[R-sig-ME] MCMCglmm zero inflated poisson model issue

[Jarrod Hadfield]

Hi,

The two reasons are most likely:

a) you are trying to estimate the residual covariance, for which there 
is no information in the data, you are trying to estimate the 
between-individual covariance for which there is information but given 
you have set the between-individual variance in zero-inflation to zero 
you don't want to estimate.  Replacing us() with idh() resolves these 
issues.

b) you have the argument singular.ok=TRUE. This will retain 
non-identifiable contrasts, but you certainly want to drop them so just 
use the default.

Cheers,

Jarrod



On 24/11/2016 14:35, Rebecca Hooper wrote:
> Dear List,
>
> I am building a zero inflated poisson model in MCMCglmm, and am
> experiencing some issues with my model, specifically with prior
> specification.
>
> My response variable is number of offspring per year, and is 85% zeros. My
> predictor variables are categorical: one has four levels (social style) and
> one has three levels (time period). The random effect is individual
> identity. My code is as follows:
>
> #### main effects model ####
>
> m1 <- MCMCglmm(N.OFFSPR.YR ~ trait-1 +
> at.level(trait,1):tp +
> at.level(trait,1):social.style,
> random=~us(trait):ANIMAL_ID,
> rcov=~us(trait):units,
> prior=prior_overdisp,
> data=rm,
> family="zipoisson",
> verbose=FALSE,
> burnin = 15000,
> pl = TRUE,
> singular.ok=TRUE,
> nitt=40000,
> thin = 20)
>
> #### interaction model ####
>
> m2 <- MCMCglmm(N.OFFSPR.YR ~ trait-1 +
> at.level(trait,1):tp +
> at.level(trait,1):social.style*tp +
> at.level(trait,1):social.style,
> random=~us(trait):ANIMAL_ID,
> rcov=~us(trait):units,
> prior=prior_overdisp,
> data=rm,
> family="zipoisson",
> verbose=FALSE,
> burnin = 15000,
> pl = TRUE,
> singular.ok=TRUE,
> nitt=40000,
> thin = 20)
>
> with prior as follows:
>
> prior_overdisp <- list(R=list(V=diag(c(1,1)),nu=0.002,
> fix=2),G=list(list(V=diag(c(1,1e-6)),nu=0.002, fix=2)))
> as described by Bolker et al (2012) in their Owl example paper.
>
> Originally, another variable was included in both these models, which was
> continuous (year of life). Both models ran with no issues when this
> variable was included - the model converged and chains mixed well. I no
> longer wish to include the continuous variable though, and without it
> neither model runs. I get the error message:
>
> Mixed model equations singular: use a (stronger) prior
>
> I don't know what to make of this, and am loathe to mess around with my
> prior specification without fully understanding what it is I am doing. I
> would be very grateful for any help and direction in the matter!
>
> Many thanks,
> Beki (master's student extremely new to bayesian stats)
>
> 	[[alternative HTML version deleted]]
>
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