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Type: Package
Title: Test Similarity Between Binary Data using Jaccard/Tanimoto Coefficients
Version: 0.1.0
Date: 2018-06-06
Author: Neo Christopher Chung <nchchung@gmail.com>, Błażej Miasojedow <bmiasojedow@gmail.com>, Michał Startek <M.Startek@mimuw.edu.pl>, Anna Gambin <aniag@mimuw.edu.pl>
Maintainer: Neo Christopher Chung <nchchung@gmail.com>
Description: Calculate statistical significance of Jaccard/Tanimoto similarity coefficients for binary data.
License: GPL-2
Encoding: UTF-8
LazyData: true
Imports: Rcpp (≥ 0.12.6), qvalue, dplyr, magrittr
LinkingTo: Rcpp
NeedsCompilation: yes
SystemRequirements: C++11
RoxygenNote: 6.0.1
Packaged: 2018-06-10 01:52:22 UTC; nc
Repository: CRAN
Date/Publication: 2018-06-14 17:53:00 UTC

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 returns a Jaccard/Tanimoto coefficient.

Examples

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

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.ev returns an expected value.

Examples

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

Compute p-value using an extreme value distribution

Description

Rahman et al. (2014) proposes a method to compute a p-value of a Jaccard/Tanimoto coefficient using an extreme value distribution. Their paper provides the following description: The mean (mu) and s.d. (sigma) of the similarity scores are used to define the z score, z = (Tw - mu)/sigma. 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-z*pi/sqrt(6) - G'(1)), where the Euler=Mascheroni constant G'(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

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")

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)

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)

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)

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)

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). For fine controls, use "jaccard.test".

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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.