Description Usage Arguments Details Value Author(s) See Also Examples
View source: R/Finalised_coding.R
This function performs the IsolateDetect methodology based on an information criterion approach, in order to detect multiple changepoints in a noisy, continuous, piecewiselinear data sequence, with the noise being Gaussian. More information on how this approach works as well as the relevant literature reference are given in Details.
1 2 
x 
A numeric vector containing the data in which you would like to find changepoints. 
th_const 
A positive real number with default value equal to 1.25. It is used to define the threshold value that will be used at the first step of the model selection based IsolateDetect method; see Details for more information. 
Kmax 
A positive integer with default value equal to 200. It is the
maximum allowed number of estimated changepoints in the solution path; see

penalty 
A character vector with names of penalty functions used. 
points 
A positive integer with default value equal to 10. It defines the distance between two consecutive end or startpoints of the right or leftexpanding intervals, respectively. 
The approach followed in cplm_ic
in order to detect the
changepoints is based on identifying the set of changepoints that minimise an
information criterion. At first, we employ sol_path_cplm
, which
overestimates the number of changepoints using th_const
in order to define the
threshold and then sorts the obtained estimates in a way that the estimate,
which is most likely to be correct appears first, whereas the least likely
to be correct, appears last. Let J be the number of estimates
that this overestimation approach returns. We will obtain a vector
b = (b_1, b_2, ..., b_J), with the estimates ordered as explained above. We
define the collection ≤ft\{M_j\right\}_{j = 0,1,…,J}, where M_0
is the empty set and M_j = ≤ft\{b_1,b_2,...,b_j\right\}. Among the collection
of models M_j, j=0,1,...,J, we select the one that minimises a predefined
Information Criterion. The obtained set of changepoints is apparently a subset of
the solution path given in sol_path_cplm
. More details can be found
in “Detecting multiple generalized changepoints by isolating single ones”,
Anastasiou and Fryzlewicz (2018), preprint.
A list with the following components:
sol_path  A vector containing the solution path. 
ic_curve  A list with values of the chosen information criteria. 
cpt_ic  A list with the changepoints detected for each information criterion considered. 
no_cpt_ic  The number of changepoints detected for each information criterion considered. 
Andreas Anastasiou, a.anastasiou@lse.ac.uk
ID_cplm
and ID
, which employ this function.
In addition, see pcm_ic
for the case of detecting changes in
a piecewiseconstant signal using the information criterion based approach.
1 2 3 4 5 6 7  single.cpt < c(seq(0, 999, 1), seq(998.5, 499, 0.5))
single.cpt.noise < single.cpt + rnorm(2000)
cpt.single.ic < cplm_ic(single.cpt.noise)
three.cpt < c(seq(0, 499, 1), seq(498.5, 249, 0.5), seq(250,1249,2), seq(1248,749,1))
three.cpt.noise < three.cpt + rnorm(2000)
cpt.three.ic < cplm_ic(three.cpt.noise)

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