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PPtree (version 2.3.0)

PP.Tree: Find PP tree structure

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

References

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

See Also

PPindex.class, PP.optimize

Examples

Run this code

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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