| SIRS {MFSIS} | R Documentation |
Model-Free Feature Screening for Ultrahigh Dimensional Data
Description
A novel feature screening procedure under a unified model framework, which covers a wide variety of commonly used parametric and semi-parametric models. This method does not require imposing a specific model structure on regression functions, and thus is particularly appealing to ultrahigh-dimensional regressions, where there are a huge number of candidate predictors but little information about the actual model forms.
Usage
SIRS(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 SIRS. 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
Zhu, L.-P., L. Li, R. Li, and L.-X. Zhu (2011). Model-free feature screening for ultrahigh-dimensional data. Journal of the American Statistical Association 106(496), 1464–1475.
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=SIRS(X,Y,n/log(n));A