ranger (version 0.16.0)

treeInfo: Tree information in human readable format

Description

Extract tree information of a ranger object.

Usage

treeInfo(object, tree = 1)

Value

A data.frame with the columns

nodeID	The nodeID, 0-indexed.
leftChild	ID of the left child node, 0-indexed.
rightChild	ID of the right child node, 0-indexed.
splitvarID	ID of the splitting variable, 0-indexed. Caution, the variable order changes if the formula interface is used.
splitvarName	Name of the splitting variable.
splitval	The splitting value. For numeric or ordinal variables, all values smaller or equal go to the left, larger values to the right. For unordered factor variables see above.
terminal	Logical, TRUE for terminal nodes.
prediction	One column with the predicted class (factor) for classification and the predicted numerical value for regression. One probability per class for probability estimation in several columns. Nothing for survival, refer to object$forest$chf for the CHF node predictions.
numSamples	Number of samples in the node (only if ranger called with node.stats = TRUE).
splitStat	Split statistics, i.e., value of the splitting criterion (only if ranger called with node.stats = TRUE).

Arguments

object

ranger object.

tree

Number of the tree of interest.

Author

Marvin N. Wright

Details

Node and variable ID's are 0-indexed, i.e., node 0 is the root node. If the formula interface is used in the ranger call, the variable ID's are usually different to the original data used to grow the tree. Refer to the variable name instead to be sure.

Splitting at unordered factors (nominal variables) depends on the option respect.unordered.factors in the ranger call. For the "ignore" and "order" approaches, all values smaller or equal the splitval value go to the left and all values larger go to the right, as usual. However, with "order" the values correspond to the order in object$forest$covariate.levels instead of the original order (usually alphabetical). In the "partition" mode, the splitval values for unordered factor are comma separated lists of values, representing the factor levels (in the original order) going to the right.

See Also

ranger

Examples

rf <- ranger(Species ~ ., data = iris)
treeInfo(rf, 1)