grf (version 0.9.5)

regression_forest: Regression forest

Description

Trains a regression forest that can be used to estimate the conditional mean function mu(x) = E[Y | X = x]

Usage

regression_forest(X, Y, sample.fraction = 0.5, mtry = NULL,
  num.trees = 2000, num.threads = NULL, min.node.size = NULL,
  honesty = TRUE, ci.group.size = 2, alpha = 0.05, lambda = 0,
  downweight.penalty = FALSE, seed = NULL)

Arguments

X

The covariates used in the regression.

Y

The outcome.

sample.fraction

Fraction of the data used to build each tree. Note: If honesty is used, these subsamples will further be cut in half.

mtry

Number of variables tried for each split.

num.trees

Number of trees grown in the forest. Note: Getting accurate confidence intervals generally requires more trees than getting accurate predictions.

num.threads

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

min.node.size

A target for the minimum number of observations in each tree leaf. Note that nodes with size smaller than min.node.size can occur, as in the original randomForest package.

honesty

Whether or not honest splitting (i.e., sub-sample splitting) should be used.

ci.group.size

The forest will grow ci.group.size trees on each subsample. In order to provide confidence intervals, ci.group.size must be at least 2.

alpha

Maximum imbalance of a split.

lambda

A tuning parameter to control the amount of split regularization (experimental).

downweight.penalty

Whether or not the regularization penalty should be downweighted (experimental).

seed

The seed for the C++ random number generator.

Value

A trained regression forest object.

Examples

# NOT RUN {
# Train a standard regression forest.
n = 50; p = 10
X = matrix(rnorm(n*p), n, p)
Y = X[,1] * rnorm(n)
r.forest = regression_forest(X, Y)

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

# Predict on out-of-bag training samples.
r.pred = predict(r.forest)

# Predict with confidence intervals; growing more trees is now recommended.
r.forest = regression_forest(X, Y, num.trees = 100)
r.pred = predict(r.forest, X.test, estimate.variance = TRUE)
# }
# NOT RUN {
# }