-----Original Message-----
From: r-sig-mixed-models-bounces at r-project.org
[mailto:r-sig-mixed-models-bounces at r-project.org] On Behalf
Of Michael Li
Sent: Wednesday, June 17, 2009 3:15 PM
To: r-sig-mixed-models at r-project.org
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