I’m quite new to stan, and I’m trying to outline *Regression and Other Stories* in R. I’m working on a simple model of the hibbs dataset (vote ~ growth). In Chapter 9 (pg. 116), there is code provided to obtain predictions “by hand”, rather than by using posterior_predict(). The code is:

m1 ← stan_glm(vote ~ growth, data = hibbs)

new = data.frame(growth = 2.0)

y_pred ← posterior_predict(m1, newdata = new)

sims ← as.matrix(m1)

a ← sims[,1]

b ← sims[,2]

sigma ← sims[,3]

n_sims ← nrow(sims)

y_pred_byhand ← as.numeric(a + b * new) + rnorm(n_sims, 0, sigma)

Is this really the same way posterior_predict() constructs its’ distribution of predictions?