CovControlOgk: Constructor function for objects of class "CovControlOgk"
In armstrtw/rrcov: Scalable Robust Estimators with High Breakdown Point

Description Usage Arguments Details Value Author(s) References Examples

This function will create a control object CovControlOgk containing the control parameters for CovOgk

1	CovControlOgk(niter = 2, beta = 0.9, mrob = NULL, vrob = rrcov:::.vrobGK, smrob = "scaleTau2", svrob = "gk")

`niter`	number of iterations, usually 1 or 2 since iterations beyond the second do not lead to improvement.
`beta`	coverage parameter for the final reweighted estimate
`mrob`	function for computing the robust univariate location and dispersion - one could use the `tau scale` defined in Yohai and Zamar (1998), see `scaleTau2`. The C version of this function defined by `smrob` is the default.
`vrob`	function for computing robust estimate of covariance between two random vectors - one could use the function proposed by Gnanadesikan and Kettenring (1972), see `covGK()`. The C version of this function defined by `svrob` is the default.
`smrob`	a string indicating the name of the function for computing the robust univariate location and dispersion - defaults to `scaleTau2` - the scale tau function defined in Yohai and Zamar (1998)
`svrob`	a string indicating the name of the function for computing robust estimate of covariance between two random vectors - defaults `gk`, the one proposed by Gnanadesikan and Kettenring (1972)

If the user does not specify a scale and covariance function to be used in the computations or specifies one by using the arguments smrob and svrob (i.e. the names of the functions as strings), a native code written in C will be called which is by far faster than the R version.

If the arguments mrob and vrob are not NULL, the specified functions will be used via the pure R implementation of the algorithm. This could be quite slow.

A CovControlOgk object

Valentin Todorov valentin.todorov@chello.at

Maronna, R.A. and Zamar, R.H. (2002) Robust estimates of location and dispersion of high-dimensional datasets; Technometrics 44(4), 307–317.

Yohai, R.A. and Zamar, R.H. (1998) High breakdown point estimates of regression by means of the minimization of efficient scale JASA 86, 403–413.

Gnanadesikan, R. and John R. Kettenring (1972) Robust estimates, residuals, and outlier detection with multiresponse data. Biometrics 28, 81–124.

Todorov V & Filzmoser P (2009), An Object Oriented Framework for Robust Multivariate Analysis. Journal of Statistical Software, 32(3), 1–47. URL http://www.jstatsoft.org/v32/i03/.

    ## the following two statements are equivalent
    ctrl1 <- new("CovControlOgk", beta=0.95)
    ctrl2 <- CovControlOgk(beta=0.95)

    data(hbk)
    CovOgk(hbk, control=ctrl1)