QR.lasso.ip {cqrReg} | R Documentation |
The function use the interior point method from quantreg to solve the quantile regression problem.
QR.lasso.ip(X,y,tau,lambda)
X |
the design matrix |
y |
response variable |
tau |
quantile level |
lambda |
The constant coefficient of penalty function. (default lambda=1) |
a list
structure is with components
beta |
the vector of estimated coefficient |
b |
intercept |
lambda |
The constant coefficient of penalty function. (default lambda=1) |
Need to install quantreg package from CRAN.
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.
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)