robustbase (version 0.9-2)

lmrob.lar: Least Absolute Residuals / L1 Regression

Description

This method 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 the robust package.

Usage

lmrob.lar(x, y, control, mf)

Arguments

x

numeric matrix for the predictors

y

numeric vector for the response

control

list as returned by lmrob.control

mf

dummy parameter.

Value

• 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 TRUE), needed for lmrob.fit.

Details

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.

References

Marazzi, A. (1993). Algorithms, routines, and S functions for robust statistics. Wadsworth & Brooks/Cole, Pacific Grove, CA.

See Also

rq from package quantreg.

Examples

data(stackloss)
X <- model.matrix(stack.loss ~ . , data = stackloss)
y <- stack.loss
lmrob.lar(X, y)