I have data (each Y_i is a vector) in the form of Y_i = X_i \beta_i + Z_i b_i + epsilon_i
Were it not for the measurement error (the epsilon_i) it's a very simple model --- nice and balanced, compound symmetry, and I'd just use lme(y ~ x1 + x2, random=~1|subj, ...) but the measurement error is throwing me off.
Because the Y_i are actually derived from other data, I am able to do some bootstrapping and get an estimate of the V-C matrix of epsilon_i.
But I haven't been able to figure out how to weight the observations properly in an lme() call.
Some searching of the archives led me to a 2004 posting (courtesy of Dave Atkins) of two functions written by Jose: varRan and varWithin. This gives me some hope (a good deal of hope, actually), but I can't understand the arguments or how to use these functions.
Here's the posting: http://tolstoy.newcastle.edu.au/R/help/04/04/0245.html Any hints would be greatly appreciated. Thanks, Todd ______________________________________________ R-help@r-project.org mailing list 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.
