[R-lang] Comparing PQL fits for logistic regression models

[David Reitter]

What are the options to compare fits of logistic regression models?

My models are fitted using 'glmmPQL' from the MASS and nlme  
libraries. The models to be compared differ in their use of  
covariates, e.g.,

model.1 <- glmmPQL(dep ~ pred1a * pred2, random=1|subj, family=binomial)
model.2 <- glmmPQL(dep ~ pred1b * pred2, random=1|subj, family=binomial)

pred1a and pred1b are correlated, and I'd like to estimate their  
relative predictive power.

Both models use the same datasets, but the covariate structure is not  
nested.

(Adjusted) R^2 are not available, which I think has to do with the  
PQL fitting algorithm (it is not a maximum likelihood fit - do I  
remember correctly?).

AIC doesn't seem to be useful either ([1]). AIC, BIC and Log- 
Likelihood are only output as "NA" using the "summary" function. If  
there is no valid Log-Likelihood available for a PQL model, we can't  
compute Nagelkerke's (Pseudo) R^2, can we?

I've thought of using compareFits and comparePred from the "nlme"  
package (glmmPQL gives me models of type "lme"), but this failed  
miserably.

The only values I am getting for my model are adjusted R^2 for "lm"  
fits. But because I'm using repeated-measures data (sampled from  
dialogue corpora), fits here seem to violate not only distribution  
assumptions of the response variable, but also independence   (IID)  
of the data points.

Any comments would be appreciated - be it to clear up some  
misconceptions on my part, or be it to solve my problem at hand.



[1] http://tolstoy.newcastle.edu.au/R/e2/help/07/06/18477.html


--
David Reitter
ICCS/HCRC, Informatics, University of Edinburgh
http://www.david-reitter.com