sol.idetect {breakfast} R Documentation

## Solution path generation via the Isolate-Detect method

### Description

This function arranges all possible change-points in the mean of the input vector, or in its linear trend, in the order of importance, via the Isolate-Detect (ID) method. It is developed to be used with the sdll and information criterion (ic) model selection rules.

### Usage

sol.idetect(
x,
type = "const",
thr_ic_cons = 0.9,
thr_ic_lin = 1.25,
points = 3
)

### Arguments

 x A numeric vector containing the data to be processed. type The model type considered. type = "const", type = "lin.cont", type = "lin.discont" mean, respectively, that the signal (mean of x) is piecewise constant, piecewise linear and continuous, and piecewise linear but not necessarily continuous. If not given, the default is type = "const" thr_ic_cons A positive real number with default value equal to 0.9. It is used to create the solution path for the piecewise-constant model. The lower the value, the longer the solution path. thr_ic_lin A positive real number with default value 1.25. Used to create the solution path if type == "lin.cont" or type == "lin.discont" 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, as described in the Isolate-Detect methodology.

### Details

The Isolate-Detect method and its algorithm is described in "Detecting multiple generalized change-points by isolating single ones", A. Anastasiou & P. Fryzlewicz (2022), Metrika, https://doi.org/10.1007/s00184-021-00821-6.

### Value

An S3 object of class cptpath, which contains the following fields:

 solutions.nested TRUE, i.e., the change-point outputs are nested solution.path Locations of possible change-points in the mean of x, arranged in decreasing order of change-point importance solution.set Empty list x Input vector x type The input parameter type cands Matrix of dimensions length(x) - 1 by 4. The first two columns are (start, end)-points of the detection intervals of the corresponding possible change-point location in the third column. The fourth column is a measure of strength of the corresponding possible change-point. The order of the rows is the same as the order returned in solution.path method The method used, which has value "idetect" here

### References

A. Anastasiou & P. Fryzlewicz (2022). Detecting multiple generalized change-points by isolating single ones. Metrika, https://doi.org/10.1007/s00184-021-00821-6.