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\name{assets-selection}
\alias{assetsSelect}
\title{Selecting Assets from Multivariate Asset Sets}
\description{
Selet assets from Multivariate Asset Sets based
on clustering.
}
\usage{
assetsSelect(x, method = c("hclust", "kmeans"), control = NULL, \dots)
}
\arguments{
\item{x}{
any rectangular time series object which can be converted by the
function \code{as.matrix()} into a matrix object, e.g. like an
object of class \code{timeSeries}, \code{data.frame}, or \code{mts}.
}
\item{method}{
a character string, which clustering method should be used?
Either \code{hclust} for hierarchical clustering of dissimilarities,
or \code{kmeans} for k-means clustering.
}
\item{control}{
a character string with two entries controlling the parameters used
in the underlying cluster algorithms. If set to NULL, then
default settings are taken: For hierarchical clustering this is
\code{method=c(measure="euclidean", method="complete")},
and for kmeans clustering this is
\code{method=c(centers=3, algorithm="Hartigan-Wong")}.
}
\item{\dots}{
optional arguments to be passed. Note, for the k-means algorithm
the number of centers has to be specified!
}
}
\value{
if \code{use="hclust"} was selected then the function returns a
S3 object of class "hclust", otherwise if \code{use="kmeans"} was
selected then the function returns an object of class "kmeans".
For details we refer to the help pages of \code{hclust} and
\code{kmeans}.
}
\details{
The function \code{assetsSelect} calls the functions \code{hclust}
or \code{kmeans} from R's \code{"stats"} package. \code{hclust}
performs a hierarchical cluster analysis on the set of dissimilarities
\code{hclust(dist(t(x)))} and \code{kmeans} performs a k-means
clustering on the data matrix itself.
Note, the hierarchical clustering method has in addition a plot method.
}
\author{
Diethelm Wuertz for the Rmetrics port.
}
\references{
Wuertz, D., Chalabi, Y., Chen W., Ellis A. (2009);
\emph{Portfolio Optimization with R/Rmetrics},
Rmetrics eBook, Rmetrics Association and Finance Online, Zurich.
}
\examples{
## LPP -
# Load Swiss Pension Fund Data:
LPP <- LPP2005REC
colnames(LPP)
## assetsSelect -
# Hierarchical Clustering:
hclust <- assetsSelect(LPP, "hclust")
plot(hclust)
## assetsSelect -
# kmeans Clustering:
assetsSelect(LPP, "kmeans", control =
c(centers = 3, algorithm = "Hartigan-Wong"))
}
\keyword{models}
|
