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Stat1

This is a statistic class that accepts one or two unidimensional arrays of data. It returns a stat array using the getStats() method. If only one array is sent, it will return the min,max, sum, median, average and standard deviation for this array. If two arrays are sent, in addition to these values for both arrays, a linear regression line is computed with its correlation r and 't'test. Median correction applied on 02/26/01.

Info

Source Code ( 173 lines )

<?php

/*
 * This is a statistic class that accepts one or two unidimensional arrays of data.
 * It returns a stat array using the getStats() method.
 * If only one array is sent, it will return the min,max, sum,  median, average and standard 
 * deviation for this array. If two arrays are sent, in addition to these values for both
 * arrays, a linear regression line is computed: m is the slope b is the Yaxis intersect,
 * r is the correlation, a measure of the quality of the data fit by the regression line (r=1 
 * being a perfect fit) and t is the "t"test, to test whether the association between X and Y 
 * data is real or merely apparent, I leave the user to interpret this test himself. (See the
 * tutorial and the table at: http://www.bmj.com/collections/statsbk/apptabb.html).
 * The regression line will be calculated only if both arrays have the same number of samples. 
 * Note that it is the responsibility of the user to make sure that his data are in linear form
 * (For the linear regression to work, Median and Average values should be close).
 * I tried to use the histogram class wrote by Jesus Castagnetto as an inheritance for
 * this script, but it was too complicated since that I needed two arrays. Probably the other
 * way around, using Stat1 as an inheritance for an histogram class, will be easier now. 
 * Tested with PHP4 beta 4.
 * (c) Alain M. Samoun 3/12/00 alain@samoun.com
 * License: Same as the GNU's Free Software Foundation or the open source PHP license as you
   prefer;-).
    
    This library is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
    Lesser General Public License for more details.

 */


class Stat1 {

/* variables */

	var $N,$NY,$MINX,$MAXX,$MAXY,$MINY,$AVGX,$AVGY,$STDVX,$STDVY,$SUMX,$SUMY,$SUMX2,$SUMY2,$SUMXY;
	var $X = array(),$Y = array();
	var $MEDX,$MEDY,$DEV,$m,$b,$r;
	var $STATS = array();

	/* Constructor */
		function Stat1($X="", $Y="",$DEV=0, $title="") {
		if ($X && $Y && $title) {
			$this->create($X,$Y,$title);
		}
	}

	/* Create the class */
	
	function create($X,$Y,$title="") {
		/* Check if we got a valid set of data*/
		(($this->N = count($X))  > 1) or die("Not enough data, number of values : ".$this->N."n");
		
		/* initialize values */
		$this->MINX = (float) min($X);
		$this->MAXX= (float) max($X);	
		
		/* Compute X Median */
		# Sort values in array X
		$XX = $X;
		sort ($XX);
		if ($this->N%2 == 0){
			$this->MEDX = (($XX[($this->N)/2])+($XX[-1+($this->N/2)]))/2;
		}else{
			$this->MEDX = $XX[floor($this->N/2)];
		}
		
		$this->NY = count($Y);
		$this->MINY = (float) min($Y);
		$this->MAXY= (float) max($Y);
		
		/* Compute Y Median */
		# Sort values in array Y
		$YY = $Y;
		sort ($YY);
		if ($this->NY%2 == 0){
			$this->MEDY = (($YY[($this->NY)/2])+($YY[-1+($this->NY/2)]))/2;
		}else{
			$this->MEDY = $YY[floor($this->NY/2)];
		}
		
		 
		$this->setTitle($title);
		
		
		
		
		/* stats */
		for ($i = 0; $i < $this->N ; $i++) {
			$this->SUMX += (float) $X[$i];
			$this->SUMX2 += (float) pow($X[$i],2);
			$this->SUMXY += (float) $X[$i]* (float)$Y[$i];
		}
		$this->AVGX = (float) $this->SUMX/ (float) $this->N;
		$this->STDVX = (float) sqrt(($this->SUMX2 - $this->N*pow($this->AVGX,2))/(float)($this->N - 1));

		
		for ($i = 0; $i < $this->NY ; $i++) {
			$this->SUMY += (float) $Y[$i];
			$this->SUMY2 += (float) pow($Y[$i],2);
		}
		$this->AVGY = (float) $this->SUMY/ (float)$this->NY;
		$this->STDVY = (float) sqrt(($this->SUMY2 - $this->NY*pow($this->AVGY,2))/(float)($this->NY - 1));
		
		if ($this->NY == $this->N){
			$this->DEV = (float) (($this->SUMX2 * $this->N)- ($this->SUMX * $this->SUMX));
			$this->m = (float)(($this->SUMXY * $this->N)- ($this->SUMX * $this->SUMY))/$this->DEV;
			$this->b = (float)(($this->SUMX2 * $this->SUMY)- ($this->SUMXY * $this->SUMX))/$this->DEV;
			$this->r = (float) ($this->SUMXY -($this->N * $this->AVGX * $this->AVGY))/(($this->N-1)*$this->STDVX * $this->STDVY);
			$this->t = (float) $this->r * sqrt(($this->N -2)/(1- pow($this->r,2)));
		}
	/* make the STATS array */
		$this->STATS =	array (
									N=>$this->N,
									NY=>$this->NY,
									MINX=>$this->MINX,
									MAXX=>$this->MAXX,
									SUMX=>$this->SUMX,
									MEDX=>$this->MEDX,
									AVGX=>$this->AVGX,
									STDVX=>$this->STDVX,
									MINY=>$this->MINY,
									MAXY=>$this->MAXY,
									SUMY=>$this->SUMY,
									MEDY=>$this->MEDY,
									AVGY=>$this->AVGY,
									STDVY=>$this->STDVY,
									m=>$this->m,
									b=>$this->b,
									r=>$this->r,
									t=>$this->t
									
								);

	}
	
	
	/* sets the Title */
	function setTitle($title) {
		$this->TITLE=$title;
	}

	/* send back STATS array */
	function getStats() {
		return $this->STATS;
	}
	
	
	/* simple printStats */
	function printStats() {
		$s = "Statistics for : ".$this->TITLE."n";
		$s .= "Number of data in X axis: $this->N n";
		$s .= sprintf("Min = %-2.4ftMax = %-2.4ftAvg = %-2.4ftMED = %-2.4ftStDev = %-2.4f n",$this->MINX,$this->MAXX,$this->AVGX,$this->MEDX, $this->STDVX  );
		$s .= "nNumber of data in Y axis: $this->NY n";
		$s .= sprintf("Min = %-2.4ftMax = %-2.4ftAvg = %-2.4ftMED = %-2.4ftStDev = %-2.4f n",$this->MINY,$this->MAXY,$this->AVGY,$this->MEDY, $this->STDVY  );
		if($this->NY ==$this->N){	
			$s .= "nLinear regression line: n";
			if ($this->b >=0){
				$s .= "Y = $this->m X + $this->b   with correlation (r)= $this->rn  "t"test (df=$this->N-2) = $this->t";
			}else{
				$s .= "Y = $this->m X  $this->b    with correlation (r)= $this->rn  "t"test (df=$this->N-2) = $this->t";
			}
		}
		echo $s;
		
		
	}
	
	
	
} /* end of Stat1 class */

?>