Assuming you are measuring Y and you have factor A fixed and factor B random, I would create a model like:
mod<-lme(Y ~ A, random=~1|B/A, mydata) VarCorr(mod1) the term "random=~1|B" tells the model that B is a random factor, adding the "/A" to get "random =~1|B/A" tells the model you want the interaction between the fixed and random factors. VarCorr gives you the variance components of the model. All is answered much better (and with examples) in Pinheiro and Bates 2000 (it's in the first chapter) and in Crawley 2002. I have posted a question similar to yours times ago, and got an excellent reply from Prof Bates; search the archives for it. If ALL your factors are random try something: mod<-lme(Y~1,random=~1|A/B, mydata) VarCorr(mod) but here I am more guessing than anything. Get Pinheiro and Bates 2000 for this. Cheers, Federico -- ================================= Federico C. F. Calboli PLEASE NOTE NEW ADDRESS Dipartimento di Biologia Via Selmi 3 40126 Bologna Italy tel (+39) 051 209 4187 fax (+39) 051 251 208 f.calboli at ucl.ac.uk ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help