SIRS: Model-Free Feature Screening for Ultrahigh Dimensional Data

In MFSIS: Model-Free Sure Independent Screening Procedures

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 [e-mail]

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

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