PSIS {MFSIS}

Ultrahigh-Dimensional Multiclass Linear Discriminant Analysis by Pairwise Sure Independence Screening

Description

A novel pairwise sure independence screening method for linear discriminant analysis with an ultrahigh-dimensional predictor. This procedure is directly applicable to the situation with many classes.

Usage

PSIS(X, Y, nsis)

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 PSIS. 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

Pan, R., Wang, H., and Li, R. (2016). Ultrahigh-dimensional multiclass linear discriminant analysis by pairwise sure independence screening. Journal of the American Statistical Association, 111(513):169–179.

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