Dear list, I have had a problem with model comparison for several months, so now I finally worked up my courage to ask for your help and hope that you can settle the question.
I have frequently encountered positive logLik values and now heard that this might be due to bug in the lmer function. However, I also recently found Douglas Bates stating that "a positive log-likelihood is acceptable in a model for a continuous response" in an S-list. Positive logLiks appear in Baayen's 2008 introductory book, always together with negative AIC and BIC. He does not seem to treat them as erroneous. Instead, if I understood correctly, he chooses the model with more negative AIC/BIC (smaller value) and more positive logLik (larger value) as the better model in these comparisons. So did I get it right and is this the way to go or is there a bug that inverts the polarity of the numbers? As second question: Is there a general rule of thumb for cases when AIC and BIC point into different directions? Does it depend on the data set? Or is it a matter of taste how much one wants to avoid overfitting? Should one trust the value that agrees with the logLik? Many thanks in advance Anja
