I am looking to implement bayesian logistic regression. However, the examples are confusing me a lot.
So far, here is what I got,
# X is predictors dataframe # Y is prediction dataframe # Say X has 4 dimensions,then I believe multNormal # should somehow take input of list of size 5 for means, # and another matrix of 5x5 for covariances multNormal <- # What goes here? post <- stan_glm(Y ~ X, family = # What goes here for logistic regression?, prior = multNormal, prior_intercept = multNormal, seed = 1)
I tried to understand https://mc-stan.org/rstanarm/articles/priors.html#informative-prior-distributions and https://mc-stan.org/rstanarm/articles/rstanarm.html, but honestly, I couldn’t understand what to do next. Couldn’t locate any documentation either.
what do I need to do next?