@stdlib/stats-incr-pcorrmat

Compute a sample Pearson product-moment correlation matrix incrementally.

https://github.com/stdlib-js/stats-incr-pcorrmat

Science Score: 44.0%

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anticorrelation correlated correlation covariance dispersion javascript math mathematics matrix node node-js nodejs sample-covariance standard-deviation statistics stats stdlib unbiased var variance

Last synced: 6 months ago · JSON representation ·

Repository

Compute a sample Pearson product-moment correlation matrix incrementally.

Basic Info

Host: GitHub
Owner: stdlib-js
License: apache-2.0
Language: JavaScript
Default Branch: main
Homepage: https://github.com/stdlib-js/stdlib
Size: 2.35 MB

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anticorrelation correlated correlation covariance dispersion javascript math mathematics matrix node node-js nodejs sample-covariance standard-deviation statistics stats stdlib unbiased var variance

Created over 4 years ago · Last pushed 11 months ago

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Readme Changelog Contributing License Code of conduct Citation Security

We believe in a future in which the web is a preferred environment for numerical computation. To help realize this future, we've built stdlib. stdlib is a standard library, with an emphasis on numerical and scientific computation, written in JavaScript (and C) for execution in browsers and in Node.js.

The library is fully decomposable, being architected in such a way that you can swap out and mix and match APIs and functionality to cater to your exact preferences and use cases.

When you use stdlib, you can be absolutely certain that you are using the most thorough, rigorous, well-written, studied, documented, tested, measured, and high-quality code out there.

To join us in bringing numerical computing to the web, get started by checking us out on GitHub, and please consider financially supporting stdlib. We greatly appreciate your continued support!

incrpcorrmat

Compute a sample Pearson product-moment correlation matrix incrementally.

A [Pearson product-moment correlation matrix][pearson-correlation] is an M-by-M matrix whose elements specified by indices `j` and `k` are the [Pearson product-moment correlation coefficients][pearson-correlation] between the jth and kth data variables. The [Pearson product-moment correlation coefficient][pearson-correlation] between random variables `X` and `Y` is defined as ```math \rho_{X,Y} = \frac{\mathop{\mathrm{cov}}(X,Y)}{\sigma_X \sigma_Y} ``` where the numerator is the [covariance][covariance] and the denominator is the product of the respective standard deviations. For a sample of size `n`, the [sample Pearson product-moment correlation coefficient][pearson-correlation] is defined as ```math r = \frac{\displaystyle\sum_{i=0}^{n-1} (x_i - \bar{x})(y_i - \bar{y})}{\displaystyle\sqrt{\sum_{i=0}^{n-1} (x_i - \bar{x})^2} \sqrt{\sum_{i=0}^{n-1} (y_i - \bar{y})^2}} ```

## Installation ```bash npm install @stdlib/stats-incr-pcorrmat ``` Alternatively, - To load the package in a website via a `script` tag without installation and bundlers, use the [ES Module][es-module] available on the [`esm`][esm-url] branch (see [README][esm-readme]). - If you are using Deno, visit the [`deno`][deno-url] branch (see [README][deno-readme] for usage intructions). - For use in Observable, or in browser/node environments, use the [Universal Module Definition (UMD)][umd] build available on the [`umd`][umd-url] branch (see [README][umd-readme]). The [branches.md][branches-url] 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 ```javascript var incrpcorrmat = require( '@stdlib/stats-incr-pcorrmat' ); ``` #### incrpcorrmat( out\[, means] ) Returns an accumulator `function` which incrementally computes a [sample Pearson product-moment correlation matrix][pearson-correlation]. ```javascript // Create an accumulator for computing a 2-dimensional correlation matrix: var accumulator = incrpcorrmat( 2 ); ``` The `out` argument may be either the order of the [correlation matrix][pearson-correlation] or a square 2-dimensional [`ndarray`][@stdlib/ndarray/ctor] for storing the [correlation matrix][pearson-correlation]. ```javascript 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 matrix: var corr = ndarray( 'float64', buffer, shape, strides, 0, 'row-major' ); var accumulator = incrpcorrmat( corr ); ``` When means are known, the function supports providing a 1-dimensional [`ndarray`][@stdlib/ndarray/ctor] containing mean values. ```javascript 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 = incrpcorrmat( 2, means ); ``` #### accumulator( \[vector] ) If provided a data vector, the accumulator function returns an updated [sample Pearson product-moment correlation matrix][pearson-correlation]. If not provided a data vector, the accumulator function returns the current [sample Pearson product-moment correlation matrix][pearson-correlation]. ```javascript 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 corr = 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 = incrpcorrmat( corr ); vec.set( 0, 2.0 ); vec.set( 1, 1.0 ); var out = accumulator( vec ); // returns var bool = ( out === corr ); // returns true vec.set( 0, 1.0 ); vec.set( 1, -5.0 ); out = accumulator( vec ); // returns vec.set( 0, 3.0 ); vec.set( 1, 3.14 ); out = accumulator( vec ); // returns out = accumulator(); // returns ```

