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

## Description

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.

## Usage

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

## Arguments

 `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.

## Value

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

## Author(s)

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

## References

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

## Examples

 ```1 2 3``` ```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) ```

IDetect documentation built on May 2, 2019, 11:04 a.m.