robustbase (version 0.92-5)

lmrob.lar: Least Absolute Residuals / L1 Regression

Description

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.

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)