sol.not {breakfast} | R Documentation |
This function arranges all possible change-points in the mean of the input vector in the order of importance, via the Narrowest-Over-Threshold (NOT) method.
sol.not(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 Narrowest-Over-Threshold method and its algorithm is described in "Narrowest-over-threshold detection of multiple change points and change-point-like features", R. Baranowski, Y. Chen and P. Fryzlewicz (2019), Journal of Royal Statistical Society: Series B, 81(3), 649–672.
An S3 object of class cptpath
, which contains the following fields:
solutions.nested |
|
solution.path |
Empty list |
solution.set |
Locations of possible change-points in the mean of |
solution.set.th |
A list that contains threshold levels corresponding to the detections in |
x |
Input vector |
M |
Input parameter |
cands |
Matrix of dimensions length( |
method |
The method used, which has value "not" here |
R. Baranowski, Y. Chen & P. Fryzlewicz (2019). Narrowest-over-threshold detection of multiple change points and change-point-like features. Journal of the Royal Statistical Society: Series B, 81(3), 649–672.
sol.idetect
, sol.tguh
, sol.wbs
, sol.wbs2
r3 <- rnorm(1000) + c(rep(0,300), rep(2,200), rep(-4,300), rep(0,200))
sol.not(r3)