MCP.sis.lrm: MCP correlation based SIS under a Linear Regression Model

In abhianik/dpdSIS: Robust Variable Screening with DPD-SIS and DPD-CSIS

MCP correlation based SIS under a Linear Regression Model

Description

Performs the MCP correlation based Robust Variable Screening (DPD-SIS) under a Linear Regression Model y=X*beta + e, with e ~ N(0, sigma^2) for some unknown sigma, using parallel computation.

Usage

MCP.sis.lrm(d, y, X)

Arguments

d	Integer. A list of the data matrices. Number of features to be selected. (1<= d <=p)
y	Vector. The response vector [n X 1].
X	Matrix. Covariate Matrix [n X p].

Details

Reference: Mu W, Xiong S. Some notes on robust sure independence screening. J App Stat. 2014; 41(10):2092–2102.

Value

SIS Vector. A d X 2 vector where the first column contains the variable index (ranked in decreasing order of importance) and the second column consist of the corresponding MDPDE of the slope

Examples

n <- 50; p <- 500;
beta <- rep(0,p); beta[c(1:5)] <- c(1,1,1,1,1);
d <- floor(n/log(n));
Sigma <- diag(p-1);
X <- mvrnorm(n, mu=rep(0,p-1), Sigma=Sigma);
X0 <- cbind(1,X)
Y <- drop(X0 %*% beta + 2*rnorm(n))

SIS<-MCP.sis.lrm(d,Y,X0)

abhianik/dpdSIS documentation built on Sept. 5, 2022, 12:40 p.m.