[R-sig-ME] Bivariate animal models with both "ill-conditioned G/R structure" and "Mixed model equations singular" errors

[Jarrod Hadfield]

Dear Stephane,

When you say crash do you mean crash in the sense of a segfault or in  
the sense that it stops with the errors:

  -Mixed model equations singular: use a (stronger) prior
  -ill-conditioned G/R structure: use proper priors if you haven't or  
rescale data if you have

If the latter, it may just require a rescaling of your continuous  
covariates by using something like scale(). If the former, it would be  
good for me to have a reproducible example as it means there is a bug.

Cheers,

Jarrod





uoting chantepie at mnhn.fr on Fri, 09 Mar 2012 10:33:42 +0100:

> Dear all,
>
> I am using MCMCglmm function to construct bivariate animal models of  
> bustard sperm production according to age-classes.
>
> The problem is that the models can stochastically crash before the  
> end of the run  (at 2000 iterations or 120000 or other) or can  
> finish. For the model which does not finish, R returns different  
> errors as:
> -Mixed model equations singular: use a (stronger) prior
> -ill-conditioned G/R structure: use proper priors if you haven't or  
> rescale data if you have
>
> For the models which reach the end, the estimations of genetic  
> additive variance appear quite good (nice bell shaped posterior  
> disctribution).
>
> The problem still remains when I remove the animal term.
> When I run univariate models, it works fine and the posteriors  
> distributions look very good.
>
> Strangely, the more data I have, the more models crash (the largest  
> amount of data I have is 65000 data for 2400 individuals for one  
> model).
>
> The model looks like:
>
> priorExp<-list(G=list(G1=list(V=diag(2), nu=2,  
> alpha.mu=rep(0,2),alpha.V=diag(2)*100000),
> G2=list(V=diag(2), nu=2, alpha.mu=rep(0,2),alpha.V=diag(2)*100000),
> G3=list(V=diag(2), nu=2, alpha.mu=rep(0,2),alpha.V=diag(2)*100000)),
> R=list(V=diag(2), nu=2))
>
> spz<-MCMCglmm(cbind(age1_2,age5_6)~trait-1 + trait:tse+  
> trait:I(tse^2)+ trait:joe + trait:I(joe^2),
>    random=~us(trait):animal+us(trait):ID+us(trait):annee ,
>    rcov=~us(trait):units,nitt=150000, thin=1000, burnin=100000,  
> prior=priorExp, verbose=TRUE, pedigree=ped,
>    family=c("gaussian","gaussian"), data=dat)
>
> For the fixed effects : I use 4 continuous parameters as correction  
> for each trait
> For the random effects: I use, individuals, years and animal parameters
>
> I have also tried more informative prior (as described in WAMWIKI)  
> but the problem was the same.
>
>
> To give you an example :
>
> Because of computing limitation, I use multi-chain process. I run  
> several times the same model (as above) and concatenate results  
> (same prior,same burning, same thin and random seed) to obtain at  
> least 1000 estimates (50 estimates by model). In this context, I ran  
> 50 bivariable models with the age-class age1_2 and the age-class  
> age5_6 but only 9 models of the 50 models reached the end.
>
> When we look fixed parameters estimates (estimate are binded for the  
> nine models : http://ubuntuone.com/3Gi8GwjcRk3P01MxJp2qLe ), we can  
> see that the estimates are really close to 0. Could it be the problem?
> When we look ramdom parameters estimates (estimate are binded for  
> the nine models : http://ubuntuone.com/42oaP9euG1m2LNipMawcHX ), the  
> residual estimates do not look very good. Could it be the problem?
>
> Last thing, if I try to add a cubic effect, all the models crash  
> (same error than before or memory mapped error).
>
> I really do not know where the problem comes from. Do you have an idea?
>
> Thanks
>
> --
> Stephane Chantepie
> CNRS Phd  candidate
> Muséum national d'Histoire naturelle
> 55 rue Buffon
> 75005 paris
> E-mail : chantepie_at_mnhn.fr
> --
>
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> R-sig-mixed-models at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>
>



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