PAC: PAC score

Description Usage Arguments Details Value See Author(s) Examples

View source: R/stat.R

Description

PAC score

Usage

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PAC(consensus_mat, x1 = seq(0.1, 0.3, by = 0.02),
    x2 = seq(0.7, 0.9, by = 0.02), trim = 0.2)

Arguments

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 x1 and x2 are more than 10.

Details

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.

Value

A single numeric score.

See

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

Author(s)

Zuguang Gu <[email protected]>

Examples

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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))

jokergoo/cola documentation built on Nov. 13, 2018, 1:22 p.m.