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

In jaccard: Test Similarity Between Binary Data 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)

Example output

$statistics
[1] -0.02460145

$pvalue
[1] 0.58527

$expectation
[1] 0.3179348

$accuracy
[1] 9.995723e-06

$error.type
[1] "average"

jaccard documentation built on May 2, 2019, 12:51 p.m.