bayesglm() (in the arm R package) and various functions in the MCMCpack package are aimed at doing Bayesian estimation of generalized linear models, but I'm not sure they're actually computing the same thing. The MCMCpack functions use Markov chain Monte Carlo to obtain a (dependent) sample from the joint posterior for the model parameters.
bayesglm(), on the other hand, produces. I'm not sure what.
It looks like
bayesglm() produces a point estimate, which would make it MAP (maximum a posteriori) estimation rather than a full Bayesian estimation, but there's a
sim() function that looks like it can be used to get posterior draws.
Can someone explain the difference in intended use for the two? Can
bayesglm() + sim() produce true posterior draws, or is it some sort of approximation?