sol.wbs2 {breakfast}  R Documentation 
Solution path generation via the Wild Binary Segmentation 2 method
Description
This function arranges all possible changepoints 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. 
M 
The maximum number of data subsamples drawn at each recursive stage of the algorithm. The default is

systematic.intervals 
Whether data subintervals for CUSUM computation are drawn systematically (TRUE; start and endpoints 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 subintervals would be drawn. The default is 
Details
The Wild Binary Segmentation 2 algorithm is described in "Detecting possibly frequent changepoints: Wild Binary Segmentation 2 and steepestdrop model selection", P. Fryzlewicz (2020), Journal of the Korean Statistical Society, 49, 10271070.
Value
An S3 object of class cptpath
, which contains the following fields:
solutions.nested 

solution.path 
Locations of possible changepoints in the mean of 
solution.set 
Empty list 
x 
Input vector 
type 
Input parameter 
M 
Input parameter 
cands 
Matrix of dimensions length( 
method 
The method used, which has value "wbs2" here 
References
P. Fryzlewicz (2020). Detecting possibly frequent changepoints: Wild Binary Segmentation 2 and steepestdrop model selection. Journal of the Korean Statistical Society, 49, 10271070.
See Also
sol.idetect
, sol.idetect_seq
, sol.not
, sol.tguh
, sol.wbs
Examples
r3 < rnorm(1000) + c(rep(0,300), rep(2,200), rep(4,300), rep(0,200))
sol.wbs2(r3)