match.cpt.ts: Associating the change-points with the component time series In ccid: Cross-Covariance Isolate Detect: a New Change-Point Method for Estimating Dynamic Functional Connectivity

Description

This function performs a contrast function based approach in order to match each change-point and time series. In simple terms, for a given change-point set this function associates each change-point with the respective data sequence (or sequences) from which it was detected.

Usage

 1 2 3 4 5 6 7 8 match.cpt.ts( X, cpt, thr_const = 1, thr_fin = thr_const * sqrt(2 * log(nrow(X))), scales = -1, count = 5 )

Arguments

 X A numerical matrix representing the multivariate periodograms. Each column contains a different periodogram which is the result of applying the wavelet transformation to the initial multivariate time series. cpt A positive integer vector with the locations of the change-points. If missing, then our approach with the L_2 aggregation is called internally to extract the change-points in X. thr_const A positive real number with default value equal to 1. It is used to define the threshold; see thr_fin. thr_fin With T the length of the data sequence, this is a positive real number with default value equal to thr_const * log(T). It is the threshold, which is used in the detection process. scales Negative integers for the wavelet scales used to create the periodograms, with a small negative integer representing a fine scale. The default value is equal to -1. count Positive integer with default value equal to 5. It can be used so that the function will return only the count most important matches of each change-points with the time series.

Value

A list with the following components:

 time_series_indicator A list of matrices. There are as many matrices as the number of change-points. Each change-point has its own matrix, with each row of the matrix representing the associated combination of time series that are associated with the respective change-point. most_important A list of matrices. There are as many matrices as the number of change-points. Each change-point has its own matrix, with each row of the matrix representing the associated combination of time series that are associated with the respective change-point. It shows the count most important time series combinations for each change-point.

Author(s)

Andreas Anastasiou, anastasiou.andreas@ucy.ac.cy

References

“Cross-covariance isolate detect: a new change-point method for estimating dynamic functional connectivity”, Anastasiou et al (2020), preprint <doi:10.1101/2020.12.20.423696>.

Examples

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 set.seed(1) num.nodes <- 40 # number of nodes etaA.1 <- 0.95 etaA.2 <- 0.05 pcor1 <- GeneNet::ggm.simulate.pcor(num.nodes, etaA = etaA.1) pcor2 <- GeneNet::ggm.simulate.pcor(num.nodes, etaA = etaA.2) n <- 100 data1 <- GeneNet::ggm.simulate.data(n, pcor1) data2 <- GeneNet::ggm.simulate.data(n, pcor2) X <- rbind(data1, data2, data1, data2) ## change-points at 100, 200, 300 sgn <- sign(stats::cor(X)) M1 <- match.cpt.ts(t(hdbinseg::gen.input(x = t(X),scales = -1, sq = TRUE, diag = FALSE, sgn = sgn))) M1

ccid documentation built on Dec. 20, 2021, 5:08 p.m.