PPtreeViz (version 2.0.3)

PPopt: Projection pursuit optimization

Description

PP optimization using various projection pursuit indices

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

Details

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

# NOT RUN {
data(iris)
PP.proj.result <- PPopt(iris[,5],as.matrix(iris[,1:4]))
PP.proj.result
# }