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$num_observations()

• RandomEffectsDataset$has_group_labels()

• RandomEffectsDataset$has_basis()

• RandomEffectsDataset$has_variance_weights()

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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.

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Method num_observations()

Return number of observations in a RandomEffectsDataset object

Usage

RandomEffectsDataset$num_observations()

Returns

Observation count

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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

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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

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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 April 4, 2025, 2:11 a.m.