jaccard.test.mca: Compute p-value using the Measure Concentration Algorithm

In ncchung/jaccard: Testing similarity between binary datasets using Jaccard/Tanimoto coefficients

Description

Compute statistical significance of Jaccard/Tanimoto similarity coefficients.

Usage

jaccard.test.mca(x, y, px = NULL, py = NULL, accuracy = 1e-05,
  error.type = "average", verbose = TRUE)

Arguments

x	a binary vector (e.g., fingerprint)
y	a binary vector (e.g., fingerprint)
px	probability of successes in x (optional)
py	probability of successes in y (optional)
accuracy	an error bound on approximating a multinomial distribution
error.type	an error type on approximating a multinomial distribution ("average", "upper", "lower")
verbose	whether to print progress messages

Value

jaccard.test.mca returns a list consisting of

statistics	centered Jaccard/Tanimoto similarity coefficient
pvalue	p-value
expectation	expectation

Examples

set.seed(1234)
x = rbinom(100,1,.5)
y = rbinom(100,1,.5)
jaccard.test.mca(x,y,accuracy = 1e-05)

ncchung/jaccard documentation built on Dec. 31, 2019, 8:20 p.m.