my questions are general in nature so I won't provide any data.
I am using the package glmmTMB in R so if my terminology is weird it is because it is a mix of this and other sources I've been using to try and answer this myself. I am using GLMMs to model abundance data in relation to physical attributes of the environment. As is expected the count data is massively zero inflated. I am using zero inflation models, not hurdle-models, as the zeros are likely a mixture of real zero observations and some false zeros due to methodological issues.
I am currently using either all of the explanatory variables from the main model:
glmmTMB(formula = RV ~ EV1 + EV2 + EV3, ziformula = ~., ...)
or a single zero inflation parameter applied to all observations:
glmmTMB(formula = RV ~ EV1 + EV2 + EV3, ziformula = ~1, ...)
Some of the variables that are not significant in the main conditional model are significant in the zero inflation conditional model.
If I want to reduce the number of explanatory variables down via a drop-1 process (using AIC), does it make sense to have different sets of explanatory variables in the main and zero inflation model specifications? For example:
glmmTMB(formula = RV ~ EV1 + EV2, ziformula = EV1 + EV3, ...)
Furthermore, if this does make sense, what are the implications, in terms of inference, when trying to interpret the selected model if it has different main and zero-inflation model specifications?