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Standard library statistical functions.

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<!-- @license Apache-2.0 Copyright (c) 2018 The Stdlib Authors. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> # incrapcorr > Compute a sample absolute [Pearson product-moment correlation coefficient][pearson-correlation] incrementally. <section class="intro"> The [Pearson product-moment correlation coefficient][pearson-correlation] between random variables `X` and `Y` is defined as <!-- <equation class="equation" label="eq:pearson_correlation_coefficient" align="center" raw="\rho_{X,Y} = \frac{\operatorname{cov}(X,Y)}{\sigma_X \sigma_Y}" alt="Equation for the Pearson product-moment correlation coefficient."> --> <div class="equation" align="center" data-raw-text="\rho_{X,Y} = \frac{\operatorname{cov}(X,Y)}{\sigma_X \sigma_Y}" data-equation="eq:pearson_correlation_coefficient"> <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@80f96253bf726f33bc71d8eb68037ab203ae4cf9/lib/node_modules/@stdlib/stats/incr/apcorr/docs/img/equation_pearson_correlation_coefficient.svg" alt="Equation for the Pearson product-moment correlation coefficient."> <br> </div> --> <!-- </equation> --> where the numerator is the [covariance][covariance] and the denominator is the product of the respective standard deviations. For a sample of size `n`, the sample [Pearson product-moment correlation coefficient][pearson-correlation] is defined as <!-- <equation class="equation" label="eq:sample_pearson_correlation_coefficient" align="center" raw="r = \frac{\displaystyle\sum_{i=0}^{n-1} (x_i - \bar{x})(y_i - \bar{y})}{\displaystyle\sqrt{\sum_{i=0}^{n-1} (x_i - \bar{x})^2} \sqrt{\sum_{i=0}^{n-1} (y_i - \bar{y})^2}}" alt="Equation for the sample Pearson product-moment correlation coefficient."> --> <!-- <div class="equation" align="center" data-raw-text="r = \frac{\displaystyle\sum_{i=0}^{n-1} (x_i - \bar{x})(y_i - \bar{y})}{\displaystyle\sqrt{\sum_{i=0}^{n-1} (x_i - \bar{x})^2} \sqrt{\sum_{i=0}^{n-1} (y_i - \bar{y})^2}}" data-equation="eq:sample_pearson_correlation_coefficient"> <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@80f96253bf726f33bc71d8eb68037ab203ae4cf9/lib/node_modules/@stdlib/stats/incr/apcorr/docs/img/equation_sample_pearson_correlation_coefficient.svg" alt="Equation for the sample Pearson product-moment correlation coefficient."> <br> </div> <!-- </equation> --> The sample **absolute** [Pearson product-moment correlation coefficient][pearson-correlation] is thus defined as the absolute value of the sample [Pearson product-moment correlation coefficient][pearson-correlation]. </section> <!-- /.intro --> <section class="usage"> ## Usage ```javascript var incrapcorr = require( '@stdlib/stats/incr/apcorr' ); ``` #### incrapcorr( \[mx, my] ) Returns an accumulator `function` which incrementally computes a sample absolute [Pearson product-moment correlation coefficient][pearson-correlation]. ```javascript var accumulator = incrapcorr(); ``` If the means are already known, provide `mx` and `my` arguments. ```javascript var accumulator = incrapcorr( 3.0, -5.5 ); ``` #### accumulator( \[x, y] ) If provided input value `x` and `y`, the accumulator function returns an updated accumulated value. If not provided input values `x` and `y`, the accumulator function returns the current accumulated value. ```javascript var accumulator = incrapcorr(); var v = accumulator( 2.0, 1.0 ); // returns 0.0 v = accumulator( 1.0, -5.0 ); // returns 1.0 v = accumulator( 3.0, 3.14 ); // returns ~0.965 v = accumulator(); // returns ~0.965 ``` </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. - In comparison to the sample [Pearson product-moment correlation coefficient][pearson-correlation], the sample absolute [Pearson product-moment correlation coefficient][pearson-correlation] is useful when only concerned with the strength of the correlation and not the direction. </section> <!-- /.notes --> <section class="examples"> ## Examples <!-- eslint no-undef: "error" --> ```javascript var randu = require( '@stdlib/random/base/randu' ); var incrapcorr = require( '@stdlib/stats/incr/apcorr' ); var accumulator; var x; var y; var i; // Initialize an accumulator: accumulator = incrapcorr(); // For each simulated datum, update the sample absolute correlation coefficient... for ( i = 0; i < 100; i++ ) { x = randu() * 100.0; y = randu() * 100.0; accumulator( x, y ); } console.log( accumulator() ); ``` </section> <!-- /.examples --> <!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. --> <section class="related"> * * * ## See Also - <span class="package-name">[`@stdlib/stats/incr/mapcorr`][@stdlib/stats/incr/mapcorr]</span><span class="delimiter">: </span><span class="description">compute a moving sample absolute Pearson product-moment correlation coefficient incrementally.</span> - <span class="package-name">[`@stdlib/stats/incr/pcorr`][@stdlib/stats/incr/pcorr]</span><span class="delimiter">: </span><span class="description">compute a sample Pearson product-moment correlation coefficient.</span> - <span class="package-name">[`@stdlib/stats/incr/pcorr2`][@stdlib/stats/incr/pcorr2]</span><span class="delimiter">: </span><span class="description">compute a squared sample Pearson product-moment correlation coefficient.</span> </section> <!-- /.related --> <!-- Section for all links. Make sure to keep an empty line after the `section` element and another before the `/section` close. --> <section class="links"> [pearson-correlation]: https://en.wikipedia.org/wiki/Pearson_correlation_coefficient [covariance]: https://en.wikipedia.org/wiki/Covariance <!-- <related-links> --> [@stdlib/stats/incr/mapcorr]: https://github.com/stdlib-js/stats/tree/main/incr/mapcorr [@stdlib/stats/incr/pcorr]: https://github.com/stdlib-js/stats/tree/main/incr/pcorr [@stdlib/stats/incr/pcorr2]: https://github.com/stdlib-js/stats/tree/main/incr/pcorr2 <!-- </related-links> --> </section> <!-- /.links -->