Please excuse my inexperience on this topic.
I am fitting a multivariate mixed model in order to find some scoring predictive mechanism.
I have a 2 negative binomials and lognormal families
I used shrinkage priors
model_mult <- brms::bf(x ~. + | Id ,family = negbinomial()) + brms::bf(y~. + | Id,family = negbinomial()) + brms::bf(z~. + | Id, family = lognormal())
priors <-c(set_prior(“horseshoe(1, par_ratio=0.2)”,class = “b”, coef = “”),set_prior(“cauchy(0, 10)”, class = “Intercept”, coef = “”))
fit_ml <- brms::brm(
data = train,
convergence issues when using binomial for x outcome
chains = 2,
cores = 2,
prior = priors
- did i set the priors correctly? if not do i need to specify responses
- Fixed effect confidence intervals are extremely large, any specific reason this could be happening?
- I tried using the binomial distribution for one of the outcome and had convergence issues, I ended up using negativebinomial , any reason this could be happening?
Thank you so much
I am sorry for asking all these questions