[R-sig-ME] MCMCglmm and prior specification
j.hadfield at ed.ac.uk
Wed Nov 25 08:08:55 CET 2009
Sorry, yesterday I should have said
when V=0 & nu=-2 the MARGINAL posterior modes should be equal to the REML
when V=0 & nu=-2 the JOINT posterior modes should be equal to the REML
Sorry, yesterday I said
Quoting Jarrod Hadfield <j.hadfield at ed.ac.uk>:
> Dear Cristina,
> I am currently writing a better guide to MCMCglmm which deals more
> extensively with prior specifications - it should be ready soon.
> In your first prior you have fixed the residual variance to be V=1, so
> it's not surprising that this gives very different answers from models
> 2 and 3.
> Often variance components are sensitive to prior information, in part
> because the data may not contain much information but also because the
> inverse-Wishart prior used is not really flexible enough (again, I am
> working on this).
> The various improper priors often lead to numerical problems which
> precludes their use, but the resulting posterior does have some useful
> when V=anything & nu=0 the joint posterior modes should be equal to
> the ML estimate
> when V=0 & nu=-2 the joint posterior modes should be equal to the REML
> estimate (in practice you will have to use V=1e-6, or something small)
> A commonly used proper prior is V=1 & nu=0.002. This is equivalent to
> the inverse gamma distribution with shape=scale=0.001. This was the
> "default" in WinBUGS for a while before Andrew Gelman showed why it
> shouldn't be - it can be informative if the variances are close to zero.
> Hope this helps,
> If anyone wants an unfinished copy of the MCMCglmm guide just email me.
> On 24 Nov 2009, at 16:42, ledonret at email.unc.edu wrote:
>> Dear all,
>> I am trying to use the MCMCglmm package to create credibility
>> intervals for random variables in my data. I'm having a bit of
>> trouble though determining what the best prior to use for each
>> model is, since the results seem to differ tremendously depending
>> on which prior I am using, for instance, I've tried these three
>> types of priors,
>> For the model,
>> Where the random factors are Family and Replicate.
>> From these priors, I get intervals for my Family effect,
>> lower upper
>> var1 0.09660338 0.8888039
>> lower upper
>> var1 0.1944570 2.120540
>> lower upper
>> var1 0.2099238 1.529794
>> I feel bad that I don't understand better how to specify the
>> components of these priors, but from what I understand, the model
>> should return similar values even if the priors are very different.
>> I've looked through the vignette thoroughly, but didn't get a
>> sense of what I was supposed to do if alternate priors returned
>> different answers. I'm not sure whether this is telling me that
>> all the information is coming from my priors (and there is, in
>> fact, no information in the data), or I am just incorrectly
>> specifying my priors.
>> Any insight would be very much appreciated! Happy holidays,
>> Cristina Ledon-Rettig
>> UNC-Chapel Hill
>> *I am using lme4 version 0.99375-28 with Mac OS X version 10.5
>> R-sig-mixed-models at r-project.org mailing list
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