| 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.