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Compute a sample Pearson product-moment correlation distance matrix incrementally.
A sample Pearson product-moment correlation distance matrix is an M-by-M matrix whose elements specified by indices j and k are the sample Pearson product-moment correlation distances between the jth and kth data variables. 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-pcorrdistmatAlternatively,
- 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).
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.
var incrpcorrdistmat = require( '@stdlib/stats-incr-pcorrdistmat' );Returns an accumulator function which incrementally computes a sample Pearson product-moment correlation distance matrix.
// Create an accumulator for computing a 2-dimensional correlation distance matrix:
var accumulator = incrpcorrdistmat( 2 );The out argument may be either the order of the correlation distance matrix or a square 2-dimensional ndarray for storing the correlation distance matrix.
var Float64Array = require( '@stdlib/array-float64' );
var ndarray = require( '@stdlib/ndarray-ctor' );
var buffer = new Float64Array( 4 );
var shape = [ 2, 2 ];
var strides = [ 2, 1 ];
// Create a 2-dimensional output correlation distance matrix:
var dist = ndarray( 'float64', buffer, shape, strides, 0, 'row-major' );
var accumulator = incrpcorrdistmat( dist );When means are known, the function supports providing a 1-dimensional ndarray containing mean values.
var Float64Array = require( '@stdlib/array-float64' );
var ndarray = require( '@stdlib/ndarray-ctor' );
var buffer = new Float64Array( 2 );
var shape = [ 2 ];
var strides = [ 1 ];
var means = ndarray( 'float64', buffer, shape, strides, 0, 'row-major' );
means.set( 0, 3.0 );
means.set( 1, -5.5 );
var accumulator = incrpcorrdistmat( 2, means );If provided a data vector, the accumulator function returns an updated sample Pearson product-moment distance correlation matrix. If not provided a data vector, the accumulator function returns the current sample Pearson product-moment correlation distance matrix.
var Float64Array = require( '@stdlib/array-float64' );
var ndarray = require( '@stdlib/ndarray-ctor' );
var buffer = new Float64Array( 4 );
var shape = [ 2, 2 ];
var strides = [ 2, 1 ];
var dist = ndarray( 'float64', buffer, shape, strides, 0, 'row-major' );
buffer = new Float64Array( 2 );
shape = [ 2 ];
strides = [ 1 ];
var vec = ndarray( 'float64', buffer, shape, strides, 0, 'row-major' );
var accumulator = incrpcorrdistmat( dist );
vec.set( 0, 2.0 );
vec.set( 1, 1.0 );
var out = accumulator( vec );
// returns <ndarray>
var bool = ( out === dist );
// returns true
vec.set( 0, 1.0 );
vec.set( 1, -5.0 );
out = accumulator( vec );
// returns <ndarray>
vec.set( 0, 3.0 );
vec.set( 1, 3.14 );
out = accumulator( vec );
// returns <ndarray>
out = accumulator();
// returns <ndarray>- Due to limitations inherent in representing numeric values using floating-point format (i.e., the inability to represent numeric values with infinite precision), the correlation distance between perfectly correlated random variables may not be
0or2. In fact, the 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 ndarray = require( '@stdlib/ndarray-ctor' );
var Float64Array = require( '@stdlib/array-float64' );
var incrpcorrdistmat = require( '@stdlib/stats-incr-pcorrdistmat' );
var dist;
var dxy;
var dyx;
var dx;
var dy;
var i;
// Initialize an accumulator for a 2-dimensional correlation distance matrix:
var accumulator = incrpcorrdistmat( 2 );
// Create a 1-dimensional data vector:
var buffer = new Float64Array( 2 );
var shape = [ 2 ];
var strides = [ 1 ];
var vec = ndarray( 'float64', buffer, shape, strides, 0, 'row-major' );
// For each simulated data vector, update the sample correlation distance matrix...
for ( i = 0; i < 100; i++ ) {
vec.set( 0, randu()*100.0 );
vec.set( 1, randu()*100.0 );
dist = accumulator( vec );
dx = dist.get( 0, 0 ).toFixed( 4 );
dy = dist.get( 1, 1 ).toFixed( 4 );
dxy = dist.get( 0, 1 ).toFixed( 4 );
dyx = dist.get( 1, 0 ).toFixed( 4 );
console.log( '[ %d, %d\n %d, %d ]', dx, dxy, dyx, dy );
}@stdlib/stats-incr/pcorrdist: compute a sample Pearson product-moment correlation distance.@stdlib/stats-incr/pcorrmat: compute a sample Pearson product-moment correlation matrix incrementally.
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.
Copyright © 2016-2024. The Stdlib Authors.
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