stats_stat_correlation

(PECL stats >= 1.0.0)

stats_stat_correlation — Returns the Pearson correlation coefficient of two data sets

说明

float stats_stat_correlation ( array $arr1 , array $arr2 )

Returns the Pearson correlation coefficient between arr1 and arr2.

参数

arr1

The first array

arr2

The second array

返回值

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

User Contributed Notes

non at dot com 19-Jan-2015 08:30
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;
}
admin at maychu dot net (Le Cong) 15-Jun-2009 06:58
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;
?>
umar dot anjum at ymail dot com 05-Mar-2009 07:05
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);
}

?>