@stdlib/stats-incr-rss
v0.2.2
Published
Compute the residual sum of squares (RSS) incrementally.
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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 inNaN
, the accumulated value isNaN
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() );
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.
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
License
See LICENSE.
Copyright
Copyright © 2016-2024. The Stdlib Authors.