@stdlib/stats-incr-rss

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incrrss

Compute the residual sum of squares (RSS) incrementally.

The residual sum of squares (also referred to as the sum of squared residuals (SSR) and the sum of squared errors (SSE)) is defined as

Installation

npm install @stdlib/stats-incr-rss

Usage

var incrrss = require( '@stdlib/stats-incr-rss' );

incrrss()

Returns an accumulator function which incrementally computes the residual sum of squares.

var accumulator = incrrss();

accumulator( [x, y] )

If provided input values x and y, the accumulator function returns an updated residual sum of squares. If not provided input values x and y, the accumulator function returns the current residual sum of squares.

var accumulator = incrrss();

var r = accumulator( 2.0, 3.0 );
// returns 1.0

r = accumulator( -1.0, -4.0 );
// returns 10.0

r = accumulator( -3.0, 5.0 );
// returns 74.0

r = accumulator();
// returns 74.0

Notes

• Input values are not type checked. If provided NaN or a value which, when used in computations, results in NaN, the accumulated value is NaN for all future invocations. If non-numeric inputs are possible, you are advised to type check and handle accordingly before passing the value to the accumulator function.

Examples

var randu = require( '@stdlib/random-base-randu' );
var incrrss = require( '@stdlib/stats-incr-rss' );

var accumulator;
var v1;
var v2;
var i;

// Initialize an accumulator:
accumulator = incrrss();

// For each simulated datum, update the residual sum of squares...
for ( i = 0; i < 100; i++ ) {
    v1 = ( randu()*100.0 ) - 50.0;
    v2 = ( randu()*100.0 ) - 50.0;
    accumulator( v1, v2 );
}
console.log( accumulator() );

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See Also

• @stdlib/stats-incr/mrss: compute a moving residual sum of squares (RSS) incrementally.
• @stdlib/stats-incr/mse: compute the mean squared error (MSE) incrementally.
• @stdlib/stats-incr/rmse: compute the root mean squared error (RMSE) incrementally.

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Notice

This package is part of stdlib, a standard library for JavaScript and Node.js, with an emphasis on numerical and scientific computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.

For more information on the project, filing bug reports and feature requests, and guidance on how to develop stdlib, see the main project repository.

Community

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License

See LICENSE.

Copyright

Copyright © 2016-2024. The Stdlib Authors.