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 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, thr_ic = 0.9, points = 3)
```

### Arguments

 `x` A numeric vector containing the data to be processed. `thr_ic` A positive real number with default value equal to 0.9. It is used to create the solution path. The lower the value, the larger the solution path vector. `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 (2021), 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` `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 (2021). Detecting multiple generalized change-points by isolating single ones. Metrika, https://doi.org/10.1007/s00184-021-00821-6.

`sol.idetect_seq`, `sol.not`, `sol.wbs`, `sol.wbs2`, `sol.tguh`,
```r3 <- rnorm(1000) + c(rep(0,300), rep(2,200), rep(-4,300), rep(0,200))