class PHPExcel_Calculation_Statistical in Loft Data Grids 6.2
Same name and namespace in other branches
- 7.2 vendor/phpoffice/phpexcel/Classes/PHPExcel/Calculation/Statistical.php \PHPExcel_Calculation_Statistical
PHPExcel_Calculation_Statistical
@category PHPExcel @package PHPExcel_Calculation @copyright Copyright (c) 2006 - 2014 PHPExcel (http://www.codeplex.com/PHPExcel)
Hierarchy
Expanded class hierarchy of PHPExcel_Calculation_Statistical
File
- vendor/
phpoffice/ phpexcel/ Classes/ PHPExcel/ Calculation/ Statistical.php, line 62
View source
class PHPExcel_Calculation_Statistical {
private static function _checkTrendArrays(&$array1, &$array2) {
if (!is_array($array1)) {
$array1 = array(
$array1,
);
}
if (!is_array($array2)) {
$array2 = array(
$array2,
);
}
$array1 = PHPExcel_Calculation_Functions::flattenArray($array1);
$array2 = PHPExcel_Calculation_Functions::flattenArray($array2);
foreach ($array1 as $key => $value) {
if (is_bool($value) || is_string($value) || is_null($value)) {
unset($array1[$key]);
unset($array2[$key]);
}
}
foreach ($array2 as $key => $value) {
if (is_bool($value) || is_string($value) || is_null($value)) {
unset($array1[$key]);
unset($array2[$key]);
}
}
$array1 = array_merge($array1);
$array2 = array_merge($array2);
return True;
}
// function _checkTrendArrays()
/**
* Beta function.
*
* @author Jaco van Kooten
*
* @param p require p>0
* @param q require q>0
* @return 0 if p<=0, q<=0 or p+q>2.55E305 to avoid errors and over/underflow
*/
private static function _beta($p, $q) {
if ($p <= 0.0 || $q <= 0.0 || $p + $q > LOG_GAMMA_X_MAX_VALUE) {
return 0.0;
}
else {
return exp(self::_logBeta($p, $q));
}
}
// function _beta()
/**
* Incomplete beta function
*
* @author Jaco van Kooten
* @author Paul Meagher
*
* The computation is based on formulas from Numerical Recipes, Chapter 6.4 (W.H. Press et al, 1992).
* @param x require 0<=x<=1
* @param p require p>0
* @param q require q>0
* @return 0 if x<0, p<=0, q<=0 or p+q>2.55E305 and 1 if x>1 to avoid errors and over/underflow
*/
private static function _incompleteBeta($x, $p, $q) {
if ($x <= 0.0) {
return 0.0;
}
elseif ($x >= 1.0) {
return 1.0;
}
elseif ($p <= 0.0 || $q <= 0.0 || $p + $q > LOG_GAMMA_X_MAX_VALUE) {
return 0.0;
}
$beta_gam = exp(0 - self::_logBeta($p, $q) + $p * log($x) + $q * log(1.0 - $x));
if ($x < ($p + 1.0) / ($p + $q + 2.0)) {
return $beta_gam * self::_betaFraction($x, $p, $q) / $p;
}
else {
return 1.0 - $beta_gam * self::_betaFraction(1 - $x, $q, $p) / $q;
}
}
// function _incompleteBeta()
// Function cache for _logBeta function
private static $_logBetaCache_p = 0.0;
private static $_logBetaCache_q = 0.0;
private static $_logBetaCache_result = 0.0;
/**
* The natural logarithm of the beta function.
*
* @param p require p>0
* @param q require q>0
* @return 0 if p<=0, q<=0 or p+q>2.55E305 to avoid errors and over/underflow
* @author Jaco van Kooten
*/
private static function _logBeta($p, $q) {
if ($p != self::$_logBetaCache_p || $q != self::$_logBetaCache_q) {
self::$_logBetaCache_p = $p;
self::$_logBetaCache_q = $q;
if ($p <= 0.0 || $q <= 0.0 || $p + $q > LOG_GAMMA_X_MAX_VALUE) {
self::$_logBetaCache_result = 0.0;
}
else {
self::$_logBetaCache_result = self::_logGamma($p) + self::_logGamma($q) - self::_logGamma($p + $q);
}
}
return self::$_logBetaCache_result;
}
// function _logBeta()
/**
* Evaluates of continued fraction part of incomplete beta function.
* Based on an idea from Numerical Recipes (W.H. Press et al, 1992).
* @author Jaco van Kooten
*/
private static function _betaFraction($x, $p, $q) {
$c = 1.0;
$sum_pq = $p + $q;
$p_plus = $p + 1.0;
$p_minus = $p - 1.0;
$h = 1.0 - $sum_pq * $x / $p_plus;
if (abs($h) < XMININ) {
$h = XMININ;
}
$h = 1.0 / $h;
$frac = $h;
$m = 1;
$delta = 0.0;
while ($m <= MAX_ITERATIONS && abs($delta - 1.0) > PRECISION) {
$m2 = 2 * $m;
// even index for d
$d = $m * ($q - $m) * $x / (($p_minus + $m2) * ($p + $m2));
$h = 1.0 + $d * $h;
if (abs($h) < XMININ) {
$h = XMININ;
}
$h = 1.0 / $h;
$c = 1.0 + $d / $c;
if (abs($c) < XMININ) {
$c = XMININ;
}
$frac *= $h * $c;
// odd index for d
$d = -($p + $m) * ($sum_pq + $m) * $x / (($p + $m2) * ($p_plus + $m2));
$h = 1.0 + $d * $h;
if (abs($h) < XMININ) {
$h = XMININ;
}
$h = 1.0 / $h;
$c = 1.0 + $d / $c;
if (abs($c) < XMININ) {
$c = XMININ;
}
$delta = $h * $c;
$frac *= $delta;
++$m;
}
return $frac;
}
// function _betaFraction()
/**
* logGamma function
*
* @version 1.1
* @author Jaco van Kooten
*
* Original author was Jaco van Kooten. Ported to PHP by Paul Meagher.
*
* The natural logarithm of the gamma function. <br />
* Based on public domain NETLIB (Fortran) code by W. J. Cody and L. Stoltz <br />
* Applied Mathematics Division <br />
* Argonne National Laboratory <br />
* Argonne, IL 60439 <br />
* <p>
* References:
* <ol>
* <li>W. J. Cody and K. E. Hillstrom, 'Chebyshev Approximations for the Natural
* Logarithm of the Gamma Function,' Math. Comp. 21, 1967, pp. 198-203.</li>
* <li>K. E. Hillstrom, ANL/AMD Program ANLC366S, DGAMMA/DLGAMA, May, 1969.</li>
* <li>Hart, Et. Al., Computer Approximations, Wiley and sons, New York, 1968.</li>
* </ol>
* </p>
* <p>
* From the original documentation:
* </p>
* <p>
* This routine calculates the LOG(GAMMA) function for a positive real argument X.
* Computation is based on an algorithm outlined in references 1 and 2.
* The program uses rational functions that theoretically approximate LOG(GAMMA)
* to at least 18 significant decimal digits. The approximation for X > 12 is from
* reference 3, while approximations for X < 12.0 are similar to those in reference
* 1, but are unpublished. The accuracy achieved depends on the arithmetic system,
* the compiler, the intrinsic functions, and proper selection of the
* machine-dependent constants.
* </p>
* <p>
* Error returns: <br />
* The program returns the value XINF for X .LE. 0.0 or when overflow would occur.
* The computation is believed to be free of underflow and overflow.
* </p>
* @return MAX_VALUE for x < 0.0 or when overflow would occur, i.e. x > 2.55E305
*/
// Function cache for logGamma
private static $_logGammaCache_result = 0.0;
private static $_logGammaCache_x = 0.0;
private static function _logGamma($x) {
// Log Gamma related constants
static $lg_d1 = -0.5772156649015329;
static $lg_d2 = 0.42278433509846713;
static $lg_d4 = 1.791759469228055;
static $lg_p1 = array(
4.945235359296727,
201.8112620856775,
2290.8383738313464,
11319.672059033808,
28557.246356716354,
38484.962284437934,
26377.487876241954,
7225.813979700288,
);
static $lg_p2 = array(
4.974607845568932,
542.4138599891071,
15506.93864978365,
184793.29044456323,
1088204.7694688288,
3338152.96798703,
5106661.678927353,
3074109.0548505397,
);
static $lg_p4 = array(
14745.0216605994,
2426813.3694867045,
121475557.40450932,
2663432449.630977,
29403789566.34554,
170266573776.5399,
492612579337.7431,
560625185622.3951,
);
static $lg_q1 = array(
67.48212550303778,
1113.3323938571993,
7738.757056935398,
27639.870744033407,
54993.102062261576,
61611.22180066002,
36351.2759150194,
8785.536302431014,
);
static $lg_q2 = array(
183.03283993705926,
7765.049321445006,
133190.38279660742,
1136705.8213219696,
5267964.117437947,
13467014.543111017,
17827365.303532742,
9533095.591844354,
);
static $lg_q4 = array(
2690.5301758708993,
639388.5654300093,
41355999.30241388,
1120872109.616148,
14886137286.788137,
101680358627.24382,
341747634550.73773,
446315818741.9713,
);
static $lg_c = array(
-0.001910444077728,
0.0008417138778129501,
-0.0005952379913043012,
0.0007936507935003503,
-0.0027777777777776816,
0.08333333333333333,
0.0057083835261,
);
// Rough estimate of the fourth root of logGamma_xBig
static $lg_frtbig = 2.25E+76;
static $pnt68 = 0.6796875;
if ($x == self::$_logGammaCache_x) {
return self::$_logGammaCache_result;
}
$y = $x;
if ($y > 0.0 && $y <= LOG_GAMMA_X_MAX_VALUE) {
if ($y <= EPS) {
$res = -log(y);
}
elseif ($y <= 1.5) {
// ---------------------
// EPS .LT. X .LE. 1.5
// ---------------------
if ($y < $pnt68) {
$corr = -log($y);
$xm1 = $y;
}
else {
$corr = 0.0;
$xm1 = $y - 1.0;
}
if ($y <= 0.5 || $y >= $pnt68) {
$xden = 1.0;
$xnum = 0.0;
for ($i = 0; $i < 8; ++$i) {
$xnum = $xnum * $xm1 + $lg_p1[$i];
$xden = $xden * $xm1 + $lg_q1[$i];
}
$res = $corr + $xm1 * ($lg_d1 + $xm1 * ($xnum / $xden));
}
else {
$xm2 = $y - 1.0;
$xden = 1.0;
$xnum = 0.0;
for ($i = 0; $i < 8; ++$i) {
$xnum = $xnum * $xm2 + $lg_p2[$i];
$xden = $xden * $xm2 + $lg_q2[$i];
}
$res = $corr + $xm2 * ($lg_d2 + $xm2 * ($xnum / $xden));
}
}
elseif ($y <= 4.0) {
// ---------------------
// 1.5 .LT. X .LE. 4.0
// ---------------------
$xm2 = $y - 2.0;
$xden = 1.0;
$xnum = 0.0;
for ($i = 0; $i < 8; ++$i) {
$xnum = $xnum * $xm2 + $lg_p2[$i];
$xden = $xden * $xm2 + $lg_q2[$i];
}
$res = $xm2 * ($lg_d2 + $xm2 * ($xnum / $xden));
}
elseif ($y <= 12.0) {
// ----------------------
// 4.0 .LT. X .LE. 12.0
// ----------------------
$xm4 = $y - 4.0;
$xden = -1.0;
$xnum = 0.0;
for ($i = 0; $i < 8; ++$i) {
$xnum = $xnum * $xm4 + $lg_p4[$i];
$xden = $xden * $xm4 + $lg_q4[$i];
}
$res = $lg_d4 + $xm4 * ($xnum / $xden);
}
else {
// ---------------------------------
// Evaluate for argument .GE. 12.0
// ---------------------------------
$res = 0.0;
if ($y <= $lg_frtbig) {
$res = $lg_c[6];
$ysq = $y * $y;
for ($i = 0; $i < 6; ++$i) {
$res = $res / $ysq + $lg_c[$i];
}
}
$res /= $y;
$corr = log($y);
$res = $res + log(SQRT2PI) - 0.5 * $corr;
$res += $y * ($corr - 1.0);
}
}
else {
// --------------------------
// Return for bad arguments
// --------------------------
$res = MAX_VALUE;
}
// ------------------------------
// Final adjustments and return
// ------------------------------
self::$_logGammaCache_x = $x;
self::$_logGammaCache_result = $res;
return $res;
}
// function _logGamma()
//
// Private implementation of the incomplete Gamma function
//
private static function _incompleteGamma($a, $x) {
static $max = 32;
$summer = 0;
for ($n = 0; $n <= $max; ++$n) {
$divisor = $a;
for ($i = 1; $i <= $n; ++$i) {
$divisor *= $a + $i;
}
$summer += pow($x, $n) / $divisor;
}
return pow($x, $a) * exp(0 - $x) * $summer;
}
// function _incompleteGamma()
//
// Private implementation of the Gamma function
//
private static function _gamma($data) {
if ($data == 0.0) {
return 0;
}
static $p0 = 1.000000000190015;
static $p = array(
1 => 76.18009172947146,
2 => -86.50532032941678,
3 => 24.01409824083091,
4 => -1.231739572450155,
5 => 0.001208650973866179,
6 => -5.395239384953E-6,
);
$y = $x = $data;
$tmp = $x + 5.5;
$tmp -= ($x + 0.5) * log($tmp);
$summer = $p0;
for ($j = 1; $j <= 6; ++$j) {
$summer += $p[$j] / ++$y;
}
return exp(0 - $tmp + log(SQRT2PI * $summer / $x));
}
// function _gamma()
/***************************************************************************
* inverse_ncdf.php
* -------------------
* begin : Friday, January 16, 2004
* copyright : (C) 2004 Michael Nickerson
* email : nickersonm@yahoo.com
*
***************************************************************************/
private static function _inverse_ncdf($p) {
// Inverse ncdf approximation by Peter J. Acklam, implementation adapted to
// PHP by Michael Nickerson, using Dr. Thomas Ziegler's C implementation as
// a guide. http://home.online.no/~pjacklam/notes/invnorm/index.html
// I have not checked the accuracy of this implementation. Be aware that PHP
// will truncate the coeficcients to 14 digits.
