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Compute a moving sample Pearson product-moment correlation distance incrementally.
The sample Pearson product-moment correlation distance is defined as
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].
npm install @stdlib/stats-incr-mpcorrdistAlternatively,
- To load the package in a website via a
scripttag without installation and bundlers, use the ES Module available on theesmbranch (see README). - If you are using Deno, visit the
denobranch (see README for usage intructions). - For use in Observable, or in browser/node environments, use the Universal Module Definition (UMD) build available on the
umdbranch (see README).
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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.
var incrmpcorrdist = require( '@stdlib/stats-incr-mpcorrdist' );Returns an accumulator function which incrementally computes a moving sample Pearson product-moment correlation distance. The window parameter defines the number of values over which to compute the moving sample Pearson product-moment correlation distance.
var accumulator = incrmpcorrdist( 3 );If means are already known, provide mx and my arguments.
var accumulator = incrmpcorrdist( 3, 5.0, -3.14 );If provided input values x and y, the accumulator function returns an updated sample Pearson product-moment correlation distance. If not provided input values x and y, the accumulator function returns the current sample Pearson product-moment correlation distance.
var accumulator = incrmpcorrdist( 3 );
var r = accumulator();
// returns null
// Fill the window...
r = accumulator( 2.0, 1.0 ); // [(2.0, 1.0)]
// returns 1.0
r = accumulator( -5.0, 3.14 ); // [(2.0, 1.0), (-5.0, 3.14)]
// returns ~2.0
r = accumulator( 3.0, -1.0 ); // [(2.0, 1.0), (-5.0, 3.14), (3.0, -1.0)]
// returns ~1.925
// Window begins sliding...
r = accumulator( 5.0, -9.5 ); // [(-5.0, 3.14), (3.0, -1.0), (5.0, -9.5)]
// returns ~1.863
r = accumulator( -5.0, 1.5 ); // [(3.0, -1.0), (5.0, -9.5), (-5.0, 1.5)]
// returns ~1.803
r = accumulator();
// returns ~1.803- Input values are not type checked. If provided
NaNor a value which, when used in computations, results inNaN, the accumulated value isNaNfor at leastW-1future invocations. If non-numeric inputs are possible, you are advised to type check and handle accordingly before passing the value to the accumulator function. - As
W(x,y) pairs are needed to fill the window buffer, the firstW-1returned values are calculated from smaller sample sizes. Until the window is full, each returned value is calculated from all provided values. - Due to limitations inherent in representing numeric values using floating-point format (i.e., the inability to represent numeric values with infinite precision), the sample correlation distance between perfectly correlated random variables may not be
0or2. In fact, the sample correlation distance is not guaranteed to be strictly on the interval[0,2]. Any computed distance should, however, be within floating-point roundoff error.
var randu = require( '@stdlib/random-base-randu' );
var incrmpcorrdist = require( '@stdlib/stats-incr-mpcorrdist' );
var accumulator;
var x;
var y;
var i;
// Initialize an accumulator:
accumulator = incrmpcorrdist( 5 );
// For each simulated datum, update the moving sample correlation distance...
for ( i = 0; i < 100; i++ ) {
x = randu() * 100.0;
y = randu() * 100.0;
accumulator( x, y );
}
console.log( accumulator() );@stdlib/stats-incr/mpcorr: compute a moving sample Pearson product-moment correlation coefficient incrementally.@stdlib/stats-incr/pcorrdist: compute a sample Pearson product-moment correlation distance.
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.
See LICENSE.
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