QR.lasso.cd: 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) use Coordinate Descent (cd) Algorithms

Description

The adaptive lasso parameter base on the estimated coefficient without penalty function. The algorithm base on greedy coordinate descent and Edgeworth's for ordinary l_1 regression. As explored by Tong Tong Wu and Kenneth Lange.

Usage

QR.lasso.cd(X,y,tau,lambda,beta,maxit,toler)

Arguments

X	the design matrix
y	response variable
tau	quantile level
lambda	The constant coefficient of penalty function. (default lambda=1)
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

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

References

Wu, T.T. and Lange, K. (2008). Coordinate Descent Algorithms for Lasso Penalized Regression. Annals of Applied Statistics, 2, No 1, 224–244.

Wu, Yichao and Liu, Yufeng (2009). Variable selection in quantile regression. Statistica Sinica, 19, 801–817.

Examples

set.seed(1)
n=100
p=2
a=2*rnorm(n*2*p, mean = 1, sd =1)
x=matrix(a,n,2*p)
beta=2*rnorm(p,1,1)
beta=rbind(matrix(beta,p,1),matrix(0,p,1))
y=x%*%beta-matrix(rnorm(n,0.1,1),n,1)
# x is 1000*20 matrix, y is 1000*1 vector, beta is 20*1 vector with last ten zero value elements. 
QR.lasso.cd(x,y,0.1)

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