PPtreeViz (version 1.3.0)

PP.Tree.class: Projection pursuit classification tree

Description

Construct the projection pursuit classification tree

Usage

PP.Tree.class(origclass, origdata, PPmethod="LDA",weight=TRUE, r=1,lambda=0.1,energy=0,maxiter=50000, ...)

Arguments

origclass

class information vector

origdata

numeric data matrix without class information

PPmethod

method for projection pursuit; "LDA", "PDA", "Lr", "GINI", and "ENTROPY"

weight

weight flag in LDA, PDA and Lr index

r

r in Lr index

lambda

lambda in PDA index

energy

parameter for the probability to take new projection

maxiter

maximum iteration number

...

arguments to be passed to methods

Value

Tree.Struct tree structure of projection pursuit classification treeprojbest.node 1 dimensional optimal projections of each node splitsplitCutoff.node cutoff values of each node splitorigclass original classorigdata original data

Details

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

References

Lee, YD, Cook, D., Park JW, and Lee, EK(2013) PPtree: Projection Pursuit Classification Tree, Electronic Journal of Statistics, 7:1369-1386.

Examples

data(iris)
Tree.result <- PP.Tree.class(iris[,5],iris[,1:4],"LDA")
Tree.result