R: Cluster analysis for compositional data

clustCoDa {robCompositions} | R Documentation

## Cluster analysis for compositional data

### Description

Clustering in orthonormal coordinates or by using the Aitchison distance

### Usage

    clustCoDa(x, k = NULL, method = "Mclust", scale = "robust",
      transformation = "pivotCoord", distMethod = NULL, iter.max = 100,
      vals = TRUE, alt = NULL, bic = NULL, verbose = TRUE)

    ## S3 method for class 'clustCoDa'
    plot(x, y, ..., normalized = FALSE,
      which.plot = "clusterMeans", measure = "silwidths")

### Arguments

`x` | compositional data represented as a data.frame
`k` | number of clusters
`method` | clustering method. One of Mclust, cmeans, kmeansHartigan, cmeansUfcl, pam, clara, fanny, ward.D2, single, hclustComplete, average, mcquitty, median, centroid
`scale` | If orthonormal coordinates should be normalized.
`transformation` | default is the isometric log-ratio transformation. Can only used when distMethod is not Aitchison.
`distMethod` | Distance measure to be used. If “Aitchison”, then transformation should be “identity”.
`iter.max` | parameter if kmeans is chosen. The maximum number of iterations allowed
`vals` | if cluster validity measures should be calculated
`alt` | a known partitioning can be provided (for special cluster validity measures)
`bic` | if TRUE then the BIC criteria is evaluated for each single cluster as validity measure
`verbose` | if TRUE additional print output is provided
`y` | the y coordinates of points in the plot, optional if x is an appropriate structure.
`...` | additional parameters for print method passed through
`normalized` | results gets normalized before plotting. Normalization is done by z-transformation applied on each variable.
`which.plot` | currently the only plot. Plot of cluster centers.
`measure` | cluster validity measure to be considered for which.plot equals “partMeans”

### Details

The compositional data set is either internally represented by orthonormal coordiantes before a cluster algorithm is applied, or - depending on the choice of parameters - the Aitchison distance is used.

### Value

all relevant information such as cluster centers, cluster memberships, and cluster statistics.

### Author(s)

Matthias Templ (accessing the basic features of hclust, Mclust, kmeans, etc. that are all written by others)

### References

M. Templ, P. Filzmoser, C. Reimann. Cluster analysis applied to regional geochemical data: Problems and possibilities. Applied Geochemistry, 23 (8), 2198–2213, 2008

Templ, Filzmoser, Reimann (2008) Cluster analysis applied to regional geochemical data: Problems and possibilities", Applied Geochemistry, 23 (2008), pages 2198 - 2213.

### Examples

    data(expenditures)
    x <- expenditures
    rr <- clustCoDa(x, k=6, scale = "robust", transformation = "pivotCoord")
    rr2 <- clustCoDa(x, k=6, distMethod = "Aitchison", scale = "none",
                     transformation = "identity")
    rr3 <- clustCoDa(x, k=6, distMethod = "Aitchison", method = "single",
                     transformation = "identity", scale = "none")
    plot(rr)
    plot(rr, normalized = TRUE)
    plot(rr, normalized = TRUE, which.plot = "partMeans")

[Package robCompositions version 2.0.9 Index]