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mvoutlier (version 2.1.1)

Multivariate Outlier Detection Based on Robust Methods

Description

Various methods for multivariate outlier detection: arw, a Mahalanobis-type method with an adaptive outlier cutoff value; locout, a method incorporating local neighborhood; pcout, a method for high-dimensional data; mvoutlier.CoDa, a method for compositional data. References are provided in the corresponding help files.

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Install

install.packages('mvoutlier')

Monthly Downloads

2,424

Version

2.1.1

License

GPL (>= 3)

Maintainer

Last Published

July 30th, 2021

Functions in mvoutlier (2.1.1)

C-horizon of the Kola Data

Adjusted Quantile Plot

Top Layer of the BSS Data

Bottom Layer of the BSS Data

Adaptive reweighted estimator for multivariate location and scatter

B-horizon of the Kola Data

Data (X coordinate) of illustrative example in paper (see below)

Background map for the BSS project

Data (Y coordinate) of illustrative example in paper (see below)

Chi-Square Plot

Interactive diagnostic plot for identifying local outliers

Moss Layer of the Kola Data

Plot Multivariate Outliers in a Map

Interpreting multivatiate outliers of CoDa

BSS background Plot

PCOut Method for Outlier Identification in High Dimensions

Distance-Distance Plot

Data of illustrative example in paper (see below)

Sign Method for Outlier Identification in High Dimensions - Sophisticated Version

Sign Method for Outlier Identification in High Dimensions - Simple Version

Correlation Plot: robust versus classical bivariate correlation

plot.mvoutlierCoDa

Plots for interpreting multivatiate outliers of CoDa

Humus Layer (O-horizon) of the Kola Data

kola.background

Background map for the Kola project

Kola background Plot

Univariate Presentation of Multivariate Outliers

Diagnostic plot for identifying local outliers with varying size of neighborhood

Diagnostic plot for identifying local outliers with fixed size of neighborhood