R: Fit a Robust Linear Model

lmRob.fit.compute {robust} | R Documentation

## 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(x, y, x1.idx = NULL, nrep = NULL, robust.control = NULL, ...)

### Arguments

`x` | a numeric matrix containing the design matrix.
`y` | a numeric vector containing the linear model response.
`x1.idx` | a numeric vector containing the indices of columns of the design matrix arising from the coding of factor variables.
`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.
`...` | 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`.

[Package robust version 0.6-0 Index]