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
Control A 1 0.81301
Control B 1 1.06061
Control C 1 1.26350
Control D 1 0.93284
Low A 2 0.79359
Low B 2 0.89111
Low C 2 1.03485
Low D 2 1.29166
Mid A 3 1.26900
Mid B 3 .
Mid C 3 1.58666
Mid D 3 1.35759
High A 4 2.02680
High B 4 1.52372
High C 4 2.19167
High D 4 1.29949
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.
If anyone could help, it will be greatly appreciated.
Thanks,
Jingyu
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