Hi,

I am currently trying to fit a quasi-poisson distribution with my data
(counts variable, which contains 50% of zero) with MCMCglmm so as to
estimate the heritability of a morphological trait. Previously I used a
"Poisson" distribution, but to be more exact I have to use a ziP
distribution. However, I am not comfortable with bayesian priors and  the
syntax of the models in MCMCglmm, and I don't understand all the terms of
the outputs ("summary.MCMCglmm(object)").

Can someone help me to

1)asses if the prior used is non-informative?

2)if the ziP model syntax have the same signification as the Poisson model?

3)explain which terms I have to use so as to calculate the heritability?



My codes are



#Poisson model



prior1.7 <- list(G = list(G1 = list(V = 1, n = 1.002), G2 = list(V = 1,n =
1.002), G3 = list(V = 1, n = 1.002),G4 = list(V = 1, n = 1.002),

 G5 = list(V = 1, n = 1.002)), R = list(V = 1, n = 1.002))



modelDEG <- MCMCglmm(DEG ~ PAR +SIZE, random = ~ animal + ANNEE + STATION +
ANNEE:STATION , pedigree = Ped, data = Data, nitt = 1000000, thin = 1000,
burnin =  50000,prior = prior1.7, verbose = FALSE,family="poisson")



Heritability=mean(modelDEG$VCV[, "animal"] / (modelDEG$VCV[, "animal"] +
modelDEG$VCV[, "units"] + modelDEG$VCV[, "STATION"]+ modelDEG$DVCV[,
"ANNEE"]+

modelDEG$VCV[, "ANNEE:STATION"]))



#ziPoisson model



zi.prior <-  list(R = list(V = diag(2), n = 1.002, fix = 2),

 G = list(G1 = list(V = 1, n = 0.002),

 G2 = list(V = 1, n = 0.002),

 G3 = list(V = 1, n = 0.002),

G4=list(V=1,n=0.002)))



model1 <- MCMCglmm(DEG ~ trait+trait: PAR +trait:SIZE,

random = ~ animal + ANNEE + STATION + ANNEE:STATION,

rcov=~us(trait):units,

pedigree = Ped, data = Data,

prior=zi.prior,

nitt = 1000000, thin = 1000, burnin =  50000,

verbose = FALSE,family="zipoisson")



Heritability= ????





Many Thanks in advance

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