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