logistic.dpd: MDPDE for Logistic Regression Model

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

MDPDE for Logistic Regression Model

Description

Computes the robust Minimum Density Power Divergence Estimator (MDPDE) under a Logistic Regression Model

Usage

logistic.dpd(y, X, alpha, method = "L-BFGS-B", p = ncol(X), Initial = matrix(rep(1, p)))

Arguments

y	Vector. The response vector [n X 1].
X	Matrix. Covariate Matrix [n X p].
alpha	Numeric. The DPD tuning parameter (0<= alpha <=1)
p	Integer. Number of columns in the covariate matrix (X). Optional.
Initial	Vector. Initial values of the parameters for the estimation process (initial of sigma needs to be given via log(sigma)). Optional. Default is 1 for all slope parameters, median of y for intercept and MAD(y) for sigma.
Method	String. Numerical optimization method to be used for the estimation process. 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".

Details

Reference: Ghosh, A., & Basu, A. (2016). Robust estimation in generalized linear models: the density power divergence approach. Test, 25(2), 269-290.

Value

A List of two elements. The first list element is a [p X 1] vector containing the parameter estimates. The second list element is an indicator if the numerical optimization within the estimation process has converged: 0 == convergence. (Same as the convergence from 'optim' function of R)

Examples

n <- 50; p <- 5;
beta <- rep(1,p); sigma<-1
SigmaX <- diag(p-1);
X <- mvrnorm(n, mu=rep(0,p-1), Sigma=SigmaX);
X0 <- cbind(1,X)
pr = 1/(1+exp(-(X0 %*% beta)))
Y = rbinom(n,1,pr)

alpha <- 0.3
MDPDE<-logistic.dpd(Y,X0,alpha)

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