PPtree_split: Projection pursuit classification tree with random variable...

In PPforest: Projection Pursuit Classification Forest

Projection pursuit classification tree with random variable selection in each split

Description

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

Usage

PPtree_split(form, data, PPmethod='LDA', 
size.p=1,  lambda = 0.1,...)

Arguments

form	A character with the name of the class variable.
data	Data frame with the complete data set.
PPmethod	index to use for projection pursuit: 'LDA', 'PDA'
size.p	proportion of variables randomly sampled in each split, default is 1, returns a PPtree.
lambda	penalty parameter in PDA index and is between 0 to 1 . If lambda = 0, no penalty parameter is added and the PDA index is the same as LDA index. If lambda = 1 all variables are treated as uncorrelated. The default value is lambda = 0.1.
...	arguments to be passed to methods

Value

An object of class PPtreeclass with components

Tree.Struct	Tree structure of projection pursuit classification tree
projbest.node	1-dim optimal projections of each split node
splitCutoff.node	cutoff values of each split node
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

#crab data set

Tree.crab <- PPtree_split('Type~.', data = crab, PPmethod = 'LDA', size.p = 0.5)
Tree.crab

PPforest documentation built on Sept. 10, 2022, 1:05 a.m.