LDAopt: PP optimization using LDA index

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

Description

PP optimization using LDA index

Usage

LDAopt(origclass,origdata,q=1,weight=TRUE,...)

Arguments

origclass	class information vector of data
origdata	data matrix without class information
q	dimension of projection vector
weight	weight flag in LDA index
...	arguments to be passed to methods

Details

Find the q-dimensional optimal projection using LDA projectin pursuit index

Value

indexbest maximum LDA index value

projbest optimal q-dimensional 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)
LDA.proj.result <- LDAopt(iris[,5],iris[,1:4])
LDA.proj.result$indexbest
LDA.proj.result$projbest

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