Compute robust estimates of multivariate location and scatter.
covRob(data, corr = FALSE, distance = TRUE, na.action = na.fail,
estim = "auto", control = covRob.control(estim, ...), ...)an object of class "covRob" with components:
an image of the call that produced the object with all the arguments named.
a numeric matrix containing the final robust estimate of the covariance/correlation matrix.
a numeric vector containing the final robust estimate of the location vector.
a numeric vector containing the squared Mahalanobis distances computed using robust estimates of covariance and location contained in cov and center. If distance = FALSE this element will me missing.
a numeric matrix containing the initial robust estimate of the covariance/correlation matrix. If there is no initial robust estimate then this element is set to NA.
a numeric vector containing the initial robust estimate of the location vector. If there is no initial robust estimate then this element is set to NA.
a numeric vector containing the squared Mahalanobis distances computed using the initial robust estimates of covariance and location contained in raw.cov and raw.center. If distance = FALSE or if there is no initial robust estimate then this element is set to NA.
a logical flag. If corr = TRUE then cov and raw.cov contain robust estimates of the correlation matrix of data.
a character string containing the name of the robust estimator.
a list containing the control parameters used by the robust estimator.
a numeric matrix or data frame containing the data.
a logical flag. If corr = TRUE then the estimated correlation matrix is computed.
a logical flag. If distance = TRUE the squared Mahalanobis distances are computed.
a function to filter missing data. The default na.fail produces an error if missing values are present. An alternative is na.omit which deletes observations that contain one or more missing values.
a character string specifying the robust estimator to be used. The choices are: "mcd" for the Fast MCD algorithm of Rousseeuw and Van Driessen, "weighted" for the Reweighted MCD, "donostah" for the Donoho-Stahel projection based estimator, "M" for the constrained M estimator provided by Rocke, "pairwiseQC" for the orthogonalized quadrant correlation pairwise estimator, and "pairwiseGK" for the Orthogonalized Gnanadesikan-Kettenring pairwise estimator. The default "auto" selects from "donostah", "mcd", and "pairwiseQC" with the goal of producing a good estimate in a reasonable amount of time.
a list of control parameters to be used in the numerical algorithms. See covRob.control for the possible control parameters and their default settings. This argument is ignored when estim = "auto".
control parameters may be passed directly when estim != "auto".
The covRob function selects a robust covariance estimator that is likely to provide a good estimate in a reasonable amount of time. Presently this selection is based on the problem size. The Donoho-Stahel estimator is used if there are less than 1000 observations and less than 10 variables or less than 5000 observations and less than 5 variables. If there are less than 50000 observations and less than 20 variables then the MCD is used. For larger problems, the Orthogonalized Quadrant Correlation estimator is used.
The MCD and Reweighted-MCD estimates (estim = "mcd" and estim = "weighted" respectively) are computed using the covMcd function in the robustbase package. By default, covMcd returns the reweighted estimate; the actual MCD estimate is contained in the components of the output list prefixed with raw.
The M estimate (estim = "M") is computed using the CovMest function in the rrcov package. For historical reasons the Robust Library uses the MCD to compute the initial estimate.
The Donoho-Stahel (estim = "donostah") estimator is computed using the CovSde function provided in the rrcov package.
The pairwise estimators (estim = "pairwisegk" and estim = "pairwiseqc") are computed using the CovOgk function in the rrcov package.
R. A. Maronna and V. J. Yohai (1995) The Behavior of the Stahel-Donoho Robust Multivariate Estimator. Journal of the American Statistical Association 90 (429), 330--341.
P. J. Rousseeuw and K. van Driessen (1999) A fast algorithm for the minimum covariance determinant estimator. Technometrics 41, 212--223.
D. L. Woodruff and D. M. Rocke (1994) Computable robust estimation of multivariate location and shape on high dimension using compound estimators. Journal of the American Statistical Association, 89, 888--896.
R. A. Maronna and R. H. Zamar (2002) Robust estimates of location and dispersion of high-dimensional datasets. Technometrics 44 (4), 307--317.
CovSde,
covMcd,
CovOgk,
CovMest,
covRob.control,
covClassic.
data(stackloss)
covRob(stackloss)
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