[R-sig-ME] Likelihood ratio statistics when one model is probably overfitting
pmilin at ff.uns.ac.rs
Wed Jun 1 14:23:21 CEST 2011
Sorry for being stubborn, but I would really like to clarify one issue
that remained open after my latest post.
In brief, I started with a model that had vector-valued random effect,
like (1+A|item). Unfortunately, it got perfect correlations between some
levels of A
(the variance covariance matrix of the random effects is singular or
nearly singular). Then, I got advice to go for scalar random effect,
like (1|A+item). Finally, I got another advice to compare two models --
vector-valued and scalar -- by means of likelihood ratio.
My question is: whether one should compare model that has (nearly)
singular variance/covariance matrix of random effects at all? Using
fit-statistics of a model that has "wrong fit" (overfit) seems
unnecessary, if not odd. (Moreover, I secretly did that, and the
Please, can anyone more experienced clarify this?
More information about the R-sig-mixed-models