ID_cplm: Multiple change-point detection for a continuous,...
In IDetect: Isolate-Detect Methodology for Multiple Change-Point Detection

Description Usage Arguments Details Value Author(s) See Also Examples

This function estimates the number and locations of multiple change-points in the noisy, continuous and piecewise-linear input vector x, using the Isolate-Detect methodology. The noise follows the normal distribution. The estimated signal, as well as the solution path defined in sol_path_cplm are also given. The function is a hybrid between the thresholding approach of win_cplm_th and the information criterion approach of cplm_ic and estimates the change-points taking into account both these approaches (see Details for more information and the relevant literature reference).

1	ID_cplm(x, thr_id = 1.4, th_ic_id = 1.25, pointsth = 3, pointsic = 10)

`x`	A numeric vector containing the data in which you would like to find change-points.
`thr_id`	A positive real number with default value equal to 1.4. It is used to define the threshold, if the thresholding approach is to be followed; see `cplm_th` for more details.
`th_ic_id`	A positive real number with default value equal to 1.25. It is useful only if the model selection based Isolate-Detect method is to be followed and it is used to define the threshold value that will be used at the first step (change-point overestimation) of the model selection approach described in `cplm_ic`.
`pointsth`	A positive integer with default value equal to 3. It is used only when the threshold based approach is to be followed and it defines the distance between two consecutive end- or start-points of the right- or left-expanding intervals, respectively.
`pointsic`	A positive integer with default value equal to 10. It is used only when the information criterion based approach is to be followed and it defines the distance between two consecutive end- or start-points of the right- or left-expanding intervals, respectively.

Firstly, this function detects the change-points using win_cplm_th. If the estimated number of change-points is larger than 100, then the result is returned and we stop. Otherwise, ID_cplm proceeds to detect the change-points using cplm_ic and this is what is returned. To sum up, ID_cplm returns a result based on cplm_ic if the estimated number of change-points is less than 100. Otherwise, the result comes from thresholding. More details can be found in “Detecting multiple generalized change-points by isolating single ones”, Anastasiou and Fryzlewicz (2018), preprint.

A list with the following components:


`cpt`	A vector with the detected change-points.
`no_cpt`	The number of change-points detected.
`fit`	A numeric vector with the estimated continuous piecewise-linear signal.
`solution_path`	A vector containing the solution path.

Andreas Anastasiou, a.anastasiou@lse.ac.uk

win_cplm_th and cplm_ic which are the functions that ID_cplm is based on. In addition, see ID_pcm for the case of detecting changes in the mean of a piecewise-constant signal. The main function ID of the package employs ID_cplm.

single.cpt <- c(seq(0, 999, 1), seq(998.5, 499, -0.5))
single.cpt.noise <- single.cpt + rnorm(2000)
cpt.single <- ID_cplm(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 <- ID_cplm(three.cpt.noise)

multi.cpt <- rep(c(seq(0,49,1), seq(48,0,-1)),20)
multi.cpt.noise <- multi.cpt + rnorm(1980)
cpt.multi <- ID_cplm(multi.cpt.noise)