[R-sig-ME] R-sig-mixed-models Digest, Vol 28, Issue 4
bates at stat.wisc.edu
Thu Apr 2 23:02:05 CEST 2009
On Thu, Apr 2, 2009 at 4:09 AM, Iasonas Lamprianou <lamprianou at yahoo.com> wrote:
> Dear all,
> I'll re-send this request since I got no reply to the first one.
> It is an issue which I face currently with lmer and MLWin and SPSS. This problem makes me feel very undomfortable. I have one standardized variable which represents the academic performance of children, and I also have information about their school and their class. I run the model with SPSS and lmer and I get the same result (both use REML). Then I use MLWin and I get different (but more reasonable results). MLWin uses IGLS and RIGLS and MCMC (all three methods agree when I use MLWin). I hereby present my numbers:
> I run the following model:
> Linear mixed model fit by REML
> Formula: Zmg_Arxiki ~ 1 + (1 | school)
> Data: data
> AIC BIC logLik deviance REMLdev
> 21693 21714 -10844 21684 21687
> Random effects:
> Groups Name Variance Std.Dev.
> school (Intercept) 0.37043 0.60863
> Residual 0.74465 0.86293
> Number of obs: 8448, groups: school, 47
> Fixed effects:
> Estimate Std. Error t value
> (Intercept) -0.13515 0.08971 -1.506
> However, this is NOT reasonable because the variable is a standardized variable and the variance should be 1.0!! SPSS gives the same results.
> If I run the same model in MlWin, I correctly get
And you know there are the correct results because ...?
> S2 for the school level=0.077
> S2 (error) = 0.923
> and the total is 1.0 (correct)!
> Could anyone please let me know what happens? Any help is welcome.
I don't have access to neither the data nor MLWin so I don't have the
advantage of knowing what the correct results are. :-)
Do you know if MLWin fits using maximum likelihood (ML) or REML? If
it uses maximum likelihood then you might try fitting the model with
lmer setting REML = FALSE and see whether that can reproduce the
By the way, why is it necessary that the sum of the variance estimates
in a mixed-effects model fit to a standardized variable must add to 1?
Especially for imbalanced data I don't think that is required.
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