pvalues.clust: Clustering Algorithm Based on p-values.
In TSclust: Time Series Clustering Utilities

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

Clustering algorithm based on p-values. Each group in the cluster solution is formed by series with associated p-values greater than a pre-specified level of significance.

1	pvalues.clust(pvalues, significance)

`pvalues`	A `dist` object containing the p-values from testing the equality of each pair of time series under study.
`significance`	The significance level.

Each element (i,j) in pvalues corresponds to the p-value obtained from checking whether or not the i-th and j-th series come from the same generating model. The clustering algorithm will only group together those series whose associated p-values are greater than the pre-specified significance level. The algorithm was originally developed for its use with the p-values obtained with in diss.AR.MAH (see Maharaj, 2000), but it can be applied to any similar test.

An integer vector of length n, the number of observations, giving for each observation the number (id) of the cluster to which it belongs.

Pablo Montero Manso, José Antonio Vilar.

Maharaj E.A. (2000) Clusters of time series. J. Classification, 17(2), 297–314.

Montero, P and Vilar, J.A. (2014) TSclust: An R Package for Time Series Clustering. Journal of Statistical Software, 62(1), 1-43. http://www.jstatsoft.org/v62/i01/.

diss.AR.MAH

## Create three sample time series
x <- cumsum(rnorm(100))
y <- cumsum(rnorm(100))
z <- sin(seq(0, pi, length.out=100))
##

## Compute the distance and check for coherent results
dd <- diss( rbind(x,y,z), "AR.MAH")
pvalues.clust( dd$p_value, 0.05 )