Compute a Jaccard/Tanimoto similarity coefficient

Description

Compute a Jaccard/Tanimoto similarity coefficient

Usage

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

Arguments

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)

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

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)

Compute p-value using the EC-BLAST method

Description

In the EC-BLAST paper, Rahman et al. (2014) provide 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\left(-e^{-z\pi/\sqrt{6} - \Gamma'(1)}\right), where the Euler-Mascheroni constant \Gamma'(1) \approx 0.577215665.

Usage

jaccard.rahman(j)

Arguments

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) https://www.nature.com/articles/nmeth.2803

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

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

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

Value

jaccard.test.asymptotic returns a list consisting of

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

Value

jaccard.test.bootstrap returns a list consisting of

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

Value

jaccard.test.exact returns a list consisting of

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

Value

jaccard.test.mca returns a list consisting of

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). Only for testing due to its use of a for-loop.

Usage

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

Arguments

Value

jaccard.test.pairwise returns a list of matrices

See Also

jaccard.test

Launch an interactive Shiny app on a local network

Description

Launch an interactive Shiny app on a local network

Usage

runJaccardApp()