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

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

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

1 2	ht_ID_pcm(x, s.ht = 3, q_ht = 300, ht_thr_id = 1, ht_th_ic_id = 0.9, p_thr = 1, p_ic = 3)

`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 (see Details).
`q_ht`	A positive integer number with default value equal to 300. If the length of `x` is less than or equal to `q_ht`, then no pre-averaging will take place.
`ht_thr_id`	A positive real number with default value equal to 1. It is used to define the threshold, if the thresholding approach is to be followed; see `pcm_th` for more details on the thresholding approach.
`ht_th_ic_id`	A positive real number with default value equal to 0.9. 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 `pcm_ic`. It is applied to the new data, which are obtained after we pre-average `x`.
`p_thr`	A positive integer with default value equal to 1. It is used only when the threshold based approach (as described in `pcm_th`) 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 (described in `pcm_ic`) 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, 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 ID_pcm on \tilde{x}_q to obtain the change-points, namely \tilde{r}_1, \tilde{r}_2, ..., \tilde{r}_{\hat{N}} in 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)*\code{s.ht} + \lfloor \code{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 (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 piecewise-constant signal.
`solution_path`	A vector containing the solution path.

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

ID_pcm and normalise, which are functions that are used in ht_ID_pcm. In addition, see ht_ID_cplm for the case of continuous and piecewise-linear signals.

single.cpt <- c(rep(4,3000),rep(0,3000))
single.cpt.student <- single.cpt + rt(6000, df = 5)
cpts_detect <- ht_ID_pcm(single.cpt.student)

three.cpt <- c(rep(4,2000),rep(0,2000),rep(-4,2000),rep(0,2000))
three.cpt.student <- three.cpt + rt(8000, df = 5)
cpts_detect_three <- ht_ID_pcm(three.cpt.student)