Kfilter_single: The Kolmogorov filter for variable screening in...

In MFSIS: Model-Free Sure Independent Screening Procedures

The Kolmogorov filter for variable screening in high-dimensional binary classification

Description

The Kolmogorov filter for variable screening in high-dimensional binary classification

Usage

Kfilter_single(X, Y, nsis = (dim(X)[1])/log(dim(X)[1]))

Arguments

X	The design matrix of dimensions n * p. Each row is an observation vector.
Y	The response vector of dimension n * 1.
nsis	Number of predictors recruited by Kfilter_single. The default is n/log(n).

Value

the labels of first nsis largest active set of all predictors

References

#' Mai, Q., & Zou, H. (2013). The Kolmogorov filter for variable screening in high-dimensional binary classification. Biometrika, 100(1), 229-234.

Examples

n=100;
p=200;
rho=0.5;
data=GendataLGM(n,p,rho)
data=cbind(data[[1]],data[[2]])
colnames(data)[1:ncol(data)]=c(paste0("X",1:(ncol(data)-1)),"Y")
data=as.matrix(data)
X=data[,1:(ncol(data)-1)];
Y=data[,ncol(data)];
A=Kfilter_single(X,Y,n/log(n));A

MFSIS documentation built on June 22, 2024, 9:42 a.m.