| qrfit.lasso {cqrReg} | R Documentation |
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.