SIS: Sure Independent Screening

In MFSIS: Model-Free Sure Independent Screening Procedures

Sure Independent Screening

Description

To overcome challenges caused by ultra-high dimensionality, Fan and Lv (2008) proposed a sure independence screening (SIS) method, which aims to screen out the redundant features by ranking their marginal Pearson correlations. The SIS method is named after the SIS property, which states the selected subset of features contains all the active ones with probability approaching one.

Usage

SIS(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 SIS. 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

Fan, J. and J. Lv (2008). Sure independence screening for ultrahigh dimensional feature space. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 70(5),849–911.

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 <- SIS(X, Y, n / log(n))
A

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