[R] Comparing variance components

[Bert Gunter]

I'll save you the trouble.

Yes, they're bigger. Or smaller. Certainly differ between experiments.  So
what? That is just the way things work.

 Google "weighting in meta-analysis" or similar for ways folks try to deal
with this.

Cheers,

Bert

On Tuesday, February 16, 2016, Wen Huang <whuang.ustc at gmail.com> wrote:

> Hi Harold,
> R
> Thank you for your input. I was not very clear. I wanted to compare the
> sigma2_A’s from the same model fitted to two different data sets. The same
> for sigma2_e’s. The motivation is when I did the same experiment at two
> different times, whether the variance due to A (sigma2_A) is bigger at one
> time versus another. The same for sigma2_e, whether the residual variance
> is bigger for one experiment versus another.
>
> Thanks,
> Wen
>
> > On Feb 16, 2016, at 12:40 PM, Doran, Harold <HDoran at air.org
> <javascript:;>> wrote:
> >
> > (adding R mixed group). You actually do not want to do this test, and
> there is no "shrinkage" here on these variances. First, there are
> conditional variances and marginal variances in the mixed model. What you
> are have below as "A" is the marginal variances of the random effects and
> there is no shrinkage on these, per se.
> >
> > The conditional means of the random effects have shrinkage and each
> conditional mean (or BLUP) has a conditional variance.
> >
> > Now, it seems very odd to want to compare the variance between A and
> then what you have as sigma2_e, which is presumably the residual variance.
> These are variances of two completely different things, so a test comparing
> them seems strange, though I suppose some theoretical reason could exists
> justifying it, I cannot imagine one though.
> >
> >
> >
> >
> >
> > -----Original Message-----
> > From: R-help [mailto:r-help-bounces at r-project.org <javascript:;>] On
> Behalf Of Wen Huang
> > Sent: Tuesday, February 16, 2016 10:57 AM
> > To: r-help at r-project.org <javascript:;>
> > Subject: [R] Comparing variance components
> >
> > Dear R-help members,
> >
> > Say I have two data sets collected at different times with the same
> design. I fit a mixed model using in R using lmer
> >
> > lmer(y ~ (1|A))
> >
> > to these data sets and get two estimates of sigma2_A and sigma2_e
> >
> > What would be a good way to compare sigma2_A and sigma2_e for these two
> data sets and obtain a P value for the hypothesis that sigma2_A1 =
> sigma2_A2? There is obvious shrinkage on these estimates, should I be
> worried about the differential levels of shrinkage on these estimates and
> how to account for that?
> >
> > Thank you for your thoughts and inputs!
> >
> >
> >
> >       [[alternative HTML version deleted]]
> >
> > ______________________________________________
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>
> ______________________________________________
> R-help at r-project.org <javascript:;> mailing list -- To UNSUBSCRIBE and
> more, see
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> PLEASE do read the posting guide
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> and provide commented, minimal, self-contained, reproducible code.



-- 
Bert Gunter

"The trouble with having an open mind is that people keep coming along and
sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )

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