https://github.com/planeshifter/fisher-transform
inference for correlation rho via fisher transformation
https://github.com/planeshifter/fisher-transform
Last synced: about 1 year ago
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inference for correlation rho via fisher transformation
- Host: GitHub
- URL: https://github.com/planeshifter/fisher-transform
- Owner: Planeshifter
- License: mit
- Created: 2015-01-30T22:36:14.000Z (over 11 years ago)
- Default Branch: master
- Last Pushed: 2023-08-23T02:07:57.000Z (almost 3 years ago)
- Last Synced: 2024-04-24T18:23:55.312Z (about 2 years ago)
- Language: JavaScript
- Homepage:
- Size: 13.7 KB
- Stars: 6
- Watchers: 5
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
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# Inference for Pearson correlation
## Installation & Usage
```
npm install fisher-transform
```
Require as follows:
```
var fisher = require('fisher-transform');
```
`fisher` exports the following functions:
## `fisherTest(rho, n, [alpha, alternative, rho_0]`)
The function parameters are:
- rho: the Pearson correlation for which inference should be carried out
- n: the number of sample observations
- alpha: the significance level of the test, default value is 0.05
- alternative: default value "two-sided", for one-sided tests options "greater" and "less" exist
- rho_0: the value of rho assumed under the null hypothesis, default value is 0
Specifically, the two-sided test is
H_0: rho = rho_0 vs. H_1: rho != rho_0
and the one-sided tests are
H_0: rho = rho_0 vs. H_1: rho >= rho_0
and
H_0: rho = rho_0 vs. H_1: rho <= rho_0
For the chosen test, its p-value is calculated. In addition, a 1-alpha confidence interval is constructed by inverting the test statistic. The function returns an object with with two keys: pvalue and CI. The former holds the pvalue, while the latter is an Array with two elements, the lower and upper bounds of the calculated confidence interval.
## `r2z(r)`
Applies the Fisher transformation to r to obtain z, where z = arctanh(r)
## `z2r(z)`
Applies the inverse Fisher transformation to z in order to recover r, where r = tanh(z)
## `zScore(r, r_0, n)`
Returns the Fisher z-score for Pearson correlation r under the null hypothesis that r = r_0. Approximately, the z-score follows a standard normal distribution.
## Unit Tests
Run tests via the command `npm test`
---
## License
[MIT license](http://opensource.org/licenses/MIT).
[npm-image]: https://badge.fury.io/js/fisher-transform.svg
[npm-url]: http://badge.fury.io/js/fisher-transform
[coveralls-image]: https://img.shields.io/coveralls/Planeshifter/fisher-transform/master.svg
[coveralls-url]: https://coveralls.io/r/Planeshifter/fisher-transform?branch=master