outCoDa {robCompositions} | R Documentation |
Outlier detection for compositional data using standard and robust statistical methods.
outCoDa(x, alpha = 0.05, method = "robust", h = 1/2)
x |
compositional data |
alpha |
significance level |
method |
either “robust” (default) or “standard” |
h |
the size of the subsets for the robust covariance estimation according the MCD-estimator for which the determinant is minimized (the default is (n+p+1)/2). |
The outlier detection procedure is based on (robust) Mahalanobis distances after a isometric logratio transformation of the data. Observations with squared Mahalanobis distance greater equal a certain quantile of the Chi-squared distribution are marked as outliers.
If method “robust” is chosen, the outlier detection is based on the homogeneous majority of the compositional data set. If method “standard” is used, standard measures of location and scatter are applied during the outlier detection procedure.
mahalDist |
resulting Mahalanobis distance |
limit |
(1 - alpha) quantile of the Chi-squared distribution |
outlierIndex |
logical vector indicating outliers and non-outliers |
It is highly recommended to use the robust version of the procedure.
Matthias Templ, Karel Hron
Egozcue J.J., V. Pawlowsky-Glahn, G. Mateu-Figueras and C. Barcel'o-Vidal (2003) Isometric logratio transformations for compositional data analysis. Mathematical Geology, 35(3) 279-300. \
Filzmoser, P., and Hron, K. (2008) Outlier detection for compositional data using robust methods. Math. Geosciences, 40 233-248.\
Rousseeuw, P.J., Van Driessen, K. (1999) A fast algorithm for the minimum covariance determinant estimator. Technometrics, 41 212-223.
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data(expenditures) oD <- outCoDa(expenditures) oD