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MFSIS (version 0.3.0)

MFSIS: Model-free feature screening procedures

Description

Through this function, we provide a unified framework to carry out model-free screening procedures including SIS (Fan and Lv (2008) <doi:10.1111/j.1467-9868.2008.00674.x>), SIRS(Zhu et al. (2011)<doi:10.1198/jasa.2011.tm10563>), DC-SIS (Li et al. (2012) <doi:10.1080/01621459.2012.695654>), MDC-SIS(Shao and Zhang (2014) <doi:10.1080/01621459.2014.887012>), Bcor-SIS (Pan et al. (2019) <doi:10.1080/01621459.2018.1462709>), PC-Screen (Liu et al. (2020) <doi:10.1080/01621459.2020.1783274>), WLS (Zhong et al.(2021) <doi:10.1080/01621459.2021.1918554>), Kfilter (Mai and Zou (2015) <doi:10.1214/14-AOS1303>), MVSIS (Cui et al. (2015) <doi:10.1080/01621459.2014.920256>), PSIS (Pan et al. (2016) <doi:10.1080/01621459.2014.998760>), CAS (Xie et al. (2020) <doi:101080/0162145920191573734>), CI-SIS (Cheng and Wang. (2022) <doi:10.1016/j.cmpb.2022.107269>) and CSIS (Cheng et al. (2023) <doi:10.1007/s00180-023-01399-5>).

Usage

MFSIS(
  X,
  Y,
  nsis = (dim(X)[1])/log(dim(X)[1]),
  method = c("SIS", "SIRS", "DCSIS", "MDCSIS", "CSIS", "PCSIS", "BcorSIS", "WLS",
    "MVSIS", "Kfilter")
)

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 the screening method. The default is n/log(n).

method

The method that you choose to perform screening procedure. method=c("SIS", "SIRS", "DCSIS", "MDCSIS", "CSIS", "PCSIS", "BcorSIS", "WLS", "MVSIS", "Kfilter","PSIS","CAS","CISIS"). If you want to know more information about this method, please use command "help(method)" for detail information.

Author

Xuewei Cheng xwcheng@hunnu.edu.cn

Examples

Run this code

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 <- MFSIS(X, Y, n / log(n), method = "CSIS")
A

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