lmrob.lar {robustbase} | R Documentation |
To compute least absolute residuals (LAR) or “L1” regression,
lmrob.lar
implements the routine L1 in Barrodale and Roberts (1974),
which is based on the simplex method of linear programming. It is a
copy of lmRob.lar
(in early 2012) from the robust package.
lmrob.lar(x, y, control, mf)
x |
numeric matrix for the predictors. |
y |
numeric vector for the response. |
control |
|
mf |
dummy parameter. |
This method is used for computing the M-S estimate and typically not to be used on its own.
A description of the Fortran subroutines used can be found in Marazzi
(1993). In the book, the main method is named RILARS
.
A list that includes the following components:
coef |
The L1-estimate of the coefficient vector |
scale |
The residual scale estimate (mad) |
resid |
The residuals |
iter |
The number of iterations required by the simplex algorithm |
status |
Return status (0: optimal, but non unique solution, 1: optimal unique solution) |
converged |
Convergence status (always |
Manuel Koller
Marazzi, A. (1993). Algorithms, routines, and S functions for robust statistics. Wadsworth & Brooks/Cole, Pacific Grove, CA.
rq
from package quantreg.
data(stackloss) X <- model.matrix(stack.loss ~ . , data = stackloss) y <- stack.loss lmrob.lar(X, y)