| WLS {MFSIS} | R Documentation |
A Model-free Variable Screening Method Based on Leverage Score
Description
An innovative and effective sampling scheme based on leverage scores via singular value decompositions has been proposed to select rows of a design matrix as a surrogate of the full data in linear regression. Analogously, variable screening can be viewed as selecting rows of the design matrix. However, effective variable selection along this line of thinking remains elusive. This method propose a weighted leverage variable screening method by using both the left and right singular vectors of the design matrix.
Usage
WLS(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 WLS. The default is n/log(n). |
Value
the labels of first nsis largest active set of all predictors.
Author(s)
Xuewei Cheng xwcheng@csu.edu.cn
References
Zhong, W., Liu, Y., & Zeng, P. (2021). A Model-free Variable Screening Method Based on Leverage Score. Journal of the American Statistical Association, (just-accepted), 1-36.
Examples
n=100;
p=200;
rho=0.5;
data=GendataLM(n,p,rho,error="gaussian")
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=WLS(X,Y,n/log(n));A