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?