| 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
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))