Computes a robust multivariate location and scatter estimate with a high breakdown point, using the ‘MVE’ (Minimum Volume Ellipsoid) estimator.
CovMve(x, alpha = 1/2, nsamp = 500, seed = NULL, trace = FALSE, control)An S4 object of class CovMve-class which is a subclass of the
virtual class CovRobust-class.
a matrix or data frame.
numeric parameter controlling the size of the subsets
over which the determinant is minimized, i.e., alpha*n
observations are used for computing the determinant. Allowed values
are between 0.5 and 1 and the default is 0.5.
number of subsets used for initial estimates or "best"
or "exact". Default is nsamp = 500. For
nsamp="best" exhaustive enumeration is done, as long as the
number of trials does not exceed 5000. For "exact",
exhaustive enumeration will be attempted however many samples are
needed. In this case a warning message will be displayed saying
that the computation can take a very long time.
starting value for random generator. Default is seed = NULL
whether to print intermediate results. Default is trace = FALSE
a control object (S4) of class CovControlMve-class
containing estimation options - same as these provided in the fucntion
specification. If the control object is supplied, the parameters from it
will be used. If parameters are passed also in the invocation statement, they will
override the corresponding elements of the control object.
Valentin Todorov valentin.todorov@chello.at and Matias Salibian-Barrera matias@stat.ubc.ca
This function computes the minimum volume ellipsoid estimator
of location and scatter and returns an S4 object of class
CovMve-class containing the estimates.
The approximate estimate is
based on a subset of size alpha*n with an enclosing ellipsoid of smallest volume.
The mean of the best found subset provides the raw estimate of the location,
and the rescaled covariance matrix is the raw estimate of scatter. The rescaling of
the raw covariance matrix is by median(dist)/qchisq(0.5, p) and this scale factor
is returned in the slot raw.cnp2. Currently no finite sample corrction factor is applied.
The Mahalanobis distances of all observations from the location estimate for the
raw covariance matrix are calculated, and those points within the 97.5
under Gaussian assumptions are declared to be good. The final (reweightd) estimates are the
mean and rescaled covariance of the good points. The reweighted covariance matrix is
rescaled by 1/pgamma(qchisq(alpha, p)/2, p/2 + 1)/alpha
(see Croux and Haesbroeck, 1999) and this scale factor is returned
in the slot cnp2.
The search for the approximate solution is made over ellipsoids determined by the
covariance matrix of p+1 of the data points and applying
a simple but effective improvement of the subsampling procedure
as described in Maronna et al. (2006), p. 198.
Although there exists no formal proof of this improvement (as for MCD and LTS),
simulations show that it can be recommended as an approximation of the MVE.
P. J. Rousseeuw and A. M. Leroy (1987) Robust Regression and Outlier Detection. Wiley.
C. Croux and G. Haesbroeck (1999). Influence function and efficiency of the minimum covariance determinant scatter matrix estimator. Journal of Multivariate Analysis, 71, 161--190.
R. A. Maronna, D. Martin and V. Yohai (2006). Robust Statistics: Theory and Methods. Wiley, New York.
Todorov V & Filzmoser P (2009), An Object Oriented Framework for Robust Multivariate Analysis. Journal of Statistical Software, 32(3), 1--47. tools:::Rd_expr_doi("10.18637/jss.v032.i03").
cov.rob from package MASS
data(hbk)
hbk.x <- data.matrix(hbk[, 1:3])
CovMve(hbk.x)
## the following three statements are equivalent
c1 <- CovMve(hbk.x, alpha = 0.75)
c2 <- CovMve(hbk.x, control = CovControlMve(alpha = 0.75))
## direct specification overrides control one:
c3 <- CovMve(hbk.x, alpha = 0.75,
control = CovControlMve(alpha=0.95))
c1
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