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

[Jarrod Hadfield]

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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>


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