Rather than estimating the variance components via the
method-of-moments estimators, have a look at the 'nlme' and 'lme4'
packages, which provide likelihood-based tools for estimating random
and mixed models (and in the case of nlme, gls models too).
Advantages of the likelihood-based approaches in
Please pardon an extremely naive question. I see related earlier
posts, but no responses which answer my particular question. In
general, I'm very confused about how to do variance decomposition with
random and mixed effects. Pointers to good tutorials or texts would
be greatly appreciated.
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