sol.wbs {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 (WBS) method.
sol.wbs(x, M = 10000, systematic.intervals = TRUE, seed = NULL)
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
|
systematic.intervals |
When drawing the sub-intervals, 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 sub-intervals would be drawn. The default is |
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.
An S3 object of class cptpath
, which contains the following fields:
solutions.nested |
|
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 "wbs" here |
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))
sol.wbs(r3)