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

Chris Howden
Founding Partner
Tricky Solutions
Tricky Solutions 4 Tricky Problems
Evidence Based Strategic Development, IP Commercialisation and Innovation, Data
Analysis, Modelling and Training

(mobile) 0410 689 945
(fax / office)
chris@trickysolutions.com.au

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On 28/06/2013, at 6:32, Mark Payne <mpa@aqua.dtu.dk> wrote:

Hi,

Thanks for the replies - there are some interesting references to
investigate there. I had thought about ROC and AUC as well, but I'm
assuming this is really only a measure of general model quality, much the
same as R-squared is in the linear world. Is there anything that could be
used to look e.g. heteroscedasticiy, over-dispersion, and especially
auto-correlation?

Mark


________________________________
Fra: Hugh Sturrock [hughsturrock@hotmail.com]
Sendt: 27. juni 2013 20:17
Til: Mark Payne; R-sig-mixed-models@r-project.org
Emne: RE: [R-sig-ME] Model validation for Presence / Absence (binomial) GLMs

Mark,

Receiver operator curves (ROC) and the area under these curves (AUC) is
often used to validate models with binary outcomes. Check out the package
verification for relevant roc commands.

It is also possible to do k-folds cross validation (i.e. split your data
into K groups and use K-1 sets as training data and the other k set as
validation data to generate predictions for every point). In the past I've
done this manually in loops, but I'm sure there is a command somewhere.

Good luck,
Hugh

Date: Thu, 27 Jun 2013 18:47:12 +0200

From: markpayneatwork@gmail.com

To: R-sig-mixed-models@r-project.org

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


Dear R-sig-me,


How can I validate the fit of a bionomial (presence/absence) GLM?


Normally in linear modelling there is a nice array of tools

(tukey-anscombe, QQ plots, residuals vs explanatory variables,

correlation plots) that can be used to convince yourself that the fit

is ok. But when you start dealing with a bionomial (presence absence)

GLM, the whole thing kind of breaks down and starts getting ugly. For

a poisson GLM, you can go for pearson residuals - but what would be

the equivalent for a bionomial GLM? Does anyone have suggestions how

to approach this problem? Are there any "best-practices" that I am

unaware of in this regard?


Best wishes,


Mark


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