// You have permission to use and distribute this function freely for
// whatever purpose you want, but please show common courtesy and give credit
// where credit is due.
// Input paramater is $p - probability - where 0 < p < 1.
// Coefficients in rational approximations
static $a = array(
1 => -39.69683028665376,
2 => 220.9460984245205,
3 => -275.9285104469687,
4 => 138.357751867269,
5 => -30.66479806614716,
6 => 2.506628277459239,
);
static $b = array(
1 => -54.47609879822406,
2 => 161.5858368580409,
3 => -155.6989798598866,
4 => 66.80131188771972,
5 => -13.28068155288572,
);
static $c = array(
1 => -0.007784894002430293,
2 => -0.3223964580411365,
3 => -2.400758277161838,
4 => -2.549732539343734,
5 => 4.374664141464968,
6 => 2.938163982698783,
);
static $d = array(
1 => 0.007784695709041462,
2 => 0.3224671290700398,
3 => 2.445134137142996,
4 => 3.754408661907416,
);
// Define lower and upper region break-points.
$p_low = 0.02425;
//Use lower region approx. below this
$p_high = 1 - $p_low;
//Use upper region approx. above this
if (0 < $p && $p < $p_low) {
// Rational approximation for lower region.
$q = sqrt(-2 * log($p));
return ((((($c[1] * $q + $c[2]) * $q + $c[3]) * $q + $c[4]) * $q + $c[5]) * $q + $c[6]) / (((($d[1] * $q + $d[2]) * $q + $d[3]) * $q + $d[4]) * $q + 1);
}
elseif ($p_low <= $p && $p <= $p_high) {
// Rational approximation for central region.
$q = $p - 0.5;
$r = $q * $q;
return ((((($a[1] * $r + $a[2]) * $r + $a[3]) * $r + $a[4]) * $r + $a[5]) * $r + $a[6]) * $q / ((((($b[1] * $r + $b[2]) * $r + $b[3]) * $r + $b[4]) * $r + $b[5]) * $r + 1);
}
elseif ($p_high < $p && $p < 1) {
// Rational approximation for upper region.
$q = sqrt(-2 * log(1 - $p));
return -((((($c[1] * $q + $c[2]) * $q + $c[3]) * $q + $c[4]) * $q + $c[5]) * $q + $c[6]) / (((($d[1] * $q + $d[2]) * $q + $d[3]) * $q + $d[4]) * $q + 1);
}
// If 0 < p < 1, return a null value
return PHPExcel_Calculation_Functions::NULL();
}
// function _inverse_ncdf()
private static function _inverse_ncdf2($prob) {
// Approximation of inverse standard normal CDF developed by
// B. Moro, "The Full Monte," Risk 8(2), Feb 1995, 57-58.
$a1 = 2.50662823884;
$a2 = -18.61500062529;
$a3 = 41.39119773534;
$a4 = -25.44106049637;
$b1 = -8.4735109309;
$b2 = 23.08336743743;
$b3 = -21.06224101826;
$b4 = 3.13082909833;
$c1 = 0.337475482272615;
$c2 = 0.976169019091719;
$c3 = 0.160797971491821;
$c4 = 0.0276438810333863;
$c5 = 0.0038405729373609;
$c6 = 0.0003951896511919;
$c7 = 3.21767881768E-5;
$c8 = 2.888167364E-7;
$c9 = 3.960315187E-7;
$y = $prob - 0.5;
if (abs($y) < 0.42) {
$z = $y * $y;
$z = $y * ((($a4 * $z + $a3) * $z + $a2) * $z + $a1) / (((($b4 * $z + $b3) * $z + $b2) * $z + $b1) * $z + 1);
}
else {
if ($y > 0) {
$z = log(-log(1 - $prob));
}
else {
$z = log(-log($prob));
}
$z = $c1 + $z * ($c2 + $z * ($c3 + $z * ($c4 + $z * ($c5 + $z * ($c6 + $z * ($c7 + $z * ($c8 + $z * $c9)))))));
if ($y < 0) {
$z = -$z;
}
}
return $z;
}
// function _inverse_ncdf2()
private static function _inverse_ncdf3($p) {
// ALGORITHM AS241 APPL. STATIST. (1988) VOL. 37, NO. 3.
// Produces the normal deviate Z corresponding to a given lower
// tail area of P; Z is accurate to about 1 part in 10**16.
//
// This is a PHP version of the original FORTRAN code that can
// be found at http://lib.stat.cmu.edu/apstat/
$split1 = 0.425;
$split2 = 5;
$const1 = 0.180625;
$const2 = 1.6;
// coefficients for p close to 0.5
$a0 = 3.3871328727963665;
$a1 = 133.14166789178438;
$a2 = 1971.5909503065513;
$a3 = 13731.69376550946;
$a4 = 45921.95393154987;
$a5 = 67265.77092700871;
$a6 = 33430.57558358813;
$a7 = 2509.0809287301227;
$b1 = 42.31333070160091;
$b2 = 687.1870074920579;
$b3 = 5394.196021424751;
$b4 = 21213.794301586597;
$b5 = 39307.89580009271;
$b6 = 28729.085735721943;
$b7 = 5226.495278852854;
// coefficients for p not close to 0, 0.5 or 1.
$c0 = 1.4234371107496835;
$c1 = 4.630337846156546;
$c2 = 5.769497221460691;
$c3 = 3.6478483247632045;
$c4 = 1.2704582524523684;
$c5 = 0.2417807251774506;
$c6 = 0.022723844989269184;
$c7 = 0.0007745450142783414;
$d1 = 2.053191626637759;
$d2 = 1.6763848301838038;
$d3 = 0.6897673349851;
$d4 = 0.14810397642748008;
$d5 = 0.015198666563616457;
$d6 = 0.0005475938084995345;
$d7 = 1.0507500716444169E-9;
// coefficients for p near 0 or 1.
$e0 = 6.657904643501103;
$e1 = 5.463784911164114;
$e2 = 1.7848265399172913;
$e3 = 0.29656057182850487;
$e4 = 0.026532189526576124;
$e5 = 0.0012426609473880784;
$e6 = 2.7115555687434876E-5;
$e7 = 2.0103343992922881E-7;
$f1 = 0.599832206555888;
$f2 = 0.1369298809227358;
$f3 = 0.014875361290850615;
$f4 = 0.0007868691311456133;
$f5 = 1.8463183175100548E-5;
$f6 = 1.421511758316446E-7;
$f7 = 2.0442631033899397E-15;
$q = $p - 0.5;
// computation for p close to 0.5
if (abs($q) <= split1) {
$R = $const1 - $q * $q;
$z = $q * ((((((($a7 * $R + $a6) * $R + $a5) * $R + $a4) * $R + $a3) * $R + $a2) * $R + $a1) * $R + $a0) / ((((((($b7 * $R + $b6) * $R + $b5) * $R + $b4) * $R + $b3) * $R + $b2) * $R + $b1) * $R + 1);
}
else {
if ($q < 0) {
$R = $p;
}
else {
$R = 1 - $p;
}
$R = pow(-log($R), 2);
// computation for p not close to 0, 0.5 or 1.
if ($R <= $split2) {
$R = $R - $const2;
$z = ((((((($c7 * $R + $c6) * $R + $c5) * $R + $c4) * $R + $c3) * $R + $c2) * $R + $c1) * $R + $c0) / ((((((($d7 * $R + $d6) * $R + $d5) * $R + $d4) * $R + $d3) * $R + $d2) * $R + $d1) * $R + 1);
}
else {
// computation for p near 0 or 1.
$R = $R - $split2;
$z = ((((((($e7 * $R + $e6) * $R + $e5) * $R + $e4) * $R + $e3) * $R + $e2) * $R + $e1) * $R + $e0) / ((((((($f7 * $R + $f6) * $R + $f5) * $R + $f4) * $R + $f3) * $R + $f2) * $R + $f1) * $R + 1);
}
if ($q < 0) {
$z = -$z;
}
}
return $z;
}
// function _inverse_ncdf3()
/**
* AVEDEV
*
* Returns the average of the absolute deviations of data points from their mean.
* AVEDEV is a measure of the variability in a data set.
*
* Excel Function:
* AVEDEV(value1[,value2[, ...]])
*
* @access public
* @category Statistical Functions
* @param mixed $arg,... Data values
* @return float
*/
public static function AVEDEV() {
$aArgs = PHPExcel_Calculation_Functions::flattenArrayIndexed(func_get_args());
// Return value
$returnValue = null;
$aMean = self::AVERAGE($aArgs);
if ($aMean != PHPExcel_Calculation_Functions::DIV0()) {
$aCount = 0;
foreach ($aArgs as $k => $arg) {
if (is_bool($arg) && (!PHPExcel_Calculation_Functions::isCellValue($k) || PHPExcel_Calculation_Functions::getCompatibilityMode() == PHPExcel_Calculation_Functions::COMPATIBILITY_OPENOFFICE)) {
$arg = (int) $arg;
}
// Is it a numeric value?
if (is_numeric($arg) && !is_string($arg)) {
if (is_null($returnValue)) {
$returnValue = abs($arg - $aMean);
}
else {
$returnValue += abs($arg - $aMean);
}
++$aCount;
}
}
// Return
if ($aCount == 0) {
return PHPExcel_Calculation_Functions::DIV0();
}
return $returnValue / $aCount;
}
return PHPExcel_Calculation_Functions::NaN();
}
// function AVEDEV()
/**
* AVERAGE
*
* Returns the average (arithmetic mean) of the arguments
*
* Excel Function:
* AVERAGE(value1[,value2[, ...]])
