PP.Tree {classPP} | R Documentation |
Find tree structure using projection pursuit in each split.
PP.Tree(PPmethod, i.class, i.data, weight = TRUE, r=NULL,lambda=NULL,...)
PPmethod |
Selected PP index
``LDA" - LDA index ``Lp" - Lp index; ``PDA" - PDA index |
i.data |
A training data without class information |
i.class |
class information |
weight |
weight flag using in LDA index |
r |
a parameter for L_r index |
lambda |
a parameter for PDA index |
... |
... |
Tree.Struct |
Tree structure |
Alpha.Keep |
1D projection of each split |
C.Keep |
spliting rule for each split |
Eun-kyung Lee
Lee, E., Cook, D., and Klinke, S.(2002) Projection Pursuit indices for supervised classification
{PPindex.class}
, {PP.optimize}
data(iris) n <- nrow(iris) tot <- c(1:n) n.train <- round(n*0.9) train <- sample(tot,n.train) test <- tot[-train] Tree.result <- PP.Tree("LDA",iris[train,5],iris[train,1:4]) Tree.result