PPTreeclass: Projection pursuit classification tree

In PPtreeViz: Projection Pursuit Classification Tree Visualization

Description

Construct the projection pursuit classification tree

Usage

PPTreeclass(formula,data, PPmethod="LDA",weight=TRUE,
                     r=1,lambda=0.1,energy=0,maxiter=50000,...)

Arguments

formula	an object of class "formula"
data	data frame
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

Details

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

Value

Tree.Struct tree structure of projection pursuit classification tree

projbest.node 1 dimensional optimal projections of each node split

splitCutoff.node cutoff values of each node split

origclass original class

origdata original data

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 <- PPTreeclass(Species~.,data = iris,"LDA")
Tree.result

PPtreeViz documentation built on Dec. 6, 2019, 9:07 a.m.