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. Help/advice would be greatly appreciated. Thanks, Paul Louisell Statistical Specialist paul.louis...@pw.utc.com 860-565-8104 Still, tomorrow's going to be another working day, and I'm trying to get some rest. That's all, I'm trying to get some rest. Paul Simon, "American Tune" ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
