heritability from longitudinal zero-inflated count data

Thanks Jarrod & Chuck. I?m not very familiar with SAS and I don?t know what?s possible with NLMIXED, but good to know that it?s possible with MCMCglmm (given a very large data set).
Paul
Aside from being unsure what is meant by pedigree-derived correlation structure, NLMIXED (SAS) can fit the model allowing for random effects in each component, chuck
On 3 Apr 2015, at 08:52, Jarrod Hadfield <j.hadfield at ed.ac.uk> wrote:

Not having a good morning!

random=~us(trait):animal+idh(at.level(trait,1):time):year

should have read

random=~us(trait):animal+idh(at.level(trait,1):year):nest

Jarrod

Quoting Jarrod Hadfield <j.hadfield at ed.ac.uk> on Fri, 03 Apr 2015 08:27:01 +0100:

Hi,

It is possible, but you will need a lot of data. For example

counts=~trait-1+x:trait,
random=~us(trait):animal+idh(at.level(trait,1):time):year

this models separate intercepts for the binary and count parts, and different regressions on x. us(trait):animal estimates the additive genetic variance in both parts, and the additive genetic variance between them.  idh(at.level(trait,1):year):nest  fits different between-nest variances for different years for the count part (trait 1).

Cheers,

Jarrod

Quoting Paul Johnson <paul.johnson at glasgow.ac.uk> on Thu, 2 Apr 2015 17:03:47 +0000:

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

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heritability from longitudinal zero-inflated count data

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