sol.tguh {breakfast}

Solution path generation via the Tail-Greedy Unbalanced Haar method

Description

This function arranges all possible change-points in the mean of the input vector in the order of importance, via the Tail-Greedy Unbalanced Haar method.

Usage

sol.tguh(x, type = "const", p = 0.01)

Arguments

x

A numeric vector containing the data to be processed

type

The model type considered. type = "const" means piecewise-constant; this is the only type currently supported in sol.tguh

p

Specifies the number of region pairs merged in each pass through the data, as the proportion of all remaining region pairs. The default is p = 0.01

Details

The Tail-Greedy Unbalanced Haar decomposition algorithm is described in "Tail-greedy bottom-up data decompositions and fast multiple change-point detection", P. Fryzlewicz (2018), The Annals of Statistics, 46, 3390–3421.

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

Input parameter type

p

Input parameter p

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 "tguh" here

References

P. Fryzlewicz (2018). Tail-greedy bottom-up data decompositions and fast multiple change-point detection. The Annals of Statistics, 46, 3390–3421.

See Also

sol.idetect, sol.idetect_seq, sol.not, sol.wbs, sol.wbs2

Examples

r3 <- rnorm(1000) + c(rep(0,300), rep(2,200), rep(-4,300), rep(0,200))
sol.tguh(r3)