Hi, to my understanding, lme from Pinheiro & Bates nlme-library should produce similar results as aov for (standard) repeated measure designs (or am I wrong here?). For example, if I analyse the machines data set with aov and with lme, I obtain similar results: library( nlme ) data( Machines ) summary(aov(score ~ Machine + Error(Worker/Machine),Machines)) anova(lme(score ~ Machine, data=Machines, random = ~ 1|Worker/Machine)) Well, the machines example contains only one factor. Now, if I try to do something similar on a design with two factors, I have trouble specifying the random factor. For example: data2 <- data.frame(sub=as.factor(c(1,1,1,1,1,1,2,2,2,2,2,2, 3,3,3,3,3,3,4,4,4,4,4,4,5,5,5,5,5,5)), a=as.factor(c(0,0,0,1,1,1,0,0,0,1,1,1,0,0, 0,1,1,1,0,0,0,1,1,1,0,0,0,1,1,1)), b=as.factor(c(1,2,3,1,2,3,1,2,3,1,2,3,1,2, 3,1,2,3,1,2,3,1,2,3,1,2,3,1,2,3)), dv=c(60.4,60.8,59.8,56.3,65.3,57.1,39.1, 44.8,48.9,45.1,47.3,48.9,56.4,58.8,60.5, 58.6,58.9,61.2,58.0,60.1,64.3,57.3,59.8, 66.1,49.6,46.7,51.5,52.8,54.9,55.9)) summary(aov(dv ~ a * b + Error(sub/(a+b)),data2)) anova(lme( dv ~ a * b, data=data2, random = ~ 1 | sub/(a+b))) The last line gives me an error. So --- how can I specify the random effects for such a case? Do I have to use the pdMat class? I played a bit with it --- but unfortunately without success. Thanks for any help Volker -- -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._