TreeExt.construct: Projection pursuit classification tree extensions

In PPtreeExt: Projection Pursuit Classification Tree Extensions

Projection pursuit classification tree extensions

Description

Construct the projection pursuit classification tree extensions

Usage

TreeExt.construct(origclass, origdata, Tree.Struct, id, rep, rep1, rep2,
projbest.node, splitCutoff.node, PPmethod, 
lambda = NULL, q = 1, weight = TRUE, srule=TRUE, tot=NULL, tol = .5,...)

Arguments

origclass	factor or numeric vector containing the class labels for each observation.
origdata	data frame with the original data without class variable
Tree.Struct	tree structure of projection pursuit classification tree
id	tree node id
rep	internal counter for nodes
rep1	internal counter for nodes
rep2	internal counter for nodes
projbest.node	bests projection node
splitCutoff.node	cutof node
PPmethod	method for projection pursuit; "LDA", "PDA"
lambda	lambda in PDA index
q	numeric value with dimension of the projected data, if it is 1 then 1D projection is used
weight	weight flag in LDA, PDA
srule	stopping rule flag; if TRUE use stopping rule, if FALSE stop only for pure or empty nodes
tot	total number of observations
tol	tolerance value for entropy stopping rule for splitting a node
...	additional arguments to pass trough

Details

Find tree structure using various projection pursuit indices of classification in each split.

This function recursively constructs a binary classification tree using projection pursuit. At each node, it finds the optimal projection direction that best separates classes, determines a cutpoint, and creates child nodes until stopping criteria are met (pure nodes, small node size, or low entropy).

Value

A list containing the complete tree structure and node information:

Tree.Struct	A matrix where each row represents a node in the projection pursuit classification tree. The matrix has 5 columns: Column 1: Node ID Column 2: ID of the left child node (or 0 if terminal node) Column 3: ID of the right child node, or the predicted class label if terminal node Column 4: Projection index (which projection vector is used at this node) Column 5: Optimization criterion value for the projection at this node
projbest.node	A matrix where each row contains the optimal projection coefficients (Alpha vector) for each split node.
splitCutoff.node	A matrix/vector containing the optimal cutpoint values used at each split node.
rep	Integer counter tracking the current node being processed (internal use).
rep1	Integer counter for assigning child node IDs (internal use).
rep2	Integer counter for tracking projection indices (internal use).

PPtreeExt documentation built on Feb. 6, 2026, 5:06 p.m.