[R-sig-ME] MCMCglmm : Difference in additive genetic variance estimated in univariate vs bivariate models

Stephane Chantepie chantepie at mnhn.fr
Thu Jul 19 17:20:24 CEST 2012

Hi Jarrod and all other,

You said "your posterior is just reflecting your prior" : I was thinking about 
this issue but how can I test it? The V I used is V=(Phenotypic variance/2)= 
0.47 so it is bigger than the posterior mode. I have tried to used V=1 but the 
Va posterior results remains the same (Va=0.10 for the univariate model 

For the bivariate model c(spz_9,spz_5) I have kept the same V=(Phenotypic 
variance/2)but nu=2.
To answer your question "if nu=1 then V*nu/(nu-1) is ..."  V*nu/0 so +∞ or -∞ 

Just to give you an idea of the my posteriors: 

The Va posterior mode resulting from the spz_9  univariate model hit the 0 
(http://ubuntuone.com/2IGcmcdqkjcVQCdZgvMhnP) whereas the Va posterior mode 
resulting from the (spz_9,spz_5) bivariate model seems to be better shaped(at 
the top http://ubuntuone.com/3PaBOJ5dF6kDIhV6ahPnlM).

To conclude, the estimation of Va with bivariate models is biologically 
consistent with senescence theories while the Va estimated with univariate 
model is not. So : Do do you think that I could use bivariate results or it is 
better to consider that I do not have enought information?

Thank a lot for your help

all the best


More information about the R-sig-mixed-models mailing list