Hi,
I am currently modelling the effect of different habitat variables on the numbers of tipulid larvae found in soil cores using MCMCglmm. The data is slightly zero inflated so I am trying a zero-altered model (among others). I have used the following priors and model:

prior1ZA = list(R = list(V=diag(2), n=0.002, fix=2), G = list(G1 = list(V=diag(2), n=0.002)))

model1ZA <- MCMCglmm(no._tips ~trait*(percent.grass2 + mean.veg.ht + mean.soil.moisture + juldate + year),random = ~ idh(trait):colony,rcov = ~ idh(trait):units,
family = "zapoisson", data = data, prior = prior1ZA, burnin = 3000, nitt = 1003000, thin=1000)


However I have read in a previous post by the immensely helpful Jarrod Hadfield that "It is usual in zero-altered models to have the zero bit and the truncated poisson bit have the same over-dispersion. You do this by fitting the  interaction rcov=~traits:units."

I thought that ensuring the poisson and the zero process have the same over-dispersion would require priors and model of the form:

prior1ZA = list(R = list(V=diag(1), n=0.002, fix=2), G = list(G1 = list(V=diag(1), n=0.002)))

model1ZA <- MCMCglmm(no._tips ~trait*(percent.grass2 + mean.veg.ht + mean.soil.moisture + juldate + year),random = ~ trait:colony, rcov = ~ trait:units,
family = "zapoisson", data = data, prior = prior1ZA, burnin = 3000, nitt = 1003000, thin=1000)


But looking at other posts I am beginning to think I am missing something and that I *can* use my priors and model (with different variances for the zero and poisson parts of the model). Is this true? Can anyone tell me which of the two residual variance and random effect structures is most advisable?

Many thanks,
Daisy





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