# Confusion about interpreting log likelihood (and likelihood ratio test) output

I am using the nlme package in R to use mixed effects models to analyze multilevel (i.e., students nested within classrooms) data.

I am confused about interpreting the log likelihood output from these functions, and while there are other questions that address why the log is taken of the likelihood statistic and why it is negative, it is not clear how these apply to interpreting these values.

In particular, a negative log likelihood is output. Since estimation methods maximize the log likelihood, I understand a larger value indicates better fit.

Does this interpretation apply to the (negative) log likelihood output, so that a larger (i.e., closer to 0) value indicates better fit, so that if I were comparing, for example, log likelihood values of -3146.9 and -2931.41 using the anova() function, and a likelihood ratio test indicated a test statistic associated with a p-value less than .05, then the model with the log likelihood of -2931.41 demonstrates better fit?

• No. we minimise the negative log likelihood. – SmallChess Mar 31 '17 at 13:37