AssetsSelection {fAssets} | R Documentation |
A collection and description of functions which
allow for the selection and clustering of individual
assets from portfolios using several kinds of
clustering approaches.
The functions are:
assetsSelect | Asset Selection from Portfolios. |
assetsSelect(x, method = c("hclust", "kmeans"), kmeans.centers = 5, kmeans.maxiter = 10, doplot = TRUE, ...)
doplot |
[assetsSelect] - a logical, should a plot be displayed? |
kmeans.centers |
[assetsSelect] - either the number of clusters or a set of initial cluster centers. If the first, a random set of rows in x are chosen as the
initial centers.
|
kmeans.maxiter |
[assetsSelect] - the maximum number of iterations allowed. |
method |
[assetsSelect] - a character string, which clustering method should be applied? Either hclust for hierarchical clustering of dissimilarities,
or kmeans for k-means clustering.
|
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 .
|
... |
optional arguments to be passed. |
Assets Selection:
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.
assetsSelect
if method="hclust"
was selected then the function returns a
S3 object of class "hclust", otherwise if method="kmeans"
was
selected then the function returns an obkject of class list. For
details we refer to the help pages of hclust
and kmeans
.
Diethelm Wuertz for the Rmetrics port.
MultivariateDistribution
.
## berndtInvest - data(berndtInvest) # Market and Interest Rate columns from data frame, berndtAssets.tS = as.timeSeries(berndtInvest)[, -c(10, 17)] head(berndtAssets.tS) ## assetsSelect - # Hierarchical Clustering: clustered = assetsSelect(berndtAssets.tS, doplot = FALSE) clusteredAssets.tS = berndtAssets.tS[, c(clustered$order[1:4])] colnames(clusteredAssets.tS) # Cluster Dendogram: par(mfrow = c(1, 1)) plot(clustered)