| PPTreeclass {PPtreeViz} | R Documentation |
Projection pursuit classification tree
Description
Construct the projection pursuit classification tree
Usage
PPTreeclass(formula,data, PPmethod="LDA",weight=TRUE,
r=1,lambda=0.1,energy=0,maxiter=50000,...)
Arguments
formula |
an object of class "formula" |
data |
data frame |
PPmethod |
method for projection pursuit; "LDA", "PDA", "Lr", "GINI", and "ENTROPY" |
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 |
... |
arguments to be passed to methods |
Details
Find tree structure using various projection pursuit indices of classification in each split.
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(iris)
Tree.result <- PPTreeclass(Species~.,data = iris,"LDA")
Tree.result