regression

What is regression?

The 'regression' npm package is a simple and efficient library for performing various types of regression analysis. It supports multiple regression models including linear, exponential, logarithmic, power, and polynomial regression. This package is useful for fitting data to a model and making predictions based on that model.

What are regression's main functionalities?

Linear Regression

Performs linear regression on a set of data points. The result includes the equation of the line, the slope, the intercept, and the R-squared value.

const regression = require('regression');
const data = [[0, 1], [32, 67], [12, 79]];
const result = regression.linear(data);
console.log(result);

Exponential Regression

Performs exponential regression on a set of data points. The result includes the equation of the curve and the R-squared value.

const regression = require('regression');
const data = [[0, 1], [32, 67], [12, 79]];
const result = regression.exponential(data);
console.log(result);

Logarithmic Regression

Performs logarithmic regression on a set of data points. The result includes the equation of the curve and the R-squared value.

const regression = require('regression');
const data = [[0, 1], [32, 67], [12, 79]];
const result = regression.logarithmic(data);
console.log(result);

Power Regression

Performs power regression on a set of data points. The result includes the equation of the curve and the R-squared value.

const regression = require('regression');
const data = [[0, 1], [32, 67], [12, 79]];
const result = regression.power(data);
console.log(result);

Polynomial Regression

Performs polynomial regression on a set of data points. The result includes the equation of the curve and the R-squared value. The order of the polynomial can be specified.

const regression = require('regression');
const data = [[0, 1], [32, 67], [12, 79]];
const result = regression.polynomial(data, { order: 2 });
console.log(result);

Other packages similar to regression

simple-statistics

The 'simple-statistics' package provides a wide range of statistical tools, including various types of regression analysis. It is more comprehensive than 'regression' as it includes additional statistical functions such as mean, median, standard deviation, and more.

ml-regression

The 'ml-regression' package is part of the 'ml' suite of machine learning tools. It offers multiple regression models including linear, polynomial, and multivariate linear regression. It is designed to be used in conjunction with other machine learning tools in the 'ml' suite.

README

regression-js

regression-js is a JavaScript module containing a collection of linear least-squares fitting methods for simple data analysis.

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 (R²)
• 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 . It returns the coefficients in the form [m, c].

regression.exponential(data[, options])

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

regression.logarithmic(data[, options])

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

regression.polynomial(data[, options])

Fits the input data to a polynomial curve with the equation . 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.

Changelog

2.0.1

• Fix broken minified distribution