In goldingn/greta: Simple and Scalable Statistical Modelling in R

Hierarchical linear regression in general conditional formulation

A hierarchical, Bayesian linear regression model using the iris data, with random intercepts and slopes for each of the three species. The slopes and intercepts for each species are correlated in this example. We allow every species to have a species specific slope for Sepal.Length.

int  <- normal(0, 10)
coef <- normal(0, 10)
sd   <- cauchy(0, 3, truncation = c(0, Inf))

n_species  <- length(unique(iris$Species))
species_id <- as.numeric(iris$Species)

Z <- model.matrix(~ Species + Sepal.Length * Species - 1, data = iris)

gamma_matrix <- multivariate_normal(matrix(0, 1, 2),
                                    diag(2),
                                    n_realisations = 3) 
gamma <- c(gamma_matrix)

wi <- as_data(iris$Sepal.Width)
Z  <- as_data(Z)
mu <- int + coef * wi + Z %*% gamma

distribution(iris$Sepal.Length) <- normal(mu, sd)