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, p = 0.01)
```

### Arguments

 `x` A numeric vector containing the data to be processed `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` `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 "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))