| covOGK {robustbase} | R Documentation |
Computes the orthogonalized pairwise covariance matrix estimate described in in Maronna and Zamar (2002). The pairwise proposal goes back to Gnanadesikan and Kettenring (1972).
covOGK(X, n.iter, sigmamu, rcov = covGK, weight.fn,
keep.data = FALSE, ...)
X |
data in something that can be coerced into a numeric matrix. |
n.iter |
number of orthogonalization iterations. Usually 1 or 2; values greater than 2 are unlikely to have any significant effect on the estimate (other than increasing the computing time). |
sigmamu |
a function that computes univariate robust location and
scale estimates. By default sigmamu should return a single
numeric value containing the robust scale (standard deviation)
estimate. When mu.too is true, sigmamu() should
return a numeric vector of length 2 containing robust location and
scale estimates. See scaleTau2 for an example. |
rcov |
function that computes a robust covariance estimate
between two vectors. The default, Gnanadesikan-Kettenring's
covGK, is simply (s^2(X+Y) - s^2(X-Y))/4 where
s() is the scale estimate sigmamu(). |
weight.fn |
a function of the robust distances and the number of variables p to compute the weights used in the reweighting step. |
keep.data |
logical indicating if the (untransformed) data matrix
X should be kept as part of the result. |
... |
additional arguments to be passed to sigmamu() and
weight.fn(). |
Typical default values for the function arguments
sigmamu, rcov, and weight.fn, are
available as well, see the Examples below,
but their names and calling sequences are
still subject to discussion and may be changed in the future.
currently a list with components
center |
robust location: numeric vector of length p. |
cov |
robust covariance matrix estimate: p x p matrix. |
wcenter, wcov |
re-weighted versions of center and
cov. |
weights |
the robustness weights used. |
distances |
the mahalanobis distances computed using
center and cov. |
......
but note that this might be radically changed to returning an
S4 classed object!
Kjell Konis konis@stats.ox.ac.uk, with modifications by Martin Maechler.
Maronna, R.A. and Zamar, R.H. (2002) Robust estimates of location and dispersion of high-dimensional datasets; Technometrics 44(4), 307–317.
Gnanadesikan, R. and John R. Kettenring (1972) Robust estimates, residuals, and outlier detection with multiresponse data. Biometrics 28, 81–124.
data(hbk)
hbk.x <- data.matrix(hbk[, 1:3])
cO1 <- covOGK(hbk.x, n.iter = 2,
sigmamu = scaleTau2, weight.fn = hard.rejection)