PPTreereg: Construct the projection pursuit regression tree

In PPtreeregViz: Projection Pursuit Regression Tree Visualization

Construct the projection pursuit regression tree

Description

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

Usage

PPTreereg(formula,data,DEPTH=NULL,Rr=1,PPmethod="LDA",
                 weight=TRUE,lambda=0.1,r=1,TOL.CV=0.1,selP=NULL,
                 energy=0,maxiter=500,
                 standardized=TRUE,even=TRUE,space=0,
                 maxFinalNode=20,maxNodeN=10,...)

Arguments

formula	an object of class "formula"
data	data frame
DEPTH	depth of the projection pursuit regression tree
Rr	cutoff rule in each node
PPmethod	method for projection pursuit; "LDA", "PDA", "Lr", "GINI", and "ENTROPY".
weight	weight flag in LDA, PDA and Lr index
lambda	lambda in PDA index
r	r in Lr index
TOL.CV	CV limit for the final node
selP	number of variables for the final node in Method 5
energy	energy parameter
maxiter	number of maximum iteration
standardized	standardize each X variable before fitting the tree structure. Default value is TRUE
even	divide evenly at each node. Default value is TRUE
space	space between two groups of dependent variable
maxFinalNode	maximum number of final node
maxNodeN	maximum number of observations in the final node
...	arguments to be passed to methods

Value

Tree.result projection pursuit regression tree result with PPtreeclass object format

MSE mean squared error of the final tree

mean.G means of the observations in the final node

sd.G standard deviations of the observations in the final node.

coef.G regression coefficients for Method 3, 4 and 5

origY original dependent variable vector

origX.mean mean of original X

origX.sd standard deviation of original X

class.origX.mean means of the each independent variables in the final node

References

...

Examples

data(mtcars)
Tree.result <- PPTreereg(mpg~.,mtcars,DEPTH=2,PPmethod="LDA")
Tree.result

PPtreeregViz documentation built on Dec. 24, 2022, 1:50 a.m.