IDtOutl-class: Class IdtOutl

In MAINT.Data: Model and Analyse Interval Data

Class IdtOutl

Description

A description of interval-valued variable outliers found by the MAINT.Data function getIdtOutl.

Slots

outliers:

A vector of indices of the interval data units flaged as outliers.

MD2:

A vector of squared robust Mahalanobis distances for all interval data units.

eta

Nominal size of the null hypothesis that a given observation is not an outlier.

RefDist

The assumed reference distributions used to find cutoffs defining the observations assumed as outliers. Alternatives are “ChiSq” and “CerioliBetaF” respectivelly for the usual Chi-squared, and the Beta and F distributions proposed by Cerioli (2010).

multiCmpCor

Whether a multicomparison correction of the nominal size (eta) for the outliers tests was performed. Alternatives are: ‘never’ – ignoring the multicomparisons and testing all entities at the ‘eta’ nominal level. ‘always’ – testing all n entitites at 1.- (1.-‘eta’^(1/n)).

NObs

Number of original observations in the original data set.

p

Number of total numerical variables (MidPoints and/or LogRanges) that may be responsible for the outliers.

h

Size of the subsets over which the trimmed likelihood was maximized when computing the robust Mahalanobis distances.

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boolRewind

A logical vector indicanting which of the data units belong to the final trimmed subsetused to compute the tle estimates.

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Methods

show

signature(object = "IdtOutl"): show S4 method for the IdtOutl-class.

plot

signature(x = "IdtOutl"): plot S4 methods for the IdtOutl-class.

getMahaD2

signature(x = "IdtOutl"): retrieves the vector of squared robust Mahalanobis distances for all data units.

geteta

signature(x = "IdtOutl"): retrieves the nominal size of the null hypothesis used to flag observations as outliers.

getRefDist

signature(x = "IdtOutl"): retrieves the assumed reference distributions used to find cutoffs defining the observations assumed as outliers.

getmultiCmpCor

signature(x = "IdtOutl"): retrieves the multicomparison correction used when flaging observations as outliers.

Author(s)

Pedro Duarte Silva <[e-mail]>
Paula Brito <mpbrito.fep.up.pt>

References

Cerioli, A. (2010), Multivariate Outlier Detection with High-Breakdown Estimators. Journal of the American Statistical Association 105 (489), 147–156.

Duarte Silva, A.P., Filzmoser, P. and Brito, P. (2017), Outlier detection in interval data. Advances in Data Analysis and Classification, 1–38.

See Also

getIdtOutl, fasttle, fulltle

MAINT.Data documentation built on April 4, 2023, 9:09 a.m.