sol.wbs2 {breakfast} | R Documentation |
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.
sol.wbs2(x, M = 1000, systematic.intervals = TRUE)
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
|
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. |
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.
An S3 object of class cptpath
, which contains the following fields:
solutions.nested |
|
fmax
solution.path |
Locations of possible change-points in the mean of |
solution.set |
Empty list |
x |
Input vector |
M |
Input parameter |
cands |
Matrix of dimensions length( |
method |
The method used, which has value "wbs2" here |
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))
sol.wbs2(r3)