[R-sig-ME] heritability from longitudinal zero-inflated count data

[Paul Johnson]

Hi all,

I’d like to fit a model with the following features:

* The data are parasite counts recorded at a number of time points (say 4) for each individual 
* Zero-inflation, i.e. a mixture of binary and count data
* Separate fixed effects for the binary and count components
* Separate random effects for both components. One of the random effects will have a pedigree-derived correlation structure (as in an animal model). The random effects will have the same structure for both components, but need to allow different variances.
* The random effect variances are allowed to vary over time.

The main aim is to estimate heritabilities for each time point, separately for the binary and count parts of the mixture, because the factors driving the two processes (encountering parasites and resistance to parasites) are expected to be driven by quite different factors, genetic and otherwise.

Can this be done outside DIY software such as JAGS? I’d be interested in knowing how close MCMCglmm can get to this model, even if it can’t do everything.

Thanks for your help,
Paul