> > If I understand correctly, you want to include the interactions > > between the random and fixed terms? > > Yes that is exactly I wanted to model. > > > This is done like: > > > > model.lme <- lme(Beta ~ Trust*Sex*Freq, > > random = ~Trust*Sex*Freq|Subj, Model) > > > > But this needs a lot of observations as quite a few > > parameters need to be estimated! > > Well, I tried this as well, but it seems R kept hanging there and > never finished the modeling. It is very likely due to some > singularity as you suspected about the large number of parameters > needed to estimate. But this is not a problem with aov. So does > it mean that I can't run a similar model to that in aov with lme?
It depends what you mean by 'similar'. You could still include some of the interactions, e.g. by random = ~(Trust+Sex+Freq)^2|Subj, or even further reduced such as ~Trust+Sex+Freq|Subj. I am not very familiar with aov, but I would suspect that the model you calcualted in aov is not really the same than the one with all possible interactions in lme. In any case, I would personally trust lme much more than aov. > but I feel this is not good enough to account for cross-subject > variations for those interactions. Why wouldn't those patterned > variance-covariance matrix specifications work as I mentioned in > my previous mail? Any more thoughts and suggestions? Sorry, I have never really worked with those. Lorenz - Lorenz Gygax Centre for proper housing of ruminants and pigs Agroscope Reckenholz-Tänikon Research Station ART ______________________________________________ R-help@stat.math.ethz.ch 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.
