Ben et al,
I think you might have to back up and think about what hypothesis you're testing when you're comparing two non-nested models. You could consider Vuong's test http://en.wikipedia.org/wiki/Vuong%27s_closeness_test ; http://fisher.osu.edu/~schroeder.9/AMIS900/Vuong1989.pdf ... alternatively, I do think comparing AICs makes sense. AIC(model,model2) will just give you a list of AIC values. bbmle::AICtab(model,model2) will give you a slightly prettier output. Keep the various limitations of AIC (asymptotic; assumes internal points -- seehttp://glmm.wikidot.com/faq) in mind too. Ben Bolker
Slightly off topic, but speaking of Vuong's tests, I wonder about the ease with which we could obtain the necessary output from lme4. To carry out the tests, I believe we need individual observations' contributions to both the likelihood and gradient (evaluated at the ML estimates). I believe these are not simple to obtain from models fit in lme4, but I wonder whether you have any hints here. Thanks, Ed
Ed Merkle, PhD Assistant Professor Department of Psychological Sciences University of Missouri Columbia, MO, USA 65211