IDetect (version 0.1.0)

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

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

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.

References

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

Examples

# NOT RUN {
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)
# }