sol.wbs {breakfast} R Documentation

## Solution path generation via the Wild Binary Segmentation 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 (WBS) method.

### Usage

```sol.wbs(x, M = 10000, systematic.intervals = TRUE, seed = NULL)
```

### Arguments

 `x` A numeric vector containing the data to be processed `M` The maximum number of all data sub-samples at the beginning of the algorithm. The default is `M = 10000` `systematic.intervals` When drawing the sub-intervals, whether to use a systematic (and fixed) or random scheme. The default is `systematic.intervals = TRUE` `seed` If a random scheme is used, a random seed can be provided so that every time the same sets of random sub-intervals would be drawn. The default is `seed = NULL`, which means that this option is not taken

### Details

The Wild Binary Segmentation algorithm is described in "Wild binary segmentation for multiple change-point detection", P. Fryzlewicz (2014), The Annals of Statistics, 42: 2243–2281.

### 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` `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 "wbs" here

### References

P. Fryzlewicz (2014). Wild binary segmentation for multiple change-point detection. The Annals of Statistics, 42(6), 2243–2281.

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