I am fitting zero-inflated negative binomial models in brms for count data that has many many zeros (90% zeroes). I fit two models:
m1<-brm(bf(Count ~ 1 + Treatment + (1|Location) + (1|id) + (1|Day/Time_Day), zi ~ 1 + (1|id))
m2<-brm(bf(Count ~ 1 + (1|id), zi ~ 1 + (1|id), shape ~ 1 + (1|id))
I know beforehand that the zero-inflation component depends heavily on the group (id) member as does likely the shape parameter (hence m2).
Both of these models give very good posterior predictive checks.
However, the LOO-PIT plots are incredibly bad. I have already done some looking and found these links:
My understanding from these discussions and the paper that @avehtari references are that the LOO-PIT plots (as done in the loo package) are not valid for zero-inflated negative binomial models for count data.
Have I interpreted this correctly? Should I just ignore the LOO-PIT plots for these models?
If this is not correct, then how do I interpret the bizarre shape of these plots?
Many thanks for any insight!