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<div align="center"> <h1>regression-js</h1> <a href="https://travis-ci.org/Tom-Alexander/regression-js"> <img src="https://travis-ci.org/Tom-Alexander/regression-js.svg?branch=master"/> </a> <a href="https://npmjs.com/package/regression"> <img src="https://img.shields.io/npm/v/regression.svg" alt="npm version" /> </a> <a href="https://npmjs.com/package/regression"> <img src="https://img.shields.io/npm/dt/regression.svg" alt="npm downloads" /> </a> <a href="https://codeclimate.com/github/Tom-Alexander/regression-js/coverage"><img src="https://codeclimate.com/github/Tom-Alexander/regression-js/badges/coverage.svg" /></a> <br/> <br/> <p> regression-js is a JavaScript module containing a collection of linear least-squares fitting methods for simple data analysis. </p> </div>

Installation

This module works on node and in the browser. It is available as the 'regression' package on npm. It is also available on a CDN.

npm

npm install --save regression

Usage

import regression from 'regression';
const result = regression.linear([[0, 1], [32, 67], [12, 79]]);
const gradient = result.equation[0];
const yIntercept = result.equation[1];

Data is passed into the model as an array. A second parameter can be used to configure the model. The configuration parameter is optional. null values are ignored. The precision option will set the number of significant figures the output is rounded to.

Configuration options

Below are the default values for the configuration parameter.

{
  order: 2,
  precision: 2,
}

Properties

equation: an array containing the coefficients of the equation
string: A string representation of the equation
points: an array containing the predicted data in the domain of the input
r2: the coefficient of determination (<i>R</i><sup>2</sup>)
predict(x): This function will return the predicted value

API

`regression.linear(data[, options])`

Fits the input data to a straight line with the equation y = mx + c . It returns the coefficients in the form [m, c].

`regression.exponential(data[, options])`

Fits the input data to a exponential curve with the equation y = ae^bx . It returns the coefficients in the form [a, b].

`regression.logarithmic(data[, options])`

Fits the input data to a logarithmic curve with the equation y = a + b ln x . It returns the coefficients in the form [a, b].

`regression.power(data[, options])`

Fits the input data to a power law curve with the equation y = ax^b . It returns the coefficients in the form [a, b].

`regression.polynomial(data[, options])`

Fits the input data to a polynomial curve with the equation anx^n ... + a1x + a0 . It returns the coefficients in the form [an..., a1, a0]. The order can be configure with the order option.

Example

const data = [[0,1],[32, 67] .... [12, 79]];
const result = regression.polynomial(data, { order: 3 });

Development

Install the dependencies with npm install
To build the assets in the dist directory, use npm run build
You can run the tests with: npm run test.