smr

This is an implementation of multiple regression in JavaScript. It is mostly incremental -- you can incrementally add observations and the coefficient calculation will still be quick for lower-dimensional problems. This is particularly useful if you want to run multiple regression in real-time or over very large datasets that won't fit into memory all at once.

Quick Start

From Node.js:

npm install smr
node

var smr = require('smr')

In the browser use browserify.

Example

var regression = new smr.Regression({ numX: 2, numY: 1 })

regression.push({ x: [10, 11], y: [100] })
regression.push({ x: [9, 12], y: [99] })

regression.calculateCoefficients() // Returns [[4.29], [5.29]]

regression.push({ x: [8, 15], y: [80] })
regression.calculateCoefficients() // Returns [[-0.16], [10.55]]
regression.hypothesize({ x: [1, 2] }) // Returns [20.93]

Formula

To calculate multiple regression, we use the following formula:

(X' * X) ^ -1 * X' * Y

Where X is a matrix of independent variables, X' is its transpose, Y is a matrix of dependent variables, and ^ -1 indicates taking the pseudoinverse.

Mechanics

Internally, we incrementally calculate the two matrix products, X' * X and X' * Y, as new observations are added. Whenever you request the coefficients, either through calculateCoefficients() or indirectly through hypothesize(), the library will find the pseudoinverse of the readily-available X' * X and multiply this by the readily-available X' * Y.

Tests

git clone https://github.com/omphalos/smr
cd smr
npm install

Then you can run unit tests with:

npm t

You can run a simple performance t with:

node ./performance.js 500

This will show the performance with a harder (500-dimensional) problem. The bottleneck with higher-dimensional problems is the pseudoinverse calculation, which is something like N^3. As an example, on a t machine, 500 dimensions takes over 11 seconds, whereas a 200-dimensional problems takes ~100 milliseconds.