Dear R-Community! For example I have a study with 4 treatment groups (10 subjects per group) and 4 visits. Additionally, the gender is taken into account. I think - and hope this is a goog idea (!) - this data can be analysed using lme as below.
In a balanced design everything is fine, but in an unbalanced design there are differences depending on fitting y~visit*treat*gender or y~gender*visit*treat - at least with anova (see example). Does this make sense? Which ordering might be the correct one? Here the example script: library(nlme) set.seed(123) # Random generation of data: NSubj<-40 # No. of subjects set.seed(1234) id<-factor(rep(c(1:NSubj),4)) # ID of subjects treat<-factor(rep(rep(1:4,each=5),4)) # Treatment 4 Levels gender<-factor(rep(rep(1:2, each=20),4)) visit<-factor(rep(1:4, each=NSubj)) y<-runif(4*NSubj) # Results # Add effects y<-y+0.01*as.integer(visit) y<-y+0.02*as.integer(gender) y<-y+0.024*as.integer(treat) df<-data.frame(id, treat, gender, visit, y) # groupedData object for lme gdat<-groupedData(y ~ visit|id, data=df) # fits - different ordering of factors fit1<-lme(y ~ visit*treat*gender, data=gdat, random = ~visit|id) anova(fit1) fit2<-lme(y ~ gender*treat*visit, data=gdat, random = ~visit|id) anova(fit2) # Result: identical (balanced design so far), ok # Now change gender of subject 1 gdat$gender[c(1,41,81,121)]<-2 # onece more fits with different ordering of factors fit1<-lme(y ~ visit*treat*gender, data=gdat, random = ~visit|id) anova(fit1) fit2<-lme(y ~ gender*treat*visit, data=gdat, random = ~visit|id) anova(fit2) # Result: There are differences!! Hope anybody can help or give me advice how to interpret these results correctly or how to avoid this problem! Is there a better possibility to analyse these data than lme? Thanks! Karl ______________________________________________ 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.
