PP.Tree: Find PP tree structure

In EK-Lee/classPP: Projection Pursuit for supervised classification

Description

Find tree structure using projection pursuit in each split.

Usage

PP.Tree(PPmethod, i.class, i.data, weight = TRUE, r=NULL, lambda=NULL,cooling=0.999,temp=1,energy=0.01, ...)

Arguments

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
cooling	parameter for optimization
temp	inital temperature for optimization
energy	parameter for simulated annealing optimization
...	...

Value

Tree.Struct	Tree structure
Alpha.Keep	1D projection of each split
C.Keep	spliting rule for each split

Author(s)

Eun-kyung Lee

References

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

See Also

PPindex.class, PP.optimize

Examples

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

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