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    stats_stat_correlation

    (PECL stats >= 1.0.0)

    stats_stat_correlation — Returns the Pearson correlation coefficient of two data sets

    Descrição

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

    Returns the Pearson correlation coefficient between arr1 and arr2.

    Parâmetros

    arr1

    The first array

    arr2

    The second array

    Valor Retornado

    Returns the Pearson correlation coefficient between arr1 and arr2, or false on failure.

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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;
    ?>
    up
    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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