[R-sig-ME] lmer: problem in crossed random effect model with verydifferent variances

[Luca Borger]

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.


Cheers,

Luca

----- 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
>
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