Description Usage Arguments Details Value References See Also Examples

Binarizes real-valued data using the multiscale BASC methods.

1 2 3 4 5 6 |

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

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

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

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

`sigma` |
If |

`na.rm` |
If set to |

The two BASC 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. BASC A calculates these step functions in such a way that each minimizes the Euclidean distance to the initial step function. BASC B obtains step functions from smoothened versions of the input function in a scale-space manner.

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

- 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 `BASCResult`

.

M. Hopfensitz, C. M<c3><bc>ssel, C. Wawra, M. Maucher, M. Kuehl, H. Neumann, and H. A. Kestler. Multiscale Binarization of Gene Expression Data for Reconstructing Boolean Networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics 9(2):487-498, 2012.).

`BinarizationResult`

,
`BASCResult`

1 2 3 4 5 6 7 8 | ```
par(mfrow=c(2,1))
result <- binarize.BASC(iris[,"Petal.Length"], method="A", tau=0.15)
print(result)
plot(result)
result <- binarize.BASC(iris[,"Petal.Length"], method="B", tau=0.15)
print(result)
plot(result)
``` |

Binarize documentation built on May 30, 2017, 8:17 a.m.

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