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Compute and return the largest possible leaf index computable by computeForestLeafIndices for the forests in a designated forest sample container.

computeMaxLeafIndex.Rd

Compute and return the largest possible leaf index computable by computeForestLeafIndices for the forests in a designated forest sample container.

Usage

computeMaxLeafIndex(model_object, covariates, forest_type, forest_inds = NULL)

Arguments

model_object

Object of type bartmodel or bcf corresponding to a BART / BCF model with at least one forest sample

covariates

Covariates to use for prediction. Must have the same dimensions / column types as the data used to train a forest.

forest_type

Which forest to use from model_object. Valid inputs depend on the model type, and whether or not a

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 1-indexing, so the first forest sample corresponds to forest_num = 1, and so on.

Value

Vector containing the largest possible leaf index computable by computeForestLeafIndices for the forests in a designated forest sample container.