admmlasso_logC: Fit l1-penalized logistic regression model using ADMM

In theandyb/aeffp: Lasso-penalized Logistic Regression via ADMM

Description

Use an ADMM approach to find the parameters for a l1-penalized logistic regression model. Finds solution to argmin_beta sum(log(1+-yX beta)) + lambda*sum(|beta|)

Usage

admmlasso_logC(X, y, lam, rho = 0.001, maxit = 1000L, tol = 0.001)

Arguments

X	Covariate matrix (no column for intercept)
y	Vector of observations (coded in -1/1)
lam	Tuning parameter for lasso penalty
rho	Tuning parameter for ADMM optimization
maxit	Maximum number of iterations
tol	Convergence criterion

Value

Vector containing updated estimate of beta vector

theandyb/aeffp documentation built on May 8, 2019, 9:09 a.m.