createForestModelConfig: Create a forest model config object

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

Create a forest model config object

Description

Create a forest model config object

Usage

createForestModelConfig(
  feature_types = 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,
  cutpoint_grid_size = 100
)

Arguments

feature_types	Vector of integer-coded feature types (integers where 0 = numeric, 1 = ordered categorical, 2 = unordered categorical)
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.
cutpoint_grid_size	Number of unique cutpoints to consider (default: 100)

Value

ForestModelConfig object

Examples

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

stochtree documentation built on April 4, 2025, 2:11 a.m.