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incrpcorrdist

Compute a sample Pearson product-moment correlation distance incrementally.

The sample Pearson product-moment correlation distance is defined as

$$d_{x,y} = 1 - r_{x,y} = 1 - \frac{\mathop{\mathrm{cov_n(x,y)}}}{\sigma_x \sigma_y}$$

where r is the sample Pearson product-moment correlation coefficient, cov(x,y) is the sample covariance, and σ corresponds to the sample standard deviation. As r resides on the interval [-1,1], d resides on the interval [0,2].

Installation

npm install @stdlib/stats-incr-pcorrdist

Alternatively,

• To load the package in a website via a script tag without installation and bundlers, use the ES Module available on the esm branch (see README).
• If you are using Deno, visit the deno branch (see README for usage intructions).
• For use in Observable, or in browser/node environments, use the Universal Module Definition (UMD) build available on the umd branch (see README).

The branches.md file summarizes the available branches and displays a diagram illustrating their relationships.

To view installation and usage instructions specific to each branch build, be sure to explicitly navigate to the respective README files on each branch, as linked to above.

Usage

var incrpcorrdist = require( '@stdlib/stats-incr-pcorrdist' );

incrpcorrdist( [mx, my] )

Returns an accumulator function which incrementally computes a sample Pearson product-moment correlation distance.

var accumulator = incrpcorrdist();

If the means are already known, provide mx and my arguments.

var accumulator = incrpcorrdist( 3.0, -5.5 );

accumulator( [x, y] )

If provided input value x and y, the accumulator function returns an updated sample correlation coefficient. If not provided input values x and y, the accumulator function returns the current sample correlation coefficient.

var accumulator = incrpcorrdist();

var d = accumulator( 2.0, 1.0 );
// returns 1.0

d = accumulator( 1.0, -5.0 );
// returns 0.0

d = accumulator( 3.0, 3.14 );
// returns ~0.035

d = accumulator();
// returns ~0.035

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 all 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.

Examples

var randu = require( '@stdlib/random-base-randu' );
var incrpcorrdist = require( '@stdlib/stats-incr-pcorrdist' );

var accumulator;
var x;
var y;
var i;

// Initialize an accumulator:
accumulator = incrpcorrdist();

// For each simulated datum, update the sample correlation distance...
for ( i = 0; i < 100; i++ ) {
    x = randu() * 100.0;
    y = randu() * 100.0;
    accumulator( x, y );
}
console.log( accumulator() );

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See Also

• @stdlib/stats-incr/covariance: compute an unbiased sample covariance incrementally.
• @stdlib/stats-incr/pcorr: compute a sample Pearson product-moment correlation coefficient.
• @stdlib/stats-incr/summary: compute a statistical summary incrementally.

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Notice

This package is part of 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, see the main project repository.

Community

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License

See LICENSE.

Copyright

Copyright © 2016-2024. The Stdlib Authors.