| SIS {MFSIS} | R Documentation |
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 xwcheng@hunnu.edu.cn
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