sol.wbs {breakfast}

Solution path generation via the Wild Binary Segmentation method

Description

This function arranges all possible change-in-mean 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 type = "const" is the only accepted value. This assumes that the mean of the input vector is piecewise-constant.

M

The maximum number of all data sub-samples at the beginning of the algorithm. The default is M = 10000

systematic.intervals

When drawing the sub-intervals, whether to use a systematic (and fixed) or random scheme. The default is systematic.intervals = TRUE

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 seed = NULL, which means that this option is not set

Details

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.

Value

An S3 object of class cptpath, which contains the following fields:

solutions.nested

TRUE, i.e., the change-point outputs are nested

solution.path

Locations of possible change-points in the mean of x, arranged in decreasing order of change-point importance

solution.set

Empty list

x

Input vector x

type

The input parameter type

M

Input parameter M

cands

Matrix of dimensions length(x) - 1 by 4. The first two columns are (start, end)-points of the detection intervals of the corresponding possible change-point location in the third column. The fourth column is a measure of strength of the corresponding possible change-point. The order of the rows is the same as the order returned in solution.path

method

The method used, which has value "wbs" here

References

P. Fryzlewicz (2014). Wild binary segmentation for multiple change-point detection. The Annals of Statistics, 42(6), 2243–2281.

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.

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)