R: Permutation Distribution Clustering Distance Matrix

pdcDist {pdc} | R Documentation

## Permutation Distribution Clustering Distance Matrix

### Description

This function computes and returns the distance matrix computed by the divergence between permutation distributions of time series.

### Usage

    pdcDist(X, m = NULL, t = NULL, divergence = symmetricAlphaDivergence)

### Arguments

`X` | A matrix representing a set of time series. Columns are time series and rows represent time points.
`m` | Embedding dimension for calculating the permutation distributions. Reasonable values range usually somewhere between 2 and 10. If no embedding dimension is chosen, the MinE heuristic is used to determine the embedding dimension automatically.
`t` | Time-delay of the embedding
`divergence` | Divergence measure between discrete distributions. Default is the symmetric alpha divergence.

### Details

A valid divergence is always non-negative.

### Value

Returns the dissimilarity between two codebooks as floating point number (larger or equal than zero).

### Author(s)

Andreas Brandmaier brandmaier@mpib-berlin.mpg.de

### References

Brandmaier, A. M. (2015). pdc: An R Package for Complexity-Based Clustering of Time Series. Journal of Statistical Software, 67(5), 1–23.

### See Also

`pdclust`

`hclust` `kmeans`

### Examples

    # create a set of time series consisting
    # of sine waves with different degrees of added noise
    # and two white noise time series
    X <- cbind(
    sin(1:500)+rnorm(500,0,.1),
    sin(1:500)+rnorm(500,0,.2),
    sin(1:500)+rnorm(500,0,.3),
    sin(1:500)+rnorm(500,0,.4),
    rnorm(500,0,1),
    rnorm(500,0,1)
    )

    # calculate the distance matrix
    D <- pdcDist(X,3)

    # and plot with lattice package, you will
    # be able to spot two clusters: a noise cluster
    # and a sine wave cluster
    require("lattice")
    levelplot(as.matrix(D), col.regions=grey.colors(100,start=0.9, end=0.3))

[Package pdc version 1.0.3 Index]