rrcov.control(alpha = 1/2, method = c("covMcd", "covComed", "ltsReg"), nsamp = 500, nmini = 300, kmini = 5, seed = NULL, tolSolve = 1e-14, scalefn = "hrv2012", maxcsteps = 200, trace = FALSE, wgtFUN = "01.original", beta, use.correction = identical(wgtFUN, "01.original"), adjust = FALSE)
alpha*n
observations
are used for computing the determinant. Allowed values are between 0.5
and 1 and the default is 0.5. rrcov.control()
is used. This currently only makes a
difference to determine the default for beta
."best"
or "exact"
. Default is nsamp = 500
.
If nsamp="best"
exhaustive enumeration is done, as far as
the number of trials do not exceed 5000. If nsamp="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. .Random.seed
and the description of the seed
argument in lmrob.control
.solve
) of the covariance matrix in mahalanobis
.trace = FALSE
.function
, specifying
how the weights for the reweighting step should be computed, see
ltsReg
, covMcd
or
covComed
, respectively. The default is specified by
"01.original"
, as the resulting weights are 0 or 1. Alternative
string specifications need to match names(.wgtFUN.covComed)
-
which currently is experimental.wgtFUN
s, see e.g., .wgtFUN.covMcd
and
.wgtFUN.covComed
.TRUE
.ltsReg()
:) whether to perform
intercept adjustment at each step. Because this can be quite time
consuming, the default is adjust = FALSE
.ltsReg
and
covMcd
, respectively.
data(Animals, package = "MASS")
brain <- Animals[c(1:24, 26:25, 27:28),]
data(hbk)
hbk.x <- data.matrix(hbk[, 1:3])
ctrl <- rrcov.control(alpha=0.75, trace=TRUE)
covMcd(hbk.x, control = ctrl)
covMcd(log(brain), control = ctrl)
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