sol.tguh {breakfast} R Documentation

## 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.

sol.idetect, sol.idetect_seq, sol.not, sol.wbs, sol.wbs2
r3 <- rnorm(1000) + c(rep(0,300), rep(2,200), rep(-4,300), rep(0,200))