Description Usage Arguments Value References
This implements the minimum variance vector covariance estimator described in Herwindiati, Djauhari, and Mashuri (2007). It is exactly analagous to the minimum covariance determinant (MCD), except the trace of the matrix is the objective function, rather than the determinant. The deterministic MCD algorithm described in Hubert, Rousseeuw, and Verdonck (2012) is adapted here, rather than adapting the fast MCD algorithm in the paper introducing the MVV.
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x |
a data frame or matrix of numeric covariates |
kappa |
the the proportion of the data to use in each subset. defaults to 0.75. must be > 0.50. |
method |
the method of measuring location and scale. see |
opts |
list of options for the various scale estimators. "b" determines the percentage bend coefficient for "pb", "eff" determines the efficiency of the "huber", "bisquare" and "mopt" scale estimators. |
a covRobust object containing the following elements:
center: multivariate mean of cleaned data set after removing outliers.
cov: covariance matrix of cleaned data set after removing outliers.
dist: the mahalanobis distances used in calculating the weights.
outliers: the indices of the outliers identified.
weights: the weights for downweighting outliers. here they are binary, with 0 marking an outlier and 1 otherwise.
Herwindiati, D. E.; Djauhari, M. A.; Mashuri, M. (2007). Robust Multivariate Outlier Labeling. Communications in Statistics - Simulation and Computation, 36(6), 1287–1294. doi:10.1080/03610910701569044
Hubert, M.; Rousseeuw, P.J.; Verdonck, T. (2012) A Deterministic Algorithm for Robust Location and Scatter, Journal of Computational and Graphical Statistics, 21(3), 618-637, doi: 10.1080/10618600.2012.672100
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