I am doing linear mixed models using lme4 and this is the results of model comparison:
> anova(lmer5,lmer6,lmer32)
Df AIC BIC logLik Chisq Chi Df Pr(>Chisq)
lmer32 9 43172 43226 -21577
lmer6 21 43190 43315 -21574 6.3081 12 0.8998
lmer5 26 43162 43317 -21555 37.9971 5 3.778e-07 ***
As you can see, the results show that one model is significantly better than the others and normally I will choose model with smallest logLik. However in this result, the logLik is negative. Do you think it is a good idea to choose model from logLik in this case, or should I choose it from AIC or BIC instead.
As no conclusion whether AIC is better than BIC, I am confused which one I should choose. What do you think?
Best Answer
For a Cox model, logLik method returns the partial likelihood.
check https://mailman.ucsd.edu/pipermail/ling-r-lang-l/2011-August/000282.html you want to have large (i.e. in direction of positive infinity) logLik and small AIC/BIC (i.e. in direction of negative infinity)
also caution BIC is only valid for comparisons of not nested models (see https://en.wikipedia.org/wiki/Bayesian_information_criterion), if models are nested orient with AIC alone, if models are not nested you can orient for both AIC and BIC