hurdle modfels in glmmadmb

[cc'ing r-sig-mixed-models]

if you assume that the zero-causing process and the non-zero 
count-determining process are independent (which is not necessarily 
true, but which I think you're more or less forced to assume by 
identifiability issues in the absence of more information), then you 
should just be able to add the two distinct AIC values for the two 
components of the model to get a single AIC value.  (Since the processes 
are independent, the log-likelihoods are additive, and the number of 
parameters in the combined model is clearly additive too.)
    You could cross-check by checking what the pscl package does (it 
deals with zero-inflated GLMs too, although not mixed zero-inflated GLMs).

Dear Ben Bolker,

I would like to thank you very much for developing the r package
glmmadmb that I am currently using to model my zero inflated data.
I have one small question regarding using two part (hurdle) models
within the glmmadmb package.
(http://glmmadmb.r-forge.r-project.org/glmmADMB.html)
Is there a way to evaluate the fit (e.g. AIC) of a two part model as a
whole and justify using it?
Or the only way to justify using a hurdle model comes from prior
hypothesis of two processes; one causing zeros versus non-zeros, and
another process explaining the non-zero counts?

Thank you in advance

Panagiotis Theodorou