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incrme

Compute the mean error (ME) incrementally.

The mean error is defined as

$$\mathop{\mathrm{ME}} = \frac{1}{n} \sum_{i=0}^{n-1} (y_i - x_i)$$

Installation

npm install @stdlib/stats-incr-me

Alternatively,

• To load the package in a website via a script tag without installation and bundlers, use the ES Module available on the esm branch (see README).
• If you are using Deno, visit the deno branch (see README for usage intructions).
• For use in Observable, or in browser/node environments, use the Universal Module Definition (UMD) build available on the umd branch (see README).

The branches.md file summarizes the available branches and displays a diagram illustrating their relationships.

To view installation and usage instructions specific to each branch build, be sure to explicitly navigate to the respective README files on each branch, as linked to above.

Usage

var incrme = require( '@stdlib/stats-incr-me' );

incrme()

Returns an accumulator function which incrementally computes the mean error.

var accumulator = incrme();

accumulator( [x, y] )

If provided input values x and y, the accumulator function returns an updated mean error. If not provided input values x and y, the accumulator function returns the current mean error.

var accumulator = incrme();

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

m = accumulator( -1.0, -4.0 );
// returns -1.0

m = accumulator( -3.0, 5.0 );
// returns 2.0

m = accumulator();
// returns 2.0

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.
• Be careful when interpreting the mean error as errors can cancel. This stated, that errors can cancel makes the mean error suitable for measuring the bias in forecasts.
• Warning: the mean error is scale-dependent and, thus, the measure should not be used to make comparisons between datasets having different scales.

Examples

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

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

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

// For each simulated datum, update the mean error...
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/mae: compute the mean absolute error (MAE) incrementally.
• @stdlib/stats-incr/mean: compute an arithmetic mean incrementally.
• @stdlib/stats-incr/mme: compute a moving mean error (ME) 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.