jonas garcia <garcia.jonas80 <at> googlemail.com> writes: > I am trying to fit some mixed models using packages lme4 and nlme. > > I did the model selection using lmer but I suspect that I may have some > autocorrelation going on in my data so I would like to have a look using the > handy correlation structures available in nlme. > > The problem is that I cannot translate my lmer model to lme: > > mod1<- lmer(y~x + (1|a:b) + (1|b:c), data=mydata) > > "a", "b" and "c" are factors with "c" nested in "b" and "b" nested in "a" > > The best I can do with lme is: > > mod2<- lme(y~x, random=list(a=~1, b=~1, c=~1), data=mydata) > > which is the same as: > > lmer(y~x + (1|a) + (1|a:b) + (1|a:b:c), data=mydata) > > I am not at all interested in random effects (1|a) and (1|a:b:c) as they are > not significant. I just need two random intercepts as specified in mod1. How > can I translate mod1 into lme language? > > Any help on this would be much appreciated.
This would probably be better on the r-sig-mixed-models list. Does random=list(~1|a:b,~1|b:c) work? I would be a little bit careful throwing out ~1|a (non-significance is not necessarily sufficient reason to discard a term from the model -- it depends a lot on your procedure), and with the interpretation of your nesting. If b is only explicitly and not implicitly nested in a (i.e. if there a levels of 'b' that occur in more than one level of 'a', for example if a corresponded to families, b corresponded to individuals, and you labeled individuals 1..N_b_i in each family) then I'm not sure how you would actually interpret b:c, as it would be crossed rather than nested. But assuming that your model specification in lmer is correct and sensible, I think my suggestion above should (?) work to get the equivalent in lme. > > Jonas ______________________________________________ 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.
