(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-boun...@r-project.org] On Behalf Of Wen Huang
Sent: Tuesday, February 16, 2016 10:57 AM
To: r-help@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!



        [[alternative HTML version deleted]]

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