assetsSelect {fAssets} | R Documentation |
Selet assets from Multivariate Asset Sets based on clustering.
assetsSelect(x, method = c("hclust", "kmeans"), control = NULL, ...)
x |
any rectangular time series object which can be converted by the
function as.matrix() into a matrix object, e.g. like an
object of class timeSeries , data.frame , or mts .
|
method |
a character string, which clustering method should be used?
Either hclust for hierarchical clustering of dissimilarities,
or kmeans for k-means clustering.
|
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
method=c(measure="euclidean", method="complete") ,
and for kmeans clustering this is
method=c(centers=3, algorithm="Hartigan-Wong") .
|
... |
optional arguments to be passed. Note, for the k-means algorithm the number of centers has to be specified! |
The function assetsSelect
calls the functions hclust
or kmeans
from R's "stats"
package. hclust
performs a hierarchical cluster analysis on the set of dissimilarities
hclust(dist(t(x)))
and kmeans
performs a k-means
clustering on the data matrix itself.
Note, the hierarchical clustering method has in addition a plot method.
if use="hclust"
was selected then the function returns a
S3 object of class "hclust", otherwise if use="kmeans"
was
selected then the function returns an object of class "kmeans".
For details we refer to the help pages of hclust
and
kmeans
.
Diethelm Wuertz for the Rmetrics port.
Wuertz, D., Chalabi, Y., Chen W., Ellis A. (2009); Portfolio Optimization with R/Rmetrics, Rmetrics eBook, Rmetrics Association and Finance Online, Zurich.
## LPP - LPP = as.timeSeries(data(LPP2005REC)) colnames(LPP) ## assetsSelect - # hclust Clustering: hclust = assetsSelect(LPP, "hclust") plot(hclust) ## assetsSelect - # kmeans Clustering: assetsSelect(LPP, "kmeans", control = c(centers = 3, algorithm = "Hartigan-Wong"))