PPTreeclass_MOD: Projection pursuit classification tree

In natydasilva/PPtreeExt: Projection Pursuit Classification Tree extensions

Projection pursuit classification tree

Description

Construct the projection pursuit classification tree extensions Find tree structure using various projection pursuit indices of classification in each split.

Usage

PPTreeclass_MOD(formula,data, PPmethod = "LDA",weight = TRUE,
                     r = 1,lambda = 0.1, energy = 0,maxiter = 50000, strule = 1,tot,...)

Arguments

formula	an object of class "formula"
data	data frame
PPmethod	method for projection pursuit; "LDA", "PDA"
weight	weight flag in LDA, PDA and Lr index
r	r in Lr index
lambda	lambda in PDA index
energy	parameter for the probability to take new projection
maxiter	maximum iteration number
strule	select the stoping rule, 1 all observations in the node belongs to the same class based, 2 node size is less than 5 3 the entropy reduction is samaller than a treshold.
tot	total obs original class
...	arguments to be passed to methods

Value

Tree.Struct tree structure of projection pursuit classification tree

projbest.node 1 dimensional optimal projections of each node split

splitCutoff.node cutoff values of each node split

origclass original class

origdata original data

References

Lee, YD, Cook, D., Park JW, and Lee, EK(2013) PPtree: Projection Pursuit Classification Tree, Electronic Journal of Statistics, 7:1369-1386.

Examples

data(penguins)
penguins <- na.omit(penguins[, -c(2,7)])
penguins_ppt <- PPTreeclass_MOD(species~bill_len + bill_dep +
  flipper_len + body_mass, data = penguins, PPmethod = "PDA")
penguins_ppt

natydasilva/PPtreeExt documentation built on June 14, 2025, 12:18 p.m.