computeForestLeafVariances: Compute vector of forest leaf scale parameters

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

Compute vector of forest leaf scale parameters

Description

Return each forest's leaf node scale parameters.

If leaf scale is not sampled for the forest in question, throws an error that the leaf model does not have a stochastic scale parameter.

Usage

computeForestLeafVariances(model_object, forest_type, forest_inds = NULL)

Arguments

model_object	Object of type bartmodel or bcfmodel corresponding to a BART / BCF model with at least one forest sample
forest_type	Which forest to use from model_object. Valid inputs depend on the model type, and whether or not a given forest was sampled in that model. 1. BART 'mean': Extracts leaf indices for the mean forest 'variance': Extracts leaf indices for the variance forest 2. BCF 'prognostic': Extracts leaf indices for the prognostic forest 'treatment': Extracts leaf indices for the treatment effect forest 'variance': Extracts leaf indices for the variance forest
forest_inds	(Optional) Indices of the forest sample(s) for which to compute leaf indices. If not provided, this function will return leaf indices for every sample of a forest. This function uses 0-indexing, so the first forest sample corresponds to forest_num = 0, and so on.

Value

Vector of size length(forest_inds) with the leaf scale parameter for each requested forest.

Examples

X <- matrix(runif(10*100), ncol = 10)
y <- -5 + 10*(X[,1] > 0.5) + rnorm(100)
bart_model <- bart(X, y, num_gfr=0, num_mcmc=10)
computeForestLeafVariances(bart_model, "mean")
computeForestLeafVariances(bart_model, "mean", 0)
computeForestLeafVariances(bart_model, "mean", c(1,3,5))

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