predict.quantile_forest: Predict with a quantile forest

In grf: Generalized Random Forests

Predict with a quantile forest

Description

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

Usage

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

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).

Value

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

Examples

# 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)

grf documentation built on June 24, 2024, 5:20 p.m.