RandomEffectsDataset: Dataset used to sample a random effects model

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

Dataset used to sample a random effects model

Description

A dataset consists of three matrices / vectors: group labels, bases, and variance weights. Variance weights are optional.

Public fields

data_ptr

External pointer to a C++ RandomEffectsDataset class

Methods

Public methods

• RandomEffectsDataset$new()

• RandomEffectsDataset$update_basis()

• RandomEffectsDataset$update_variance_weights()

• RandomEffectsDataset$num_observations()

• RandomEffectsDataset$num_basis()

• RandomEffectsDataset$get_group_labels()

• RandomEffectsDataset$get_basis()

• RandomEffectsDataset$get_variance_weights()

• RandomEffectsDataset$has_group_labels()

• RandomEffectsDataset$has_basis()

• RandomEffectsDataset$has_variance_weights()

---

Method new()

Create a new RandomEffectsDataset object.

Usage

RandomEffectsDataset$new(group_labels, basis, variance_weights = NULL)

Arguments

group_labels

Vector of group labels

basis

Matrix of bases used to define the random effects regression (for an intercept-only model, pass an array of ones)

variance_weights

(Optional) Vector of observation-specific variance weights

Returns

A new RandomEffectsDataset object.

---

Method update_basis()

Update basis matrix in a dataset

Usage

RandomEffectsDataset$update_basis(basis)

Arguments

basis

Updated matrix of bases used to define random slopes / intercepts

---

Method update_variance_weights()

Update variance_weights in a dataset

Usage

RandomEffectsDataset$update_variance_weights(
  variance_weights,
  exponentiate = F
)

Arguments

variance_weights

Updated vector of variance weights used to define individual variance / case weights

exponentiate

Whether or not input vector should be exponentiated before being written to the RandomEffectsDataset's variance weights. Default: F.

---

Method num_observations()

Return number of observations in a RandomEffectsDataset object

Usage

RandomEffectsDataset$num_observations()

Returns

Observation count

---

Method num_basis()

Return dimension of the basis matrix in a RandomEffectsDataset object

Usage

RandomEffectsDataset$num_basis()

Returns

Basis vector count

---

Method get_group_labels()

Return group labels as an R vector

Usage

RandomEffectsDataset$get_group_labels()

Returns

Group label data

---

Method get_basis()

Return bases as an R matrix

Usage

RandomEffectsDataset$get_basis()

Returns

Basis data

---

Method get_variance_weights()

Return variance weights as an R vector

Usage

RandomEffectsDataset$get_variance_weights()

Returns

Variance weight data

---

Method has_group_labels()

Whether or not a dataset has group label indices

Usage

RandomEffectsDataset$has_group_labels()

Returns

True if group label vector is loaded, false otherwise

---

Method has_basis()

Whether or not a dataset has a basis matrix

Usage

RandomEffectsDataset$has_basis()

Returns

True if basis matrix is loaded, false otherwise

---

Method has_variance_weights()

Whether or not a dataset has variance weights

Usage

RandomEffectsDataset$has_variance_weights()

Returns

True if variance weights are loaded, false otherwise

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