Hi Ben, thanks for your reply. Your suggestion does not work indeed:
lme(y ~ x, random=list(~1|a:b, ~1|b:c), data=mydata) Error in getGroups.data.frame(dataMix, groups) : Invalid formula for groups Here is a reproducible example of my data: set.seed(123) library(lme4) library(nlme) y<- rnorm(30) x<-rnorm(30) a<- factor(sort(rep(c("alpha", "beta", "charlie"), 10))) b<- factor(rep(c("rho", "epsilon", "lambda"), 10)) c<- factor(c(sort(rep(1:2, 5)), sort(rep(3:4, 5)), sort(rep(5:6, 5)))) mydata<- data.frame(y,x,a,b,c) mod1<- lmer(y ~ x + (1|a:b) + (1|b:c), data=mydata) mod2.lme<- lme(y ~ x, random=list(a=~1, b=~1, c=~1), data=mydata) mod2.lmer<- lmer(y ~ x + (1|a) + (1|a:b) + (1|a:b:c), data=mydata) My objective is to specify mod1 using function lme. Anyone knows how to do it? Thanks J On Mon, Sep 12, 2011 at 9:43 PM, Ben Bolker <bbol...@gmail.com> wrote: > 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<http://www.r-project.org/posting-guide.html> > and provide commented, minimal, self-contained, reproducible code. > [[alternative HTML version deleted]] ______________________________________________ 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.