Regarding the example here with standard package and stan_glm() function for Bayesian logistic regression
library(rstanarm) options(mc.cores = parallel::detectCores()) t_prior <- student_t(df = 7, location = 0, scale = 2.5) post1 <- stan_glm(outcome ~ ., data = diabetes, family = binomial(link = "logit"), prior = t_prior, prior_intercept = t_prior, QR=TRUE, seed = 14124869)
The OR is
OR = post1$measure[2,1]
I would like to ask some questions please:
- How I can use non-informative prior (flat)? with different cutoff points?
- How I can perform queries since its Bayesian, like P(OR>0.2)?
- How I can get the plot of prior, likelihood, and the posterior as well?
Thanks in advance.