lmRob
, but not supposed to be called by the
users directly.
lmRob.fit.compute(x2, y, x1=NULL, x1.idx=NULL, nrep=NULL, robust.control=lmRob.robust.control(...), genetic.control=NULL, ...)
x1
as column numbers of the whole
predictor matrix.
"Exhaustive"
resampling
is being used, the value of
nrep
is ignored.
lmRob.robust.control()
for the possible control parameters and their
default settings.
lmRob.genetic.control()
for the possible control parameters.
"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.