Description Usage Arguments Details Value See Author(s) Examples

PAC score

1 2 |

`consensus_mat` |
a consensus matrix. |

`x1` |
lower bound to define "ambiguous clustering". The value can be a vector. |

`x2` |
upper bound to define "ambihuous clustering". The value can be a vector. |

`trim` |
percent of extreme values to trim if combinations of |

This a variant of the orignial PAC (proportion of ambiguous clustering) method.

For each `x_1i`

in `x1`

and `x_2j`

in `x2`

, `PAC_k = F(x_2j) - F(x_1i)`

where `F(x)`

is the ecdf of the consensus matrix (the lower triangle matrix without diagnals).
The final PAC is the mean of all `PAC_k`

by removing top `trim/2`

percent and bottom `trim/2`

percent of all values.

A single numeric score.

See https://www.nature.com/articles/srep06207 for explanation of PAC score.

Zuguang Gu <[email protected]>

1 2 3 4 5 6 | ```
data(cola_rl)
PAC(get_consensus(cola_rl[1, 1], k = 2))
PAC(get_consensus(cola_rl[1, 1], k = 3))
PAC(get_consensus(cola_rl[1, 1], k = 4))
PAC(get_consensus(cola_rl[1, 1], k = 5))
PAC(get_consensus(cola_rl[1, 1], k = 6))
``` |

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