[R-sig-ME] Undefined R-structure MCMCglmm

[Christopher David Desjardins]

Thanks Jarrod. I'll let you know if I have success.
Chris

On 7/20/09 3:20 AM, Jarrod Hadfield wrote:
> Hi Chris,
>
> Zip models are essentially bivariate in MCMCglmm with the first 
> "trait" being the poisson part and the second "trait" the 
> zero-inflation part.  An appropriate models would be something of the 
> form:
>
> fit1 <- MCMCglmm(sus ~ trait + trait:ethnic + trait:sped + trait:ell + 
> trait:risk + trait:male + trait:grade-1, random=~us(trait):id.f, 
> rcov~idh(trait):units, data=enroll.long.m, verbose=TRUE, DIC=TRUE, 
> family="zipoisson")
>
> This model is probably the least parsimonious model. Each fixed effect 
> can have an effect on the zero-inflation and the poisson process 
> (hence the interaction with trait) and there are separate id.f random 
> effects for each process with the covariance estimated.  Because there 
> is no information to estimate this covariance within a data point I 
> usually set it to zero using the idh(...) structure. Moreover, the 
> residual variance of the zero-inflation cannot be estimated as in 
> standard binary analyses so it should be fixed (I fix at 1 usually). 
> The appropriate prior in this case would be something like
>
> prior=list(R=list(V=matrix(V=diag(c(v1, 1)), fix=2, n=2), 
> G=list(G1=list(V=V2, n=2)))
>
> where v1 is the prior for the Poisson over-dispersion, and V2 is a 2x2 
> matrix specifying the covariance matrix for id.f.
>
> You may also want to consider not having a random effect for the zero 
> inflation, but only for the Poisson part, in which case you can set 
> this value close to zero.
>
> prior=list(R=list(V=matrix(V=diag(c(v1, 1)), fix=2, n=2), 
> G=list(G1=list(V=diag(c(v2, 1e-10)), n=2)))
>
> where v2 is now the id.f variance component  for the Poisson part.  
> Note, you cannot do this for the residual variance or the chain will 
> not mix.
>
> You can also do the same for the fixed effects by placing strong 
> priors around zero for the zero-inflation coefficients.
>
> Typically ZIP and high dimensional multinomial models are the hardest 
> to fit, and mixing may be a problem. Specifying pl=TRUE and looking at 
> the posterior traces of the latent variables can often be a better 
> indicator of problem than looking at the traces of  the fixed effects 
> and variance components. I usually see problems with zip mdoels when 
> the mean of the Poisson process is so high that zero's are not 
> expected from the Poisson process alone.
>
> I'd be interested to know how you get on.
>
> Cheers,
>
> Jarrod
>
>
> On 19 Jul 2009, at 23:38, Christopher David Desjardins wrote:
>
>> Hi,
>> I am getting the following error when fitting a zip model in MCMCglmm.
>>
>> > fit1 <- MCMCglmm(sus ~ 1 + ethnic + sped + ell + risk + male + 
>> grade, random=~id.f, data=enroll.long.m, verbose=TRUE, DIC=TRUE, 
>> family="zipoisson") # Random intercept only model
>> Error in MCMCglmm(sus ~ 1 + ethnic + sped + ell + risk + male + 
>> grade,  :
>>  R-structure does not define unique residual for each data point
>>
>> I was able to run fit a poisson model but my data is zero-inflated.
>>
>> Thanks,
>> Chris
>>
>> -- 
>> Christopher David Desjardins
>> Ph.D. Student
>> Quantitative Methods in Education
>> Department of Educational Psychology
>> University of  Minnesota
>> http://cddesjardins.wordpress.com/
>>
>> _______________________________________________
>> R-sig-mixed-models at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>
>

-- 
Christopher David Desjardins
Ph.D. Student
Quantitative Methods in Education
Department of Educational Psychology
University of  Minnesota
http://cddesjardins.wordpress.com/