Hello,

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(

model_mult,

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