Predict with a quantile forest — predict.quantile

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

# S3 method for 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 prediction. 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

# \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)
# }