[R-sig-ME] lmer: problem in crossed random effect model with verydifferent variances
lborger at uoguelph.ca
Wed Jun 17 21:56:36 CEST 2009
>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
> 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?
> R-sig-mixed-models at r-project.org mailing list
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