# 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

 ```1 2``` ```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

 ```1 2 3 4``` ```set.seed(1234) x = rbinom(100,1,.5) y = rbinom(100,1,.5) jaccard.test.mca(x,y,accuracy = 1e-05) ```

### Example output

```\$statistics
 -0.02460145

\$pvalue
 0.58527

\$expectation
 0.3179348

\$accuracy
 9.995723e-06

\$error.type
 "average"
```

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