JaFF <el.romaro <at> gmail.com> writes: > > > Hi folks, > > I have a dataset from a trial measuring the subjects' pupils. There are many > measurements, all of which must be analysed in a similar fashion; so if I > get the analysis right for one of them, I've got them all. For simplicity, > let us call any measurement we may be interested as "response". The study > design is an unbalanced latin square, with 5 periods, 5 treatments and 6 > subjects. Each subject has two measurements: left and right eyes. The model > is as follows, with ":" denoting interaction... > > Fixed Effects = (Subject + Period + Dose):Eye > Random Effects = Subject:Period + Subject:Period:Eye >
> My main question is how to make this happen in R. I know that "aov" is not > suitable. If you need any more information, I will do my best to provide it > to the best of my knowledge. Doesn't "treatment" appear in fixed effects somewhere? Perhaps you mean (Treatment+Period+Dose):Eye? Translating your specification directly (substituting 'treatment' for 'subject' in the fixed effects) I would say lmer(response~(Treatment+Period+Dose):Eye + (Eye|Subject:Period), data=...) should be OK. Do you really want interactions only (:) rather than crossing (*) for the fixed effects? You will get a model with the same number of parameters either way, but parcelled out among effects differently. ______________________________________________ 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.
