Description Usage Arguments Details Value Author(s) References See Also Examples

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

1 | ```
pdcDist(X, m = NULL, t = NULL, divergence = symmetricAlphaDivergence)
``` |

`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. |

A valid divergence is always non-negative.

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

Andreas Brandmaier brandmaier@mpib-berlin.mpg.de

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

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))
``` |

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