# [R-sig-ME] zipoisson in MCMCglmm

Tue May 12 00:14:31 CEST 2009

```Hi Josh,

As with the random effect specification you probably want to form
interactions with the reserved variable "trait". These interactions
allow you to model fixed effects for both processes.  Something like:

~ trait+trait:fixed1 + trait:fixed2-1

I usually use -1 so that the contrasts are within traits, rather than
trait2-trait1. For zipossion models I would also save the posterior
distribution of latent variables (pl=TRUE) to make sure its mixing
properly by plotting the mcmc traces.

Most importantly, you should probably fix the residual variance of the
zero-inflation process to something (I use 1) because it cannot be
identified from the data:

priors <- list(R=list(V=diag(2),n=2, fix=2), G=list(G1=list(V=diag(2),
n=2), G2=list(V=diag(2), n=2)))

fix=2 fixes the bottom right diagonal matrix starting at [2,2] (in
this case the zero-inflation variance).

Cheers,

Jarrod

Quoting Josh Van Buskirk <jvb at zool.uzh.ch>:

>
> Dear all,
>
> Does anyone have experience working with Jarrod Hadfield's MCMCglmm
> package with a zero-inflated Poisson distribution? After fitting the
> model, I'm having trouble obtaining the fixed effect coefficients from
> the logistic (inflated) and Poisson parts of the model. I'm interested
> in estimating how the fixed effects influence both processes.
>
> In this example, many random genotypes are sampled within many random
> populations. There are two fixed effects. The response variable is
> highly zero-inflated.
>
> priors <- list(R=list(V=diag(2),n=2), G=list(G1=list(V=diag(2), n=2),
> G2=list(V=diag(2), n=2)))
> model <- MCMCglmm(
>         response ~ fixed1 + fixed2 ,
>         random = ~idh(trait):Population + idh(trait):Genotype,
>         family = "zipoisson",
>         prior = priors,
>         rcov = ~idh(trait):units,
>         data = mydata )
>
> After fitting the model, the object called model\$VCV contains 8
> variance components, which makes a little bit of sense: zero-inflated
> and Poisson parts of two random effects (Population and Genotype), plus
> the same for the residual.
>
> However, the object model\$Sol contains estimates for three fixed
> effects (intercept, fixed1, fixed2). I expected there to be twice as
> many, because fixed effects can influence both the logistic and Poisson
> parts of the model. In fact, I'm not sure which process these estimates
> refer to (Poisson or logistic).
>
> Any insight here?
>
> Many thanks,
>
> Josh Van Buskirk
> University of Zurich
> jvb at zool.uzh.ch
>
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