lmer: problem in crossed random effect model with verydifferent variances

Hello,

I probably should have used log-transformed y.

does the discrepancy between lmer&SAS persist if using log(y) (and are the
distributional assumptions of the model reasonably met with the
log-transformed response?). Furthermore, I think SAS and lme4 use different
algorithms, which might contribute to differences in the estimates.

----- Original Message ----- From: "Michael Li" <wuolong at gmail.com>
To: <r-sig-mixed-models at r-project.org>
Sent: Wednesday, June 17, 2009 3:15 PM
Subject: [R-sig-ME] lmer: problem in crossed random effect model with
verydifferent variances

Hi, I ?remember seeing this mentioned somewhere but couldn't find it.

I used lmer to fit a simple linear mixed model with two crossed random
effects, day and analyst, with no other fixed effects. ?So the syntax
is something like:

lmer (y ~ (1 | day) + (1 | analyst), data = data)

I can also fit the same model in PROC MIXED. Most of the time I got
the same answers. ?But there seems to be a problem with lmer when one
of the random effect has a much smaller variance than others.

In my case, SAS would give random effect variances of 1552, 599133 and
213814 for analyst, day and residual effects, respectively but lmer
gives 2x10^-12, 599050, and 214680. ?Basically all parameter estimates
are the same (more or less), except that lmer gives very tiny estimate
for the random effect of 'analyst'.

I probably should have used log-transformed y. ?But aside from that,
how can I get lmer to give a sensible answer? ?Or is SAS giving the
right answer?

Thanks,

Michael