Hello,
All the help I've read (including Pinheiro and Bates book, 'Mixed Effects
Models in S and S-PLUS') regarding how to fit a linear mixed-effects model
where variances change with a factor's levels indicates this is done
through the 'weights' argument to 'lme', using something like
'weights=varIdent(form=~v|g)' where 'v' is a variance covariate and 'g' is
the grouping factor whose strata have different random effect variances.
My question: Suppose I have more than 1 variance covariate, say v1, ...,
vk, and I want _each_ of these to have variances that change with the
levels of g giving a total of k*nlevels(g) parameters (k*nlevels(g) - k
allowing for identifiability). How is this handled in the nlme package? A
simple example would be random slope and intercepts, _both_ of which have
variances changing with the levels of g. I haven't found any examples of
this online or in Pinheiro & Bates, and I haven't been able to figure this
out using the various varFunc/pdMat classes. I'd use the 'lme4' package
(instead of nlme), but I need the correlated residuals structure (e.g.,
'corAR1', 'corARMA') provided in nlme.
Help/advice would be greatly appreciated.
Thanks,
Paul Louisell
Statistical Specialist
Paul.Louisell at pw.utc.com
860-565-8104
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