"varFunc" classes

I'm too lazy to see if anyone has answered this one already, so ...

Dear lme4 authors,

I am sure you are very busy so I will just ask my question very quickly. I
was reading the book "Mixed-effects models in S and S-plus" by Pinheiro and
Bates. On the top of page 208 of this book, there is a Table 5.1 that
implements various "varFunc" classes. One of these classes would seem to be
what I need for my data: varIdent - different variances per stratum. I do
know that different subets in my data have very different variances you see,
so I would need to include this.

However this book relates to S-plus and I am not sure if this has been
implemented in R, in the glmer package? My data are continuous so I would
just need this for lmer (and not glmer). If it has not been implemented is
there any "workaround"?

It has been implemented in R, but in the nlme package rather than the
lme4 package (which contains lmer and glmer).

   Historical note: nlme is the package associated with Pinheiro and
Bates's 2000 book. R's first "stable beta" version (according to
Wikipedia) was released in 2000.  Doug Bates has been involved in R
since the beginning (or almost?).

  If you need to do this in lme4::lmer, you can do it in a sort of
clunky way by setting up dummy variables for group differences and
adding an individual-level random effect, e.g.


data(Orthodont,package="nlme")

O2 <- transform(Orthodont,
                obs=seq(nrow(Orthodont))) ## observation-level variance


## since Female var < Male var, we have to use a dummy for Male
## to add extra variance for males (won't work the other way because
## we can't have a negative variance)

m1 <-lmer(distance ~ age + (age|Subject) +
         (0+dummy(Sex,"Male")|obs),
     control=lmerControl(check.nobs.vs.nlev="ignore",
                         check.nobs.vs.nRE="ignore"),
     O2)

library(nlme)
m2 <- lme(distance~age,random=~age|Subject,
          data=Orthodont,
          weights=varIdent(form=~1|Sex))
summary(m2)

all.equal(c(logLik(m1)),c(logLik(m2)))
all.equal(c(fixef(m1)),c(fixef(m2)),tolerance=1e-6)

Thanks in advance for any advice, Dan Jackson


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