Feng, Jingyu wrote: > > I'am trying to develop some code if R, which would correspond to what I > did in SAS. > The data look like: > > Treatment Replicate group1 GSI > > .. > The SAS code is: > proc mixed data=data_name order=data method=ml; *scoring=10; > classes group1; > model GSI=group1/residual influence solution; > repeated /group=group1; > > run; > > Basically, I need different variance for each treatment group. I want to > do the similar thing in R. > > Here is what I get so far: > lm1<-lme(response~treatment,data=o,random=~1|as.factor(dummy),weights=varIdent(form=~1|treatment),method="ML") > > There should no random term in the model. However If I don't specify one, > lme won't work, so I made a dummy variable, which equals to 1 for every > observation. > >
The much underused (quote Frank Harrell) gls in package nlme should do that. Quote PB (p250): It can be viewed as an lme function without the random argument. Dieter -- View this message in context: http://www.nabble.com/Fit-unequal-variance-model-in-R-tp22829549p22830566.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.