*
* @access public
* @category Statistical Functions
* @param mixed $arg,... Data values
* @return float
*/
public static function AVERAGE() {
$returnValue = $aCount = 0;
// Loop through arguments
foreach (PHPExcel_Calculation_Functions::flattenArrayIndexed(func_get_args()) as $k => $arg) {
if (is_bool($arg) && (!PHPExcel_Calculation_Functions::isCellValue($k) || PHPExcel_Calculation_Functions::getCompatibilityMode() == PHPExcel_Calculation_Functions::COMPATIBILITY_OPENOFFICE)) {
$arg = (int) $arg;
}
// Is it a numeric value?
if (is_numeric($arg) && !is_string($arg)) {
if (is_null($returnValue)) {
$returnValue = $arg;
}
else {
$returnValue += $arg;
}
++$aCount;
}
}
// Return
if ($aCount > 0) {
return $returnValue / $aCount;
}
else {
return PHPExcel_Calculation_Functions::DIV0();
}
}
// function AVERAGE()
/**
* AVERAGEA
*
* Returns the average of its arguments, including numbers, text, and logical values
*
* Excel Function:
* AVERAGEA(value1[,value2[, ...]])
*
* @access public
* @category Statistical Functions
* @param mixed $arg,... Data values
* @return float
*/
public static function AVERAGEA() {
// Return value
$returnValue = null;
$aCount = 0;
// Loop through arguments
foreach (PHPExcel_Calculation_Functions::flattenArrayIndexed(func_get_args()) as $k => $arg) {
if (is_bool($arg) && !PHPExcel_Calculation_Functions::isMatrixValue($k)) {
}
else {
if (is_numeric($arg) || is_bool($arg) || is_string($arg) && $arg != '') {
if (is_bool($arg)) {
$arg = (int) $arg;
}
elseif (is_string($arg)) {
$arg = 0;
}
if (is_null($returnValue)) {
$returnValue = $arg;
}
else {
$returnValue += $arg;
}
++$aCount;
}
}
}
// Return
if ($aCount > 0) {
return $returnValue / $aCount;
}
else {
return PHPExcel_Calculation_Functions::DIV0();
}
}
// function AVERAGEA()
/**
* AVERAGEIF
*
* Returns the average value from a range of cells that contain numbers within the list of arguments
*
* Excel Function:
* AVERAGEIF(value1[,value2[, ...]],condition)
*
* @access public
* @category Mathematical and Trigonometric Functions
* @param mixed $arg,... Data values
* @param string $condition The criteria that defines which cells will be checked.
* @param mixed[] $averageArgs Data values
* @return float
*/
public static function AVERAGEIF($aArgs, $condition, $averageArgs = array()) {
// Return value
$returnValue = 0;
$aArgs = PHPExcel_Calculation_Functions::flattenArray($aArgs);
$averageArgs = PHPExcel_Calculation_Functions::flattenArray($averageArgs);
if (empty($averageArgs)) {
$averageArgs = $aArgs;
}
$condition = PHPExcel_Calculation_Functions::_ifCondition($condition);
// Loop through arguments
$aCount = 0;
foreach ($aArgs as $key => $arg) {
if (!is_numeric($arg)) {
$arg = PHPExcel_Calculation::_wrapResult(strtoupper($arg));
}
$testCondition = '=' . $arg . $condition;
if (PHPExcel_Calculation::getInstance()
->_calculateFormulaValue($testCondition)) {
if (is_null($returnValue) || $arg > $returnValue) {
$returnValue += $arg;
++$aCount;
}
}
}
// Return
if ($aCount > 0) {
return $returnValue / $aCount;
}
else {
return PHPExcel_Calculation_Functions::DIV0();
}
}
// function AVERAGEIF()
/**
* BETADIST
*
* Returns the beta distribution.
*
* @param float $value Value at which you want to evaluate the distribution
* @param float $alpha Parameter to the distribution
* @param float $beta Parameter to the distribution
* @param boolean $cumulative
* @return float
*
*/
public static function BETADIST($value, $alpha, $beta, $rMin = 0, $rMax = 1) {
$value = PHPExcel_Calculation_Functions::flattenSingleValue($value);
$alpha = PHPExcel_Calculation_Functions::flattenSingleValue($alpha);
$beta = PHPExcel_Calculation_Functions::flattenSingleValue($beta);
$rMin = PHPExcel_Calculation_Functions::flattenSingleValue($rMin);
$rMax = PHPExcel_Calculation_Functions::flattenSingleValue($rMax);
if (is_numeric($value) && is_numeric($alpha) && is_numeric($beta) && is_numeric($rMin) && is_numeric($rMax)) {
if ($value < $rMin || $value > $rMax || $alpha <= 0 || $beta <= 0 || $rMin == $rMax) {
return PHPExcel_Calculation_Functions::NaN();
}
if ($rMin > $rMax) {
$tmp = $rMin;
$rMin = $rMax;
$rMax = $tmp;
}
$value -= $rMin;
$value /= $rMax - $rMin;
return self::_incompleteBeta($value, $alpha, $beta);
}
return PHPExcel_Calculation_Functions::VALUE();
}
// function BETADIST()
/**
* BETAINV
*
* Returns the inverse of the beta distribution.
*
* @param float $probability Probability at which you want to evaluate the distribution
* @param float $alpha Parameter to the distribution
* @param float $beta Parameter to the distribution
* @param float $rMin Minimum value
* @param float $rMax Maximum value
* @param boolean $cumulative
* @return float
*
*/
public static function BETAINV($probability, $alpha, $beta, $rMin = 0, $rMax = 1) {
$probability = PHPExcel_Calculation_Functions::flattenSingleValue($probability);
$alpha = PHPExcel_Calculation_Functions::flattenSingleValue($alpha);
$beta = PHPExcel_Calculation_Functions::flattenSingleValue($beta);
$rMin = PHPExcel_Calculation_Functions::flattenSingleValue($rMin);
$rMax = PHPExcel_Calculation_Functions::flattenSingleValue($rMax);
if (is_numeric($probability) && is_numeric($alpha) && is_numeric($beta) && is_numeric($rMin) && is_numeric($rMax)) {
if ($alpha <= 0 || $beta <= 0 || $rMin == $rMax || $probability <= 0 || $probability > 1) {
return PHPExcel_Calculation_Functions::NaN();
}
if ($rMin > $rMax) {
$tmp = $rMin;
$rMin = $rMax;
$rMax = $tmp;
}
$a = 0;
$b = 2;
$i = 0;
while ($b - $a > PRECISION && $i++ < MAX_ITERATIONS) {
$guess = ($a + $b) / 2;
$result = self::BETADIST($guess, $alpha, $beta);
if ($result == $probability || $result == 0) {
$b = $a;
}
elseif ($result > $probability) {
$b = $guess;
}
else {
$a = $guess;
}
}
if ($i == MAX_ITERATIONS) {
return PHPExcel_Calculation_Functions::NA();
}
return round($rMin + $guess * ($rMax - $rMin), 12);
}
return PHPExcel_Calculation_Functions::VALUE();
}
// function BETAINV()
/**
* BINOMDIST
*
* Returns the individual term binomial distribution probability. Use BINOMDIST in problems with
* a fixed number of tests or trials, when the outcomes of any trial are only success or failure,
* when trials are independent, and when the probability of success is constant throughout the
* experiment. For example, BINOMDIST can calculate the probability that two of the next three
* babies born are male.
*
* @param float $value Number of successes in trials
* @param float $trials Number of trials
* @param float $probability Probability of success on each trial
* @param boolean $cumulative
* @return float
*
* @todo Cumulative distribution function
*
*/
public static function BINOMDIST($value, $trials, $probability, $cumulative) {
$value = floor(PHPExcel_Calculation_Functions::flattenSingleValue($value));
$trials = floor(PHPExcel_Calculation_Functions::flattenSingleValue($trials));
$probability = PHPExcel_Calculation_Functions::flattenSingleValue($probability);
if (is_numeric($value) && is_numeric($trials) && is_numeric($probability)) {
if ($value < 0 || $value > $trials) {
return PHPExcel_Calculation_Functions::NaN();
}
if ($probability < 0 || $probability > 1) {
return PHPExcel_Calculation_Functions::NaN();
}
if (is_numeric($cumulative) || is_bool($cumulative)) {
if ($cumulative) {
$summer = 0;
for ($i = 0; $i <= $value; ++$i) {
$summer += PHPExcel_Calculation_MathTrig::COMBIN($trials, $i) * pow($probability, $i) * pow(1 - $probability, $trials - $i);
}
return $summer;
}
else {
return PHPExcel_Calculation_MathTrig::COMBIN($trials, $value) * pow($probability, $value) * pow(1 - $probability, $trials - $value);
}
}
}
return PHPExcel_Calculation_Functions::VALUE();
}
// function BINOMDIST()
/**
* CHIDIST
*
* Returns the one-tailed probability of the chi-squared distribution.
*
* @param float $value Value for the function
* @param float $degrees degrees of freedom
* @return float
*/
public static function CHIDIST($value, $degrees) {
$value = PHPExcel_Calculation_Functions::flattenSingleValue($value);
$degrees = floor(PHPExcel_Calculation_Functions::flattenSingleValue($degrees));
if (is_numeric($value) && is_numeric($degrees)) {
if ($degrees < 1) {
return PHPExcel_Calculation_Functions::NaN();
}
if ($value < 0) {
if (PHPExcel_Calculation_Functions::getCompatibilityMode() == PHPExcel_Calculation_Functions::COMPATIBILITY_GNUMERIC) {
return 1;
}
return PHPExcel_Calculation_Functions::NaN();
}
return 1 - self::_incompleteGamma($degrees / 2, $value / 2) / self::_gamma($degrees / 2);
}
return PHPExcel_Calculation_Functions::VALUE();
}
// function CHIDIST()
/**
* CHIINV
*
* Returns the one-tailed probability of the chi-squared distribution.
*
* @param float $probability Probability for the function
* @param float $degrees degrees of freedom
* @return float
*/
public static function CHIINV($probability, $degrees) {
$probability = PHPExcel_Calculation_Functions::flattenSingleValue($probability);
$degrees = floor(PHPExcel_Calculation_Functions::flattenSingleValue($degrees));
if (is_numeric($probability) && is_numeric($degrees)) {
$xLo = 100;
$xHi = 0;
$x = $xNew = 1;
$dx = 1;
$i = 0;
while (abs($dx) > PRECISION && $i++ < MAX_ITERATIONS) {
// Apply Newton-Raphson step
$result = self::CHIDIST($x, $degrees);
$error = $result - $probability;
if ($error == 0.0) {
$dx = 0;
}
elseif ($error < 0.0) {
$xLo = $x;
}
else {
$xHi = $x;
}
// Avoid division by zero
if ($result != 0.0) {
$dx = $error / $result;
$xNew = $x - $dx;
}
// If the NR fails to converge (which for example may be the
// case if the initial guess is too rough) we apply a bisection
// step to determine a more narrow interval around the root.
if ($xNew < $xLo || $xNew > $xHi || $result == 0.0) {
$xNew = ($xLo + $xHi) / 2;
$dx = $xNew - $x;
}
$x = $xNew;
}
if ($i == MAX_ITERATIONS) {
return PHPExcel_Calculation_Functions::NA();
}
return round($x, 12);
}
return PHPExcel_Calculation_Functions::VALUE();
}
// function CHIINV()
/**
* CONFIDENCE
*
* Returns the confidence interval for a population mean
*
* @param float $alpha
* @param float $stdDev Standard Deviation
* @param float $size
* @return float
*
*/
public static function CONFIDENCE($alpha, $stdDev, $size) {
$alpha = PHPExcel_Calculation_Functions::flattenSingleValue($alpha);
$stdDev = PHPExcel_Calculation_Functions::flattenSingleValue($stdDev);
$size = floor(PHPExcel_Calculation_Functions::flattenSingleValue($size));
if (is_numeric($alpha) && is_numeric($stdDev) && is_numeric($size)) {
if ($alpha <= 0 || $alpha >= 1) {
return PHPExcel_Calculation_Functions::NaN();
}
if ($stdDev <= 0 || $size < 1) {
return PHPExcel_Calculation_Functions::NaN();
}
return self::NORMSINV(1 - $alpha / 2) * $stdDev / sqrt($size);
}
return PHPExcel_Calculation_Functions::VALUE();
}
// function CONFIDENCE()
/**
* CORREL
*
* Returns covariance, the average of the products of deviations for each data point pair.
