| IdtOutl-class {MAINT.Data} | R Documentation |
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 <psilva@porto.ucp.pt>
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.