Given a trained forest and test data, compute the kernel weights for each test point.

During normal prediction, these weights (named alpha in the GRF paper) are computed as an intermediate step towards producing estimates. This function allows for examining the weights directly, so they could be potentially be used as the input to a different analysis.

get_forest_weights(forest, newdata = NULL, num.threads = NULL)

Arguments

forest	The trained forest.
newdata	Points at which predictions should be made. If NULL, makes out-of-bag predictions on the training set instead (i.e., provides predictions at Xi using only trees that did not use the i-th training example).
num.threads	Number of threads used in training. If set to NULL, the software automatically selects an appropriate amount.

Value

A sparse matrix where each row represents a test sample, and each column is a sample in the training data. The value at (i, j) gives the weight of training sample j for test sample i.

Examples

# \donttest{
p <- 10
n <- 100
X <- matrix(2 * runif(n * p) - 1, n, p)
Y <- (X[, 1] > 0) + 2 * rnorm(n)
rrf <- regression_forest(X, Y, mtry = p)
forest.weights.oob <- get_forest_weights(rrf)

n.test <- 15
X.test <- matrix(2 * runif(n.test * p) - 1, n.test, p)
forest.weights <- get_forest_weights(rrf, X.test)
# }