sol.wbs2 {breakfast} R Documentation

## Solution path generation via the Wild Binary Segmentation 2 method

### Description

This function arranges all possible change-points in the mean of the input vector in the order of importance, via the Wild Binary Segmentation 2 method.

### Usage

```sol.wbs2(x, M = 1000, systematic.intervals = TRUE)
```

### Arguments

 `x` A numeric vector containing the data to be processed. `M` The maximum number of data sub-samples drawn at each recursive stage of the algorithm. The default is `M = 1000`. Setting `M = 0` executes the standard binary segmentation. `systematic.intervals` Whether data sub-intervals for CUSUM computation are drawn systematically (TRUE; start- and end-points taken from an approximately equispaced grid) or randomly (FALSE; obtained uniformly with replacement). The default is TRUE.

### Details

The Wild Binary Segmentation 2 algorithm is described in "Detecting possibly frequent change-points: Wild Binary Segmentation 2 and steepest-drop model selection", P. Fryzlewicz (2020), Journal of the Korean Statistical Society, 49, 1027-1070.

### Value

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

 `solutions.nested` `TRUE`, i.e., the change-point outputs are nested

fmax

 `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` `M` Input parameter `M` `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 "wbs2" here

### References

P. Fryzlewicz (2020). Detecting possibly frequent change-points: Wild Binary Segmentation 2 and steepest-drop model selection. Journal of the Korean Statistical Society, 49, 1027-1070.

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