I have a question regarding computing the Bayes Factor for a predictor of interest in a hurdle poisson model. With a linear model, I would fit the model with and without the predictor and use both of them in the bayes_factor() command. However, the 2 models I am effectively fitting simultaneously in a hurdle model make it hard for me to wrap my head around. Is it possible to simply fit the models specified below and calculate the Bayes Factor? And that would then be the Bayes Factor for the addition of the predictor predicting both the 0s and the counts? Or would I only remove the predictor on one of the two processes in fit2 and then have the Bayes Factor for the addition of the predictor on either one of the two processes? Or does this approach not work at all in a hurdle model? I am confused at this point.
fit1 <- brm(bf(outcome ~ 1 + predictor + (1 | pp), hu ~ 1 + outcome + (1 | pp)), data = data, family = hurdle_poisson()) fit2 <- brm(bf(outcome ~ 1 + (1 | pp), hu ~ 1 + (1 | pp)), data = data, family = hurdle_poisson()) bayes_factor(fit1, fit2)
Thanks in advance for any help!