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
The University of Aberdeen is a charity registered in Scotland, No SC013683.
[[alternative HTML version deleted]]