@jazeee/regression
v2.0.11
Published
Javascript least squares data fitting methods
Downloads
10
Maintainers
Readme
@jazeee/regression
This package is a fork of https://travis-ci.org/Tom-Alexander/regression-js, which is no longer maintained.
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 '@jazeee/regression' package on npm.
npm
npm install --save @jazeee/regression
Usage
import regression from '@jazeee/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 equationstring
: A string representation of the equationpoints
: an array containing the predicted data in the domain of the inputresiduals
: an array containing the difference between predicted data and source data in the domain of the inputr2
: 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, usenpm run build
- You can run the tests with:
npm run test
ornpm run test -- --watch
. npm run lint --fix