On Feb 16, 2016, at 12:40 PM, Doran, Harold <HDoran at air.org> 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] On Behalf Of Wen Huang
Sent: Tuesday, February 16, 2016 10:57 AM
To: r-help at r-project.org
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!
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