Linear Regression in Javascript

Question

I want to do Least Squares Fitting in Javascript in a web browser.

Currently users enter data point information using HTML text inputs and then I grab that data with jQuery and graph it with Flot.

After the user had entered in their data points I would like to present them with a "line of best fit". I imagine I would calculate the linear, polynomial, exponential and logarithmic equations and then choose the one with the highest R^2 value.

I can't seem to find any libraries that will help me to do this though. I stumbled upon jStat, but it is completely missing documentation (as far as I can find) and after digging through the the source code it doesn't seem to have any linear regression functionality built in--I'm basing this purely on function names however.

Does anyone know any Javascript libraries that offer simple regression analysis?

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The hope would be that I could use the library like so...

If I had some set of scatter points in an array var points = [[3,4],[15,45],...[23,78]], I would be able to hand that to some function like lin_reg(points) and it would return something like [7.12,3] if the linear equation was y = 7.12 x + 3.

Answer 1

The simplest solution I found for the question at hand can be found in the following post: http://trentrichardson.com/2010/04/06/compute-linear-regressions-in-javascript/

Note that in addition to the linear equation, it also returns the R2 score, which can be useful.

** EDIT **

Here is the actual code snippet:

function linearRegression(y,x){
        var lr = {};
        var n = y.length;
        var sum_x = 0;
        var sum_y = 0;
        var sum_xy = 0;
        var sum_xx = 0;
        var sum_yy = 0;

        for (var i = 0; i < y.length; i++) {

            sum_x += x[i];
            sum_y += y[i];
            sum_xy += (x[i]*y[i]);
            sum_xx += (x[i]*x[i]);
            sum_yy += (y[i]*y[i]);
        } 

        lr['slope'] = (n * sum_xy - sum_x * sum_y) / (n*sum_xx - sum_x * sum_x);
        lr['intercept'] = (sum_y - lr.slope * sum_x)/n;
        lr['r2'] = Math.pow((n*sum_xy - sum_x*sum_y)/Math.sqrt((n*sum_xx-sum_x*sum_x)*(n*sum_yy-sum_y*sum_y)),2);

        return lr;
}

To use this you just need to pass it two arrays, known_y's and known_x's, so this is what you might pass:

var known_y = [1, 2, 3, 4];
var known_x = [5.2, 5.7, 5.0, 4.2];

var lr = linearRegression(known_y, known_x);
// now you have:
// lr.slope
// lr.intercept
// lr.r2