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# incrpcorrdist

> Compute a [sample Pearson product-moment correlation distance][pearson-correlation] incrementally.

<section class="intro">

The [sample Pearson product-moment correlation distance][pearson-correlation] is defined as

<!-- <equation class="equation" label="eq:pearson_distance" align="center" raw="d_{x,y} = 1 - r_{x,y} = 1 - \frac{\operatorname{cov_n(x,y)}}{\sigma_x \sigma_y}" alt="Equation for the Pearson product-moment correlation distance."> -->

<div class="equation" align="center" data-raw-text="d_{x,y} = 1 - r_{x,y} = 1 - \frac{\operatorname{cov_n(x,y)}}{\sigma_x \sigma_y}" data-equation="eq:pearson_distance">
    <img src="https://cdn.rawgit.com/stdlib-js/stdlib/27e2a43c70db648bb5bbc3fd0cdee050c25adc0b/lib/node_modules/@stdlib/stats/incr/pcorrdist/docs/img/equation_pearson_distance.svg" alt="Equation for the Pearson product-moment correlation distance.">
    <br>
</div>

<!-- </equation> -->

where `r` is the [sample Pearson product-moment correlation coefficient][pearson-correlation], `cov(x,y)` is the sample covariance, and `σ` corresponds to the sample standard deviation. As `r` resides on the interval `[-1,1]`, `d` resides on the interval `[0,2]`.

</section>

<!-- /.intro -->

<section class="usage">

## Usage

```javascript
var incrpcorrdist = require( '@stdlib/stats/incr/pcorrdist' );
```

#### incrpcorrdist( \[mx, my] )

Returns an accumulator `function` which incrementally computes a [sample Pearson product-moment correlation distance][pearson-correlation].

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

If the means are already known, provide `mx` and `my` arguments.

```javascript
var accumulator = incrpcorrdist( 3.0, -5.5 );
```

#### accumulator( \[x, y] )

If provided input value `x` and `y`, the accumulator function returns an updated [sample correlation coefficient][pearson-correlation]. If not provided input values `x` and `y`, the accumulator function returns the current [sample correlation coefficient][pearson-correlation].

```javascript
var accumulator = incrpcorrdist();

var d = accumulator( 2.0, 1.0 );
// returns 1.0

d = accumulator( 1.0, -5.0 );
// returns 0.0

d = accumulator( 3.0, 3.14 );
// returns ~0.035

d = accumulator();
// returns ~0.035
```

</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.

</section>

<!-- /.notes -->

<section class="examples">

## Examples

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

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

var accumulator;
var x;
var y;
var i;

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

// For each simulated datum, update the sample correlation distance...
for ( i = 0; i < 100; i++ ) {
    x = randu() * 100.0;
    y = randu() * 100.0;
    accumulator( x, y );
}
console.log( accumulator() );
```

</section>

<!-- /.examples -->

<section class="links">

[pearson-correlation]: https://en.wikipedia.org/wiki/Pearson_correlation_coefficient

</section>

<!-- /.links -->
