PPopt: Projection pursuit optimization

In EK-Lee/PPtreeViz: Projection Pursuit Classification Tree Visualization

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

Details

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

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

EK-Lee/PPtreeViz documentation built on May 6, 2019, 3:09 p.m.