*
* @param array of mixed Data Series Y
* @param array of mixed Data Series X
* @return float
*/
public static function CORREL($yValues, $xValues = null) {
if (is_null($xValues) || !is_array($yValues) || !is_array($xValues)) {
return PHPExcel_Calculation_Functions::VALUE();
}
if (!self::_checkTrendArrays($yValues, $xValues)) {
return PHPExcel_Calculation_Functions::VALUE();
}
$yValueCount = count($yValues);
$xValueCount = count($xValues);
if ($yValueCount == 0 || $yValueCount != $xValueCount) {
return PHPExcel_Calculation_Functions::NA();
}
elseif ($yValueCount == 1) {
return PHPExcel_Calculation_Functions::DIV0();
}
$bestFitLinear = trendClass::calculate(trendClass::TREND_LINEAR, $yValues, $xValues);
return $bestFitLinear
->getCorrelation();
}
// function CORREL()
/**
* COUNT
*
* Counts the number of cells that contain numbers within the list of arguments
*
* Excel Function:
* COUNT(value1[,value2[, ...]])
*
* @access public
* @category Statistical Functions
* @param mixed $arg,... Data values
* @return int
*/
public static function COUNT() {
// Return value
$returnValue = 0;
// Loop through arguments
$aArgs = PHPExcel_Calculation_Functions::flattenArrayIndexed(func_get_args());
foreach ($aArgs as $k => $arg) {
if (is_bool($arg) && (!PHPExcel_Calculation_Functions::isCellValue($k) || PHPExcel_Calculation_Functions::getCompatibilityMode() == PHPExcel_Calculation_Functions::COMPATIBILITY_OPENOFFICE)) {
$arg = (int) $arg;
}
// Is it a numeric value?
if (is_numeric($arg) && !is_string($arg)) {
++$returnValue;
}
}
// Return
return $returnValue;
}
// function COUNT()
/**
* COUNTA
*
* Counts the number of cells that are not empty within the list of arguments
*
* Excel Function:
* COUNTA(value1[,value2[, ...]])
*
* @access public
* @category Statistical Functions
* @param mixed $arg,... Data values
* @return int
*/
public static function COUNTA() {
// Return value
$returnValue = 0;
// Loop through arguments
$aArgs = PHPExcel_Calculation_Functions::flattenArray(func_get_args());
foreach ($aArgs as $arg) {
// Is it a numeric, boolean or string value?
if (is_numeric($arg) || is_bool($arg) || is_string($arg) && $arg != '') {
++$returnValue;
}
}
// Return
return $returnValue;
}
// function COUNTA()
/**
* COUNTBLANK
*
* Counts the number of empty cells within the list of arguments
*
* Excel Function:
* COUNTBLANK(value1[,value2[, ...]])
*
* @access public
* @category Statistical Functions
* @param mixed $arg,... Data values
* @return int
*/
public static function COUNTBLANK() {
// Return value
$returnValue = 0;
// Loop through arguments
$aArgs = PHPExcel_Calculation_Functions::flattenArray(func_get_args());
foreach ($aArgs as $arg) {
// Is it a blank cell?
if (is_null($arg) || is_string($arg) && $arg == '') {
++$returnValue;
}
}
// Return
return $returnValue;
}
// function COUNTBLANK()
/**
* COUNTIF
*
* Counts the number of cells that contain numbers within the list of arguments
*
* Excel Function:
* COUNTIF(value1[,value2[, ...]],condition)
*
* @access public
* @category Statistical Functions
* @param mixed $arg,... Data values
* @param string $condition The criteria that defines which cells will be counted.
* @return int
*/
public static function COUNTIF($aArgs, $condition) {
// Return value
$returnValue = 0;
$aArgs = PHPExcel_Calculation_Functions::flattenArray($aArgs);
$condition = PHPExcel_Calculation_Functions::_ifCondition($condition);
// Loop through arguments
foreach ($aArgs as $arg) {
if (!is_numeric($arg)) {
$arg = PHPExcel_Calculation::_wrapResult(strtoupper($arg));
}
$testCondition = '=' . $arg . $condition;
if (PHPExcel_Calculation::getInstance()
->_calculateFormulaValue($testCondition)) {
// Is it a value within our criteria
++$returnValue;
}
}
// Return
return $returnValue;
}
// function COUNTIF()
/**
* COVAR
*
* Returns covariance, the average of the products of deviations for each data point pair.
*
* @param array of mixed Data Series Y
* @param array of mixed Data Series X
* @return float
*/
public static function COVAR($yValues, $xValues) {
if (!self::_checkTrendArrays($yValues, $xValues)) {
return PHPExcel_Calculation_Functions::VALUE();
}
$yValueCount = count($yValues);
$xValueCount = count($xValues);
if ($yValueCount == 0 || $yValueCount != $xValueCount) {
return PHPExcel_Calculation_Functions::NA();
}
elseif ($yValueCount == 1) {
return PHPExcel_Calculation_Functions::DIV0();
}
$bestFitLinear = trendClass::calculate(trendClass::TREND_LINEAR, $yValues, $xValues);
return $bestFitLinear
->getCovariance();
}
// function COVAR()
/**
* CRITBINOM
*
* Returns the smallest value for which the cumulative binomial distribution is greater
* than or equal to a criterion value
*
* See http://support.microsoft.com/kb/828117/ for details of the algorithm used
*
* @param float $trials number of Bernoulli trials
* @param float $probability probability of a success on each trial
* @param float $alpha criterion value
* @return int
*
* @todo Warning. This implementation differs from the algorithm detailed on the MS
* web site in that $CumPGuessMinus1 = $CumPGuess - 1 rather than $CumPGuess - $PGuess
* This eliminates a potential endless loop error, but may have an adverse affect on the
* accuracy of the function (although all my tests have so far returned correct results).
*
*/
public static function CRITBINOM($trials, $probability, $alpha) {
$trials = floor(PHPExcel_Calculation_Functions::flattenSingleValue($trials));
$probability = PHPExcel_Calculation_Functions::flattenSingleValue($probability);
$alpha = PHPExcel_Calculation_Functions::flattenSingleValue($alpha);
if (is_numeric($trials) && is_numeric($probability) && is_numeric($alpha)) {
if ($trials < 0) {
return PHPExcel_Calculation_Functions::NaN();
}
if ($probability < 0 || $probability > 1) {
return PHPExcel_Calculation_Functions::NaN();
}
if ($alpha < 0 || $alpha > 1) {
return PHPExcel_Calculation_Functions::NaN();
}
if ($alpha <= 0.5) {
$t = sqrt(log(1 / ($alpha * $alpha)));
$trialsApprox = 0 - ($t + (2.515517 + 0.802853 * $t + 0.010328 * $t * $t) / (1 + 1.432788 * $t + 0.189269 * $t * $t + 0.001308 * $t * $t * $t));
}
else {
$t = sqrt(log(1 / pow(1 - $alpha, 2)));
$trialsApprox = $t - (2.515517 + 0.802853 * $t + 0.010328 * $t * $t) / (1 + 1.432788 * $t + 0.189269 * $t * $t + 0.001308 * $t * $t * $t);
}
$Guess = floor($trials * $probability + $trialsApprox * sqrt($trials * $probability * (1 - $probability)));
if ($Guess < 0) {
$Guess = 0;
}
elseif ($Guess > $trials) {
$Guess = $trials;
}
$TotalUnscaledProbability = $UnscaledPGuess = $UnscaledCumPGuess = 0.0;
$EssentiallyZero = 9.999999999999999E-12;
$m = floor($trials * $probability);
++$TotalUnscaledProbability;
if ($m == $Guess) {
++$UnscaledPGuess;
}
if ($m <= $Guess) {
++$UnscaledCumPGuess;
}
$PreviousValue = 1;
$Done = False;
$k = $m + 1;
while (!$Done && $k <= $trials) {
$CurrentValue = $PreviousValue * ($trials - $k + 1) * $probability / ($k * (1 - $probability));
$TotalUnscaledProbability += $CurrentValue;
if ($k == $Guess) {
$UnscaledPGuess += $CurrentValue;
}
if ($k <= $Guess) {
$UnscaledCumPGuess += $CurrentValue;
}
if ($CurrentValue <= $EssentiallyZero) {
$Done = True;
}
$PreviousValue = $CurrentValue;
++$k;
}
$PreviousValue = 1;
$Done = False;
$k = $m - 1;
while (!$Done && $k >= 0) {
$CurrentValue = $PreviousValue * $k + 1 * (1 - $probability) / (($trials - $k) * $probability);
$TotalUnscaledProbability += $CurrentValue;
if ($k == $Guess) {
$UnscaledPGuess += $CurrentValue;
}
if ($k <= $Guess) {
$UnscaledCumPGuess += $CurrentValue;
}
if ($CurrentValue <= $EssentiallyZero) {
$Done = True;
}
$PreviousValue = $CurrentValue;
--$k;
}
$PGuess = $UnscaledPGuess / $TotalUnscaledProbability;
$CumPGuess = $UnscaledCumPGuess / $TotalUnscaledProbability;
// $CumPGuessMinus1 = $CumPGuess - $PGuess;
$CumPGuessMinus1 = $CumPGuess - 1;
while (True) {
if ($CumPGuessMinus1 < $alpha && $CumPGuess >= $alpha) {
return $Guess;
}
elseif ($CumPGuessMinus1 < $alpha && $CumPGuess < $alpha) {
$PGuessPlus1 = $PGuess * ($trials - $Guess) * $probability / $Guess / (1 - $probability);
$CumPGuessMinus1 = $CumPGuess;
$CumPGuess = $CumPGuess + $PGuessPlus1;
$PGuess = $PGuessPlus1;
++$Guess;
}
elseif ($CumPGuessMinus1 >= $alpha && $CumPGuess >= $alpha) {
$PGuessMinus1 = $PGuess * $Guess * (1 - $probability) / ($trials - $Guess + 1) / $probability;
$CumPGuess = $CumPGuessMinus1;
$CumPGuessMinus1 = $CumPGuessMinus1 - $PGuess;
$PGuess = $PGuessMinus1;
--$Guess;
}
}
}
return PHPExcel_Calculation_Functions::VALUE();
}
// function CRITBINOM()
/**
* DEVSQ
*
* Returns the sum of squares of deviations of data points from their sample mean.
*
* Excel Function:
* DEVSQ(value1[,value2[, ...]])
*
* @access public
* @category Statistical Functions
* @param mixed $arg,... Data values
* @return float
*/
public static function DEVSQ() {
$aArgs = PHPExcel_Calculation_Functions::flattenArrayIndexed(func_get_args());
// Return value
$returnValue = null;
$aMean = self::AVERAGE($aArgs);
if ($aMean != PHPExcel_Calculation_Functions::DIV0()) {
$aCount = -1;
foreach ($aArgs as $k => $arg) {
// Is it a numeric value?
if (is_bool($arg) && (!PHPExcel_Calculation_Functions::isCellValue($k) || PHPExcel_Calculation_Functions::getCompatibilityMode() == PHPExcel_Calculation_Functions::COMPATIBILITY_OPENOFFICE)) {
$arg = (int) $arg;
}
if (is_numeric($arg) && !is_string($arg)) {
if (is_null($returnValue)) {
$returnValue = pow($arg - $aMean, 2);
}
else {
$returnValue += pow($arg - $aMean, 2);
}
++$aCount;
}
}
// Return
if (is_null($returnValue)) {
return PHPExcel_Calculation_Functions::NaN();
}
else {
return $returnValue;
}
}
return self::NA();
}
// function DEVSQ()
/**
* EXPONDIST
*
* Returns the exponential distribution. Use EXPONDIST to model the time between events,
* such as how long an automated bank teller takes to deliver cash. For example, you can
* use EXPONDIST to determine the probability that the process takes at most 1 minute.
