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    stats_stat_correlation

    (PECL stats >= 1.0.0)

    stats_stat_correlation — Возвращает коэффициент корреляции Пирсона двух наборов данных

    Описание

    stats_stat_correlation(array $arr1, array $arr2): float

    Возвращает коэффициент корреляции Пирсона для arr1 и arr2.

    Список параметров

    arr1

    Первый массив

    arr2

    Второй массив

    Возвращаемые значения

    Возвращает коэффициент корреляции Пирсона для arr1 и arr2, или false в случае возникновения ошибки.

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    User Contributed Notes 3 notes

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    17
    non at dot com
    9 years ago
    undefined for me, thus I've implemented my own correlation which is much faster and simpler than the one provided above.

    function Corr($x, $y){

    $length= count($x);
    $mean1=array_sum($x) / $length;
    $mean2=array_sum($y) / $length;

    $a=0;
    $b=0;
    $axb=0;
    $a2=0;
    $b2=0;

    for($i=0;$i<$length;$i++)
    {
    $a=$x[$i]-$mean1;
    $b=$y[$i]-$mean2;
    $axb=$axb+($a*$b);
    $a2=$a2+ pow($a,2);
    $b2=$b2+ pow($b,2);
    }

    $corr= $axb / sqrt($a2*$b2);

    return $corr;
    }
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    1
    admin at maychu dot net (Le Cong)
    15 years ago
    Please note that this function is reserved for two arrays with continued numbers inside (just integers).
    I tested this function and found that it calculate the Pearson's Correlation Coefficient of two arrays.
    ---
    Here's suggested documentation:

    stats_stat_correlation — Calculate the Pearson's Correlation Coefficient of two arrays of continued numbers.

    Parameters:
    arr1 = array (integer1a, interger2a ...)
    arr2 = array (integer1b, interger2b ...))
    (Note that the count of elements in two arrays must be equal)

    Return value: Pearson's Correlation Coefficient in decimal format (ex. 0.934399822094)

    Code examples:

    <?php
    // Provided by admin@maychu.net
    $array_x = array(5,3,6,7,4,2,9,5);
    $array_y = array(4,3,4,8,3,2,10,5);
    $pearson = stats_stat_correlation($array_x,$array_y);
    echo     $pearson;
    ?>
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    2
    umar dot anjum at ymail dot com
    15 years ago
    I tried to use this function, but got a not-defined error. Anyway, I have created a set of functions to replace this:

    <?php

    //Since Correlation needs two arrays, I am hardcoding them
    $array1[0] = 59.3;
    $array1[1] = 61.2;
    $array1[2] = 56.8
    $array1    [3] = 97.55;

    $array2[0] = 565.82;
    $array2[1] = 54.568;
    $array2[2] = 84.22;
    $array2[3] = 483.55;

    //To find the correlation of the two arrays, simply call the 
    //function Correlation that takes two arrays:

    $correlation = Correlation($array1, $array2);

    //Displaying the calculated Correlation:
    print $correlation;

    //The functions that work behind the scene to calculate the
    //correlation

    function Correlation($arr1, $arr2)
    {       
            $correlation = 0;
       
            $k = SumProductMeanDeviation($arr1, $arr2);
            $ssmd1 = SumSquareMeanDeviation($arr1);
            $ssmd2 = SumSquareMeanDeviation($arr2);
       
            $product = $ssmd1 * $ssmd2;
       
            $res = sqrt($product);
       
            $correlation = $k / $res;
       
        return     $correlation;
    }

    function     SumProductMeanDeviation($arr1, $arr2)
    {
            $sum = 0;
       
            $num = count($arr1);
       
        for(    $i=0; $i<$num; $i++)
        {
                $sum = $sum + ProductMeanDeviation($arr1, $arr2, $i);
        }
       
        return     $sum;
    }

    function     ProductMeanDeviation($arr1, $arr2, $item)
    {
        return (    MeanDeviation($arr1, $item) * MeanDeviation($arr2, $item));
    }

    function     SumSquareMeanDeviation($arr)
    {
            $sum = 0;
       
            $num = count($arr);
       
        for(    $i=0; $i<$num; $i++)
        {
                $sum = $sum + SquareMeanDeviation($arr, $i);
        }
       
        return     $sum;
    }

    function     SquareMeanDeviation($arr, $item)
    {
        return     MeanDeviation($arr, $item) * MeanDeviation($arr, $item);
    }

    function     SumMeanDeviation($arr)
    {
            $sum = 0;
       
            $num = count($arr);
       
        for(    $i=0; $i<$num; $i++)
        {
                $sum = $sum + MeanDeviation($arr, $i);
        }
       
        return     $sum;
    }

    function     MeanDeviation($arr, $item)
    {
            $average = Average($arr);
       
        return     $arr[$item] - $average;
    }   

    function     Average($arr)
    {
            $sum = Sum($arr);
            $num = count($arr);
       
        return     $sum/$num;
    }

    function     Sum($arr)
    {
        return     array_sum($arr);
    }

    ?>
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