[R-sig-ME] independent random effects with equal variances

[Jarrod Hadfield]

Hi,

You can do it in MCMCglmm, but then you have to worry about priors etc. ....

m1<-MCMCglmm(y ~ x, random=~idv(R1+R2), .....)

Cheers,

Jarrod






Quoting Christos Hatzis <christos.hatzis at nuverabio.com> on Wed, 4 May  
2011 15:38:40 -0400:

> Yes, that's right.
> You've thought about these issues much harder than I have.
>
> -----Original Message-----
> From: dmbates at gmail.com [mailto:dmbates at gmail.com] On Behalf Of Douglas
> Bates
> Sent: Wednesday, May 04, 2011 2:33 PM
> To: christos.hatzis at nuverabio.com
> Cc: Hae Kyung Im; r-sig-mixed-models at r-project.org
> Subject: Re: [R-sig-ME] independent random effects with equal variances
>
> On Wed, May 4, 2011 at 1:02 PM, Christos Hatzis
> <christos.hatzis at nuverabio.com> wrote:
>> Independence, equal variance and the implicit normality assumption
> wouldn't
>> imply that these random effects are IID from the same N(0, sigma)
>> distribution?  Wouldn't then this be equivalent to
>
>> y ~ x + (1|R)
>
>> where R is the "combined" random effect?
>
> Except that you can't define a factor R with the combined levels.
> Consider, for example, the Penicillin data in the lme4 package.  A
> model with independent random effects having one variance for the
> plate and one variance for the sample random effects has the model
> formula
>
> diameter ~ 1 + (1|plate) + (1|sample)
>
> for a total of 30 random effects (24 plates and 6 samples).  You can't
> generate a factor with 30 levels that can be decomposed into 24 levels
> for plate and 6 levels for samples.
>
>> -----Original Message-----
>> From: r-sig-mixed-models-bounces at r-project.org
>> [mailto:r-sig-mixed-models-bounces at r-project.org] On Behalf Of Douglas
> Bates
>> Sent: Wednesday, May 04, 2011 1:56 PM
>> To: Hae Kyung Im
>> Cc: r-sig-mixed-models at r-project.org
>> Subject: Re: [R-sig-ME] independent random effects with equal variances
>>
>> On Wed, May 4, 2011 at 10:47 AM, Hae Kyung Im <haky at uchicago.edu> wrote:
>>> Dear list,
>>
>>> does anyone know of an easy way to enforce equal variance for two
>> independent random effects?
>>
>>> So I would like to fit this model with equal variances for R1 and R2
>>
>>> y ~ x + (1|R1) + (1|R2)
>>
>> I don't think that would be easily done under the current setup.
>>
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>



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