Compute vector of forest leaf indices

Compute and return a vector representation of a forest's leaf predictions for every observation in a dataset.

The vector has a "row-major" format that can be easily re-represented as as a CSR sparse matrix: elements are organized so that the first n elements correspond to leaf predictions for all n observations in a dataset for the first tree in an ensemble, the next n elements correspond to predictions for the second tree and so on. The "data" for each element corresponds to a uniquely mapped column index that corresponds to a single leaf of a single tree (i.e. if tree 1 has 3 leaves, its column indices range from 0 to 2, and then tree 2's leaf indices begin at 3, etc...).

Usage

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

Value

List of vectors. Each vector is of size num_obs * num_trees, where num_obs = nrow(covariates) and num_trees is the number of trees in the relevant forest of model_object.