Extract raw sample values for each of the random effect parameter terms.

Usage

# S3 method for class 'bartmodel'
getRandomEffectSamples(object, ...)

Arguments

object: Object of type bcf containing draws of a Bayesian causal forest model and associated sampling outputs.
...: Other parameters to be used in random effects extraction

Value

List of arrays. The alpha array has dimension (num_components, num_samples) and is simply a vector if num_components = 1. The xi and beta arrays have dimension (num_components, num_groups, num_samples) and is simply a matrix if num_components = 1. The sigma array has dimension (num_components, num_samples) and is simply a vector if num_components = 1.

Examples

n <- 100
p <- 5
X <- matrix(runif(n*p), ncol = p)
f_XW <- (
    ((0 <= X[,1]) & (0.25 > X[,1])) * (-7.5) + 
    ((0.25 <= X[,1]) & (0.5 > X[,1])) * (-2.5) + 
    ((0.5 <= X[,1]) & (0.75 > X[,1])) * (2.5) + 
    ((0.75 <= X[,1]) & (1 > X[,1])) * (7.5)
)
snr <- 3
group_ids <- rep(c(1,2), n %/% 2)
rfx_coefs <- matrix(c(-1, -1, 1, 1), nrow=2, byrow=TRUE)
rfx_basis <- cbind(1, runif(n, -1, 1))
rfx_term <- rowSums(rfx_coefs[group_ids,] * rfx_basis)
E_y <- f_XW + rfx_term
y <- E_y + rnorm(n, 0, 1)*(sd(E_y)/snr)
test_set_pct <- 0.2
n_test <- round(test_set_pct*n)
n_train <- n - n_test
test_inds <- sort(sample(1:n, n_test, replace = FALSE))
train_inds <- (1:n)[!((1:n) %in% test_inds)]
X_test <- X[test_inds,]
X_train <- X[train_inds,]
y_test <- y[test_inds]
y_train <- y[train_inds]
group_ids_test <- group_ids[test_inds]
group_ids_train <- group_ids[train_inds]
rfx_basis_test <- rfx_basis[test_inds,]
rfx_basis_train <- rfx_basis[train_inds,]
rfx_term_test <- rfx_term[test_inds]
rfx_term_train <- rfx_term[train_inds]
bart_model <- bart(X_train = X_train, y_train = y_train, 
                   group_ids_train = group_ids_train, rfx_basis_train = rfx_basis_train, 
                   X_test = X_test, group_ids_test = group_ids_test, rfx_basis_test = rfx_basis_test, 
                   num_gfr = 100, num_burnin = 0, num_mcmc = 100, sample_tau = TRUE)
rfx_samples <- getRandomEffectSamples(bart_model)