Binomial model variance and repeatability estimates with MCMCglmm

Thanks, I was aware of that for categorical and some of the other  
families but thought I could get away with it here and I wasn't  
quite sure otherwise how to calculate the relevant ratio (thanks for  
providing that too).
With smaller sample sizes repeatability still seems to get  
misestimated and stuck close to zero even with long runs but running  
multiple chains seem to resolve that.

Thanks again,
Ned

On 10/21/2014 3:05 PM, Jarrod Hadfield wrote:

Hi,

The residual variance of a binary response cannot be estimated, so use

prior1 = list(R = list(V = 1, fix=1),
             G = list(G1 = list(V = 1, nu = 0.002)))

In this example it is more efficient to aggregate success/failures  
of an individual into a multi-trial binomial response and use:

prior2 = list(R = list(V = 1, nu=0.002))

sim.mcmc2<-MCMCglmm(cbind(Fail,Success)~1,
                   family="multinomial2", prior=prior2,
                   nitt = 260000, thin = 200, burnin = 60000,
                   verbose=FALSE,data=ind.data)

sim.mcmc2$VCV/(sim.mcmc2$VCV+pi^2/3)

Cheers,

Jarrod