Description Usage Arguments Value Author(s) References Examples
This function performs a contrast function based approach in order to match each changepoint and time series. In simple terms, for a given changepoint set this function associates each changepoint with the respective data sequence (or sequences) from which it was detected.
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
)

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
changepoints. If missing, then our approach with the L_2
aggregation is called internally to extract the changepoints in

thr_const 
A positive real number with default value equal to 1. It is
used to define the threshold; see 
thr_fin 
With 
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 
A list with the following components:
time_series_indicator  A list of matrices. There are as many matrices as 
the number of changepoints. Each changepoint has its own matrix, with  
each row of the matrix representing the associated combination of time  
series that are associated with the respective changepoint.  
most_important  A list of matrices. There are as many matrices as 
the number of changepoints. Each changepoint has its own matrix, with  
each row of the matrix representing the associated combination of time  
series that are associated with the respective changepoint. It shows the  
count most important time series combinations for each changepoint.

Andreas Anastasiou, anastasiou.andreas@ucy.ac.cy
“Crosscovariance isolate detect: a new changepoint method for estimating dynamic functional connectivity”, Anastasiou et al (2020), preprint <doi:10.1101/2020.12.20.423696>.
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) ## changepoints 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

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