MFSIS (version 0.3.0)

CISIS: Model-Free Feature screening Based on Concordance Index for Ultra-High Dimensional Categorical Data

Description

The proposed method is based on the concordance index which measures concordance between random vectors. A model-free and robust feature screening method for ultrahigh-dimensional categorical data. The performance is quite robust in the presence of heavy-tailed error distributions, extremely unbalance responses, and category-adaptive data.

Usage

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

Author

Xuewei Cheng

References

Cheng X, Wang H. A generic model-free feature screening procedure for ultra-high dimensional data with categorical response[J]. Computer Methods and Programs in Biomedicine, 2023, 229: 107269.

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