Gordon Smyth <[EMAIL PROTECTED]> writes: > On page 165 of Mixed-Effects Models in S and S-Plus by Pinheiro and > Bates there is an example of using lme() in the nlme package to fit a > model with crossed random factors. The example assumes though that the > data is grouped. Is it possible to use lme() to fit crossed random > factors when the data is not grouped? > > E.g., y <- rnorm(12); a=gl(4,1,12); b=gl(3,4,12). Can I fit an > additive model like y~a+b but with a and b random? > > Everything I've tried gives an error: > > > lme(y~1,random=~1|(a+b)) > Error in switch(mode(object), name = , numeric = , call = object, > character = as.name(object), : > [[ cannot be of mode ( > > lme(y~1,random=~a+b) > Error in getGroups.data.frame(dataMix, groups) : > Invalid formula for groups >
A standard trick is to define a grouping with one level: one <- rep(1,length(y) lme(...., random=~pdBlocked(.....)|one) (Sorry, I'm a little rusty on the syntax, but just follow the example in P&B) AFAIR, it also works with random=list(a=~1,one=~b) and vice versa. (The model is the same but you get different DF calculations, none of which are correct in the completely balanced case...) -- O__ ---- Peter Dalgaard Blegdamsvej 3 c/ /'_ --- Dept. of Biostatistics 2200 Cph. N (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~~~~~~~~~ - ([EMAIL PROTECTED]) FAX: (+45) 35327907 ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
