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

[chantepie at mnhn.fr]

Dear Jarrod and others,

It is not a segfault error, the runs actually stop with one of these two
errors.

My covariates have already been rescaled (centered to be precise). I have
tried to center-reduce the covariates (by usind scale) but the result was the
same.

Also, when I use cubic term for "tse" and "joe" , the models stop running
before 1000 iterations.

Could the problem come from the number of fixed parameters to estimate? It
seems strange because I have a quiet big data set.

kind regards

stephane

Jarrod Hadfield <j.hadfield at ed.ac.uk> a écrit :

> 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
>> --
>>
>> _______________________________________________
>> R-sig-mixed-models at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>>
>
>
>
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