model.lp {breakfast} R Documentation

## Estimating change-points in the piecewise-constant mean of a noisy data sequence via the localised pruning

### Description

This function estimates the number and locations of change-points in the piecewise-constant mean of a noisy data sequence via the localised pruning method, which performs a Schwarz criterion-based model selection on the given candidate set in a localised way.

### Usage

```model.lp(
cptpath.object,
min.d = 5,
penalty = c("log", "polynomial"),
pen.exp = 1.01,
do.thr = TRUE,
th.const = 0.5
)
```

### Arguments

 `cptpath.object` A solution-path object, returned by a `sol.[name]` routine. Note that the field `cptpath.object\$x` contains the input data sequence. `min.d` A number specifying the minimal spacing between change points; `min.d = 5` by default `penalty` A string specifying the type of penalty term to be used in Schwarz criterion; possible values are: `"log"` Use `penalty = log(length(x))^pen.exp` `"polynomial"` Use `penalty = length(x)^pen.exp` `pen.exp` Exponent for the penalty term (see `penalty`) `do.thr` If `do.thr = TRUE`, mild threshoding on the CUSUM test statistics is performed after internal standardisation step in order to "pre-prune down" the candidates `th.const` A constant multiplied to `sqrt(2*log(length(x)))` to form a mild threshold; if not supplied, a default value (`0.5*` a value suggested in Fryzlewicz (2020) is used, see `th.const` in `model.sdll`

### Details

Further information can be found in Cho and Kirch (2021).

### Value

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

 `solution.path` The solution path method used to obtain `cptpath.object` `model.selection` The model selection method used to return the final change-point estimators object, here its value is `"lp"` `no.of.cpt` The number of estimated change-points in the piecewise-constant mean of the vector `cptpath.object\$x` `cpts` The locations of estimated change-points in the piecewise-constant mean of the vector `cptpath.object\$x`. These are the end-points of the corresponding constant-mean intervals `est` An estimate of the piecewise-constant mean of the vector `cptpath.object\$x`; the values are the sample means of the data (replicated a suitable number of times) between each pair of consecutive detected change-points

### References

H. Cho & C. Kirch (2021) Two-stage data segmentation permitting multiscale change points, heavy tails and dependence. arXiv preprint arXiv:1910.12486.

`sol.idetect`, `sol.idetect_seq`, `sol.not`, `sol.tguh`, `sol.wbs`, `sol.wbs2`, `breakfast`
```f <- rep(rep(c(0, 1), each = 50), 10)