
Dear Zoltan,

Answering your first question, Nagelkerke's R2 is usually used in generalized models with binomial outcome. You may find a brief explanation in Steyerberg 2009.

Best,

Aitor 

--------------------------------------------------
From: "Zoltan Botta-Dukat" <bdz@botanika.hu>
Sent: Tuesday, March 09, 2010 8:58 AM
To: <r-sig-ecology@r-project.org>
Subject: [R-sig-eco] explained variation in GAM/GAMM

> Dear All,
> 
> I would like to estimate the variation explained by different sets of 
> predictors. My response is binary, and I want to capture the possible 
> non-linear relationships, thus first I chooose GAM with binomial family.
> Summary of the model gives adjusted R-squared and explained deviance (in
> %). The latter seems to be what I need, and I'm not sure what  the 
> R-squared is in this situation. Could someone explain it?
> 
> Then, I recognzed that residuals are highly spatially autocorrelated, so
> I changed to GAMM. It handle the spatial autocorrelation, but the 
> summary of gam part of the fitted model gives adjusted R-squared only, 
> but not the expalined variance. R-squared is lower in GAMM than in GAM, 
> so I think  considering spatial autocorrelation is necessary. My 
> question is whether I can use this adjusted R-squared or I should 
> calculate something else, for example Efron's pseudo-R-squared, from the
> predicted values.
> 
> Thanks for your advice
> 
> Zoltan
> 
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