PPtreeViz (version 1.0.3)

PPopt: PP optimization using various PP indices

Description

Find the q-dim optimal projection using various projectin pursuit indices with class information

Usage

PPopt(origclass,origdata,q=1,PPmethod="LDA",weight=TRUE,r=1,lambda=0.1,
             energy=0,cooling=0.999,TOL=0.0001,maxiter = 50000)

Arguments

origclass

class information vector

origdata

data matrix without class information

q

dimension of projection matrix

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

energy parameter

cooling

cooling parameter

TOL

tolerance

maxiter

number of maximum iteration

Value

• indexbest maximum LDA index value projbest optimal q-dim projection matrix origclass original class information vector origdata original data matrix without class information

References

Lee, EK., Cook, D., Klinke, S., and Lumley, T.(2005) Projection Pursuit for exploratory supervised classification, Journal of Computational and Graphical statistics, 14(4):831-846.

Examples

data(iris)
PP.proj.result <- PPopt(iris[,5],as.matrix(iris[,1:4]))
PP.proj.result