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Functions and methods dealing with the rearrangement of column names of 'timeSeries' objects.

orderColnamesReturns ordered column names of a time Series,
sortColnamesReturns sorted column names of a time Series,
sampleColnamesReturns sampled column names of a time Series,
statsColnamesReturns statistically rearranged column names,
pcaColnamesReturns PCA correlation ordered column names,
hclustColnamesReturns hierarchical clustered column names.


orderColnames(x, ...)
sortColnames(x, ...)  
sampleColnames(x, ...) 
statsColnames(x, FUN = colMeans, ...)
pcaColnames(x, robust = FALSE, ...)
hclustColnames(x, method = c("euclidean", "complete"), ...)



an object of class timesSeries or any other rectangular object which can be transformed by the function as.matrix into a numeric matrix.


a character string indicating which statistical function should be applied. By default statistical ordering operates on the column means of the time series.


a character string with two elements. The first determines the choice of the distance measure, see dist, and the second determines the choice of the agglomeration method, see hclust.


a logical flag which indicates if robust correlations should be used.


further arguments to be passed to the underlying functions doing the main work, see section ‘Details’.


These functions reorder the column names of a "timeSeries" object according to some statistical measure.

Statistically Motivated Rearrangement

The function 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 colStats but custom functions can be used that compute for example risk or any other statistical measure. The ... argument allows to pass additional arguments to the underlying function FUN.

PCA Ordering of the Correlation Matrix

The function pcaColnames rearranges the column names according to the PCA ordered correlation matrix. The argument robust allsows to select between the use of the standard cor and computation of robust correlations using the function covMcd from contributed R package robustbase. The ... argument allows to pass additional arguments to the two underlying functions cor or covMcd. E.g., adding method="kendall" to the argument list calculates Kendall's rank correlations instead the default which calculates Person's correlations.

Ordering by Hierarchical Clustering

The function pcaColnames uses the hierarchical clustering approach hclust to rearrange the column names of the time series.


for 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


## Load Swiss Pension Fund Benchmark Data -
   data <- LPP2005REC[,1:6]
## Abbreviate Column Names -
#> [1] "SBI" "SPI" "SII" "LMI" "MPI" "ALT"

## Sort Alphabetically - 
#> [1] "ALT" "LMI" "MPI" "SBI" "SII" "SPI"
## Sort by Column Names by Hierarchical Clustering -
#> [1] "SII" "SBI" "LMI" "SPI" "MPI" "ALT"
   head(data[, hclustColnames(data)])
#> GMT 
#>                     SII          SBI          LMI          SPI         MPI
#> 2005-11-01 -0.003190926 -0.000612745 -0.001108882  0.008414595 0.001548062
#> 2005-11-02 -0.004117638 -0.002762009 -0.001175939  0.002519342 0.000342876
#> 2005-11-03 -0.005209409 -0.001153092 -0.000992456  0.012707292 0.010502959
#> 2005-11-04 -0.001127165 -0.003235750 -0.001198528 -0.000702757 0.011679558
#> 2005-11-07 -0.001795839  0.001310970  0.000360366  0.006205226 0.002709618
#> 2005-11-08  0.002103374  0.000539312  0.002327040  0.000329260 0.000346843
#>                     ALT
#> 2005-11-01 -0.002572971
#> 2005-11-02 -0.001141604
#> 2005-11-03  0.005007442
#> 2005-11-04  0.009482677
#> 2005-11-07  0.004723952
#> 2005-11-08  0.000853619