cqr.fit.lasso {cqrReg}R Documentation

Composite Quantile Regression (cqr) with Adaptive Lasso Penalty (lasso)

Description

Composite quantile regression (cqr) find the estimated coefficient which minimize the absolute error for various quantile level. High level function for estimating and selecting parameter by composite quantile regression with adaptive lasso penalty.

Usage

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

Arguments

X

the design matrix

y

response variable

tau

vector of quantile level

method

"mm" for majorize and minimize method,"cd" for coordinate descent method, "admm" for Alternating method of mulipliers method

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

cqr.fit.lasso(x,y,tau) work properly only if the least square estimation is good.


[Package cqrReg version 1.2.1 Index]