sample_bart_posterior_predictive: Sample from the posterior predictive distribution for...

In stochtree: Stochastic Tree Ensembles (XBART and BART) for Supervised Learning and Causal Inference

Sample from the posterior predictive distribution for outcomes modeled by BART

Description

Sample from the posterior predictive distribution for outcomes modeled by BART

Usage

sample_bart_posterior_predictive(
  model_object,
  X = NULL,
  leaf_basis = NULL,
  rfx_group_ids = NULL,
  rfx_basis = NULL,
  num_draws_per_sample = NULL
)

Arguments

model_object	A fitted BART model object of class bartmodel.
X	A matrix or data frame of covariates. Required if the BART model depends on covariates (e.g., contains a mean or variance forest).
leaf_basis	A matrix of bases for mean forest models with regression defined in the leaves. Required for "leaf regression" models.
rfx_group_ids	A vector of group IDs for random effects model. Required if the BART model includes random effects.
rfx_basis	A matrix of bases for random effects model. Required if the BART model includes random effects.
num_draws_per_sample	The number of posterior predictive samples to draw for each posterior sample. Defaults to a heuristic based on the number of samples in a BART model (i.e. if the BART model has >1000 draws, we use 1 draw from the likelihood per sample, otherwise we upsample to ensure intervals are based on at least 1000 posterior predictive draws).

Value

Array of posterior predictive samples with dimensions (num_observations, num_posterior_samples, num_draws_per_sample) if num_draws_per_sample > 1, otherwise (num_observations, num_posterior_samples).

Examples

n <- 100
p <- 5
X <- matrix(rnorm(n * p), nrow = n, ncol = p)
y <- 2 * X[,1] + rnorm(n)
bart_model <- bart(y_train = y, X_train = X)
ppd_samples <- sample_bart_posterior_predictive(
  model_object = bart_model, X = X
)

stochtree documentation built on Nov. 22, 2025, 9:06 a.m.