I'm working with a latent variable model and I'd like to predict a negative binomial count response variable. I found the clever cludge that @AdminRobK previously shared to use thresholds fixed to parametric quantiles for negative binomial distribution (#7856 [6]; 180313.R [7] ). I think this approach will work well for me. The issue I've got is that my dependent variable is clearly zero-inflated. I've also got some minor observation-number differences that I'd like to incorporate as an offset.
I'm not exactly sure how to marry a zero-inflation model with the threshold parameterization of the negative binomial Rob used there? Can anyone offer a suggestion?
For the offset, with this parameterization, would it be correct to just add the log(number of observations) as a predictor in my model with a fixed coefficient of 1.0?
Thanks!