createForestModelConfig: Create ForestModelConfig Object

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

Create ForestModelConfig Object

Description

Create a forest model config object

This function is intended for advanced use cases in which users require detailed control of sampling algorithms and data structures. Minimal input validation and error checks are performed – users are responsible for providing the correct inputs. For tutorials on the "proper" usage of the stochtree's advanced workflow, we provide several vignettes at https://stochtree.ai/

Usage

createForestModelConfig(
  feature_types = NULL,
  sweep_update_indices = NULL,
  num_trees = NULL,
  num_features = NULL,
  num_observations = NULL,
  variable_weights = NULL,
  leaf_dimension = 1,
  alpha = 0.95,
  beta = 2,
  min_samples_leaf = 5,
  max_depth = -1,
  leaf_model_type = 1,
  leaf_model_scale = NULL,
  variance_forest_shape = 1,
  variance_forest_scale = 1,
  cloglog_forest_shape = 2,
  cloglog_forest_rate = 2,
  cutpoint_grid_size = 100,
  num_features_subsample = NULL
)

Arguments

feature_types	Vector of integer-coded feature types (integers where 0 = numeric, 1 = ordered categorical, 2 = unordered categorical)
sweep_update_indices	Vector of (0-indexed) indices of trees to update in a sweep
num_trees	Number of trees in the forest being sampled
num_features	Number of features in training dataset
num_observations	Number of observations in training dataset
variable_weights	Vector specifying sampling probability for all p covariates in ForestDataset
leaf_dimension	Dimension of the leaf model (default: 1)
alpha	Root node split probability in tree prior (default: 0.95)
beta	Depth prior penalty in tree prior (default: 2.0)
min_samples_leaf	Minimum number of samples in a tree leaf (default: 5)
max_depth	Maximum depth of any tree in the ensemble in the model. Setting to -1 does not enforce any depth limits on trees. Default: -1.
leaf_model_type	Integer specifying the leaf model type (0 = constant leaf, 1 = univariate leaf regression, 2 = multivariate leaf regression). Default: 0.
leaf_model_scale	Scale parameter used in Gaussian leaf models (can either be a scalar or a q x q matrix, where q is the dimensionality of the basis and is only >1 when leaf_model_int = 2). Calibrated internally as 1/num_trees, propagated along diagonal if needed for multivariate leaf models.
variance_forest_shape	Shape parameter for IG leaf models (applicable when leaf_model_type = 3). Default: 1.
variance_forest_scale	Scale parameter for IG leaf models (applicable when leaf_model_type = 3). Default: 1.
cloglog_forest_shape	Shape parameter for conditional gamma component of cloglog leaf models (applicable when leaf_model_type = 4). Default: 1.
cloglog_forest_rate	Rate parameter for conditional gamma component of cloglog leaf models (applicable when leaf_model_type = 4). Default: 1.
cutpoint_grid_size	Number of unique cutpoints to consider (default: 100)
num_features_subsample	Number of features to subsample for the GFR algorithm

Value

ForestModelConfig object

Examples

config <- createForestModelConfig(num_trees = 10, num_features = 5, num_observations = 100)

stochtree documentation built on April 29, 2026, 1:08 a.m.