Solved – If the log-likelihood is negative, should we choose it? – model comparison

lme4-nlmer

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

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