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.
lmRob.fit.compute(x, y, x1.idx = NULL, nrep = NULL, robust.control = NULL, ...)a numeric matrix containing the design matrix.
a numeric vector containing the linear model response.
a numeric vector containing the indices of columns of the design matrix arising from the coding of factor variables.
the number of random subsamples to be drawn. If "Exhaustive" resampling is being used, the value of nrep is ignored.
a list of control parameters to be used in the numerical algorithms. See lmRob.control for the possible control parameters and their default settings.
additional arguments.
an object of class "lmRob". See lmRob.object for a complete description of the object returned.
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.