PPtree_split | R Documentation |
Find tree structure using various projection pursuit indices of classification in each split.
PPtree_split(form, data, PPmethod='LDA',
size.p=1, lambda = 0.1,...)
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 |
... |
arguments to be passed to methods |
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 |
Lee, YD, Cook, D., Park JW, and Lee, EK (2013) PPtree: Projection pursuit classification tree, Electronic Journal of Statistics, 7:1369-1386.
#crab data set
Tree.crab <- PPtree_split('Type~.', data = crab, PPmethod = 'LDA', size.p = 0.5)
Tree.crab
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