I am trying to use the MCMCglmm package to run a bivariate plasticity model
to assess plasticity in the timing of egg laying and the relationship with
reproductive success. The issue I am having is with the prior
specification, which keep returning errors.

#The prior I am using is a flat prior:

prior.bi<-list(R = list(V = diag(2), nu = 0.002), G = list(G1 = list(V =
diag(4), nu = 0.002)))

#And when I set the following values:

chain = 20
thin = 500
burnin = thin * 100
nitt = burnin + (thin * chain)

#and run this model:

model <- MCMCglmm(cbind(savg.nf, sld)
~ trait - 1 + trait:st1 + trait:factor(min.age) + trait:yr, random =
~us(trait+st1:trait):f.id, rcov = ~us(trait):units, prior=prior, family =
c("gaussian","gaussian"), data = data,
burnin =burnin, nitt = nitt, thin = thin, verbose = FALSE)

#I get the following error:

*Error in MCMCglmm(cbind(savg.nf <http://savg.nf/>, sld) ~ trait - 1 +
trait:st1 + trait:factor(min.age) +  :ill-conditioned G/R structure: use
proper priors if you haven't or rescale data if you have*
I thought that I was using a proper prior, and I have already scaled the
data. What is the meaning of "ill-conditioned G/R structure"?

Help would be greatly appreciated!

-David

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