@stdlib/stats-incr-mpcorr2
v0.2.3TypeScript
Compute a moving squared sample Pearson product-moment correlation coefficient incrementally.
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Published by The Stdlib Authors
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incrmpcorr2
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> Compute a moving squared sample [Pearson product-moment correlation coefficient][pearson-correlation] incrementally.
The [Pearson product-moment correlation coefficient][pearson-correlation] between random variables X and Y is defined as
where the numerator is the [covariance][covariance] and the denominator is the product of the respective standard deviations.
For a sample of size W, the sample [Pearson product-moment correlation coefficient][pearson-correlation] is defined as
The squared sample [Pearson product-moment correlation coefficient][pearson-correlation] is thus defined as the square of the sample [Pearson product-moment correlation coefficient][pearson-correlation].
Installation
``bash`
npm install @stdlib/stats-incr-mpcorr2
Usage
`javascript`
var incrmpcorr2 = require( '@stdlib/stats-incr-mpcorr2' );
#### incrmpcorr2( window\[, mx, my] )
Returns an accumulator function which incrementally computes a moving squared sample [Pearson product-moment correlation coefficient][pearson-correlation]. The window parameter defines the number of values over which to compute the moving squared sample [Pearson product-moment correlation coefficient][pearson-correlation].
`javascript`
var accumulator = incrmpcorr2( 3 );
If means are already known, provide mx and my arguments.
`javascript`
var accumulator = incrmpcorr2( 3, 5.0, -3.14 );
#### accumulator( \[x, y] )
If provided input values x and y, the accumulator function returns an updated accumulated value. If not provided input values x and y, the accumulator function returns the current accumulated value.
`javascript
var accumulator = incrmpcorr2( 3 );
var r2 = accumulator();
// returns null
// Fill the window...
r2 = accumulator( 2.0, 1.0 ); // [(2.0, 1.0)]
// returns 0.0
r2 = accumulator( -5.0, 3.14 ); // [(2.0, 1.0), (-5.0, 3.14)]
// returns ~1.0
r2 = accumulator( 3.0, -1.0 ); // [(2.0, 1.0), (-5.0, 3.14), (3.0, -1.0)]
// returns ~0.86
// Window begins sliding...
r2 = accumulator( 5.0, -9.5 ); // [(-5.0, 3.14), (3.0, -1.0), (5.0, -9.5)]
// returns ~0.74
r2 = accumulator( -5.0, 1.5 ); // [(3.0, -1.0), (5.0, -9.5), (-5.0, 1.5)]
// returns ~0.64
r2 = accumulator();
// returns ~0.64
`
Notes
- Input values are not type checked. If provided NaN or a value which, when used in computations, results in NaN, the accumulated value is NaN for at least W-1 future invocations. If non-numeric inputs are possible, you are advised to type check and handle accordingly before passing the value to the accumulator function.W
- As (x,y) pairs are needed to fill the window buffer, the first W-1 returned values are calculated from smaller sample sizes. Until the window is full, each returned value is calculated from all provided values.
- In comparison to the sample [Pearson product-moment correlation coefficient][pearson-correlation], the squared sample [Pearson product-moment correlation coefficient][pearson-correlation] is useful for emphasizing strong correlations.
Examples
`javascript
var randu = require( '@stdlib/random-base-randu' );
var incrmpcorr2 = require( '@stdlib/stats-incr-mpcorr2' );
var accumulator;
var x;
var y;
var i;
// Initialize an accumulator:
accumulator = incrmpcorr2( 5 );
// For each simulated datum, update the moving squared sample correlation coefficient...
for ( i = 0; i < 100; i++ ) {
x = randu() * 100.0;
y = randu() * 100.0;
accumulator( x, y );
}
console.log( accumulator() );
`
*
See Also
- [@stdlib/stats-incr/mapcorr][@stdlib/stats/incr/mapcorr]: compute a moving sample absolute Pearson product-moment correlation coefficient incrementally.
- [@stdlib/stats-incr/mpcorr][@stdlib/stats/incr/mpcorr]: compute a moving sample Pearson product-moment correlation coefficient incrementally.
- [@stdlib/stats-incr/pcorr2][@stdlib/stats/incr/pcorr2]: compute a squared sample Pearson product-moment correlation coefficient.
*
Notice
This package is part of [stdlib][stdlib], a standard library for JavaScript and Node.js, with an emphasis on numerical and scientific computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.
For more information on the project, filing bug reports and feature requests, and guidance on how to develop [stdlib][stdlib], see the main project [repository][stdlib].
#### Community
[![Chat][chat-image]][chat-url]
---
License
See [LICENSE][stdlib-license].
Copyright
Copyright © 2016-2026. The Stdlib [Authors][stdlib-authors].
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[umd]: https://github.com/umdjs/umd
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[deno-url]: https://github.com/stdlib-js/stats-incr-mpcorr2/tree/deno
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[umd-url]: https://github.com/stdlib-js/stats-incr-mpcorr2/tree/umd
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[stdlib-license]: https://raw.githubusercontent.com/stdlib-js/stats-incr-mpcorr2/main/LICENSE
[pearson-correlation]: https://en.wikipedia.org/wiki/Pearson_correlation_coefficient
[covariance]: https://en.wikipedia.org/wiki/Covariance
[@stdlib/stats/incr/mapcorr]: https://www.npmjs.com/package/@stdlib/stats-incr-mapcorr
[@stdlib/stats/incr/mpcorr]: https://www.npmjs.com/package/@stdlib/stats-incr-mpcorr
[@stdlib/stats/incr/pcorr2]: https://www.npmjs.com/package/@stdlib/stats-incr-pcorr2