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https://github.com/stdlib-js/stats-base-dnanvariancetk

Calculate the variance of a double-precision floating-point strided array ignoring NaN values and using a one-pass textbook algorithm.
https://github.com/stdlib-js/stats-base-dnanvariancetk

array deviation dispersion javascript math mathematics node node-js nodejs sample-variance standard-deviation statistics stats stdlib strided strided-array typed unbiased var variance

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Calculate the variance of a double-precision floating-point strided array ignoring NaN values and using a one-pass textbook algorithm.

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README 

        



  

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# dnanvariancetk



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



> Calculate the [variance][variance] of a double-precision floating-point strided array ignoring `NaN` values and using a one-pass textbook algorithm.



The population [variance][variance] of a finite size population of size `N` is given by



```math

\sigma^2 = \frac{1}{N} \sum_{i=0}^{N-1} (x_i - \mu)^2

```



where the population mean is given by



```math

\mu = \frac{1}{N} \sum_{i=0}^{N-1} x_i

```



Often in the analysis of data, the true population [variance][variance] is not known _a priori_ and must be estimated from a sample drawn from the population distribution. If one attempts to use the formula for the population [variance][variance], the result is biased and yields a **biased sample variance**. To compute an **unbiased sample variance** for a sample of size `n`,



```math

s^2 = \frac{1}{n-1} \sum_{i=0}^{n-1} (x_i - \bar{x})^2

```



where the sample mean is given by



```math

\bar{x} = \frac{1}{n} \sum_{i=0}^{n-1} x_i

```



The use of the term `n-1` is commonly referred to as Bessel's correction. Note, however, that applying Bessel's correction can increase the mean squared error between the sample variance and population variance. Depending on the characteristics of the population distribution, other correction factors (e.g., `n-1.5`, `n+1`, etc) can yield better estimators.



## Installation



```bash

npm install @stdlib/stats-base-dnanvariancetk

```



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 dnanvariancetk = require( '@stdlib/stats-base-dnanvariancetk' );

```



#### dnanvariancetk( N, correction, x, stride )



Computes the [variance][variance] of a double-precision floating-point strided array `x` ignoring `NaN` values and using a one-pass textbook algorithm.



```javascript

var Float64Array = require( '@stdlib/array-float64' );



var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );



var v = dnanvariancetk( x.length, 1, x, 1 );

// returns ~4.3333

```



The function has the following parameters:



-   **N**: number of indexed elements.

-   **correction**: degrees of freedom adjustment. Setting this parameter to a value other than `0` has the effect of adjusting the divisor during the calculation of the [variance][variance] according to `n-c` where `c` corresponds to the provided degrees of freedom adjustment and `n` corresponds to the number of non-`NaN` indexed elements. When computing the [variance][variance] of a population, setting this parameter to `0` is the standard choice (i.e., the provided array contains data constituting an entire population). When computing the unbiased sample [variance][variance], setting this parameter to `1` is the standard choice (i.e., the provided array contains data sampled from a larger population; this is commonly referred to as Bessel's correction).

-   **x**: input [`Float64Array`][@stdlib/array/float64].

-   **stride**: index increment for `x`.



The `N` and `stride` parameters determine which elements in `x` are accessed at runtime. For example, to compute the [variance][variance] of every other element in `x`,



```javascript

var Float64Array = require( '@stdlib/array-float64' );

var floor = require( '@stdlib/math-base-special-floor' );



var x = new Float64Array( [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0, NaN ] );

var N = floor( x.length / 2 );



var v = dnanvariancetk( N, 1, x, 2 );

// returns 6.25

```



Note that indexing is relative to the first index. To introduce an offset, use [`typed array`][mdn-typed-array] views.



```javascript

var Float64Array = require( '@stdlib/array-float64' );

var floor = require( '@stdlib/math-base-special-floor' );



var x0 = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN ] );

var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element



var N = floor( x0.length / 2 );



var v = dnanvariancetk( N, 1, x1, 2 );

// returns 6.25

```



#### dnanvariancetk.ndarray( N, correction, x, stride, offset )



Computes the [variance][variance] of a double-precision floating-point strided array ignoring `NaN` values and using a one-pass textbook algorithm and alternative indexing semantics.



```javascript

var Float64Array = require( '@stdlib/array-float64' );



var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );



var v = dnanvariancetk.ndarray( x.length, 1, x, 1, 0 );

// returns ~4.33333

```



The function has the following additional parameters:



-   **offset**: starting index for `x`.



While [`typed array`][mdn-typed-array] views mandate a view offset based on the underlying `buffer`, the `offset` parameter supports indexing semantics based on a starting index. For example, to calculate the [variance][variance] for every other value in `x` starting from the second value



```javascript

var Float64Array = require( '@stdlib/array-float64' );

var floor = require( '@stdlib/math-base-special-floor' );



var x = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );

var N = floor( x.length / 2 );



var v = dnanvariancetk.ndarray( N, 1, x, 2, 1 );

