# PPtree_split

##### 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 structure of projection pursuit classification tree

1-dim optimal projections of each split node

cutoff values of each split node

original class

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

```
# 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)*