@stdlib/stats-incr-grubbs

Grubbs' test for outliers.

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

Science Score: 57.0%

This score indicates how likely this project is to be science-related based on various indicators:

✓
CITATION.cff file
Found CITATION.cff file
✓
codemeta.json file
Found codemeta.json file
✓
.zenodo.json file
Found .zenodo.json file
✓
DOI references
Found 6 DOI reference(s) in README
○
Academic publication links
○
Committers with academic emails
○
Institutional organization owner
○
JOSS paper metadata
○
Scientific vocabulary similarity
Low similarity (14.3%) to scientific vocabulary

Keywords

deviate esd extreme grubbs hypothesis hypothesis-test javascript math mathematics maximum node node-js nodejs normalized residual statistics stats stdlib studentized test

Last synced: 4 months ago · JSON representation ·

Repository

Grubbs' test for outliers.

Basic Info

Host: GitHub
Owner: stdlib-js
License: apache-2.0
Language: JavaScript
Default Branch: main
Homepage: https://github.com/stdlib-js/stdlib
Size: 5.43 MB

Statistics

Stars: 6
Watchers: 3
Forks: 0
Open Issues: 0
Releases: 0

Topics

deviate esd extreme grubbs hypothesis hypothesis-test javascript math mathematics maximum node node-js nodejs normalized residual statistics stats stdlib studentized test

Created over 4 years ago · Last pushed 5 months ago

Metadata Files

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!

incrgrubbs

Grubbs' test for outliers.

[Grubbs' test][grubbs-test] (also known as the **maximum normalized residual test** or **extreme studentized deviate test**) is a statistical test used to detect outliers in a univariate dataset assumed to come from a normally distributed population. [Grubbs' test][grubbs-test] is defined for the hypothesis: - **H_0**: the dataset does **not** contain outliers. - **H_1**: the dataset contains **exactly** one outlier. The [Grubbs' test][grubbs-test] statistic for a two-sided alternative hypothesis is defined as ```math G = \frac{\max_{i=0,\ldots,N-1} |Y_i - \bar{Y}|}{s} ``` where `s` is the sample standard deviation. The [Grubbs test][grubbs-test] statistic is thus the largest absolute deviation from the sample mean in units of the sample standard deviation. The [Grubbs' test][grubbs-test] statistic for the alternative hypothesis that the minimum value is an outlier is defined as ```math G = \frac{\bar{Y} - Y_{\textrm{min}}}{s} ``` The [Grubbs' test][grubbs-test] statistic for the alternative hypothesis that the maximum value is an outlier is defined as ```math G = \frac{Y_{\textrm{max}} - \bar{Y}}{s} ``` For a two-sided test, the hypothesis that a dataset does **not** contain an outlier is rejected at significance level α if ```math G > \frac{N-1}{\sqrt{N}} \sqrt{\frac{t^2_{\alpha/(2N),N-2}}{N - 2 + t^2_{\alpha/(2N),N-2}}} ``` where `t` denotes the upper critical value of the _t_-distribution with `N-2` degrees of freedom and a significance level of `α/(2N)`. For a one-sided test, the hypothesis that a dataset does **not** contain an outlier is rejected at significance level α if ```math G > \frac{N-1}{\sqrt{N}} \sqrt{\frac{t^2_{\alpha/N,N-2}}{N - 2 + t^2_{\alpha/N,N-2}}} ``` where `t` denotes the upper critical value of the _t_-distribution with `N-2` degrees of freedom and a significance level of `α/N`.

## Installation ```bash npm install @stdlib/stats-incr-grubbs ``` 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 incrgrubbs = require( '@stdlib/stats-incr-grubbs' ); ``` #### incrgrubbs( \[options] ) Returns an accumulator `function` which incrementally performs [Grubbs' test][grubbs-test] for outliers. ```javascript var accumulator = incrgrubbs(); ``` The function accepts the following `options`: - **alpha**: significance level. Default: `0.05`. - **alternative**: alternative hypothesis. The option may be one of the following values: - `'two-sided'`: test whether the minimum or maximum value is an outlier. - `'min'`: test whether the minimum value is an outlier. - `'max'`: test whether the maximum value is an outlier. Default: `'two-sided'`. - **init**: number of data points the accumulator should use to compute initial statistics **before** testing for an outlier. Until the accumulator is provided the number of data points specified by this option, the accumulator returns `null`. Default: `100`. #### accumulator( \[x] ) If provided an input value `x`, the accumulator function returns updated test results. If not provided an input value `x`, the accumulator function returns the current test results. ```javascript var rnorm = require( '@stdlib/random-base-normal' ); var opts = { 'init': 0 }; var accumulator = incrgrubbs( opts ); var results = accumulator( rnorm( 10.0, 5.0 ) ); // returns null results = accumulator( rnorm( 10.0, 5.0 ) ); // returns null results = accumulator( rnorm( 10.0, 5.0 ) ); // returns

@stdlib/stats-incr-grubbs

Science Score: 57.0%

Keywords

Repository

Basic Info

Statistics

Topics

Metadata Files

README.md

incrgrubbs

Owner

Citation (CITATION.cff)

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

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

Rankings

Maintainers (4)

Funding

Dependencies