ccid (version 1.2.0)

match.cpt.ts: Associating the change-points with the component time series

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

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

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

# NOT RUN {
  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
# }