+bees$X250PCURB+bees$X250PCOSR+bees$X250PCWOO+ 
> (1|TRANSECT), data = bees,
> family =poisson)

  It may work for now but it's ugly and maybe eventually problematic to use
bees$ inside the formula:

hy25<-glmer(Bh~DATE+X250PCURB+X250PCOSR+X250PCWOO+(1|TRANSECT), 
  data = bees, family =poisson)

 would be clearer.

[snip]

> In total there are 338 observations of 42 subjects, 
> is there a general method for extracting a dispersion
> parameter from models such as these 
> (there are models for 7 spp. at 3 different scales) where there are
> differing numbers of predictors and random effects?

  Don't quite understand the question.  Poisson models don't
have dispersion parameters.  Do you want to extract the random-effects
variances and covariances (?VarCorr) ? Compute an estimate
of overdispersion (sum(residuals(model,type="pearson")^2), and
and see http://glmm.wikidot.com/faq ?

  Ben Bolker