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robust (version 0.3-0)

lmRob.fit.compute: Fit a Robust Linear Model

Description

Fits a robust linear model with high breakdown point and high efficiency estimates. This is used by lmRob, but not supposed to be called by the users directly.

Usage

lmRob.fit.compute(x2, y, x1 = NULL, x1.idx = NULL, nrep = NULL, robust.control = NULL, genetic.control = NULL, ...)

Arguments

x2
numeric vector or matrix for the continuous predictors.
y
numeric vector for the response in a linear model.
x1
numeric vector or matrix for the discrete predictors.
x1.idx
numeric vector giving the index of x1 as column numbers of the whole predictor matrix.
nrep
the number of random subsamples to be drawn. If "Exhaustive" resampling is being used, the value of nrep is ignored.
robust.control
a list of control parameters to be used in the numerical algorithms. See lmRob.control for the possible control parameters and their default settings.
genetic.control
a list of control parameters to be used in the genetic algorithm, if chosen.
...
additional arguments.

Value

  • an object of class "lmRob". See lmRob.object for a complete description of the object returned.

References

Gervini, D., and Yohai, V. J. (1999). A class of robust and fully efficient regression estimates, mimeo, Universidad de Buenos Aires.

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

Maronna, R. A., and Yohai, V. J. (1999). Robust regression with both continuous and categorical predictors, mimeo, Universidad de Buenos Aires.

Yohai, V. (1988). High breakdown-point and high efficiency estimates for regression, Annals of Statistics, 15, 642-665.

Yohai, V., Stahel, W. A., and Zamar, R. H. (1991). A procedure for robust estimation and inference in linear regression, in Stahel, W. A. and Weisberg, S. W., Eds., Directions in robust statistics and diagnostics, Part II. Springer-Verlag.

See Also

lmRob, lmRob.control.