Calculate the regularization path of an SDForest

This function calculates the variable importance of an SDForest and the out-of-bag performance for different complexity parameters.

# S3 method for class 'SDForest'
regPath(object, cp_seq = NULL, X = NULL, Y = NULL, Q = NULL, copy = TRUE, ...)

Arguments

object

an SDForest object

cp_seq

A sequence of complexity parameters. If NULL, the sequence is calculated automatically using only relevant values.

X

The training data, if NULL the data from the forest object is used.

Y

The training response variable, if NULL the data from the forest object is used.

Q

The transformation matrix, if NULL the data from the forest object is used.

copy

Whether the tree should be copied for the regularization path. If FALSE, the pruning is done in place and will change the SDForest. This might be reasonable, if the SDForest is to large to copy.

...

Further arguments passed to or from other methods.

Value

An object of class paths containing

cp

The sequence of complexity parameters.

varImp_path

A matrix with the variable importance for each complexity parameter.

loss_path

A matrix with the out-of-bag performance for each complexity parameter.

cp_min

The complexity parameter with the lowest out-of-bag performance.

type

Path type

See also

plot.paths plotOOB regPath.SDTree prune get_cp_seq SDForest

Author

Markus Ulmer

Examples

set.seed(1)
n <- 10
X <- matrix(rnorm(n * 5), nrow = n)
y <- sign(X[, 1]) * 3 + sign(X[, 2]) + rnorm(n)
model <- SDForest(x = X, y = y, Q_type = 'no_deconfounding', cp = 0.5)
paths <- regPath(model)
plotOOB(paths)

plot(paths)

# \donttest{
plot(paths, plotly = TRUE)

# }