Model validation for Presence / Absence, (binomial) GLMs

Highland Statistics Ltd <highstat 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).

The statement on  'unidentifiable for Bernoulli 
responses'....well...apparently this is not that trivial.
See:  http://www.highstat.com/BGGLM.htm

Follow the link to: the Discussion Board....
Go to: Chapter 1 Introduction to generalized linear models
And see the topic: Can binary logistic models be overdispersed?

Alain

That's an interesting document: I think the bottom line is:
  
  * if the Bernoulli data can be grouped, i.e. if there are
in general multiple observations with the same set of covariates,
then overdispersion can be identified, because the data are
really equivalent to a binomial response within the groups.

  For example, the trivial example

grp  resp
A    1
A    0
A    1
B    0
B    0
B    1

is equivalent to:

grp  successes total
A    2         3
B    1         3

this sort of aggregation can be done with plyr::ddply, or
in lots of different ways -- it's generally faster and 
more efficient to fit the data in the grouped form than
in the binary form.

It might have been more convenient for readers to be able to
go directly to the link:

http://www.highstat.com/BGS/GLMGLMM/pdfs/
 HILBE-Can_binary_logistic_models_be_overdispersed2Jul2013.pdf
{URL broken to make Gmane  happy}

rather than having to click 5 times to get through the
Highland Statistics material to the PDF of interest ...