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The function color.plot plots the (two-dimensional) data using different symbols according to the robust mahalanobis distance based on the mcd estimator with adjustment and using different colors according to the euclidean distances of the observations.
● Data Source:
CranContrib
● Keywords: dplot
● Alias: color.plot
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locoutPercent
(Package: mvoutlier) :
Diagnostic plot for identifying local outliers with fixed size of neighborhood
Computes global and pairwise Mahalanobis distances for visualizing global and local multivariate outliers. The size of the neighborhood (number of neighbors) is fixed, but the fraction of neighbors is varying.
● Data Source:
CranContrib
● Keywords: multivariate, robust
● Alias: locoutPercent
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sign1
(Package: mvoutlier) :
Sign Method for Outlier Identification in High Dimensions - Simple Version
Fast algorithm for identifying multivariate outliers in high-dimensional and/or large datasets, using spatial signs, see Filzmoser, Maronna, and Werner (CSDA, 2007). The computation of the distances is based on Mahalanobis distances.
● Data Source:
CranContrib
● Keywords: multivariate, robust
● Alias: sign1
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The function symbol.plot plots the (two-dimensional) data using different symbols according to the robust mahalanobis distance based on the mcd estimator with adjustment.
● Data Source:
CranContrib
● Keywords: dplot
● Alias: symbol.plot
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The function chisq.plot plots the ordered robust mahalanobis distances of the data against the quantiles of the Chi-squared distribution. By user interaction this plotting is iterated each time leaving out the observation with the greatest distance.
● Data Source:
CranContrib
● Keywords: dplot
● Alias: chisq.plot
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uni.plot
(Package: mvoutlier) :
Univariate Presentation of Multivariate Outliers
The function uni.plot plots each variable of x parallel in a one-dimensional scatter plot and in addition marks multivariate outliers.
● Data Source:
CranContrib
● Keywords: dplot
● Alias: uni.plot
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plot.mvoutlierCoDa
(Package: mvoutlier) :
Plots for interpreting multivatiate outliers of CoDa
Plots the computed information by mvoutlier.CoDa for supporting the interpretation of multivariate outliers in case of compositional data.
● Data Source:
CranContrib
● Keywords: multivariate, robust
● Alias: plot.mvoutlierCoDa
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mvoutlier.CoDa
(Package: mvoutlier) :
Interpreting multivatiate outliers of CoDa
Computes the basis information for plot functions supporting the interpretation of multivariate outliers in case of compositional data.
● Data Source:
CranContrib
● Keywords: multivariate, robust
● Alias: mvoutlier.CoDa
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sign2
(Package: mvoutlier) :
Sign Method for Outlier Identification in High Dimensions - Sophisticated Version
Fast algorithm for identifying multivariate outliers in high-dimensional and/or large datasets, using spatial signs, see Filzmoser, Maronna, and Werner (CSDA, 2007). The computation of the distances is based on principal components.
● Data Source:
CranContrib
● Keywords: multivariate, robust
● Alias: sign2
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dd.plot
(Package: mvoutlier) :
Distance-Distance Plot
The function dd.plot plots the classical mahalanobis distance of the data against the robust mahalanobis distance based on the mcd estimator. Different symbols (see function symbol.plot) and colours (see function color.plot) are used depending on the mahalanobis and euclidean distance of the observations (see Filzmoser et al., 2005).
● Data Source:
CranContrib
● Keywords: dplot
● Alias: dd.plot
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