cqr.fit {cqrReg} | R Documentation |
Composite quantile regression (cqr) find the estimated coefficient which minimize the absolute error for various quantile level. High level function for estimating parameter by composite quantile regression.
cqr.fit(X,y,tau,beta,method,maxit,toler,rho)
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,"ip" for interior point mehod |
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) |
a list
structure is with components
beta |
the vector of estimated coefficient |
b |
intercept |
cqr.fit(x,y,tau) work properly only if the least square estimation is good. Interior point method is done by quantreg.