Package 'jaccard'

Compute a Jaccard/Tanimoto similarity coefficient

Description

Compute a Jaccard/Tanimoto similarity coefficient

Usage

jaccard(x, y, center = FALSE, px = NULL, py = NULL)

Arguments

x	a binary vector (e.g., fingerprint)
y	a binary vector (e.g., fingerprint)
center	whether to center the Jaccard/Tanimoto coefficient by its expectation
px	probability of successes in x (optional)
py	probability of successes in y (optional)

Value

jaccard.test.bootstrap returns an expected value.

Examples

set.seed(1234)
x = rbinom(100,1,.5)
y = rbinom(100,1,.5)
jaccard(x,y)

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Compute an expected Jaccard/Tanimoto similarity coefficient under independence

Description

Compute an expected Jaccard/Tanimoto similarity coefficient under independence

Usage

jaccard.ev(x, y, px = NULL, py = NULL)

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)

Value

jaccard.test.bootstrap returns an expected value.

Examples

set.seed(1234)
x = rbinom(100,1,.5)
y = rbinom(100,1,.5)
jaccard.ev(x,y)

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Compute p-value using the EC-BLAST method

Description

In the EC-BLAST paper, Rahman et al. (2014) provide the following description: The mean (μ) and s.d. (σ) of the similarity scores are used to define the z score, z = (Tw – μ)/σ. For the purpose of calculating the P value, only hits with T > 0 are considered. The P value w is derived from the z score using an extreme value distribution P = 1 – exp(−e−zπ/√(6) − Γ′ (1)), where the Euler-Mascheroni constant Γ′ (1) ≈ 0.577215665.

Usage

jaccard.rahman(j)

Arguments

j	a numeric vector of observed Jaccard coefficients (uncentered)

Value

jaccard.rahman returns a numeric vector of p-values

References

Rahman, Cuesta, Furnham, Holliday, and Thornton (2014) EC-BLAST: a tool to automatically search and compare enzyme reactions. Nature Methods, 11(2) http://www.nature.com/nmeth/journal/v11/n2/full/nmeth.2803.html

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Test for Jaccard/Tanimoto similarity coefficients

Description

Compute statistical significance of Jaccard/Tanimoto similarity coefficients between binary vectors, using four different methods.

Usage

jaccard.test(x, y, method = "mca", px = NULL, py = NULL, verbose = TRUE,
  ...)

Arguments

x	a binary vector (e.g., fingerprint)
y	a binary vector (e.g., fingerprint)
method	a method to compute a p-value ("mca", "bootstrap", "asymptotic", or "exact")
px	probability of successes in x (optional)
py	probability of successes in y (optional)
verbose	whether to print progress messages
...	optional arguments for specific computational methods

Details

There exist four methods to compute p-values of Jaccard/Tanimoto similarity coefficients: mca, bootstrap, asymptotic, and exact. This is simply a wrapper function for corresponding four functions in this package: jaccard.test.mca, jaccard.test.bootstrap, jaccard.test.asymptotic, and jaccard.test.exact.

We recommand using either mca or bootstrap methods, since the exact solution is slow for a moderately large vector and asymptotic approximation may be inaccurate depending on the input vector size. The bootstrap method uses resampling with replacement binary vectors to compute a p-value (see optional arguments). The mca method uses the measure concentration algorithm that estimates the multinomial distribution with a known error bound (specified by an optional argument accuracy).

Value

jaccard.test returns a list mainly consisting of

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

Optional arguments for method="bootstrap"

fix

whether to fix (i.e., not resample) x and/or y

B

a total bootstrap iteration

seed

a seed for a random number generator

Optional arguments for method="mca"

accuracy

an error bound on approximating a multinomial distribution

error.type

an error type on approximating a multinomial distribution ("average", "upper", "lower")

seed

a seed for the random number generator.

See Also

jaccard.test.bootstrap jaccard.test.mca jaccard.test.exact jaccard.test.asymptotic

Examples

set.seed(1234)
x = rbinom(100,1,.5)
y = rbinom(100,1,.5)
jaccard.test(x,y,method="bootstrap")
jaccard.test(x,y,method="mca")
jaccard.test(x,y,method="exact")
jaccard.test(x,y,method="asymptotic")

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Compute p-value using an asymptotic approximation

Description

Compute statistical significance of Jaccard/Tanimoto similarity coefficients.

Usage

jaccard.test.asymptotic(x, y, px = NULL, py = NULL, 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)
verbose	whether to print progress messages

Value

jaccard.test.asymptotic 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.asymptotic(x,y)

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Compute p-value using the bootstrap procedure

Description

Compute statistical significance of Jaccard/Tanimoto similarity coefficients.

Usage

jaccard.test.bootstrap(x, y, px = NULL, py = NULL, verbose = TRUE,
  fix = "x", B = 1000, seed = NULL)

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)
verbose	whether to print progress messages
fix	whether to fix (i.e., not resample) x and/or y
B	a total bootstrap iteration
seed	a seed for a random number generator

Value

jaccard.test.bootstrap 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.bootstrap(x,y,B=500)

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Compute p-value using the exact solution

Description

Compute statistical significance of Jaccard/Tanimoto similarity coefficients.

Usage

jaccard.test.exact(x, y, px = NULL, py = NULL, 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)
verbose	whether to print progress messages

Value

jaccard.test.exact 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.exact(x,y)

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Compute p-value using the Measure Concentration Algorithm

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)

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Pair-wise tests for Jaccard/Tanimoto similarity coefficients

Description

Given a data matrix, it computes pair-wise Jaccard/Tanimoto similarity coefficients and p-values among rows (variables). Only for testing due to its use of a for-loop.

Usage

jaccard.test.pairwise(dat, method = "mca", verbose = TRUE,
  compute.qvalue = TRUE, ...)

Arguments

dat	a data matrix
method	a method to compute a p-value ("mca", "bootstrap", "asymptotic", or "exact")
verbose	whether to print progress messages
compute.qvalue	whether to compute q-values
...	optional arguments for specific computational methods

Value

jaccard.test.pairwise returns a list of matrices

statistics	Jaccard/Tanimoto similarity coefficients
pvalues	p-values
qvalues	q-values

See Also

jaccard.test

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Launch an interactive Shiny app on a local network

Description

Launch an interactive Shiny app on a local network

Usage

runJaccardApp()

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