grf (version 2.3.2)

predict.quantile_forest: Predict with a quantile forest

Description

Gets estimates of the conditional quantiles of Y given X using a trained forest.

Usage

# S3 method for quantile_forest
predict(object, newdata = NULL, quantiles = NULL, num.threads = NULL, ...)

Value

A list with elements `predictions`: a matrix with predictions at each test point for each desired quantile.

Arguments

object

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). Note that this matrix should have the number of columns as the training matrix, and that the columns must appear in the same order.

quantiles

Vector of quantiles at which estimates are required. If NULL, the quantiles used to train the forest is used. Default is NULL.

num.threads

Number of threads used in training. If set to NULL, the software automatically selects an appropriate amount.

...

Additional arguments (currently ignored).

Examples

# \donttest{
# Train a quantile forest.
n <- 50
p <- 10
X <- matrix(rnorm(n * p), n, p)
Y <- X[, 1] * rnorm(n)
q.forest <- quantile_forest(X, Y, quantiles = c(0.1, 0.5, 0.9))

# Predict on out-of-bag training samples.
q.pred <- predict(q.forest)

# Predict using the forest.
X.test <- matrix(0, 101, p)
X.test[, 1] <- seq(-2, 2, length.out = 101)
q.pred <- predict(q.forest, X.test)
# }