*
* @param float $value Value of the function
* @param float $lambda The parameter value
* @param boolean $cumulative
* @return float
*/
public static function EXPONDIST($value, $lambda, $cumulative) {
$value = PHPExcel_Calculation_Functions::flattenSingleValue($value);
$lambda = PHPExcel_Calculation_Functions::flattenSingleValue($lambda);
$cumulative = PHPExcel_Calculation_Functions::flattenSingleValue($cumulative);
if (is_numeric($value) && is_numeric($lambda)) {
if ($value < 0 || $lambda < 0) {
return PHPExcel_Calculation_Functions::NaN();
}
if (is_numeric($cumulative) || is_bool($cumulative)) {
if ($cumulative) {
return 1 - exp(0 - $value * $lambda);
}
else {
return $lambda * exp(0 - $value * $lambda);
}
}
}
return PHPExcel_Calculation_Functions::VALUE();
}
// function EXPONDIST()
/**
* FISHER
*
* Returns the Fisher transformation at x. This transformation produces a function that
* is normally distributed rather than skewed. Use this function to perform hypothesis
* testing on the correlation coefficient.
*
* @param float $value
* @return float
*/
public static function FISHER($value) {
$value = PHPExcel_Calculation_Functions::flattenSingleValue($value);
if (is_numeric($value)) {
if ($value <= -1 || $value >= 1) {
return PHPExcel_Calculation_Functions::NaN();
}
return 0.5 * log((1 + $value) / (1 - $value));
}
return PHPExcel_Calculation_Functions::VALUE();
}
// function FISHER()
/**
* FISHERINV
*
* Returns the inverse of the Fisher transformation. Use this transformation when
* analyzing correlations between ranges or arrays of data. If y = FISHER(x), then
* FISHERINV(y) = x.
*
* @param float $value
* @return float
*/
public static function FISHERINV($value) {
$value = PHPExcel_Calculation_Functions::flattenSingleValue($value);
if (is_numeric($value)) {
return (exp(2 * $value) - 1) / (exp(2 * $value) + 1);
}
return PHPExcel_Calculation_Functions::VALUE();
}
// function FISHERINV()
/**
* FORECAST
*
* Calculates, or predicts, a future value by using existing values. The predicted value is a y-value for a given x-value.
*
* @param float Value of X for which we want to find Y
* @param array of mixed Data Series Y
* @param array of mixed Data Series X
* @return float
*/
public static function FORECAST($xValue, $yValues, $xValues) {
$xValue = PHPExcel_Calculation_Functions::flattenSingleValue($xValue);
if (!is_numeric($xValue)) {
return PHPExcel_Calculation_Functions::VALUE();
}
if (!self::_checkTrendArrays($yValues, $xValues)) {
return PHPExcel_Calculation_Functions::VALUE();
}
$yValueCount = count($yValues);
$xValueCount = count($xValues);
if ($yValueCount == 0 || $yValueCount != $xValueCount) {
return PHPExcel_Calculation_Functions::NA();
}
elseif ($yValueCount == 1) {
return PHPExcel_Calculation_Functions::DIV0();
}
$bestFitLinear = trendClass::calculate(trendClass::TREND_LINEAR, $yValues, $xValues);
return $bestFitLinear
->getValueOfYForX($xValue);
}
// function FORECAST()
/**
* GAMMADIST
*
* Returns the gamma distribution.
*
* @param float $value Value at which you want to evaluate the distribution
* @param float $a Parameter to the distribution
* @param float $b Parameter to the distribution
* @param boolean $cumulative
* @return float
*
*/
public static function GAMMADIST($value, $a, $b, $cumulative) {
$value = PHPExcel_Calculation_Functions::flattenSingleValue($value);
$a = PHPExcel_Calculation_Functions::flattenSingleValue($a);
$b = PHPExcel_Calculation_Functions::flattenSingleValue($b);
if (is_numeric($value) && is_numeric($a) && is_numeric($b)) {
if ($value < 0 || $a <= 0 || $b <= 0) {
return PHPExcel_Calculation_Functions::NaN();
}
if (is_numeric($cumulative) || is_bool($cumulative)) {
if ($cumulative) {
return self::_incompleteGamma($a, $value / $b) / self::_gamma($a);
}
else {
return 1 / (pow($b, $a) * self::_gamma($a)) * pow($value, $a - 1) * exp(0 - $value / $b);
}
}
}
return PHPExcel_Calculation_Functions::VALUE();
}
// function GAMMADIST()
/**
* GAMMAINV
*
* Returns the inverse of the beta distribution.
*
* @param float $probability Probability at which you want to evaluate the distribution
* @param float $alpha Parameter to the distribution
* @param float $beta Parameter to the distribution
* @return float
*
*/
public static function GAMMAINV($probability, $alpha, $beta) {
$probability = PHPExcel_Calculation_Functions::flattenSingleValue($probability);
$alpha = PHPExcel_Calculation_Functions::flattenSingleValue($alpha);
$beta = PHPExcel_Calculation_Functions::flattenSingleValue($beta);
if (is_numeric($probability) && is_numeric($alpha) && is_numeric($beta)) {
if ($alpha <= 0 || $beta <= 0 || $probability < 0 || $probability > 1) {
return PHPExcel_Calculation_Functions::NaN();
}
$xLo = 0;
$xHi = $alpha * $beta * 5;
$x = $xNew = 1;
$error = $pdf = 0;
$dx = 1024;
$i = 0;
while (abs($dx) > PRECISION && $i++ < MAX_ITERATIONS) {
// Apply Newton-Raphson step
$error = self::GAMMADIST($x, $alpha, $beta, True) - $probability;
if ($error < 0.0) {
$xLo = $x;
}
else {
$xHi = $x;
}
$pdf = self::GAMMADIST($x, $alpha, $beta, False);
// Avoid division by zero
if ($pdf != 0.0) {
$dx = $error / $pdf;
$xNew = $x - $dx;
}
// If the NR fails to converge (which for example may be the
// case if the initial guess is too rough) we apply a bisection
// step to determine a more narrow interval around the root.
if ($xNew < $xLo || $xNew > $xHi || $pdf == 0.0) {
$xNew = ($xLo + $xHi) / 2;
$dx = $xNew - $x;
}
$x = $xNew;
}
if ($i == MAX_ITERATIONS) {
return PHPExcel_Calculation_Functions::NA();
}
return $x;
}
return PHPExcel_Calculation_Functions::VALUE();
}
// function GAMMAINV()
/**
* GAMMALN
*
* Returns the natural logarithm of the gamma function.
*
* @param float $value
* @return float
*/
public static function GAMMALN($value) {
$value = PHPExcel_Calculation_Functions::flattenSingleValue($value);
if (is_numeric($value)) {
if ($value <= 0) {
return PHPExcel_Calculation_Functions::NaN();
}
return log(self::_gamma($value));
}
return PHPExcel_Calculation_Functions::VALUE();
}
// function GAMMALN()
/**
* GEOMEAN
*
* Returns the geometric mean of an array or range of positive data. For example, you
* can use GEOMEAN to calculate average growth rate given compound interest with
* variable rates.
*
* Excel Function:
* GEOMEAN(value1[,value2[, ...]])
*
* @access public
* @category Statistical Functions
* @param mixed $arg,... Data values
* @return float
*/
public static function GEOMEAN() {
$aArgs = PHPExcel_Calculation_Functions::flattenArray(func_get_args());
$aMean = PHPExcel_Calculation_MathTrig::PRODUCT($aArgs);
if (is_numeric($aMean) && $aMean > 0) {
$aCount = self::COUNT($aArgs);
if (self::MIN($aArgs) > 0) {
return pow($aMean, 1 / $aCount);
}
}
return PHPExcel_Calculation_Functions::NaN();
}
// GEOMEAN()
/**
* GROWTH
*
* Returns values along a predicted emponential trend
*
* @param array of mixed Data Series Y
* @param array of mixed Data Series X
* @param array of mixed Values of X for which we want to find Y
* @param boolean A logical value specifying whether to force the intersect to equal 0.
* @return array of float
*/
public static function GROWTH($yValues, $xValues = array(), $newValues = array(), $const = True) {
$yValues = PHPExcel_Calculation_Functions::flattenArray($yValues);
$xValues = PHPExcel_Calculation_Functions::flattenArray($xValues);
$newValues = PHPExcel_Calculation_Functions::flattenArray($newValues);
$const = is_null($const) ? True : (bool) PHPExcel_Calculation_Functions::flattenSingleValue($const);
$bestFitExponential = trendClass::calculate(trendClass::TREND_EXPONENTIAL, $yValues, $xValues, $const);
if (empty($newValues)) {
$newValues = $bestFitExponential
->getXValues();
}
$returnArray = array();
foreach ($newValues as $xValue) {
$returnArray[0][] = $bestFitExponential
->getValueOfYForX($xValue);
}
return $returnArray;
}
// function GROWTH()
/**
* HARMEAN
*
* Returns the harmonic mean of a data set. The harmonic mean is the reciprocal of the
* arithmetic mean of reciprocals.
*
* Excel Function:
* HARMEAN(value1[,value2[, ...]])
*
* @access public
* @category Statistical Functions
* @param mixed $arg,... Data values
* @return float
*/
public static function HARMEAN() {
// Return value
$returnValue = PHPExcel_Calculation_Functions::NA();
// Loop through arguments
$aArgs = PHPExcel_Calculation_Functions::flattenArray(func_get_args());
if (self::MIN($aArgs) < 0) {
return PHPExcel_Calculation_Functions::NaN();
}
$aCount = 0;
foreach ($aArgs as $arg) {
// Is it a numeric value?
if (is_numeric($arg) && !is_string($arg)) {
if ($arg <= 0) {
return PHPExcel_Calculation_Functions::NaN();
}
if (is_null($returnValue)) {
$returnValue = 1 / $arg;
}
else {
$returnValue += 1 / $arg;
}
++$aCount;
}
}
// Return
if ($aCount > 0) {
return 1 / ($returnValue / $aCount);
}
else {
return $returnValue;
}
}
// function HARMEAN()
/**
* HYPGEOMDIST
*
* Returns the hypergeometric distribution. HYPGEOMDIST returns the probability of a given number of
* sample successes, given the sample size, population successes, and population size.
*
* @param float $sampleSuccesses Number of successes in the sample
* @param float $sampleNumber Size of the sample
* @param float $populationSuccesses Number of successes in the population
* @param float $populationNumber Population size
* @return float
*
*/
public static function HYPGEOMDIST($sampleSuccesses, $sampleNumber, $populationSuccesses, $populationNumber) {
$sampleSuccesses = floor(PHPExcel_Calculation_Functions::flattenSingleValue($sampleSuccesses));
$sampleNumber = floor(PHPExcel_Calculation_Functions::flattenSingleValue($sampleNumber));
$populationSuccesses = floor(PHPExcel_Calculation_Functions::flattenSingleValue($populationSuccesses));
$populationNumber = floor(PHPExcel_Calculation_Functions::flattenSingleValue($populationNumber));
if (is_numeric($sampleSuccesses) && is_numeric($sampleNumber) && is_numeric($populationSuccesses) && is_numeric($populationNumber)) {
if ($sampleSuccesses < 0 || $sampleSuccesses > $sampleNumber || $sampleSuccesses > $populationSuccesses) {
return PHPExcel_Calculation_Functions::NaN();
}
if ($sampleNumber <= 0 || $sampleNumber > $populationNumber) {
return PHPExcel_Calculation_Functions::NaN();
}
if ($populationSuccesses <= 0 || $populationSuccesses > $populationNumber) {
return PHPExcel_Calculation_Functions::NaN();
}
return PHPExcel_Calculation_MathTrig::COMBIN($populationSuccesses, $sampleSuccesses) * PHPExcel_Calculation_MathTrig::COMBIN($populationNumber - $populationSuccesses, $sampleNumber - $sampleSuccesses) / PHPExcel_Calculation_MathTrig::COMBIN($populationNumber, $sampleNumber);
}
return PHPExcel_Calculation_Functions::VALUE();
}
// function HYPGEOMDIST()
/**
* INTERCEPT
*
* Calculates the point at which a line will intersect the y-axis by using existing x-values and y-values.
