lmRob.fit.compute {robust} | R Documentation |
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(x2, y, x1 = NULL, x1.idx = NULL, nrep = NULL, robust.control = NULL, genetic.control = NULL, ...)
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. |
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.