Description Usage Arguments Details Value See Also Examples

Trinarizes real-valued data using the multiscale TASC method.

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`method` |
Chooses the TASC method to use (see details), i.e. either "A" or "B". |

`vect` |
A real-valued vector of data to trinarize. |

`tau` |
This parameter adjusts the sensitivity and the specificity of the statistical testing procedure that rates the quality of the trinarization. Defaults to 0.01. |

`numberOfSamples` |
The number of samples for the bootstrap test. Defaults to 999. |

`sigma` |
If |

`na.rm` |
If set to |

`error` |
Determines which error should be used for the data points between two thresholds, the "mean" error (default) to the thresholds or the "min" error. |

The two TASC methods can be subdivided into three steps:

- Compute a series of step functions:
An initial step function is obtained by rearranging the original time series measurements in increasing order. Then, step functions with fewer discontinuities are calculated. TASC A calculates these step functions in such a way that each minimizes the Euclidean distance to the initial step function. TASC B obtains step functions from smoothened versions of the input function in a scale-space manner.

- Find strongest discontinuities in each step function:
A strong discontinuity is a high jump size (derivative) in combination with a low approximation error. For TASC a pair of strongest discontinuities is determined.

- Estimate location and variation of the strongest discontinuities:
Based on these estimates, data values can be excluded from further analyses.

Returns an object of class `TASCResult`

.

`TrinarizationResult`

,
`TASCResult`

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