Description Usage Arguments Value Examples
Predict with a drf forest
1 2 3 4 5 6 7 8 9 10 
object 
The trained drf forest. 
newdata 
Points at which predictions should be made. If NULL, makes outofbag predictions on the training set instead (i.e., provides predictions at Xi using only trees that did not use the ith training example). Note that this matrix (or vector) should have the number of columns as the training matrix, and that the columns must appear in the same order. 
transformation 
a function giving a transformation of the responses, by default if NULL, the identity 
functional 
which type of statistical functional. One option between:

num.threads 
Number of threads used in training. If set to NULL, the software automatically selects an appropriate amount. 
custom.functional 
a function giving the custom functional when 
... 
additional parameters. 
a list containing an entry with the same name as the functional selected.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29  # Train a distributional random forest with CART splitting rule.
n < 100
p < 2
X < matrix(rnorm(n * p), n, p)
Y < X + matrix(rnorm(n * p), ncol=p)
drf.forest < drf(X = X, Y = Y)
# Predict conditional correlation.
X.test < matrix(0, 101, p)
X.test[, 1] < seq(2, 2, length.out = 101)
cor.pred < predict(drf.forest, X.test, functional = "cor")
# Predict on outofbag training samples.
cor.oob.pred < predict(drf.forest, functional = "cor")
# Train a distributional random forest with "FourierMMD" splitting rule.
n < 100
p < 2
X < matrix(rnorm(n * p), n, p)
Y < X + matrix(rnorm(n * p), ncol=p)
drf.forest < drf(X = X, Y = Y, splitting.rule = "FourierMMD", num.features = 10)
# Predict conditional correlation.
X.test < matrix(0, 101, p)
X.test[, 1] < seq(2, 2, length.out = 101)
cor.pred < predict(drf.forest, X.test, functional = "cor")
# Predict on outofbag training samples.
cor.oob.pred < predict(drf.forest, functional = "cor")

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.