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\name{r6pack}
\alias{r6pack}
\title{Robust Distance based observation orderings based on robust "Six pack"}
\description{
Compute six initial robust estimators of multivariate location and
\dQuote{scatter} (scale); then, for each, compute the distances
\eqn{d_{ij}}{d_ij} and take the \code{h} (\eqn{h > n/2}) observations
with smallest distances. Then compute the statistical distances based
on these h observations.
Return the indices of the observations sorted in increasing order.
}
\usage{
r6pack(x, h, full.h, scaled = TRUE, scalefn = rrcov.control()$scalefn)
}
\arguments{
\item{x}{n x p data matrix}
\item{h}{integer, typically around (and slightly larger than) \eqn{n/2}.}
\item{full.h}{logical specifying if the full (length n) observation
ordering should be returned; otherwise only the first \code{h} are.
For \code{.detmcd()}, \code{full.h=FALSE} is typical.}
\item{scaled}{logical indicating if the data \code{x} is
already scaled; if false, we apply \code{x <- doScale(x, median,
scalefn)}.}
\item{scalefn}{a \code{\link{function}(u)} to compute a robust
univariate scale of u.}
}% args
\details{%% --> ../R/detmcd.R
The six initial estimators are
\enumerate{
\item Hyperbolic tangent of standardized data
\item Spearmann correlation matrix
\item Tukey normal scores
\item Spatial sign covariance matrix
\item BACON
\item Raw OGK estimate for scatter
}
}
\references{
Hubert, M., Rousseeuw, P. J. and Verdonck, T. (2012)
A deterministic algorithm for robust location and scatter.
Journal of Computational and Graphical Statistics \bold{21}, 618--637.
}
\value{
a \eqn{h' \times 6}{h' x 6} \code{\link{matrix}} of observation
indices, i.e., with values from \eqn{1,\dots,n}{1..n}. If
\code{full.h} is true, \eqn{h' = n}, otherwise \eqn{h' = h}.
}
\author{Valentin Todorov, based on the original Matlab code by
Tim Verdonck and Mia Hubert. Martin Maechler for tweaks
(performance etc), and \code{full.h}.
}
\seealso{
\code{\link{covMcd}(*, nsamp = "deterministic")};
\code{\link[rrcov]{CovSest}(*, nsamp = "sdet")} from package \CRANpkg{rrcov}.
}
\examples{
data(pulpfiber)
dim(m.pulp <- data.matrix(pulpfiber)) # 62 x 8
dim(fr6 <- r6pack(m.pulp, h = 40, full.h= FALSE)) # h x 6 = 40 x 6
dim(fr6F <- r6pack(m.pulp, h = 40, full.h= TRUE )) # n x 6 = 62 x 6
stopifnot(identical(fr6, fr6F[1:40,]))
\dontshow{
stopifnot(apply(fr6[1:10,], 2L,
function(col) c(1,3,6,35,36,38) \%in\% col))
}
}
\keyword{robust}
\keyword{multivariate}
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