QR.lasso.ip: 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 Interior Point (ip) Method

Description

The function use the interior point method from quantreg to solve the quantile regression problem.

Usage

QR.lasso.ip(X,y,tau,lambda)

Arguments

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

Value

a list structure is with components

beta	the vector of estimated coefficient
b	intercept
lambda	The constant coefficient of penalty function. (default lambda=1)

Note

Need to install quantreg package from CRAN.

References

Koenker, R. and S. Portnoy (1997). The Gaussian Hare and the Laplacian Tortoise: Computability of squared-error vs. absolute-error estimators, with discussion, Statistical Science, 12, 279-300.

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. 
#you should install Rmosek first to run following command
#QR.lasso.ip(x,y,0.1)

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