# Extract raw sample values for each of the random effect parameter terms. ```r ## S3 method for class 'bcfmodel' getRandomEffectSamples(object, ...) ``` ## Arguments - `object`: Object of type `bcfmodel` containing draws of a Bayesian causal forest model and associated sampling outputs. - `...`: Other parameters to be used in random effects extraction ## Returns 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 ```r n <- 500 p <- 5 X <- matrix(runif(n*p), ncol = p) mu_x <- ( ((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) ) pi_x <- ( ((0 <= X[,1]) & (0.25 > X[,1])) * (0.2) + ((0.25 <= X[,1]) & (0.5 > X[,1])) * (0.4) + ((0.5 <= X[,1]) & (0.75 > X[,1])) * (0.6) + ((0.75 <= X[,1]) & (1 > X[,1])) * (0.8) ) tau_x <- ( ((0 <= X[,2]) & (0.25 > X[,2])) * (0.5) + ((0.25 <= X[,2]) & (0.5 > X[,2])) * (1.0) + ((0.5 <= X[,2]) & (0.75 > X[,2])) * (1.5) + ((0.75 <= X[,2]) & (1 > X[,2])) * (2.0) ) Z <- rbinom(n, 1, pi_x) E_XZ <- mu_x + Z*tau_x snr <- 3 rfx_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[rfx_group_ids,] * rfx_basis) y <- E_XZ + rfx_term + rnorm(n, 0, 1)*(sd(E_XZ)/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,] pi_test <- pi_x[test_inds] pi_train <- pi_x[train_inds] Z_test <- Z[test_inds] Z_train <- Z[train_inds] y_test <- y[test_inds] y_train <- y[train_inds] mu_test <- mu_x[test_inds] mu_train <- mu_x[train_inds] tau_test <- tau_x[test_inds] tau_train <- tau_x[train_inds] rfx_group_ids_test <- rfx_group_ids[test_inds] rfx_group_ids_train <- rfx_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] mu_params <- list(sample_sigma_leaf = TRUE) tau_params <- list(sample_sigma_leaf = FALSE) bcf_model <- bcf(X_train = X_train, Z_train = Z_train, y_train = y_train, propensity_train = pi_train, rfx_group_ids_train = rfx_group_ids_train, rfx_basis_train = rfx_basis_train, X_test = X_test, Z_test = Z_test, propensity_test = pi_test, rfx_group_ids_test = rfx_group_ids_test, rfx_basis_test = rfx_basis_test, num_gfr = 10, num_burnin = 0, num_mcmc = 10, prognostic_forest_params = mu_params, treatment_effect_forest_params = tau_params) rfx_samples <- getRandomEffectSamples(bcf_model) ```

getRandomEffectSamples.bcfmodel function