Nicolas Ris <Nicolas.Ris <at> sophia.inra.fr> writes: > I am trying to analyse the data of the box 10.5 in the Biometry from > Sokal and Rohlf (2001) using R. This is a three-level nested anova with > equal sample size : 3 different treatments are compared ; 2 rats (coded > 1 or 2) / treatment are studied ; 3 preparations (coded 1, 2 or 3) / > rats are available ; 2 readings of the glycogen content / preparations > are realised. Treatment is fixed whereas Rats (nested in Treatment) and > Prep (nested in Rats) are random effects. > > According to a previous discussion found in the R-help archives (January > 2007), I have tried the following formula : > > box105.lme<-lme(content~treatment, box105.gd, random=~1|rats/prep) > However, the formula summary(box105.lme) gives wrong estimates for the
Since your factors are codes as numbers, the first thing I would check if these are really factors in your data frame. If these are numeric, you will get a regression and degrees of freedom are totally off. Try box105.gd$rats = as.factor(box105.gd$rats) box105.gd$prep = as.factor(box105.gd$prep) I am sure there will be some differences to Sokal/Rohlf afterwards, but you should come closer. Please post a complete, self-running examples if you have more questions. Dieter ______________________________________________ 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.