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

[Jarrod Hadfield]

Hi Stephane,

Its very hard to know what could be causing the problem with out  
having the data. The three things that you could try are:


  a) scale the response variable

  b) work out if some terms really are weakly identified (MCMCglmm  
drops non-identified terms in the fixed effects by default)

  c) the error can appear I think if the residual covariance matrix  
becomes singular. This could happen, amongst other reasons, because  
non-modelled non-genetic effects are contributing to the resemblance  
between relatives and inflating Va by so much that Ve goes to zero.

Either i) running it for some iterations before it terminates or ii)  
fit the model in ASReml should give you some idea which of these  
problems are most likely.

Cheers,

Jarrod




Quoting chantepie at mnhn.fr on Fri, 09 Mar 2012 17:33:52 +0100:

> 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
>>>
>>>
>>
>>
>>
>> -- 
>> The University of Edinburgh is a charitable body, registered in
>> Scotland, with registration number SC005336.
>>
>>
>>
>
>
>
>
>



-- 
The University of Edinburgh is a charitable body, registered in
Scotland, with registration number SC005336.