model.ic {breakfast} | R Documentation |

## Estimating change-points or change-point-type features in the mean of a noisy data sequence via the strengthened Schwarz information criterion

### Description

This function estimates the number and locations of change-points or change-point-type features in the mean of a noisy data sequence via the sSIC (strengthened Schwarz information criterion) method.

### Usage

```
model.ic(cptpath.object, alpha = 1.01, q.max = NULL)
```

### Arguments

`cptpath.object` |
A solution-path object, returned by a |

`alpha` |
The parameter associated with the sSIC. The default value is 1.01. Note that the SIC is recovered when alpha = 1. |

`q.max` |
The maximum number of features allowed. If nothing or |

### Details

The model selection method for algorithms that produce nested solution path is described in "Wild binary segmentation for multiple change-point detection", P. Fryzlewicz (2014), The Annals of Statitics, 42: 2243–2281. The corresponding description for those that produce non-nested solution set can be found in "Narrowest-over-threshold detection of multiple change points and change-point-like features", R. Baranowski, Y. Chen and P. Fryzlewicz (2019), Journal of Royal Statistical Society: Series B, 81(3), 649–672.

### Value

An S3 object of class `cptmodel`

, which contains the following fields:

`solution.path` |
The solution path method used to obtain |

`type` |
The model type used, inherited from the given |

`model.selection` |
The model selection method used to return the final change-point or change-point-type feature estimators object, here its value is |

`no.of.cpt` |
The number of estimated features in the mean of the vector |

`cpts` |
The locations of estimated features in the mean of the vector |

`est` |
An estimate of the mean of the vector |

### References

P. Fryzlewicz (2014). Wild binary segmentation for multiple change-point detection. *The Annals of Statistics*, 42(6), 2243–2281.

R. Baranowski, Y. Chen & P. Fryzlewicz (2019). Narrowest-over-threshold detection of multiple change points and change-point-like features. *Journal of the Royal Statistical Society: Series B*, 81(3), 649–672.

### See Also

`sol.idetect`

, `sol.not`

, `sol.tguh`

, `sol.wbs`

, `sol.wbs2`

, `breakfast`

### Examples

```
x <- c(rep(0, 100), rep(1, 100), rep(0, 100)) + rnorm(300)
model.ic(sol.wbs(x))
model.ic(sol.not(x))
```

*breakfast*version 2.4 Index]