sol.wbs {breakfast}  R Documentation 
Solution path generation via the Wild Binary Segmentation method
Description
This function arranges all possible changeinmean features of the input vector in the order of importance, via the Wild Binary Segmentation (WBS) method.
Usage
sol.wbs(x, type = "const", M = 10000, systematic.intervals = TRUE, seed = NULL)
Arguments
x 
A numeric vector containing the data to be processed 
type 
The model type considered. Currently 
M 
The maximum number of all data subsamples at the beginning of the algorithm. The default is

systematic.intervals 
When drawing the subintervals, whether to use a systematic (and fixed) or random scheme. The default is 
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 algorithm is described in "Wild binary segmentation for multiple changepoint 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 

solution.path 
Locations of possible changepoints in the mean of 
solution.set 
Empty list 
x 
Input vector 
type 
The input parameter 
M 
Input parameter 
cands 
Matrix of dimensions length( 
method 
The method used, which has value "wbs" here 
References
P. Fryzlewicz (2014). Wild binary segmentation for multiple changepoint detection. The Annals of Statistics, 42(6), 2243–2281.
R. Baranowski, Y. Chen & P. Fryzlewicz (2019). Narrowestoverthreshold detection of multiple change points and changepointlike features. Journal of the Royal Statistical Society: Series B, 81(3), 649–672.
See Also
sol.idetect
, sol.idetect_seq
, sol.not
, sol.tguh
, sol.wbs2
Examples
r3 < rnorm(1000) + c(rep(0,300), rep(2,200), rep(4,300), rep(0,200))
sol.wbs(r3)