[R-sig-ME] MCMCglmm Prior for a Binary Trait with a Random Interaction w/ "animal"

[Pierre de Villemereuil]

Hi Cameron,

Both V and alpha.V are matrices and should be of same dimensions (which seems to be 2 in your case?).

A long time ago, I wrote a script to be able to visualise the prior distributions in the mono- and multivariate case of the extended parameters. I'm afraid it's not in the best shape as I wrote it quickly, but maybe it can help:
https://github.com/devillemereuil/prior-MCMCglmm/blob/master/priors.R

(Note that this script assumes only the second trait is binomial for the multivariate case, but you can change this easily by setting fix = 1 instead of fix = 2).

I don't guarantee that keeping nu = 1000 for the multivariate case is the best solution (or even a sane one, as it could be extremely informative on the covariances), so using the script to visualise the priors, especially for the correlations might be a good idea.

Hope this helps,
Pierre.

Le jeudi 27 février 2020, 23:04:35 CET Cameron So a écrit :
> Hi all,
> 
> I am trying to measure the genetic variance in plasticity when different plant genotypes are planted into two different environments. The trait of interest is binary (germination).
> 
> Without fitting an interaction term, the prior for a binary trait follows a Chi square distribution of df = 1, based on suggestions in de Villemereuil's (2012) paper. However, I wish to add an interaction term with my "animal" term (which is, actually in my case, the ID of a plant individual). If I wish to keep this prior for an interaction between the environment (a.k.a treatment), should I specify the covariance matrix in the alpha.V or V section of the prior?
> 
> Essentially, I want to incorporate suggestions made in Arnold et al. (2019)'s paper.
> 
> Below is my code:
> 
> prior_germ <- list(R = list(V = 1, fix = 1),
>                  G = list(G1 = list(V = 1, nu = 1000, alpha.mu = 0, alpha.V = diag(2)),
>                               G2 = list(V = 1, nu = 1000, alpha.mu = 0, alpha.V = diag(2))))
> 
> MCMCglmm(germ ~ treatment + plot, random = ~idh(treatment):animal + ~idh(treatment):animal,
>                         ginverse = list(animal = Ainv),
>                         family = "threshold", data = plastic.Germination, prior = prior_germ, #Bernoulli distribution
>                         nitt = 1100000, thin = 500, burnin = 100000, verbose = T, pr = TRUE, trunc = TRUE)
> 
> 
> Thanks for any advice in advance!
> 
> 
> Cameron
> 
> ______
> 
> Cameron So
> 
> Master's Student | Plant Evolutionary Responses to Climate Change | Weis Lab
> Department of Ecology & Evolutionary Biology
> University of Toronto
> ESC2083, St. George Campus
> 
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