PP.optimize {classPP}R Documentation

Find optimal Projection by maximizing selected PPindex

Description

Find optimal projection using PP index.

Usage

PP.optimize.random(PPmethod,projdim,data,class,std=TRUE,cooling=0.99,temp=1,r=NULL,lambda=NULL,weight=TRUE)
PP.optimize.anneal(PPmethod,projdim,data,class,std=TRUE,cooling=0.999,temp=1,energy=0.01,r=NULL,lambda=NULL,weight=TRUE)
PP.optimize.Huber(PPmethod,projdim,data,class,std=TRUE,cooling=0.99,temp=1,r=NULL,lambda=NULL,weight=TRUE)
PP.optimize.plot(PP.opt, data, class,std=TRUE)

Arguments

PPmethod Selected PP index
``LDA" - LDA index
``Lp" - Lp index;
``PDA" - PDA index
projdim dimension of projection that you want to find
data data without class information
class class information
std decide whether data will be standardized or not before applying projection pursuit
weight weight flag using in LDA index
cooling parameter for optimization
temp inital temperature for optimization
energy parameter for simulated annealing optimization
r a parameter for L_r index
lambda a parameter for PDA index
PP.opt the optimal projection

Value

index.best PP index for optimal projected data
proj.best optimal projection

Author(s)

Eun-kyung Lee

References

Lee E., Cook D., and Klinke, S. (2002) Projection Pursuit indices for supervised classification

See Also

{PPindex.class}

Examples

data(iris)

PP.opt<-PP.optimize.random("LDA",1,iris[,1:4],iris[,5],cooling=0.999,temp=1)

PP.opt$index.best
PP.optimize.plot(PP.opt,iris[,1:4],iris[,5])

PP.opt<-PP.optimize.anneal("LDA",1,iris[,1:4],iris[,5],cooling=0.999,temp=1,energy=0.01)
PP.opt$index.best

PP.optimize.plot(PP.opt,iris[,1:4],iris[,5])

PP.opt<-PP.optimize.Huber("LDA",2,iris[,1:4],iris[,5],cooling=0.999,r=1)
PP.opt$index.best
PP.optimize.plot(PP.opt,iris[,1:4],iris[,5])


[Package classPP version 1.0.2 Index]