sol.wcm {breakfast}  R Documentation 
Solution path generation via the Wild Contrast Maximisation 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.wcm(
x,
type = "const",
M = 100,
min.d = NULL,
Q = floor(log(length(x))^1.9),
max.iter = 5
)
Arguments
x 
A numeric vector containing the data to be processed. 
type 
The type of changepoint models fitted to the data; currently the class of piecewise constant signals ( 
M 
The maximum number of data subsamples drawn at each recursive stage of the algorithm. The default is 
min.d 
The minimum distance between candidate changepoint estimators;
if 
Q 
The maximum number of allowable changepoints.
The default is 
max.iter 
The maximum number of candidate changepoint models considered; if a model with the number of changepoint estimators exceeding 
Details
The Wild Contrast Maximisation (WCM) algorithm generates a nested sequence of candidate models by identifying large gaps in the solution path generated by WBS2, which aids the model selection step in the presence of large random fluctuations due to serial dependence. See Cho and Fryzlewicz (2023) for further details.
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 
A list of candidate changepoint models. Each model contains possible changepoints in the mean of 
x 
Input vector 
type 
The type of the changepoint model considered, which has value "const" here 
M 
Input parameter 
cands 
Matrix of dimensions 
method 
The method used, which has value "wcm" here 
References
H. Cho & P. Fryzlewicz (2024) Multiple change point detection under serial dependence: Wild contrast maximisation and gappy Schwarz algorithm. Journal of Time Series Analysis, 45(3): 479–494.
See Also
Examples
set.seed(111)
f < rep(c(0, 5, 2, 8, 1, 2), c(100, 200, 200, 50, 200, 250))
x < f + arima.sim(list(ar = c(.75, .5), ma = c(.8, .7, .6, .5, .4, .3)), n = length(f), sd = 1)
sol.wcm(x)$solution.set