PP.optimize {classPP} | R Documentation |
Find optimal projection using PP index.
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)
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 |
index.best |
PP index for optimal projected data |
proj.best |
optimal projection |
Eun-kyung Lee
Lee E., Cook D., and Klinke, S. (2002) Projection Pursuit indices for supervised classification
{PPindex.class}
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])