Dear Pierre,

thanks a lot! It does help, but I will need time to fully understand the 
paper.

Just one further question if I may, what prior would you use for a 
covariance then?

Many thanks again,

All the best

Celine

> Dear Céline !
>
> One usual "non informative" prior on variance component is V=1, and 
> nu=0.002, which correspond to a inverse-Gamma(0.001,0.001). This is 
> usual, but that is not to say that it is really non informative. 
> Indeed, inverse-Gamma(e,e) is weakly informative, since the posterior 
> can depend on the choice of e.
>
> Concerning the WAMwiki suggestion to use the phenotypic variance to 
> set the prior, this quite not orthodox since no information coming 
> from your current dataset should be used to define the prior (but you 
> could use previous data to parametrize your prior).
>
> I would suggest you to refit your model considering V=1 and nu=0.002 
> as a (so-called) non informative prior. Other solutions exist like 
> using the parameter expansion and a chi2 distribution by setting 
> V=1,nu=1000,alpha.mu=0 and alpha.V=1, which is also weakly informative 
> (it has more weight in variance values less than 10).
>
> For more information about priors on variance component, and parameter 
> expansion, it would suggest you to read :
>
>    1. A. Gelman, « Prior distributions for variance parameters in
>       hierarchical models », /Bayesian analysis/ 1, n^o . 3 (2006):
>       515--533.
>
> In the hope I'm helping.
>
> Pierre de Villemereuil.
>
>
> Le 25/04/2011 13:26, Celine Teplitsky a écrit :
>> Dear all,
>>
>> I realise that Jarrod is doing field work, but I'm really hoping 
>> someone can answer my question while he's not around.
>>
>> I am running animal models estimating covariances between life 
>> history traits, and I'm having trouble knowing which prior to use.
>>
>> Thing is, if I use a prior as described on the Wam wiki site with 
>> V=PhenotypicVar/4 (as I have 3 random effects + residual), I have 
>> very nice results, with some significant genetic correlations between 
>> some life history traits.
>>
>> However, one reviewer asked about prior sensitivity because CI were 
>> pretty large, so I went back to MCMCglmm course notes and saw that 
>> non informative prior were supposed to be V=diag(nbDimV)*0 and 
>> n=nbDimV-3. This led to an error message about G being ill 
>> conditioned, so I tried with diag(nbDimV)*0.001 and diag(nbDimV)*0.01 
>> instead of diag(nbDimV)*0, and diag(nbDimV)*0.01 worked... But then I 
>> have the posterior of additive genetic variance collapsing on 0 for 
>> some trait. So my guess would be that I should use those latest 
>> priors, and believe my nice results did not exist. But as Hadfield et 
>> al paper and the Wam wiki website do not recommend those priors, I am 
>> a bit confused. Could someone help me figure out what would be the 
>> right thing to do?
>>
>> All my apologies if this is a silly question, but I'm feeling a bit 
>> lost here
>>
>> Thanks a lot in advance
>>
>> Celine
>>
>


-- 

Celine Teplitsky
Département Ecologie et Gestion de la Biodiversité UMR 7204
Unité Conservation des Espèces, Restauration et Suivi des Populations
Case Postale 51
55 rue Buffon 75005 Paris

Webpage :http://www2.mnhn.fr/cersp/spip.php?rubrique96
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