By David Smith
Mixed models (which include random effects, essentially parameters drawn from a random distribution) are tricky beasts. Throw non-Normal distributions into the mix for Generalized Linear Mixed Models (GLMMs), or go non-linear, and things get trickier still. It was a new field of Statistics when I was working on the Oswald package for S-PLUS, and even 20 years later some major questions have yet to be fully answered (like, how do you calculate the degrees of freedom for a significance test?).
These days lme4, nlme and MCMCglmm are the go-to R packages for mixed models, and if you’re using them you likely have questions. The r-sig-mixed-models FAQ is a good compendium of answers, and includes plenty of references for further reading. You can also join in the discussions on mixed models at the r-sig-mixed-models mailing list.
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