(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! ______________________________________________ R-help at 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.
Comparing variance components
7 messages · Doran, Harold, Bert Gunter, Thompson,Paul +2 more
Hi Harold, 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> 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! [[alternative HTML version deleted]]
______________________________________________ R-help at 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.
Hi Harold, 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> 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! [[alternative HTML version deleted]]
______________________________________________ R-help at 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.
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]]
______________________________________________ R-help at r-project.org <javascript:;> mailing list -- To UNSUBSCRIBE and
PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help at 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]]
Are you computing two estimates of reliability and wishing to compare them? One possible method is to set both into the same design, treat the design effect (Exp 1, Exp 2) as a fixed effect, and compare them with a standard F test. -----Original Message----- From: R-sig-mixed-models [mailto:r-sig-mixed-models-bounces at r-project.org] On Behalf Of Wen Huang Sent: Tuesday, February 16, 2016 11:57 AM To: Doran, Harold Cc: r-sig-mixed-models at r-project.org Subject: Re: [R-sig-ME] [R] Comparing variance components Hi Harold, 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> 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! [[alternative HTML version deleted]]
______________________________________________ R-help at 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.
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Hi Paul, Thank you. That is a neat idea. How would you implement that? Could you write an example code on how the model should be fitted? Sorry for my ignorance. Thanks, Wen
On Feb 16, 2016, at 1:18 PM, Thompson,Paul <Paul.Thompson at SanfordHealth.org> wrote: Are you computing two estimates of reliability and wishing to compare them? One possible method is to set both into the same design, treat the design effect (Exp 1, Exp 2) as a fixed effect, and compare them with a standard F test. -----Original Message----- From: R-sig-mixed-models [mailto:r-sig-mixed-models-bounces at r-project.org] On Behalf Of Wen Huang Sent: Tuesday, February 16, 2016 11:57 AM To: Doran, Harold Cc: r-sig-mixed-models at r-project.org Subject: Re: [R-sig-ME] [R] Comparing variance components Hi Harold, 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> 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! [[alternative HTML version deleted]]
______________________________________________ R-help at 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.
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Hi Wen, The question sounds sensible to me, but you can't do what you want to do in lmer because it does not allow heterogenous variances for the residuals. You can do it in nlme: model.lme.a<- lme(y~Exp, random=~1|G, data=my_data) model.lme.b<- lme(y~Exp, random=~0+Exp|G, weights=varIdent(form=~1|Exp), data=my_data) or MCMCglmm (or asreml if you have it): model.mcmc.a<- MCMCglmm(y~Exp, random=~G, data=my_data) model.mcmc.b<- MCMCglmm(y~Exp, random=~idh(Exp):G, rcov=~idh(Exp):units, data=my_data) The first model assumes common variances for each experiment, the second allows the variances to differ. You can comapre model.lme.a and model.lme.b using a likelihood ratio test (2 parameters) or you can compare the posterior distributions in the Bayesian model. Note that this assumes that the levels of the random effect differ in the two epxeriments (and they have been given separate lables). If there is overlap then an additional assumption of model.a is that the random effects have a correlation of 1 between the two experiments when they are associated with the same factor level. Cheers, Jarrod
On 16/02/2016 20:28, Wen Huang wrote:
Hi Paul, Thank you. That is a neat idea. How would you implement that? Could you write an example code on how the model should be fitted? Sorry for my ignorance. Thanks, Wen
On Feb 16, 2016, at 1:18 PM, Thompson,Paul <Paul.Thompson at SanfordHealth.org> wrote: Are you computing two estimates of reliability and wishing to compare them? One possible method is to set both into the same design, treat the design effect (Exp 1, Exp 2) as a fixed effect, and compare them with a standard F test. -----Original Message----- From: R-sig-mixed-models [mailto:r-sig-mixed-models-bounces at r-project.org] On Behalf Of Wen Huang Sent: Tuesday, February 16, 2016 11:57 AM To: Doran, Harold Cc: r-sig-mixed-models at r-project.org Subject: Re: [R-sig-ME] [R] Comparing variance components Hi Harold, 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> 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! [[alternative HTML version deleted]]
______________________________________________ R-help at 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.
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