## Examples ```javascript var randu = require( '@stdlib/random-base-randu' ); var ndarray = require( '@stdlib/ndarray-ctor' ); var Float64Array = require( '@stdlib/array-float64' ); var incrpcorrmat = require( '@stdlib/stats-incr-pcorrmat' ); var corr; var rxy; var ryx; var rx; var ry; var i; // Initialize an accumulator for a 2-dimensional correlation matrix: var accumulator = incrpcorrmat( 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 matrix... for ( i = 0; i < 100; i++ ) { vec.set( 0, randu()*100.0 ); vec.set( 1, randu()*100.0 ); corr = accumulator( vec ); rx = corr.get( 0, 0 ).toFixed( 4 ); ry = corr.get( 1, 1 ).toFixed( 4 ); rxy = corr.get( 0, 1 ).toFixed( 4 ); ryx = corr.get( 1, 0 ).toFixed( 4 ); console.log( '[ %d, %d\n %d, %d ]', rx, rxy, ryx, ry ); } ```

* * * ## 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 © 2016-2025. The Stdlib [Authors][stdlib-authors].

Owner

Name: stdlib
Login: stdlib-js
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Website: https://stdlib.io
Twitter: stdlibjs
Repositories: 3,692
Profile: https://github.com/stdlib-js

Standard library for JavaScript.

Citation (CITATION.cff)

cff-version: 1.2.0
title: stdlib
message: >-
  If you use this software, please cite it using the
  metadata from this file.

type: software

authors:
  - name: The Stdlib Authors
    url: https://github.com/stdlib-js/stdlib/graphs/contributors

repository-code: https://github.com/stdlib-js/stdlib
url: https://stdlib.io

abstract: |
  Standard library for JavaScript and Node.js.

keywords:
  - JavaScript
  - Node.js
  - TypeScript
  - standard library
  - scientific computing
  - numerical computing
  - statistical computing

license: Apache-2.0 AND BSL-1.0

date-released: 2016

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npmjs.org: @stdlib/stats-incr-pcorrmat

Compute a sample Pearson product-moment correlation matrix incrementally.

Homepage: https://stdlib.io
License: Apache-2.0
Latest release: 0.2.2
published over 1 year ago

Versions: 11
Dependent Packages: 3
Dependent Repositories: 3
Downloads: 113 Last month

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Average: 11.4%

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planeshifter rreusser stdlib-bot kgryte

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@stdlib/bench ^0.0.x development
@stdlib/constants-float64-eps ^0.0.x development
@stdlib/math-base-special-abs ^0.0.x development
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istanbul ^0.4.1 development
tap-spec 5.x.x development
tape git+https://github.com/kgryte/tape.git#fix/globby development
@stdlib/array-float64 ^0.0.x
@stdlib/assert-is-positive-integer ^0.0.x
@stdlib/assert-is-square-matrix ^0.0.x
@stdlib/assert-is-vector-like ^0.0.x
@stdlib/math-base-special-sqrt ^0.0.x
@stdlib/ndarray-base-ctor ^0.0.x
@stdlib/ndarray-base-numel ^0.0.x
@stdlib/ndarray-ctor ^0.0.x
@stdlib/string-format ^0.0.x
@stdlib/types ^0.0.x

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@stdlib/stats-incr-pcorrmat

Science Score: 44.0%

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