[R-sig-ME] Predictions from zero-inflated or hurdle models

[Ruben Arslan]

Dear list,

I wanted to ask: Is there any (maybe just back of the envelope) way to
obtain a response prediction for zero-inflated or hurdle type models?
I've fit such models in MCMCglmm, but I don't work in ecology and my
previous experience with explaining such models to "my audience" did not
bode well. When it comes to humans, the researchers I presented to are not
used to offspring count being zero-inflated (or acquainted with that
concept), but in my historical data with high infant mortality, it is (in
modern data it's actually slightly underdispersed).

Currently I'm using lme4 and simply splitting my models into two stages
(finding a mate and having offspring).
That's okay too, but in one population the effect of interest is not
clearly visible in either stage, only when both are taken together (but
then the outcome is zero-inflated).
I expect to be given a hard time for this and hence thought I'd use a
binomial model with the outcome offspring>0 as my main model, but that
turns out to be hard to explain too and doesn't really do the data justice.

Basically I don't want to be forced to discuss my smallest population as a
non-replication of the effect because I was insufficiently able to explain
the statistics behind my reasoning that the effect shows.

Best regards,

Ruben Arslan

Georg August University Göttingen
Biological Personality Psychology
Georg Elias Müller Institute of Psychology
Goßlerstr. 14
37073 Göttingen
Germany
Tel.: +49 551 3920704
https://psych.uni-goettingen.de/en/arslan
https://formr.org

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