| PPopt {PPtreeViz} | R Documentation |
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 |
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