ht_ID_plm: Apply the Isolate-Detect methodology for multiple...
In IDetect: Isolate-Detect Method for Multiple Change-Point Detection

ht_ID_plm

R Documentation

Apply the Isolate-Detect methodology for multiple change-point detection in the slope of a vector with non Gaussian noise

Description

Using the Isolate-Detect methodology, this function estimates the number and locations of multiple change-points in the piecewise-linear mean of a noisy input vector x, with noise that is not normally distributed. It also gives the estimated signal, as well as the solution path (see Details for the relevant literature reference).

Usage

ht_ID_plm(
  x,
  s_ht = 3,
  l_ht = 300,
  ht_thr_id = 1.4,
  ht_th_ic_id = 1.25,
  p_thr = 1,
  p_ic = 3
)

Arguments

`x`	A numeric vector containing the data in which you would like to find change-points.
`s_ht`	A positive integer number with default value equal to 3. It is used to define the way we pre-average the given data sequence.
`l_ht`	A positive integer number with default value equal to 300. If the length of `x` is less than or equal to `l_ht`, then no pre-averaging will take place.
`ht_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. In this case, the change-points are estimated by thresholding with threshold equal to `sigma * thr_id * sqrt(2 * log(l))`, where `l` is the length of the newly obtained data, after pre-averaging takes place through the `normalise` function.
`ht_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. It is applied to the new data, which are obtained after we take average values on `x`.
`p_thr`	A positive integer with default value equal to 1. 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.
`p_ic`	A positive integer with default value equal to 3. 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.

Details

Firstly, in this function we call normalise, in order to create a new data sequence, \tilde{x}, by taking averages of observations in x. Then, we employ link{ID_plm} on \tilde{x}_q to obtain the change-points, namely \tilde{r}_1, \tilde{r}_2, ..., \tilde{r}_{\hat{N}} in an increasing order. To obtain the original location of the change-points with, on average, the highest accuracy we define

\hat{r}_k = (\tilde{r}_{k}-1)*s_ht + \lfloor s_ht/2 + 0.5 \rfloor, k=1, 2,..., \hat{N}.

More details can be found in “Detecting multiple generalized change-points by isolating single ones”, Anastasiou and Fryzlewicz (2017), preprint.

Value

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 piecewise-linear mean signal.

solution_path A vector containing the solution path.

Author(s)

Andreas Anastasiou, anastasiou.andreas@ucy.ac.cy

Examples

single.cpt <- c(seq(0, 1999, 1), seq(1998, -1, -1))
single.cpt.student <- single.cpt + rt(4000, df = 5)
cpt.single <- ht_ID_plm(single.cpt.student)

three.cpt <- c(seq(0, 3998, 2), seq(3996, -2, -2), seq(0,3998,2), seq(3996,-2,-2))
three.cpt.student <- three.cpt + rt(8000, df = 5)
cpt.three <- ht_ID_plm(three.cpt.student)

IDetect documentation built on May 7, 2026, 5:09 p.m.