GSE (version 4.2-1)

get-methods: Accessor methods to the essential slots of classes CovRobMiss, TSGS, GSE, emve, and HuberPairwise

Description

Accessor methods to the slots of objects of classes CovRobMiss, TSGS, GSE, emve, and HuberPairwise

Usage

getLocation(object)
getScatter(object)
getDist(object)
getDistAdj(object)
getDim(object)
getMissing(object)
getOutliers(object, cutoff)
getScale(obj)
getFiltDat(object)

Arguments

obj,object

an object of any of the following classes CovRobMiss-class, GSE-class, emve-class, and HuberPairwise-class. For function getScale the package defines a method for objects of class GSE-class objects are allowed.

cutoff

optional argument for getOutliers - quantiles of chi-square to be used as a threshold for outliers detection, defaults to 0.99

Details

getLocation

signature(object = "CovRobMiss"): return the estimated location vector

getScatter

signature(object = "CovRobMiss", cutoff = "numeric"): return the estimated scatter matrix

getDist

signature(object = "CovRobMiss"): return the squared partial Mahalanobis distances

getDistAdj

signature(object = "CovRobMiss"): return the adjusted squared partial Mahalanobis distances

getDim

signature(object = "CovRobMiss"): return the dimension of observed entries for each case

getMissing

signature(object = "CovRobMiss"): return the case number with completely missing data, if any

getOutliers

signature(object = "CovRobMiss", cutoff = "numeric"): return the case number(s) adjusted squared distances above (1 - cutoff)th quantile of chi-square p-degrees of freedom.

getScale

signature(object = "CovRobMissSc"): return either the estimated generalized S-scale or MVE-scale. See GSE and emve for details.

getFiltDat

signature(object = "TSGS"): return filtered data matrix from the first step of 2SGS.

Examples

if (FALSE) {
data(boston)
res <- GSE(boston)

## extract estimated location
getLocation(res)

## extract estimated scatter
getScatter(res)

## extract estimated adjusted distances
getDistAdj(res)

## extract outliers
getOutliers(res)
}