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. I.Random.seed and the description of the seed
argument in lmrob.control.solve) of the covariance matrix in mahalanobis.trace = FALSE.wgtFUNs, 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)Run the code above in your browser using DataLab