Description Usage Arguments Value Examples

View source: R/detectSliding.R

Change point detection using PCA and sliding method

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 |

`Y` |
data: Y = length*dim |

`wd` |
window size for sliding averages |

`L` |
the number of factors |

`Del` |
Delta away from the boundary restriction |

`q` |
methods in calculating long-run variance of the test statistic. Defaul is "andrew" "fixed" = length^1/3 or user specify the length |

`alpha` |
significance level of the test |

`nboot` |
the number of bootstrap sample for pvalue. Defauls is 199. |

`n.cl` |
number of cores in parallel computing. The default is (machine cores - 1) |

`bsize` |
block size for the Block Wild Boostrapping. Default is log(length), "sqrt" uses sqrt(length), "adaptive" deterines block size usign data dependent selection of Andrews |

`bootTF` |
determine whether the threshold is calculated from bootstrap or asymptotic |

`scaleTF` |
scale the variance into 1 |

`diagTF` |
include diagonal term of covariance matrix or not |

`plotTF` |
Draw plot to see test statistic and threshould |

**sW** The test statistic

**L** The number of factors used in the procedure

**q** The estimated vecorized autocovariance on each regime.

**crit** The critical vlaue to identify change point

**bsize** The block size of the bootstrap

**diagTF** If TRUE, the diagonal entry of covariance matrix is used in detecting connectivity changes.

**bootTF** If TRUE, boostrap is used to find critical value

**scaleTF** If TRUE, the multivariate signal is studentized to have zero mean and unit variance.

1 | ```
out4 = detectSliding(changesim, wd=40, L=2, n.cl=1)
``` |

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