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.u...@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 <hdo...@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-boun...@r-project.org <javascript:;>] On > Behalf Of Wen Huang > > Sent: Tuesday, February 16, 2016 10:57 AM > > To: r-help@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]] > > > > ______________________________________________ > > R-help@r-project.org <javascript:;> mailing list -- To UNSUBSCRIBE and > more, see https://stat.ethz.ch/mailman/listinfo/r-help > > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > > and provide commented, minimal, self-contained, reproducible code. > > ______________________________________________ > R-help@r-project.org <javascript:;> mailing list -- To UNSUBSCRIBE and > more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > 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 ) [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
