Compute the mean error (ME) incrementally.

The mean error is defined as

upper M upper E equals StartFraction 1 Over n EndFraction sigma-summation Underscript i equals 0 Overscript n minus 1 Endscripts left-parenthesis y Subscript i Baseline minus x Subscript i Baseline right-parenthesis


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


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


  • 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.


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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