Dear all, I am analysing a data set based on 6 groups of individuals. Each group is observed for 10 days. 5 days with one manipulation 5 days with another manipulation. I therefore have 6 replicate groups (n=6) each with one mean measurement for manipulation A and manipulation B. Each group consists of a set of males and females. An independent group of males for each group replicate, however there are only 2 sets of females each replicated 3 times within the six groups.
data takes the from: group response female set treatment 1 3.0 A high 2 3.0 B high 3 2.8 A high 4 2.6 B high 5 2.6 A high 6 2.9 B high 1 1.5 A low 2 1.4 B low 3 1.7 A low 4 1.9 B low 5 2.0 A low 6 2.1 B low The order of treatment is counterbalanced and I would assume I would choose to fit the model: > model1<-lme(response~treat, random=~1|femaleset/group) or > model2<-lmer(response~treat+(1|femaleset/group)) However I am concerned with two aspcts: my small sample size of course but also the use of a random effect of female set only has two levels (A and B). Is there a more appropriate way to handle this analysis? A glm with female group as an explanatory for instance such as: model3<-glm(response~treatment+femaleset+treatment*femaleset). Although yhis will not properly account for the pseudoreplication. Ant assistance or help would be greatly appreciated. Best Colin -- View this message in context: http://r.789695.n4.nabble.com/lmer-with-2-random-effects-with-only-two-levels-tp3536791p3536791.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ 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.