*
* @param array of mixed Data Series Y
* @param array of mixed Data Series X
* @return float
*/
public static function INTERCEPT($yValues, $xValues) {
if (!self::_checkTrendArrays($yValues, $xValues)) {
return PHPExcel_Calculation_Functions::VALUE();
}
$yValueCount = count($yValues);
$xValueCount = count($xValues);
if ($yValueCount == 0 || $yValueCount != $xValueCount) {
return PHPExcel_Calculation_Functions::NA();
}
elseif ($yValueCount == 1) {
return PHPExcel_Calculation_Functions::DIV0();
}
$bestFitLinear = trendClass::calculate(trendClass::TREND_LINEAR, $yValues, $xValues);
return $bestFitLinear
->getIntersect();
}
// function INTERCEPT()
/**
* KURT
*
* Returns the kurtosis of a data set. Kurtosis characterizes the relative peakedness
* or flatness of a distribution compared with the normal distribution. Positive
* kurtosis indicates a relatively peaked distribution. Negative kurtosis indicates a
* relatively flat distribution.
*
* @param array Data Series
* @return float
*/
public static function KURT() {
$aArgs = PHPExcel_Calculation_Functions::flattenArrayIndexed(func_get_args());
$mean = self::AVERAGE($aArgs);
$stdDev = self::STDEV($aArgs);
if ($stdDev > 0) {
$count = $summer = 0;
// Loop through arguments
foreach ($aArgs as $k => $arg) {
if (is_bool($arg) && !PHPExcel_Calculation_Functions::isMatrixValue($k)) {
}
else {
// Is it a numeric value?
if (is_numeric($arg) && !is_string($arg)) {
$summer += pow(($arg - $mean) / $stdDev, 4);
++$count;
}
}
}
// Return
if ($count > 3) {
return $summer * ($count * ($count + 1) / (($count - 1) * ($count - 2) * ($count - 3))) - 3 * pow($count - 1, 2) / (($count - 2) * ($count - 3));
}
}
return PHPExcel_Calculation_Functions::DIV0();
}
// function KURT()
/**
* LARGE
*
* Returns the nth largest value in a data set. You can use this function to
* select a value based on its relative standing.
*
* Excel Function:
* LARGE(value1[,value2[, ...]],entry)
*
* @access public
* @category Statistical Functions
* @param mixed $arg,... Data values
* @param int $entry Position (ordered from the largest) in the array or range of data to return
* @return float
*
*/
public static function LARGE() {
$aArgs = PHPExcel_Calculation_Functions::flattenArray(func_get_args());
// Calculate
$entry = floor(array_pop($aArgs));
if (is_numeric($entry) && !is_string($entry)) {
$mArgs = array();
foreach ($aArgs as $arg) {
// Is it a numeric value?
if (is_numeric($arg) && !is_string($arg)) {
$mArgs[] = $arg;
}
}
$count = self::COUNT($mArgs);
$entry = floor(--$entry);
if ($entry < 0 || $entry >= $count || $count == 0) {
return PHPExcel_Calculation_Functions::NaN();
}
rsort($mArgs);
return $mArgs[$entry];
}
return PHPExcel_Calculation_Functions::VALUE();
}
// function LARGE()
/**
* LINEST
*
* Calculates the statistics for a line by using the "least squares" method to calculate a straight line that best fits your data,
* and then returns an array that describes the line.
*
* @param array of mixed Data Series Y
* @param array of mixed Data Series X
* @param boolean A logical value specifying whether to force the intersect to equal 0.
* @param boolean A logical value specifying whether to return additional regression statistics.
* @return array
*/
public static function LINEST($yValues, $xValues = NULL, $const = TRUE, $stats = FALSE) {
$const = is_null($const) ? TRUE : (bool) PHPExcel_Calculation_Functions::flattenSingleValue($const);
$stats = is_null($stats) ? FALSE : (bool) PHPExcel_Calculation_Functions::flattenSingleValue($stats);
if (is_null($xValues)) {
$xValues = range(1, count(PHPExcel_Calculation_Functions::flattenArray($yValues)));
}
if (!self::_checkTrendArrays($yValues, $xValues)) {
return PHPExcel_Calculation_Functions::VALUE();
}
$yValueCount = count($yValues);
$xValueCount = count($xValues);
if ($yValueCount == 0 || $yValueCount != $xValueCount) {
return PHPExcel_Calculation_Functions::NA();
}
elseif ($yValueCount == 1) {
return 0;
}
$bestFitLinear = trendClass::calculate(trendClass::TREND_LINEAR, $yValues, $xValues, $const);
if ($stats) {
return array(
array(
$bestFitLinear
->getSlope(),
$bestFitLinear
->getSlopeSE(),
$bestFitLinear
->getGoodnessOfFit(),
$bestFitLinear
->getF(),
$bestFitLinear
->getSSRegression(),
),
array(
$bestFitLinear
->getIntersect(),
$bestFitLinear
->getIntersectSE(),
$bestFitLinear
->getStdevOfResiduals(),
$bestFitLinear
->getDFResiduals(),
$bestFitLinear
->getSSResiduals(),
),
);
}
else {
return array(
$bestFitLinear
->getSlope(),
$bestFitLinear
->getIntersect(),
);
}
}
// function LINEST()
/**
* LOGEST
*
* Calculates an exponential curve that best fits the X and Y data series,
* and then returns an array that describes the line.
*
* @param array of mixed Data Series Y
* @param array of mixed Data Series X
* @param boolean A logical value specifying whether to force the intersect to equal 0.
* @param boolean A logical value specifying whether to return additional regression statistics.
* @return array
*/
public static function LOGEST($yValues, $xValues = null, $const = True, $stats = False) {
$const = is_null($const) ? True : (bool) PHPExcel_Calculation_Functions::flattenSingleValue($const);
$stats = is_null($stats) ? False : (bool) PHPExcel_Calculation_Functions::flattenSingleValue($stats);
if (is_null($xValues)) {
$xValues = range(1, count(PHPExcel_Calculation_Functions::flattenArray($yValues)));
}
if (!self::_checkTrendArrays($yValues, $xValues)) {
return PHPExcel_Calculation_Functions::VALUE();
}
$yValueCount = count($yValues);
$xValueCount = count($xValues);
foreach ($yValues as $value) {
if ($value <= 0.0) {
return PHPExcel_Calculation_Functions::NaN();
}
}
if ($yValueCount == 0 || $yValueCount != $xValueCount) {
return PHPExcel_Calculation_Functions::NA();
}
elseif ($yValueCount == 1) {
return 1;
}
$bestFitExponential = trendClass::calculate(trendClass::TREND_EXPONENTIAL, $yValues, $xValues, $const);
if ($stats) {
return array(
array(
$bestFitExponential
->getSlope(),
$bestFitExponential
->getSlopeSE(),
$bestFitExponential
->getGoodnessOfFit(),
$bestFitExponential
->getF(),
$bestFitExponential
->getSSRegression(),
),
array(
$bestFitExponential
->getIntersect(),
$bestFitExponential
->getIntersectSE(),
$bestFitExponential
->getStdevOfResiduals(),
$bestFitExponential
->getDFResiduals(),
$bestFitExponential
->getSSResiduals(),
),
);
}
else {
return array(
$bestFitExponential
->getSlope(),
$bestFitExponential
->getIntersect(),
);
}
}
// function LOGEST()
/**
* LOGINV
*
* Returns the inverse of the normal cumulative distribution
*
* @param float $probability
* @param float $mean
* @param float $stdDev
* @return float
*
* @todo Try implementing P J Acklam's refinement algorithm for greater
* accuracy if I can get my head round the mathematics
* (as described at) http://home.online.no/~pjacklam/notes/invnorm/
*/
public static function LOGINV($probability, $mean, $stdDev) {
$probability = PHPExcel_Calculation_Functions::flattenSingleValue($probability);
$mean = PHPExcel_Calculation_Functions::flattenSingleValue($mean);
$stdDev = PHPExcel_Calculation_Functions::flattenSingleValue($stdDev);
if (is_numeric($probability) && is_numeric($mean) && is_numeric($stdDev)) {
if ($probability < 0 || $probability > 1 || $stdDev <= 0) {
return PHPExcel_Calculation_Functions::NaN();
}
return exp($mean + $stdDev * self::NORMSINV($probability));
}
return PHPExcel_Calculation_Functions::VALUE();
}
// function LOGINV()
/**
* LOGNORMDIST
*
* Returns the cumulative lognormal distribution of x, where ln(x) is normally distributed
* with parameters mean and standard_dev.
*
* @param float $value
* @param float $mean
* @param float $stdDev
* @return float
*/
public static function LOGNORMDIST($value, $mean, $stdDev) {
$value = PHPExcel_Calculation_Functions::flattenSingleValue($value);
$mean = PHPExcel_Calculation_Functions::flattenSingleValue($mean);
$stdDev = PHPExcel_Calculation_Functions::flattenSingleValue($stdDev);
if (is_numeric($value) && is_numeric($mean) && is_numeric($stdDev)) {
if ($value <= 0 || $stdDev <= 0) {
return PHPExcel_Calculation_Functions::NaN();
}
return self::NORMSDIST((log($value) - $mean) / $stdDev);
}
return PHPExcel_Calculation_Functions::VALUE();
}
// function LOGNORMDIST()
/**
* MAX
*
* MAX returns the value of the element of the values passed that has the highest value,
* with negative numbers considered smaller than positive numbers.
*
* Excel Function:
* MAX(value1[,value2[, ...]])
*
* @access public
* @category Statistical Functions
* @param mixed $arg,... Data values
* @return float
*/
public static function MAX() {
// Return value
$returnValue = null;
// Loop through arguments
$aArgs = PHPExcel_Calculation_Functions::flattenArray(func_get_args());
foreach ($aArgs as $arg) {
// Is it a numeric value?
if (is_numeric($arg) && !is_string($arg)) {
if (is_null($returnValue) || $arg > $returnValue) {
$returnValue = $arg;
}
}
}
// Return
if (is_null($returnValue)) {
return 0;
}
return $returnValue;
}
// function MAX()
/**
* MAXA
*
* Returns the greatest value in a list of arguments, including numbers, text, and logical values
*
* Excel Function:
* MAXA(value1[,value2[, ...]])
*
* @access public
* @category Statistical Functions
* @param mixed $arg,... Data values
* @return float
*/
public static function MAXA() {
// Return value
$returnValue = null;
// Loop through arguments
$aArgs = PHPExcel_Calculation_Functions::flattenArray(func_get_args());
foreach ($aArgs as $arg) {
// Is it a numeric value?
if (is_numeric($arg) || is_bool($arg) || is_string($arg) && $arg != '') {
if (is_bool($arg)) {
$arg = (int) $arg;
}
elseif (is_string($arg)) {
$arg = 0;
}
if (is_null($returnValue) || $arg > $returnValue) {
$returnValue = $arg;
}
}
}
// Return
if (is_null($returnValue)) {
return 0;
}
return $returnValue;
}
// function MAXA()
/**
* MAXIF
*
* Counts the maximum value within a range of cells that contain numbers within the list of arguments
*
* Excel Function:
* MAXIF(value1[,value2[, ...]],condition)
*
* @access public
* @category Mathematical and Trigonometric Functions
* @param mixed $arg,... Data values
* @param string $condition The criteria that defines which cells will be checked.
* @return float
*/
public static function MAXIF($aArgs, $condition, $sumArgs = array()) {
// Return value
$returnValue = null;
$aArgs = PHPExcel_Calculation_Functions::flattenArray($aArgs);
$sumArgs = PHPExcel_Calculation_Functions::flattenArray($sumArgs);
if (empty($sumArgs)) {
$sumArgs = $aArgs;
}
$condition = PHPExcel_Calculation_Functions::_ifCondition($condition);
// Loop through arguments
foreach ($aArgs as $key => $arg) {
if (!is_numeric($arg)) {
$arg = PHPExcel_Calculation::_wrapResult(strtoupper($arg));
}
$testCondition = '=' . $arg . $condition;
if (PHPExcel_Calculation::getInstance()
->_calculateFormulaValue($testCondition)) {
if (is_null($returnValue) || $arg > $returnValue) {
$returnValue = $arg;
}
}
}
// Return
return $returnValue;
}
// function MAXIF()
/**
* MEDIAN
*
* Returns the median of the given numbers. The median is the number in the middle of a set of numbers.
