IdtOutl-methods: Plot method for class IdtOutl in Package 'MAINT.Data'

In MAINT.Data: Model and Analyse Interval Data

Plot method for class IdtOutl in Package ‘MAINT.Data’

Description

Plots robust Mahalanobis distances and outlier cut-offs for an object describing potential outliers in a interval-valued data set

Usage

  ## S4 method for signature 'IdtOutl,missing'
plot(x, scale=c("linear","log"), RefDist=getRefDist(x), eta=geteta(x), 
  multiCmpCor=getmultiCmpCor(x), ...)

Arguments

x	An IData object of class IdtOutl describing potential interval-valued ouliters.
scale	The scale of the axis for the robust Mahalanobis distances.
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). By default uses the one selected in the creation of the object ‘x’.
eta	Nominal size of the null hypothesis that a given observation is not an outlier. By default uses the one selected in the creation of the object ‘x’.
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)). By default uses the one selected in the creation of the object ‘x’.
...	Further arguments to be passed to methods.

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.
Journal of Computational and Graphical Statistics 14, 910–927.

See Also

getIdtOutl, fasttle, fulltle

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