Minimum Covariance Determinant Estimation - Generic Function

DESCRIPTION:

Returns a list containing estimates of the robust multivariate location, the robust covariance matrix, and (optionally) the robust correlation matrix, based on the Minimum Covariance Determinant (MCD) estimator proposed by Rousseeuw (1984, 1985).

This function is generic (see Methods); method functions can be written to handle specific classes of data. Classes which already have methods for this function include:

formula

USAGE:

cov.mcd(object, ...)

REQUIRED ARGUMENTS:

object
a vector, a matrix, a data.frame or a formula.

OPTIONAL ARGUMENTS:

...
methods may have additional arguments.

VALUE:

an object of class "mcd" representing the minimum covariance determinant estimates. See mcd.object for details.

DETAILS:

The computational details for minimum covariance determinant estimates are described in Rousseeuw and Van Driessen (1999).

NOTE:

The current version is limited to 50 variables i.e. the maximum covariance matrix that can be estimated is 50 by 50.

REFERENCES:

Rousseeuw, P. J. (1984). Least median of squares regression. Journal of the American Statistical Association 79, 871-881.

Rousseeuw, P. J. (1985). Multivariate estimation with high breakdown point. In: Mathematical Statistics and Applications, edited by W. Grossmann, G. Pflug, I. Vincze and W. Wertz. Reidel: Dordrecht, 283-297.

Rousseeuw, P. J. and Van Driessen K. (1999). A fast algorithm for the minimum covariance determinant estimator. Technometrics 41, 212-223.

SEE ALSO:

, , , , , .

EXAMPLES:

cov.mcd(air)
cov.mcd(~ ozone+wind+temperature+radiation, data=air)