Description Usage Arguments Value Author(s) References Examples
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.
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
change-points. If missing, then our approach with the L_2
aggregation is called internally to extract the change-points 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 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.
|
Andreas Anastasiou, anastasiou.andreas@ucy.ac.cy
“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>.
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
|
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