*
* Excel Function:
* MEDIAN(value1[,value2[, ...]])
*
* @access public
* @category Statistical Functions
* @param mixed $arg,... Data values
* @return float
*/
public static function MEDIAN() {
// Return value
$returnValue = PHPExcel_Calculation_Functions::NaN();
$mArgs = array();
// Loop through arguments
$aArgs = PHPExcel_Calculation_Functions::flattenArray(func_get_args());
foreach ($aArgs as $arg) {
// Is it a numeric value?
if (is_numeric($arg) && !is_string($arg)) {
$mArgs[] = $arg;
}
}
$mValueCount = count($mArgs);
if ($mValueCount > 0) {
sort($mArgs, SORT_NUMERIC);
$mValueCount = $mValueCount / 2;
if ($mValueCount == floor($mValueCount)) {
$returnValue = ($mArgs[$mValueCount--] + $mArgs[$mValueCount]) / 2;
}
else {
$mValueCount == floor($mValueCount);
$returnValue = $mArgs[$mValueCount];
}
}
// Return
return $returnValue;
}
// function MEDIAN()
/**
* MIN
*
* MIN returns the value of the element of the values passed that has the smallest value,
* with negative numbers considered smaller than positive numbers.
*
* Excel Function:
* MIN(value1[,value2[, ...]])
*
* @access public
* @category Statistical Functions
* @param mixed $arg,... Data values
* @return float
*/
public static function MIN() {
// Return value
$returnValue = null;
// Loop through arguments
$aArgs = PHPExcel_Calculation_Functions::flattenArray(func_get_args());
foreach ($aArgs as $arg) {
// Is it a numeric value?
if (is_numeric($arg) && !is_string($arg)) {
if (is_null($returnValue) || $arg < $returnValue) {
$returnValue = $arg;
}
}
}
// Return
if (is_null($returnValue)) {
return 0;
}
return $returnValue;
}
// function MIN()
/**
* MINA
*
* Returns the smallest value in a list of arguments, including numbers, text, and logical values
*
* Excel Function:
* MINA(value1[,value2[, ...]])
*
* @access public
* @category Statistical Functions
* @param mixed $arg,... Data values
* @return float
*/
public static function MINA() {
// Return value
$returnValue = null;
// Loop through arguments
$aArgs = PHPExcel_Calculation_Functions::flattenArray(func_get_args());
foreach ($aArgs as $arg) {
// Is it a numeric value?
if (is_numeric($arg) || is_bool($arg) || is_string($arg) && $arg != '') {
if (is_bool($arg)) {
$arg = (int) $arg;
}
elseif (is_string($arg)) {
$arg = 0;
}
if (is_null($returnValue) || $arg < $returnValue) {
$returnValue = $arg;
}
}
}
// Return
if (is_null($returnValue)) {
return 0;
}
return $returnValue;
}
// function MINA()
/**
* MINIF
*
* Returns the minimum value within a range of cells that contain numbers within the list of arguments
*
* Excel Function:
* MINIF(value1[,value2[, ...]],condition)
*
* @access public
* @category Mathematical and Trigonometric Functions
* @param mixed $arg,... Data values
* @param string $condition The criteria that defines which cells will be checked.
* @return float
*/
public static function MINIF($aArgs, $condition, $sumArgs = array()) {
// Return value
$returnValue = null;
$aArgs = PHPExcel_Calculation_Functions::flattenArray($aArgs);
$sumArgs = PHPExcel_Calculation_Functions::flattenArray($sumArgs);
if (empty($sumArgs)) {
$sumArgs = $aArgs;
}
$condition = PHPExcel_Calculation_Functions::_ifCondition($condition);
// Loop through arguments
foreach ($aArgs as $key => $arg) {
if (!is_numeric($arg)) {
$arg = PHPExcel_Calculation::_wrapResult(strtoupper($arg));
}
$testCondition = '=' . $arg . $condition;
if (PHPExcel_Calculation::getInstance()
->_calculateFormulaValue($testCondition)) {
if (is_null($returnValue) || $arg < $returnValue) {
$returnValue = $arg;
}
}
}
// Return
return $returnValue;
}
// function MINIF()
//
// Special variant of array_count_values that isn't limited to strings and integers,
// but can work with floating point numbers as values
//
private static function _modeCalc($data) {
$frequencyArray = array();
foreach ($data as $datum) {
$found = False;
foreach ($frequencyArray as $key => $value) {
if ((string) $value['value'] == (string) $datum) {
++$frequencyArray[$key]['frequency'];
$found = True;
break;
}
}
if (!$found) {
$frequencyArray[] = array(
'value' => $datum,
'frequency' => 1,
);
}
}
foreach ($frequencyArray as $key => $value) {
$frequencyList[$key] = $value['frequency'];
$valueList[$key] = $value['value'];
}
array_multisort($frequencyList, SORT_DESC, $valueList, SORT_ASC, SORT_NUMERIC, $frequencyArray);
if ($frequencyArray[0]['frequency'] == 1) {
return PHPExcel_Calculation_Functions::NA();
}
return $frequencyArray[0]['value'];
}
// function _modeCalc()
/**
* MODE
*
* Returns the most frequently occurring, or repetitive, value in an array or range of data
*
* Excel Function:
* MODE(value1[,value2[, ...]])
*
* @access public
* @category Statistical Functions
* @param mixed $arg,... Data values
* @return float
*/
public static function MODE() {
// Return value
$returnValue = PHPExcel_Calculation_Functions::NA();
// Loop through arguments
$aArgs = PHPExcel_Calculation_Functions::flattenArray(func_get_args());
$mArgs = array();
foreach ($aArgs as $arg) {
// Is it a numeric value?
if (is_numeric($arg) && !is_string($arg)) {
$mArgs[] = $arg;
}
}
if (!empty($mArgs)) {
return self::_modeCalc($mArgs);
}
// Return
return $returnValue;
}
// function MODE()
/**
* NEGBINOMDIST
*
* Returns the negative binomial distribution. NEGBINOMDIST returns the probability that
* there will be number_f failures before the number_s-th success, when the constant
* probability of a success is probability_s. This function is similar to the binomial
* distribution, except that the number of successes is fixed, and the number of trials is
* variable. Like the binomial, trials are assumed to be independent.
*
* @param float $failures Number of Failures
* @param float $successes Threshold number of Successes
* @param float $probability Probability of success on each trial
* @return float
*
*/
public static function NEGBINOMDIST($failures, $successes, $probability) {
$failures = floor(PHPExcel_Calculation_Functions::flattenSingleValue($failures));
$successes = floor(PHPExcel_Calculation_Functions::flattenSingleValue($successes));
$probability = PHPExcel_Calculation_Functions::flattenSingleValue($probability);
if (is_numeric($failures) && is_numeric($successes) && is_numeric($probability)) {
if ($failures < 0 || $successes < 1) {
return PHPExcel_Calculation_Functions::NaN();
}
if ($probability < 0 || $probability > 1) {
return PHPExcel_Calculation_Functions::NaN();
}
if (PHPExcel_Calculation_Functions::getCompatibilityMode() == PHPExcel_Calculation_Functions::COMPATIBILITY_GNUMERIC) {
if ($failures + $successes - 1 <= 0) {
return PHPExcel_Calculation_Functions::NaN();
}
}
return PHPExcel_Calculation_MathTrig::COMBIN($failures + $successes - 1, $successes - 1) * pow($probability, $successes) * pow(1 - $probability, $failures);
}
return PHPExcel_Calculation_Functions::VALUE();
}
// function NEGBINOMDIST()
/**
* NORMDIST
*
* Returns the normal distribution for the specified mean and standard deviation. This
* function has a very wide range of applications in statistics, including hypothesis
* testing.
*
* @param float $value
* @param float $mean Mean Value
* @param float $stdDev Standard Deviation
* @param boolean $cumulative
* @return float
*
*/
public static function NORMDIST($value, $mean, $stdDev, $cumulative) {
$value = PHPExcel_Calculation_Functions::flattenSingleValue($value);
$mean = PHPExcel_Calculation_Functions::flattenSingleValue($mean);
$stdDev = PHPExcel_Calculation_Functions::flattenSingleValue($stdDev);
if (is_numeric($value) && is_numeric($mean) && is_numeric($stdDev)) {
if ($stdDev < 0) {
return PHPExcel_Calculation_Functions::NaN();
}
if (is_numeric($cumulative) || is_bool($cumulative)) {
if ($cumulative) {
return 0.5 * (1 + PHPExcel_Calculation_Engineering::_erfVal(($value - $mean) / ($stdDev * sqrt(2))));
}
else {
return 1 / (SQRT2PI * $stdDev) * exp(0 - pow($value - $mean, 2) / (2 * ($stdDev * $stdDev)));
}
}
}
return PHPExcel_Calculation_Functions::VALUE();
}
// function NORMDIST()
/**
* NORMINV
*
* Returns the inverse of the normal cumulative distribution for the specified mean and standard deviation.
*
* @param float $value
* @param float $mean Mean Value
* @param float $stdDev Standard Deviation
* @return float
*
*/
public static function NORMINV($probability, $mean, $stdDev) {
$probability = PHPExcel_Calculation_Functions::flattenSingleValue($probability);
$mean = PHPExcel_Calculation_Functions::flattenSingleValue($mean);
$stdDev = PHPExcel_Calculation_Functions::flattenSingleValue($stdDev);
if (is_numeric($probability) && is_numeric($mean) && is_numeric($stdDev)) {
if ($probability < 0 || $probability > 1) {
return PHPExcel_Calculation_Functions::NaN();
}
if ($stdDev < 0) {
return PHPExcel_Calculation_Functions::NaN();
}
return self::_inverse_ncdf($probability) * $stdDev + $mean;
}
return PHPExcel_Calculation_Functions::VALUE();
}
// function NORMINV()
/**
* NORMSDIST
*
* Returns the standard normal cumulative distribution function. The distribution has
* a mean of 0 (zero) and a standard deviation of one. Use this function in place of a
* table of standard normal curve areas.
*
* @param float $value
* @return float
*/
public static function NORMSDIST($value) {
$value = PHPExcel_Calculation_Functions::flattenSingleValue($value);
return self::NORMDIST($value, 0, 1, True);
}
// function NORMSDIST()
/**
* NORMSINV
*
* Returns the inverse of the standard normal cumulative distribution
*
* @param float $value
* @return float
*/
public static function NORMSINV($value) {
return self::NORMINV($value, 0, 1);
}
// function NORMSINV()
/**
* PERCENTILE
*
* Returns the nth percentile of values in a range..
*
* Excel Function:
* PERCENTILE(value1[,value2[, ...]],entry)
*
* @access public
* @category Statistical Functions
* @param mixed $arg,... Data values
* @param float $entry Percentile value in the range 0..1, inclusive.
* @return float
*/
public static function PERCENTILE() {
$aArgs = PHPExcel_Calculation_Functions::flattenArray(func_get_args());
// Calculate
$entry = array_pop($aArgs);
if (is_numeric($entry) && !is_string($entry)) {
if ($entry < 0 || $entry > 1) {
return PHPExcel_Calculation_Functions::NaN();
}
$mArgs = array();
foreach ($aArgs as $arg) {
// Is it a numeric value?
if (is_numeric($arg) && !is_string($arg)) {
$mArgs[] = $arg;
}
}
$mValueCount = count($mArgs);
if ($mValueCount > 0) {
sort($mArgs);
$count = self::COUNT($mArgs);
$index = $entry * ($count - 1);
$iBase = floor($index);
if ($index == $iBase) {
return $mArgs[$index];
}
else {
$iNext = $iBase + 1;
$iProportion = $index - $iBase;
return $mArgs[$iBase] + ($mArgs[$iNext] - $mArgs[$iBase]) * $iProportion;
}
}
}
return PHPExcel_Calculation_Functions::VALUE();
}
// function PERCENTILE()
/**
* PERCENTRANK
*
* Returns the rank of a value in a data set as a percentage of the data set.
