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 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"
nlme & varIdent
2 messages · Louisell, Paul T PW, Thierry Onkelinx
2 days later
Dear Paul, Note that variance functions work on the residuals, not the random effect variances. I can't comment further on this as your question is not very clear to me. Can you provide a more detailed example. E.g. the formula and who you want to variance or correlation functions to work. Best regards, ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance Kliniekstraat 25 1070 Anderlecht Belgium To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher The plural of anecdote is not data. ~ Roger Brinner The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. ~ John Tukey 2016-11-12 0:52 GMT+01:00 Louisell, Paul T PW <Paul.Louisell at pw.utc.com>:
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 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"
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