MFSIS (version 0.3.0)

MVSIS: Model-Free Feature Screening for Ultrahigh Dimensional Discriminant Analysis

Description

A marginal feature screening procedure based on empirical conditional distribution function. The response variable is categorical in discriminant analysis. This method uses the conditional distribution function to construct a new index for feature screening.

Usage

MVSIS(X, Y, nsis)

Value

the labels of first nsis largest active set of all predictors

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 MVSIS. The default is n/log(n).

Author

Xuewei Cheng

References

Cui, H., Li, R., & Zhong, W. (2015). Model-free feature screening for ultrahigh dimensional discriminant analysis. Journal of the American Statistical Association, 110(510), 630-641.

Examples

n <- 100
p <- 200
rho <- 0.5
data <- GendataLGM(n, p, rho)
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 <- MVSIS(X, Y, n / log(n))
A