model.thresh {breakfast} R Documentation

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

### Description

This function estimates the number and locations of change-points in the piecewise-constant mean of a noisy data sequence via thresholding.

### Usage

```model.thresh(
cptpath.object,
th_const = 1.15
)
```

### Arguments

 `cptpath.object` A solution-path object, returned by a `sol.[name]` routine. Note that the field `sols.object\$x` contains the input data sequence. `sigma` An estimate of the standard deviation of the noise in the data `cptpath.object\$x`. Can be a functional of `cptpath.object\$x` or a specific value if known. The default is the Median Absolute Deviation of the vector `diff(cptpath.object\$x)/sqrt(2)`, tuned to the Gaussian distribution. Note that `model.thresh` works particularly well when the noise is i.i.d. Gaussian. `th_const` A positive real number with default value equal to 1. It is used to define the threshold for the detection process.

### 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 `"thresh"` `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

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