grf (version 0.9.4)

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 = c(0.1,
  0.5, 0.9), 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).

quantiles

Vector of quantiles at which estimates are required.

num.threads

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

...

Additional arguments (currently ignored).

Value

Predictions at each test point for each desired quantile.

Examples

# NOT RUN {
# 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)

# }