pdc.dist: Permutation Distribution Clustering Distance Matrix In pdc: Permutation Distribution Clustering

Description

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

Usage

 `1` ```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.

`pdclust`
`hclust` `kmeans`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20``` ```# 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)) ```