qrfit.lasso: Quantile Regression (qr) with Adaptive Lasso Penalty (lasso)

In cqrReg: Quantile, Composite Quantile Regression and Regularized Versions

Quantile Regression (qr) with Adaptive Lasso Penalty (lasso)

Description

High level function for estimating and selecting parameter by quantile regression with adaptive lasso penalty.

Usage

qrfit.lasso(X,y,tau,lambda,beta,method,maxit,toler,rho)

Arguments

X	the design matrix
y	response variable
tau	quantile level
method	"mm" for majorize and minimize method,"cd" for coordinate descent method, "admm" for Alternating method of mulipliers method,"ip" for interior point mehod
lambda	The constant coefficient of penalty function. (default lambda=1)
rho	augmented Lagrangian parameter
beta	initial value of estimate coefficient (default naive guess by least square estimation)
maxit	maxim iteration (default 200)
toler	the tolerance critical for stop the algorithm (default 1e-3)

Value

a list structure is with components

beta	the vector of estimated coefficient
b	intercept

Note

qrfit.lasso(x,y,tau) work properly only if the least square estimation is good. Interior point method is done by quantreg.

cqrReg documentation built on June 7, 2022, 9:06 a.m.