sol_path_cplm: The solution path for the case of continuous piecewise-linear...
In IDetect: Isolate-Detect Methodology for Multiple Change-Point Detection

Description Usage Arguments Value Author(s) References Examples

This function starts by over-estimating the number of true change-points. After that, following an approach based on the values of a suitable contrast function, it sorts the estimated change-points in a way that the estimation, which is most-likely to be correct appears first, whereas the least likely to be correct, appears last. The routine is typically not called directly by the user; it is employed in cplm_ic. For more details, see References.

1	sol_path_cplm(x, thr_ic = 1.25, points = 3)

`x`	A numeric vector containing the data in which you would like to find change-points.
`thr_ic`	A positive real number with default value equal to 1.25. It is used to define the threshold. The change-points are estimated by thresholding with threshold equal to `sigma * thr_ic * sqrt(2 * log(T))`, where `T` is the length of the data sequence `x` and `sigma = mad(diff(diff(x)))/6`. Because, we would like to overestimate the number of the true change-points in `x`, it is suggested to keep `thr_ic` smaller than 1.4, which is the default value used as the threshold constant in the function `win_cplm_th`.
`points`	A positive integer with default value equal to 3. It defines the distance between two consecutive end- or start-points of the right- or left-expanding intervals, respectively.

The solution path for the case of continuous piecewise-linear signals.

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

Anastasiou, A. and Fryzlewicz, P. (2018). Detecting multiple generalized change-points by isolating single ones.

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three.cpt <- c(seq(0, 499, 1), seq(498.5, 249, -0.5), seq(250.5,999,1.5), seq(998,499,-1))
three.cpt.noise <- three.cpt + rnorm(2000)
solution.path <- sol_path_cplm(three.cpt.noise)