dpd.sis.lrm: DPD-SIS under a Linear Regression Model with unknwon error...

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

DPD-SIS under a Linear Regression Model with unknwon error variance

Description

Performs the Density Power Divergence 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

dpd.sis.lrm(d, y, X, alpha, Method = "L-BFGS-B", p = ncol(X), Initial = matrix(rep(1, p)))

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].
alpha	Numeric. The DPD tuning parameter (0<= alpha <=1)
Method	String. Numerical optimization method to be used for computation of marginal slopes. Possible options are "L-BFGS-B","Nelder-Mead", "BFGS", "CG", which are the same as the input of 'optim' function in R. Optional. Default is "L-BFGS-B".
p	Integer. Number of columns in the covariate matrix (X). Optional.
Initial	Vector. Initial values of the marginal slope parameter for estimation process. Default: 1 for all parameters. Optional.

Details

Reference: Ghosh, A. and Thoresen, M. (2021). A Robust Variable Screening procedure for Ultra-high dimensional data. Statistical Methods in Medical Research, 30(8), 1816–1832.

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))

alpha <- 0.3
SIS<-dpd.sis.lrm(d,Y,X0,alpha)

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