inference for correlation rho via fisher transformation
npm install fisher-transform[![NPM version][npm-image]][npm-url]
[![Coverage Status][coveralls-image]][coveralls-url]
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`