# compute-covariance

Computes the covariance between one or more numeric arrays.

## Stats

StarsIssuesVersionUpdatedCreatedSize
compute-covariance
1.0.19 years ago9 years ago

Covariance
!NPM versionnpm-imagenpm-url !Build Statustravis-imagetravis-url !Coverage Statuscoveralls-imagecoveralls-url !Dependenciesdependencies-imagedependencies-url
Computes the covariance between one or more numeric arrays.

## Installation

``\$ npm install compute-covariance``

For use in the browser, use browserify.

## Usage

To use the module,
``var cov = require( 'compute-covariance' );``

#### cov( arr1, arr2,...,opts )

Computes the covariance between one or more numeric arrays.
``````var x = [ 1, 2, 3, 4, 5 ],
y = [ 5, 4, 3, 2, 1 ];

var mat = cov( x, y );
// returns [[2.5,-2.5],[-2.5,2.5]]``````

Note: for univariate input, the returned covariance matrix contains a single element equal to the variance.
If the number of arrays is dynamic, you may want the flexibility to compute the covariance of an arbitrary `array` collection. To this end, `cov` also accepts an `array` of `arrays`.
``````var mat = cov( [x,y] );
// returns [[2.5,-2.5],[-2.5,2.5]]``````

By default, each element of the covariance matrix is an unbiased covariance estimate. Hence, the covariance matrix is the sample covariance matrix. For those cases where you want a biased estimate (i.e., population statistics), set the `bias` option to `true`.
``````var mat = cov( x, y, {'bias': true});
// returns [[2,-2],[-2,2]]``````

## Examples

``````var cov = require( 'compute-covariance' );

// Simulate some data...
var N = 100,
x = new Array( N ),
y = new Array( N ),
z = new Array( N );

for ( var i = 0; i < N; i++ ) {
x[ i ] = Math.round( Math.random()*100 );
y[ i ] = Math.round( Math.random()*100 );
z[ i ] = 100 - x[ i ];
}
var mat = cov( x, y, z );
console.log( mat );``````

To run the example code from the top-level application directory,
``\$ node ./examples/index.js``

## Tests

### Unit

Unit tests use the Mocha test framework with Chai assertions. To run the tests, execute the following command in the top-level application directory:
``\$ make test``

All new feature development should have corresponding unit tests to validate correct functionality.

### Test Coverage

This repository uses Istanbul as its code coverage tool. To generate a test coverage report, execute the following command in the top-level application directory:
``\$ make test-cov``

Istanbul creates a `./reports/coverage` directory. To access an HTML version of the report,
``\$ make view-cov``