time-to-botec/js/node_modules/@stdlib/stats/incr/cv/README.md

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# incrcv

> Compute the [coefficient of variation][coefficient-of-variation] (CV) incrementally.

<section class="intro">

The [corrected sample standard deviation][sample-stdev] is defined as

<!-- <equation class="equation" label="eq:corrected_sample_standard_deviation" align="center" raw="s = \sqrt{\frac{1}{n-1} \sum_{i=0}^{n-1} ( x_i - \bar{x} )^2}" alt="Equation for the corrected sample standard deviation."> -->

<div class="equation" align="center" data-raw-text="s = \sqrt{\frac{1}{n-1} \sum_{i=0}^{n-1} ( x_i - \bar{x} )^2}" data-equation="eq:corrected_sample_standard_deviation">
    <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@6dd7df93fd2c6e40fd0662844acf8b69b887dcec/lib/node_modules/@stdlib/stats/incr/cv/docs/img/equation_corrected_sample_standard_deviation.svg" alt="Equation for the corrected sample standard deviation.">
    <br>
</div>

<!-- </equation> -->

and the [arithmetic mean][arithmetic-mean] is defined as

<!-- <equation class="equation" label="eq:arithmetic_mean" align="center" raw="\bar{x} = \frac{1}{n} \sum_{i=0}^{n-1} x_i" alt="Equation for the arithmetic mean."> -->

<div class="equation" align="center" data-raw-text="\bar{x} = \frac{1}{n} \sum_{i=0}^{n-1} x_i" data-equation="eq:arithmetic_mean">
    <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@6dd7df93fd2c6e40fd0662844acf8b69b887dcec/lib/node_modules/@stdlib/stats/incr/cv/docs/img/equation_arithmetic_mean.svg" alt="Equation for the arithmetic mean.">
    <br>
</div>

<!-- </equation> -->

The [coefficient of variation][coefficient-of-variation] (also known as **relative standard deviation**, RSD) is defined as

<!-- <equation class="equation" label="eq:coefficient_of_variation" align="center" raw="c_v = \frac{s}{\bar{x}}" alt="Equation for the coefficient of variation (CV)."> -->

<div class="equation" align="center" data-raw-text="c_v = \frac{s}{\bar{x}}" data-equation="eq:coefficient_of_variation">
    <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@6dd7df93fd2c6e40fd0662844acf8b69b887dcec/lib/node_modules/@stdlib/stats/incr/cv/docs/img/equation_coefficient_of_variation.svg" alt="Equation for the coefficient of variation (CV).">
    <br>
</div>

<!-- </equation> -->

</section>

<!-- /.intro -->

<section class="usage">

## Usage

```javascript
var incrcv = require( '@stdlib/stats/incr/cv' );
```

#### incrcv( \[mean] )

Returns an accumulator `function` which incrementally computes the [coefficient of variation][coefficient-of-variation].

```javascript
var accumulator = incrcv();
```

If the mean is already known, provide a `mean` argument.

```javascript
var accumulator = incrcv( 3.0 );
```

#### accumulator( \[x] )

If provided an input value `x`, the accumulator function returns an updated accumulated value. If not provided an input value `x`, the accumulator function returns the current accumulated value.

```javascript
var accumulator = incrcv();

var cv = accumulator( 2.0 );
// returns 0.0

cv = accumulator( 1.0 ); // => s^2 = ((2-1.5)^2+(1-1.5)^2) / (2-1)
// returns ~0.47

cv = accumulator( 3.0 ); // => s^2 = ((2-2)^2+(1-2)^2+(3-2)^2) / (3-1)
// returns 0.5

cv = accumulator();
// returns 0.5
```

</section>

<!-- /.usage -->

<section class="notes">

## 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.
-   The [coefficient of variation][coefficient-of-variation] is typically computed on nonnegative values. The measure may lack meaning for data which can assume both positive and negative values.
-   For small and moderately sized samples, the accumulated value tends to be too low and is thus a **biased** estimator. Provided the generating distribution is known (e.g., a normal distribution), you may want to adjust the accumulated value or use an alternative implementation providing an unbiased estimator.

</section>

<!-- /.notes -->

<section class="examples">

## Examples

<!-- eslint no-undef: "error" -->

```javascript
var randu = require( '@stdlib/random/base/randu' );
var incrcv = require( '@stdlib/stats/incr/cv' );

var accumulator;
var v;
var i;

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

// For each simulated datum, update the coefficient of variation...
for ( i = 0; i < 100; i++ ) {
    v = randu() * 100.0;
    accumulator( v );
}
console.log( accumulator() );
```

</section>

<!-- /.examples -->

<section class="links">

[coefficient-of-variation]: https://en.wikipedia.org/wiki/Coefficient_of_variation

[arithmetic-mean]: https://en.wikipedia.org/wiki/Arithmetic_mean

[sample-stdev]: https://en.wikipedia.org/wiki/Standard_deviation

</section>

<!-- /.links -->