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Compute vector of forest leaf scale parameters

Source: R/kernel.R
computeForestLeafVariances.Rd

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")
#>  [1] 0.008578778 0.009245339 0.008350627 0.007593320 0.008786625 0.008127476
#>  [7] 0.008252439 0.006815952 0.006999405 0.006511144
computeForestLeafVariances(bart_model, "mean", 0)
#> [1] 0.008578778
computeForestLeafVariances(bart_model, "mean", c(1,3,5))
#> [1] 0.009245339 0.007593320 0.008127476