cop {COP}R Documentation

This function is for selecting variables for index models

Description

This packages is for selecting variables for index models using correlation pursuit method. Correlation pursuit (COP) can be viewed as a generalization of the conventional linear stepwise regression method to semi-parametric regression models. Unlike the conventional stepwise, COP selects variables that maximize the correlation between a transformed response and a linear combination of the predictors. A sequential selection strategy is used to select variables on multiple linear combinations.

Usage

cop(x, y, signif.in, signif.out, H, my.range)

Arguments

x is a n by p matrix for the covariates
y is a response variable
signif.in is the p-value to add a new variable to the current selected subset of variables
signif.out is the p-value to delete a variable from the current selected subset of variables
H is the number of slices
my.range is the maximum number of variables that you want to selected

Value

return a list with 3 components

id The label of variables that selected
lambda The eigenvalues that obtained from each step
my.sel The variables selected from each direction

Author(s)

Wenxuan Zhong

References

Zhong, W. Zhang, T. Zhu, Y. Liu, J.S.(2009) Correlation pursuit: Stepwise Variable Selection for Index Models, see also http://www.stat.uiuc.edu/~wenxuan/paper/juncopcombine2.pdf

Examples

x=mvrnorm(40,rep(0,8),diag(1,8))
beta=c(3,1.5,0,0,2,0,0,0)
y=x%*%beta+0.1*rnorm(40,0,1)

my.cop=cop(x,y,signif.in=0.01,signif.out=0.05,H=4,my.range=8)


[Package COP version 1.0 Index]