ridge_greta.R

# fake data
n <- 1000
m <- 50
x <- matrix(rnorm(n * m), n, m)
b <- rnorm(m, 0, 2) * rbinom(m, 1, 0.2)
eta <- x %*% b
y <- rnorm(n, eta, 0.3)

library (greta)

# parameters & priors
alpha <- normal(0, 1)
sigma <- normal(0, 1, truncation = c(0, Inf))
tau <- cauchy(0, 5)
beta <- normal(0, tau, dim = m)

# model
mu <- alpha + x %*% beta
distribution(y) <- normal(mu, sigma)

# inference
m <- model(beta)
draws <- mcmc(m)

# plot
est <- summary(draws)[[1]][, "Mean"]
plot(est ~ b)
abline(0, 1)