# incrmae
> Compute the [mean absolute error][mean-absolute-error] (MAE) incrementally.
The [mean absolute error][mean-absolute-error] is defined as
## Usage
```javascript
var incrmae = require( '@stdlib/stats/incr/mae' );
```
#### incrmae()
Returns an accumulator `function` which incrementally computes the [mean absolute error][mean-absolute-error].
```javascript
var accumulator = incrmae();
```
#### accumulator( \[x, y] )
If provided input values `x` and `y`, the accumulator function returns an updated [mean absolute error][mean-absolute-error]. If not provided input values `x` and `y`, the accumulator function returns the current [mean absolute error][mean-absolute-error].
```javascript
var accumulator = incrmae();
var m = accumulator( 2.0, 3.0 );
// returns 1.0
m = accumulator( -1.0, -4.0 );
// returns 2.0
m = accumulator( -3.0, 5.0 );
// returns 4.0
m = accumulator();
// returns 4.0
```
## 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.
- **Warning**: the [mean absolute error][mean-absolute-error] is scale-dependent and, thus, the measure should **not** be used to make comparisons between datasets having different scales.
## Examples
```javascript
var randu = require( '@stdlib/random/base/randu' );
var incrmae = require( '@stdlib/stats/incr/mae' );
var accumulator;
var v1;
var v2;
var i;
// Initialize an accumulator:
accumulator = incrmae();
// For each simulated datum, update the mean absolute 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() );
```
[mean-absolute-error]: https://en.wikipedia.org/wiki/Mean_absolute_error
