grf (version 2.3.2)

predict.multi_regression_forest: Predict with a multi regression forest

Description

Gets estimates of E[Y_i | X = x] using a trained multi regression forest.

Usage

# S3 method for multi_regression_forest
predict(object, newdata = NULL, num.threads = NULL, drop = FALSE, ...)

Value

A list containing `predictions`: a matrix of predictions for each outcome.

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.

num.threads

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

drop

If TRUE, coerce the prediction result to the lowest possible dimension. Default is FALSE.

...

Additional arguments (currently ignored).

Examples

# \donttest{
# Train a standard regression forest.
n <- 500
p <- 5
X <- matrix(rnorm(n * p), n, p)
Y <-  X[, 1, drop = FALSE] %*% cbind(1, 2) + rnorm(n)
mr.forest <- multi_regression_forest(X, Y)

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

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