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\name{orderColnames}
\alias{orderColnames}
\alias{sortColnames}
\alias{sampleColnames}
\alias{statsColnames}
\alias{pcaColnames}
\alias{hclustColnames}
\title{Reorder column names of a time series}
\description{
Functions and methods dealing with the rearrangement
of column names of 'timeSeries' objects.
\cr
\tabular{ll}{
\code{orderColnames} \tab Returns ordered column names of a time Series, \cr
\code{sortColnames} \tab Returns sorted column names of a time Series, \cr
\code{sampleColnames} \tab Returns sampled column names of a time Series, \cr
\code{statsColnames} \tab Returns statistically rearranged column names, \cr
\code{pcaColnames} \tab Returns PCA correlation ordered column names, \cr
\code{hclustColnames} \tab Returns hierarchical clustered column names. }
}
\usage{
orderColnames(x, \dots)
sortColnames(x, \dots)
sampleColnames(x, \dots)
statsColnames(x, FUN = colMeans, \dots)
pcaColnames(x, robust = FALSE, \dots)
hclustColnames(x, method = c("euclidean", "complete"), \dots)
}
\arguments{
\item{x}{
an object of class \code{timesSeries} or any other rectangular
object which can be transformed by the function \code{as.matrix}
into a numeric matrix.
}
\item{FUN}{
a character string indicating which statistical function should be
applied. By default statistical ordering operates on the column
means of the time series.
}
\item{method}{
a character string with two elements. The first determines the
choice of the distance measure, see \code{\link[stats]{dist}}, and
the second determines the choice of the agglomeration method, see
\code{\link[stats]{hclust}}.
}
\item{robust}{
a logical flag which indicates if robust correlations should be
used.
}
\item{\dots}{
further arguments to be passed to the underlying functions doing the
main work, see section \sQuote{Details}.
}
}
\details{
These functions reorder the column names of a \code{"timeSeries"}
object according to some statistical measure.
\bold{Statistically Motivated Rearrangement}
The function \code{statsColnames} rearranges the column names
according to a statical measure. These measure must operate on the
columns of the time series and return a vector of values which can
be sorted. Typical functions ar those listed in help page
\code{colStats} but custom functions can be used that compute for
example risk or any other statistical measure. The \code{\dots}
argument allows to pass additional arguments to the underlying
function \code{FUN}.
\bold{PCA Ordering of the Correlation Matrix}
The function \code{pcaColnames} rearranges the column names
according to the PCA ordered correlation matrix. The argument
\code{robust} allsows to select between the use of the standard
\code{cor} and computation of robust correlations using
the function \code{covMcd} from contributed R package
\code{robustbase}. The \code{\dots} argument allows to pass
additional arguments to the two underlying functions \code{cor}
or \code{covMcd}. E.g., adding \code{method="kendall"}
to the argument list calculates Kendall's rank correlations
instead the default which calculates Person's correlations.\cr
\bold{Ordering by Hierarchical Clustering}
The function \code{pcaColnames} uses the hierarchical clustering
approach \code{hclust} to rearrange the column names of the
time series.
}
\value{
for \code{orderColnames}, an integer vector representing the
permutaion that will sort the column names,
for the other functions, a character vector giving the rearranged
column names
}
\examples{
## Load Swiss Pension Fund Benchmark Data -
data <- LPP2005REC[,1:6]
## Abbreviate Column Names -
colnames(data)
## Sort Alphabetically -
sortColnames(data)
## Sort by Column Names by Hierarchical Clustering -
hclustColnames(data)
head(data[, hclustColnames(data)])
}
\keyword{chron}
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