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=lmRob.robust.control(...), genetic.control=NULL, ...) 

REQUIRED ARGUMENTS:

x2
numeric vector or matrix for the continuous predictors.
y
numeric vector for the response in a linear model.

OPTIONAL ARGUMENTS:

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.robust.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. See lmRob.genetic.control() for the possible control parameters.

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:

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