Hello,

I have a general question about how prediction works in MCMCglmm.

I am modeling a dataset using cluster as a random effect and so I have set
pr=TRUE.

prior=list(R=list(V=1, nu=0.002), G=list
(G1=list(V = 1, nu = 0.002)))
model<-MCMCglmm(MM ~ size + tel + h_age + edu + Mdied + fmar1549+ born +
died ,  random=~cluster, data=survey, prior=prior, family="poisson", nitt =
800000, burnin=50000, thin = 300, saveX=TRUE, verbose=FALSE, pl=TRUE,
pr=TRUE, DIC=TRUE)


 I am then using the Sol matrix and multiplying this by an external dataset
to predict. For example, if there are 9000 rows of data, and 2500
iterations, there is a final product matrix of dim 9000 * 2500 , which is
then exponentiated and the sum of each column is taken, so the result is a
vector of length 2500, representing the MM count obtained from each
iteration.

In MCMCglmm course notes dated Oct 11th, pg 77,  there is the following
command: prediction.1<-W.1%*%posterior.mode(m4a.1$Sol).
What I do not understand is why VCV is not incorporated in the calculation?
When I included VCV in my matrix, my results are way off but why is this?
Shouldn't the residuals be included in the prediction to account for error?
Your help in this matter is greatly appreciated.

Thank you,
Yalda

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