PP.Tree: Find PP tree structure

Description Usage Arguments Value Author(s) References See Also Examples

Description

Find tree structure using projection pursuit in each split.

Usage

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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

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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


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