// returns 6.25

```



## Notes



-   If `N <= 0`, both functions return `NaN`.

-   If `n - c` is less than or equal to `0` (where `c` corresponds to the provided degrees of freedom adjustment and `n` corresponds to the number of non-`NaN` indexed elements), both functions return `NaN`.

-   Some caution should be exercised when using the one-pass textbook algorithm. Literature overwhelmingly discourages the algorithm's use for two reasons: 1) the lack of safeguards against underflow and overflow and 2) the risk of catastrophic cancellation when subtracting the two sums if the sums are large and the variance small. These concerns have merit; however, the one-pass textbook algorithm should not be dismissed outright. For data distributions with a moderately large standard deviation to mean ratio (i.e., **coefficient of variation**), the one-pass textbook algorithm may be acceptable, especially when performance is paramount and some precision loss is acceptable (including a risk of returning a negative variance due to floating-point rounding errors!). In short, no single "best" algorithm for computing the variance exists. The "best" algorithm depends on the underlying data distribution, your performance requirements, and your minimum precision requirements. When evaluating which algorithm to use, consider the relative pros and cons, and choose the algorithm which best serves your needs.



## Examples



```javascript

var randu = require( '@stdlib/random-base-randu' );

var round = require( '@stdlib/math-base-special-round' );

var Float64Array = require( '@stdlib/array-float64' );

var dnanvariancetk = require( '@stdlib/stats-base-dnanvariancetk' );



var x;

var i;



x = new Float64Array( 10 );

for ( i = 0; i < x.length; i++ ) {

    x[ i ] = round( (randu()*100.0) - 50.0 );

}

console.log( x );



var v = dnanvariancetk( x.length, 1, x, 1 );

console.log( v );

```



* * *



## References



-   Ling, Robert F. 1974. "Comparison of Several Algorithms for Computing Sample Means and Variances." _Journal of the American Statistical Association_ 69 (348). American Statistical Association, Taylor & Francis, Ltd.: 859–66. doi:[10.2307/2286154][@ling:1974a].



* * *



## See Also



-   [`@stdlib/stats-base/dvariancetk`][@stdlib/stats/base/dvariancetk]: calculate the variance of a double-precision floating-point strided array using a one-pass textbook algorithm.

-   [`@stdlib/stats-base/dnanvariance`][@stdlib/stats/base/dnanvariance]: calculate the variance of a double-precision floating-point strided array ignoring NaN values.

-   [`@stdlib/stats-base/nanvariancetk`][@stdlib/stats/base/nanvariancetk]: calculate the variance of a strided array ignoring NaN values and using a one-pass textbook algorithm.

-   [`@stdlib/stats-base/snanvariancetk`][@stdlib/stats/base/snanvariancetk]: calculate the variance of a single-precision floating-point strided array ignoring NaN values and using a one-pass textbook algorithm.



* * *



## 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 © 2016-2024. The Stdlib [Authors][stdlib-authors].



[npm-image]: http://img.shields.io/npm/v/@stdlib/stats-base-dnanvariancetk.svg

[npm-url]: https://npmjs.org/package/@stdlib/stats-base-dnanvariancetk



[test-image]: https://github.com/stdlib-js/stats-base-dnanvariancetk/actions/workflows/test.yml/badge.svg?branch=main

[test-url]: https://github.com/stdlib-js/stats-base-dnanvariancetk/actions/workflows/test.yml?query=branch:main



[coverage-image]: https://img.shields.io/codecov/c/github/stdlib-js/stats-base-dnanvariancetk/main.svg

[coverage-url]: https://codecov.io/github/stdlib-js/stats-base-dnanvariancetk?branch=main



[chat-image]: https://img.shields.io/gitter/room/stdlib-js/stdlib.svg

[chat-url]: https://app.gitter.im/#/room/#stdlib-js_stdlib:gitter.im



[stdlib]: https://github.com/stdlib-js/stdlib



[stdlib-authors]: https://github.com/stdlib-js/stdlib/graphs/contributors



[umd]: https://github.com/umdjs/umd

[es-module]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Modules



[deno-url]: https://github.com/stdlib-js/stats-base-dnanvariancetk/tree/deno

[deno-readme]: https://github.com/stdlib-js/stats-base-dnanvariancetk/blob/deno/README.md

[umd-url]: https://github.com/stdlib-js/stats-base-dnanvariancetk/tree/umd

[umd-readme]: https://github.com/stdlib-js/stats-base-dnanvariancetk/blob/umd/README.md

[esm-url]: https://github.com/stdlib-js/stats-base-dnanvariancetk/tree/esm

[esm-readme]: https://github.com/stdlib-js/stats-base-dnanvariancetk/blob/esm/README.md

[branches-url]: https://github.com/stdlib-js/stats-base-dnanvariancetk/blob/main/branches.md



[stdlib-license]: https://raw.githubusercontent.com/stdlib-js/stats-base-dnanvariancetk/main/LICENSE



[variance]: https://en.wikipedia.org/wiki/Variance



[@stdlib/array/float64]: https://github.com/stdlib-js/array-float64



[mdn-typed-array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/TypedArray



[@ling:1974a]: https://doi.org/10.2307/2286154



[@stdlib/stats/base/dvariancetk]: https://github.com/stdlib-js/stats-base-dvariancetk



[@stdlib/stats/base/dnanvariance]: https://github.com/stdlib-js/stats-base-dnanvariance



[@stdlib/stats/base/nanvariancetk]: https://github.com/stdlib-js/stats-base-nanvariancetk



[@stdlib/stats/base/snanvariancetk]: https://github.com/stdlib-js/stats-base-snanvariancetk