ForestDataset: Dataset used to sample a forest

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

Dataset used to sample a forest

Description

A dataset consists of three matrices / vectors: covariates, bases, and variance weights. Both the basis vector and variance weights are optional.

Public fields

data_ptr

External pointer to a C++ ForestDataset class

Methods

Public methods

• ForestDataset$new()

• ForestDataset$update_basis()

• ForestDataset$num_observations()

• ForestDataset$num_covariates()

• ForestDataset$num_basis()

• ForestDataset$has_basis()

• ForestDataset$has_variance_weights()

---

Method new()

Create a new ForestDataset object.

Usage

ForestDataset$new(covariates, basis = NULL, variance_weights = NULL)

Arguments

covariates

Matrix of covariates

basis

(Optional) Matrix of bases used to define a leaf regression

variance_weights

(Optional) Vector of observation-specific variance weights

Returns

A new ForestDataset object.

---

Method update_basis()

Update basis matrix in a dataset

Usage

ForestDataset$update_basis(basis)

Arguments

basis

Updated matrix of bases used to define a leaf regression

---

Method num_observations()

Return number of observations in a ForestDataset object

Usage

ForestDataset$num_observations()

Returns

Observation count

---

Method num_covariates()

Return number of covariates in a ForestDataset object

Usage

ForestDataset$num_covariates()

Returns

Covariate count

---

Method num_basis()

Return number of bases in a ForestDataset object

Usage

ForestDataset$num_basis()

Returns

Basis count

---

Method has_basis()

Whether or not a dataset has a basis matrix

Usage

ForestDataset$has_basis()

Returns

True if basis matrix is loaded, false otherwise

---

Method has_variance_weights()

Whether or not a dataset has variance weights

Usage

ForestDataset$has_variance_weights()

Returns

True if variance weights are loaded, false otherwise

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