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](https://www.npmjs.com/package/regression). It is also available on a [CDN](https://cdnjs.com/libraries/regression). ### npm ``` 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. ### Configuration options Below are the default values for the configuration parameter. ```javascript { 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 ![y = mx + c](http://mathurl.com/ycqyhets.png). 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](http://mathurl.com/zuys53z.png). 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](http://mathurl.com/zye394m.png). 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](http://mathurl.com/gojkazs.png). 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](http://mathurl.com/hxz543o.png). 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`.

	Stars	Issues	Version	Updated	Created
@stdlib/stdlib			0.2.0	3 months ago	8 years ago
@tensorflow/tfjs	18,119	475	4.18.0	6 days ago	6 years ago
ml	2,500	26	6.0.0	3 years ago	10 years ago
regression	932	46	2.0.1	6 years ago	10 years ago
vega-statistics			1.9.0	a year ago	8 years ago

@stdlib/stdlib vs @tensorflow/tfjs vs ml vs regression vs vega-statistics

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