[R-sig-ME] Model validation for Presence / Absence (binomial) GLMs

[Ben Bolker]

Chris Howden <chris at ...> writes:

> 
> This is something I always battle with given the plethora of great model
> fitting methods available for other models.
> 
> I always use a variant of Hugh's suggestion and look at the % of correct
> predictions between models as a quick model fitting statistic.
> 
> And for overdispersion I believe one way is to fit individual level random
> effects and see if this is a substantively better model. There is more on
> this in the wiki http://glmm.wikidot.com/faq

  Yes, but this is unidentifiable for Bernoulli responses (as also
explained there).

  It's not as systematic, but where possible I like to compare 
parametric fits to a less-parametric fit, either a (marginal)
GAM fit or binning the data and computing (marginal) mean proportions (and
possibly binomial CIs) within bins (the latter is essentially
the basis of the Hosmer-Lemeshow test).  The effects of other
variables might lead to either a false positive or a false
negative when comparing non-parametric marginal to parametric
conditional predictions, but it's a start.

  Ben Bolker