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.

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``
npm install --save regression
`

Usage

`javascript
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.

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Below are the default values for the configuration parameter.
`javascript
{
order: 2,
precision: 2,
}
`

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- 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

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

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

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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].

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

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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

`javascript
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`.