This function computes robust estimators for multivariate location and scatter.
covRob(X, type = "auto", maxit = 50, tol = 1e-04, corr = FALSE)A list with class “covClassic” with the following components:
The location estimate.
The scatter matrix estimate, scaled for consistency at the normal distribution.
The correlation matrix estimate, if the argument cor equals TRUE. Otherwise it is set to NULL.
Robust Mahalanobis distances
weights
an image of the call that produced the object with all the arguments named. The matched call.
The location estimate. Same as center above.
The scatter matrix estimate, scaled for consistency at the normal distribution. Same as cov above.
a data matrix with observations in rows.
a string indicating which estimator to compute. Valid options are "Rocke" for Rocke's S-estimator, "MM" for an MM-estimator with a SHR rho function, or "auto" (default) which selects "Rocke" if the number of variables is greater than or equal to 10, and "MM" otherwise.
Maximum number of iterations, defaults to 50.
Tolerance for convergence, defaults to 1e-4.
A logical value. If TRUE a correlation matrix is included in the element cor of the returned object. Defaults to FALSE.
Ricardo Maronna, rmaronna@retina.ar
This function computes robust estimators for multivariate location and scatter.
The default behaviour (type = "auto") computes a "Rocke" estimator
(as implemented in covRobRocke) if the number
of variables is greater than or equal to 10, and an MM-estimator with a
SHR rho function (as implemented in covRobMM) otherwise.
covRobRocke, covRobMM
data(bus)
X0 <- as.matrix(bus)
X1 <- X0[,-9]
tmp <- covRob(X1)
round(tmp$cov[1:10, 1:10], 3)
tmp$mu
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