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fisher-transform

0.2.0 • Public • Published

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`

Keywords

install

`npm i fisher-transform`

27

0.2.0