npm package: @stdlib/stats-kstest
Description: One-sample Kolmogorov-Smirnov goodness-of-fit test.
Author: The Stdlib Authors
Latest version: 0.2.3

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# Kolmogorov-Smirnov Goodness-of-Fit Test

[![NPM version][npm-image]][npm-url] [![Build Status][test-image]][test-url] [![Coverage Status][coverage-image]][coverage-url] <!-- [![dependencies][dependencies-image]][dependencies-url] -->

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

<section class="installation">

## Installation

```bash
npm install @stdlib/stats-kstest
```

</section>

<section class="usage">

## 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.

<!-- run-disable -->

```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.

<!-- run-disable -->

```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, ... }
```

</section>

<!-- /.usage -->

<section class="examples">

## Examples

<!-- eslint no-undef: "error" -->

```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
*/
```

</section>

<!-- /.examples -->

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<section class="related">

</section>

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<section class="main-repo" >

* * *

## 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].

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---

## License

See [LICENSE][stdlib-license].


## Copyright

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