# PPtree_split

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##### Projection pursuit classification tree with random variable selection in each split

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

Keywords
tree
##### 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.

• PPtree_split
##### Examples
# NOT RUN {
#crab data set

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

# }

Documentation reproduced from package PPforest, version 0.1.1, License: GPL (>= 2)

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