jurgen_vercauteren <jurgen_vercauteren <at> hotmail.com> writes:
> I am analysing my data wit a mixed model. I used SAS but I want to redo the > same analysis in R. Here the SAS code and what I wrote in R. It seems to > work but the results are not the same. I don't know how to specify the class > variable in R or specify the variance matrix. Can you please help me? > > Thanks > Jurgen > > ## SAS: > proc glimmix data=trend method=RSPL; > class pid; > model mdrfinal (event = '1') = time therapy comment / dist=binary > link=logit solution cl; > random intercept / subject=pid type=un; > run; > > ## R: > model_GLMM<- glmmPQL(mdrfinal ~ time + therapy + comment, data = data, > family = binomial, random = ~1| time) > summary(model_GLMM) This question would probably get a better reception on the r-sig-mixed-models <at> r-project.org mailing list. I think you want random = ~1|pid , although with an unstructured variance-covariance matrix you may want random=~factor(time)|pid -- keep in mind that's what on the RHS of the bar is a grouping factor, the LHS of the bar is the random effect(s) term. If you do ~1|pid you'll get a compound-symmetry model with positive rho; if ftime is a factor variable and you do ~ftime|pid you'll get an unstructured matrix. ______________________________________________ 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.
