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stats_stat_correlation

(PECL stats >= 1.0.0)

stats_stat_correlationRenvoie le coefficient de corrélation de Pearson de deux ensembles de données

Description

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

Renvoie le coefficient de corrélation de Pearson entre arr1 et arr2.

Liste de paramètres

arr1

Le premier tableau

arr2

Le second tableau

Valeurs de retour

Renvoie le coefficient de corrélation de Pearson entre arr1 et arr2, ou false en cas d'échec.

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User Contributed Notes 3 notes

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16
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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