@stdlib/stats-kstest

One-sample Kolmogorov-Smirnov goodness-of-fit test.

https://github.com/stdlib-js/stats-kstest

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goodness-of-fit hypothesis javascript math mathematics node node-js nodejs statistics stats stdlib summary test

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Repository

One-sample Kolmogorov-Smirnov goodness-of-fit test.

Basic Info

Host: GitHub
Owner: stdlib-js
License: apache-2.0
Language: JavaScript
Default Branch: main
Homepage: https://github.com/stdlib-js/stdlib
Size: 5.93 MB

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goodness-of-fit hypothesis javascript math mathematics node node-js nodejs statistics stats stdlib summary test

Created over 4 years ago · Last pushed 9 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!

Kolmogorov-Smirnov Goodness-of-Fit Test

One-sample Kolmogorov-Smirnov goodness-of-fit test.

## Installation ```bash npm install @stdlib/stats-kstest ``` 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 kstest = require( '@stdlib/stats-kstest' ); ``` #### kstest( x, y\[, ...params]\[, opts] ) For a numeric [array][mdn-array] or [typed array][mdn-typed-array] `x`, a Kolmogorov-Smirnov goodness-of-fit is computed for the null hypothesis that the values of `x` come from the distribution specified by `y`. `y` can be either a [string][mdn-string] with the name of the distribution to test against, or a [function][mdn-function]. In the latter case, `y` is expected to be the cumulative distribution function (CDF) of the distribution to test against, with its first parameter being the value at which to evaluate the CDF and the remaining parameters constituting the parameters of the distribution. The parameters of the distribution are passed as additional arguments after `y` from `kstest` to the chosen CDF. The function returns an object holding the calculated test statistic `statistic` and the `pValue` of the test. ```javascript var factory = require( '@stdlib/random-base-uniform' ).factory; var runif; var out; var x; var i; runif = factory( 0.0, 1.0, { 'seed': 8798 }); x = new Array( 100 ); for ( i = 0; i < x.length; i++ ) { x[ i ] = runif(); } out = kstest( x, 'uniform', 0.0, 1.0 ); // returns { 'pValue': ~0.703, 'statistic': ~0.069, ... } ``` The returned object comes with a `.print()` method which when invoked will print a formatted output of the hypothesis test results. `print` accepts a `digits` option that controls the number of decimal digits displayed for the outputs and a `decision` option, which when set to `false` will hide the test decision. ```javascript console.log( out.print() ); /* e.g., => Kolmogorov-Smirnov goodness-of-fit test. Null hypothesis: the CDF of `x` is equal equal to the reference CDF. pValue: 0.7039 statistic: 0.0689 Test Decision: Fail to reject null in favor of alternative at 5% significance level */ ``` The function accepts the following `options`: - **alpha**: `number` in the interval `[0,1]` giving the significance level of the hypothesis test. Default: `0.05`. - **alternative**: Either `two-sided`, `less` or `greater`. Indicates whether the alternative hypothesis is that the true distribution of `x` is not equal to the reference distribution specified by `y` (`two-sided`), whether it is `less` than the reference distribution or `greater` than the reference distribution. Default: `two-sided`. - **sorted**: `boolean` indicating if the `x` [array][mdn-array] is in sorted order (ascending). Default: `false`. By default, the test is carried out at a significance level of `0.05`. To choose a custom significance level, set the `alpha` option. ```javascript out = kstest( x, 'uniform', 0.0, 1.0, { 'alpha': 0.1 }); console.log( out.print() ); /* e.g., => Kolmogorov-Smirnov goodness-of-fit test. Null hypothesis: the CDF of `x` is equal equal to the reference CDF. pValue: 0.7039 statistic: 0.0689 Test Decision: Fail to reject null in favor of alternative at 10% significance level */ ``` By default, the function tests the null hypothesis that the true distribution of `x` and the reference distribution `y` are equal to each other against the alternative that they are not equal. To carry out a one-sided hypothesis test, set the `alternative` option to either `less` or `greater`. ```javascript var factory = require( '@stdlib/random-base-uniform' ).factory; var runif; var out; var x; var i; runif = factory( 0.0, 1.0, { 'seed': 8798 }); x = new Array( 100 ); for ( i = 0; i < x.length; i++ ) { x[ i ] = runif(); } out = kstest( x, 'uniform', 0.0, 1.0, { 'alternative': 'less' }); // returns { 'pValue': ~0.358, 'statistic': ~0.07, ... } out = kstest( x, 'uniform', 0.0, 1.0, { 'alternative': 'greater' }); // returns { 'pValue': ~0.907, 'statistic': ~0.02, ... } ``` To perform the Kolmogorov-Smirnov test, the data has to be sorted in ascending order. If the data in `x` are already sorted, set the `sorted` option to `true` to speed up computation. ```javascript x = [ 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 ]; out = kstest( x, 'uniform', 0.0, 1.0, { 'sorted': true }); // returns { 'pValue': ~1, 'statistic': 0.1, ... } ```

## Examples ```javascript var kstest = require( '@stdlib/stats-kstest' ); var factory = require( '@stdlib/random-base-normal' ).factory; var rnorm; var table; var out; var i; var x; rnorm = factory({ 'seed': 4839 }); // Values drawn from a Normal(3,1) distribution x = new Array( 100 ); for ( i = 0; i < 100; i++ ) { x[ i ] = rnorm( 3.0, 1.0 ); } // Test against N(0,1) out = kstest( x, 'normal', 0.0, 1.0 ); table = out.print(); /* e.g., returns Kolmogorov-Smirnov goodness-of-fit test. Null hypothesis: the CDF of `x` is equal to the reference CDF. statistic: 0.847 pValue: 0 Test Decision: Reject null in favor of alternative at 5% significance level */ // Test against N(3,1) out = kstest( x, 'normal', 3.0, 1.0 ); table = out.print(); /* e.g., returns Kolmogorov-Smirnov goodness-of-fit test. Null hypothesis: the CDF of `x` is equal to the reference CDF. statistic: 0.0733 pValue: 0.6282 Test Decision: Fail to reject null in favor of alternative at 5% significance level */ ```

* * * ## 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].

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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
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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-kstest

One-sample Kolmogorov-Smirnov goodness-of-fit test.

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

Versions: 11
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@stdlib/stats-kstest

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Kolmogorov-Smirnov Goodness-of-Fit Test

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