OutlierSign1-class: Class '"OutlierSign1"' - Outlier identification in high...

In rrcovHD: Robust Multivariate Methods for High Dimensional Data

Class "OutlierSign1" - Outlier identification in high dimensions using the SIGN1 algorithm

Description

Fast algorithm for identifying multivariate outliers in high-dimensional and/or large datasets, using spatial signs, see Filzmoser, Maronna, and Werner (CSDA, 2007). The computation of the distances is based on Mahalanobis distances.

Objects from the Class

Objects can be created by calls of the form new("OutlierSign1", ...) but the usual way of creating OutlierSign1 objects is a call to the function OutlierSign1() which serves as a constructor.

Slots

covobj:

A list containing intermediate results of the SIGN1 algorithm for each class

call, counts, grp, wt, flag, method, singularity:

from the "Outlier" class.

Extends

Class "Outlier", directly.

Methods

getCutoff

Return the cutoff value used to identify outliers

getDistance

Return a vector containing the computed distances

Author(s)

Valentin Todorov [e-mail]

References

P. Filzmoser, R. Maronna and M. Werner (2008). Outlier identification in high dimensions, Computational Statistics & Data Analysis, Vol. 52 1694–1711.

Filzmoser P & Todorov V (2013). Robust tools for the imperfect world, Information Sciences 245, 4–20. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.ins.2012.10.017")}.

See Also

OutlierSign1, OutlierSign2, Outlier

Examples

showClass("OutlierSign1")

rrcovHD documentation built on Sept. 12, 2024, 7:11 a.m.