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spm-regression

v1.2.1

Published

Javascript least squares data fitting methods

Downloads

8

Readme

regression.js is a javascript library containing a collection of least squares fitting methods for finding a trend in a set of data. It currently contains methods for linear, exponential, logarithmic, power and polynomial trends.

Usage

Most regressions require only two parameters - the regression method (linear, exponential, logarithmic, power or polynomial) and a data source. A third parameter can be used to define the degree of a polynomial when a polynomial regression is required.

regression.js will return an object containing an equation array and a points array.

Linear regression

equation: [gradient, y-intercept] in the form y = mx + c

var data = [[0,1],[32, 67] .... [12, 79]];
var result = regression('linear', data);

Linear regression through the origin

equation: [gradient] in the form y = mx

var data = [[0,1],[32, 67] .... [12, 79]];
var result = regression('linearThroughOrigin', data);

Exponential regression

equation: [a, b] in the form y = ae^bx

Logarithmic regression

equation: [a, b] in the form y = a + b ln x

Power law regression

equation: [a, b] in the form y = ax^b

Polynomial regression

equation: [a0, .... , an] in the form a0x^0 ... + anx^n

var data = [[0,1],[32, 67] .... [12, 79]];
var result = regression('polynomial', data, 4);

Lastvalue

Not exactly a regression. Uses the last value to fill the blanks when forecasting.

Filling the blanks and forecasting

var data = [[0,1], [32, null] .... [12, 79]];

If you use a null value for data, regressionjs will fill it using the trend.