[R-sig-ME] glmer vs. MCMCglmm
Herve.Chapuis at tours.inra.fr
Wed Apr 1 18:08:00 CEST 2009
Hope my questionsare not too stupid, as I could easily be defined as a
I intend to estimate the genetic variance for a binary trait (disease
resistance) in a simulated population.
First, I have used lmer and glmer. However, sometimes I have false
convergences, and I can't specify "nAGQ=5" when fitting two random
effects (sire AND dam).
This is the reason why I have decided to give a glimpse at MCMCglmm.
Well, what can I do when I have this kind of error message :
"Erreur dans MCMCglmm(Y ~ 1, random = ~PERE, family = "categorical",
data = PERF) :
ill-conditioned G/R structure: use proper priors if you haven't or
rescale data if you have". ???
How to specify proper priors ? If I have a binary trait, the residual
variance can't be estimated, so that it has to be fixed, isn't it ?
unless I use a proper prior.
In another design, I obtain results, but the sampled sire variance far
exceeds the parameter space boundaries, leading to an abnormaly high
heritability coefficient (above 1). The glmer estimate, on the other
hand, is much in adequation with the expected value.
I am still trying to implement an animal model as specified in the
MCMCglmm manual, but I can't figure out how an heritability can be
estimated so high without a big mistake. But I can't see it.
Any help will be greatly appreciated.
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