OutlierMahdist-class: Class 'OutlierMahdist' - Outlier identification using robust...

In rrcovHD: Robust Multivariate Methods for High Dimensional Data

Description

Holds the results of outlier identification using robust mahalanobis distances computed by robust multivarite location and covarince matrix.

Objects from the Class

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

Slots

covobj:

A list containing the robust estimates of multivariate location and covariance matrix for each class

call:

Object of class "language"

counts:

Number of observations in each class

grp:

Grouping variable

wt:

Weights

flag:

0/1 flags identifying the outliers

method:

Method used to compute the robust estimates of multivariate location and covariance matrix

singularity:

a list with singularity information for the covariance matrix (or NULL of not singular)

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

Todorov V & Filzmoser P (2009). An Object Oriented Framework for Robust Multivariate Analysis. Journal of Statistical Software, 32(3), 1–47. doi: 10.18637/jss.v032.i03.

Filzmoser P & Todorov V (2013). Robust tools for the imperfect world, Information Sciences 245, 4–20. doi: 10.1016/j.ins.2012.10.017.

See Also

OutlierMahdist, Outlier-class

Examples

showClass("OutlierMahdist")

rrcovHD documentation built on April 23, 2021, 9:08 a.m.