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, type = "const", M = 1000, systematic.intervals = TRUE, seed = NULL)


### Arguments

 x A numeric vector containing the data to be processed. type The model type considered. type = "const" means piecewise-constant; this is the only type currently supported in sol.wbs2 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. 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 set

### 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 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 Input parameter type 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))