*
* @param array of number An array of, or a reference to, a list of numbers.
* @param number The number whose rank you want to find.
* @param number The number of significant digits for the returned percentage value.
* @return float
*/
public static function PERCENTRANK($valueSet, $value, $significance = 3) {
$valueSet = PHPExcel_Calculation_Functions::flattenArray($valueSet);
$value = PHPExcel_Calculation_Functions::flattenSingleValue($value);
$significance = is_null($significance) ? 3 : (int) PHPExcel_Calculation_Functions::flattenSingleValue($significance);
foreach ($valueSet as $key => $valueEntry) {
if (!is_numeric($valueEntry)) {
unset($valueSet[$key]);
}
}
sort($valueSet, SORT_NUMERIC);
$valueCount = count($valueSet);
if ($valueCount == 0) {
return PHPExcel_Calculation_Functions::NaN();
}
$valueAdjustor = $valueCount - 1;
if ($value < $valueSet[0] || $value > $valueSet[$valueAdjustor]) {
return PHPExcel_Calculation_Functions::NA();
}
$pos = array_search($value, $valueSet);
if ($pos === False) {
$pos = 0;
$testValue = $valueSet[0];
while ($testValue < $value) {
$testValue = $valueSet[++$pos];
}
--$pos;
$pos += ($value - $valueSet[$pos]) / ($testValue - $valueSet[$pos]);
}
return round($pos / $valueAdjustor, $significance);
}
// function PERCENTRANK()
/**
* PERMUT
*
* Returns the number of permutations for a given number of objects that can be
* selected from number objects. A permutation is any set or subset of objects or
* events where internal order is significant. Permutations are different from
* combinations, for which the internal order is not significant. Use this function
* for lottery-style probability calculations.
*
* @param int $numObjs Number of different objects
* @param int $numInSet Number of objects in each permutation
* @return int Number of permutations
*/
public static function PERMUT($numObjs, $numInSet) {
$numObjs = PHPExcel_Calculation_Functions::flattenSingleValue($numObjs);
$numInSet = PHPExcel_Calculation_Functions::flattenSingleValue($numInSet);
if (is_numeric($numObjs) && is_numeric($numInSet)) {
$numInSet = floor($numInSet);
if ($numObjs < $numInSet) {
return PHPExcel_Calculation_Functions::NaN();
}
return round(PHPExcel_Calculation_MathTrig::FACT($numObjs) / PHPExcel_Calculation_MathTrig::FACT($numObjs - $numInSet));
}
return PHPExcel_Calculation_Functions::VALUE();
}
// function PERMUT()
/**
* POISSON
*
* Returns the Poisson distribution. A common application of the Poisson distribution
* is predicting the number of events over a specific time, such as the number of
* cars arriving at a toll plaza in 1 minute.
*
* @param float $value
* @param float $mean Mean Value
* @param boolean $cumulative
* @return float
*
*/
public static function POISSON($value, $mean, $cumulative) {
$value = PHPExcel_Calculation_Functions::flattenSingleValue($value);
$mean = PHPExcel_Calculation_Functions::flattenSingleValue($mean);
if (is_numeric($value) && is_numeric($mean)) {
if ($value < 0 || $mean <= 0) {
return PHPExcel_Calculation_Functions::NaN();
}
if (is_numeric($cumulative) || is_bool($cumulative)) {
if ($cumulative) {
$summer = 0;
for ($i = 0; $i <= floor($value); ++$i) {
$summer += pow($mean, $i) / PHPExcel_Calculation_MathTrig::FACT($i);
}
return exp(0 - $mean) * $summer;
}
else {
return exp(0 - $mean) * pow($mean, $value) / PHPExcel_Calculation_MathTrig::FACT($value);
}
}
}
return PHPExcel_Calculation_Functions::VALUE();
}
// function POISSON()
/**
* QUARTILE
*
* Returns the quartile of a data set.
*
* Excel Function:
* QUARTILE(value1[,value2[, ...]],entry)
*
* @access public
* @category Statistical Functions
* @param mixed $arg,... Data values
* @param int $entry Quartile value in the range 1..3, inclusive.
* @return float
*/
public static function QUARTILE() {
$aArgs = PHPExcel_Calculation_Functions::flattenArray(func_get_args());
// Calculate
$entry = floor(array_pop($aArgs));
if (is_numeric($entry) && !is_string($entry)) {
$entry /= 4;
if ($entry < 0 || $entry > 1) {
return PHPExcel_Calculation_Functions::NaN();
}
return self::PERCENTILE($aArgs, $entry);
}
return PHPExcel_Calculation_Functions::VALUE();
}
// function QUARTILE()
/**
* RANK
*
* Returns the rank of a number in a list of numbers.
*
* @param number The number whose rank you want to find.
* @param array of number An array of, or a reference to, a list of numbers.
* @param mixed Order to sort the values in the value set
* @return float
*/
public static function RANK($value, $valueSet, $order = 0) {
$value = PHPExcel_Calculation_Functions::flattenSingleValue($value);
$valueSet = PHPExcel_Calculation_Functions::flattenArray($valueSet);
$order = is_null($order) ? 0 : (int) PHPExcel_Calculation_Functions::flattenSingleValue($order);
foreach ($valueSet as $key => $valueEntry) {
if (!is_numeric($valueEntry)) {
unset($valueSet[$key]);
}
}
if ($order == 0) {
rsort($valueSet, SORT_NUMERIC);
}
else {
sort($valueSet, SORT_NUMERIC);
}
$pos = array_search($value, $valueSet);
if ($pos === False) {
return PHPExcel_Calculation_Functions::NA();
}
return ++$pos;
}
// function RANK()
/**
* RSQ
*
* Returns the square of the Pearson product moment correlation coefficient through data points in known_y's and known_x's.
*
* @param array of mixed Data Series Y
* @param array of mixed Data Series X
* @return float
*/
public static function RSQ($yValues, $xValues) {
if (!self::_checkTrendArrays($yValues, $xValues)) {
return PHPExcel_Calculation_Functions::VALUE();
}
$yValueCount = count($yValues);
$xValueCount = count($xValues);
if ($yValueCount == 0 || $yValueCount != $xValueCount) {
return PHPExcel_Calculation_Functions::NA();
}
elseif ($yValueCount == 1) {
return PHPExcel_Calculation_Functions::DIV0();
}
$bestFitLinear = trendClass::calculate(trendClass::TREND_LINEAR, $yValues, $xValues);
return $bestFitLinear
->getGoodnessOfFit();
}
// function RSQ()
/**
* SKEW
*
* Returns the skewness of a distribution. Skewness characterizes the degree of asymmetry
* of a distribution around its mean. Positive skewness indicates a distribution with an
* asymmetric tail extending toward more positive values. Negative skewness indicates a
* distribution with an asymmetric tail extending toward more negative values.
*
* @param array Data Series
* @return float
*/
public static function SKEW() {
$aArgs = PHPExcel_Calculation_Functions::flattenArrayIndexed(func_get_args());
$mean = self::AVERAGE($aArgs);
$stdDev = self::STDEV($aArgs);
$count = $summer = 0;
// Loop through arguments
foreach ($aArgs as $k => $arg) {
if (is_bool($arg) && !PHPExcel_Calculation_Functions::isMatrixValue($k)) {
}
else {
// Is it a numeric value?
if (is_numeric($arg) && !is_string($arg)) {
$summer += pow(($arg - $mean) / $stdDev, 3);
++$count;
}
}
}
// Return
if ($count > 2) {
return $summer * ($count / (($count - 1) * ($count - 2)));
}
return PHPExcel_Calculation_Functions::DIV0();
}
// function SKEW()
/**
* SLOPE
*
* Returns the slope of the linear regression line through data points in known_y's and known_x's.
*
* @param array of mixed Data Series Y
* @param array of mixed Data Series X
* @return float
*/
public static function SLOPE($yValues, $xValues) {
if (!self::_checkTrendArrays($yValues, $xValues)) {
return PHPExcel_Calculation_Functions::VALUE();
}
$yValueCount = count($yValues);
$xValueCount = count($xValues);
if ($yValueCount == 0 || $yValueCount != $xValueCount) {
return PHPExcel_Calculation_Functions::NA();
}
elseif ($yValueCount == 1) {
return PHPExcel_Calculation_Functions::DIV0();
}
$bestFitLinear = trendClass::calculate(trendClass::TREND_LINEAR, $yValues, $xValues);
return $bestFitLinear
->getSlope();
}
// function SLOPE()
/**
* SMALL
*
* Returns the nth smallest value in a data set. You can use this function to
* select a value based on its relative standing.
*
* Excel Function:
* SMALL(value1[,value2[, ...]],entry)
*
* @access public
* @category Statistical Functions
* @param mixed $arg,... Data values
* @param int $entry Position (ordered from the smallest) in the array or range of data to return
* @return float
*/
public static function SMALL() {
$aArgs = PHPExcel_Calculation_Functions::flattenArray(func_get_args());
// Calculate
$entry = array_pop($aArgs);
if (is_numeric($entry) && !is_string($entry)) {
$mArgs = array();
foreach ($aArgs as $arg) {
// Is it a numeric value?
if (is_numeric($arg) && !is_string($arg)) {
$mArgs[] = $arg;
}
}
$count = self::COUNT($mArgs);
$entry = floor(--$entry);
if ($entry < 0 || $entry >= $count || $count == 0) {
return PHPExcel_Calculation_Functions::NaN();
}
sort($mArgs);
return $mArgs[$entry];
}
return PHPExcel_Calculation_Functions::VALUE();
}
// function SMALL()
/**
* STANDARDIZE
*
* Returns a normalized value from a distribution characterized by mean and standard_dev.
*
* @param float $value Value to normalize
* @param float $mean Mean Value
* @param float $stdDev Standard Deviation
* @return float Standardized value
*/
public static function STANDARDIZE($value, $mean, $stdDev) {
$value = PHPExcel_Calculation_Functions::flattenSingleValue($value);
$mean = PHPExcel_Calculation_Functions::flattenSingleValue($mean);
$stdDev = PHPExcel_Calculation_Functions::flattenSingleValue($stdDev);
if (is_numeric($value) && is_numeric($mean) && is_numeric($stdDev)) {
if ($stdDev <= 0) {
return PHPExcel_Calculation_Functions::NaN();
}
return ($value - $mean) / $stdDev;
}
return PHPExcel_Calculation_Functions::VALUE();
}
// function STANDARDIZE()
/**
* STDEV
*
* Estimates standard deviation based on a sample. The standard deviation is a measure of how
* widely values are dispersed from the average value (the mean).
*
* Excel Function:
* STDEV(value1[,value2[, ...]])
*
* @access public
* @category Statistical Functions
* @param mixed $arg,... Data values
* @return float
*/
public static function STDEV() {
$aArgs = PHPExcel_Calculation_Functions::flattenArrayIndexed(func_get_args());
// Return value
$returnValue = null;
$aMean = self::AVERAGE($aArgs);
if (!is_null($aMean)) {
$aCount = -1;
foreach ($aArgs as $k => $arg) {
if (is_bool($arg) && (!PHPExcel_Calculation_Functions::isCellValue($k) || PHPExcel_Calculation_Functions::getCompatibilityMode() == PHPExcel_Calculation_Functions::COMPATIBILITY_OPENOFFICE)) {
$arg = (int) $arg;
}
// Is it a numeric value?
if (is_numeric($arg) && !is_string($arg)) {
if (is_null($returnValue)) {
$returnValue = pow($arg - $aMean, 2);
}
else {
$returnValue += pow($arg - $aMean, 2);
}
++$aCount;
}
}
// Return
if ($aCount > 0 && $returnValue >= 0) {
return sqrt($returnValue / $aCount);
}
}
return PHPExcel_Calculation_Functions::DIV0();
}
// function STDEV()
/**
* STDEVA
*
* Estimates standard deviation based on a sample, including numbers, text, and logical values
*
* Excel Function:
* STDEVA(value1[,value2[, ...]])
*
* @access public
* @category Statistical Functions
* @param mixed $arg,... Data values
* @return float
*/
public static function STDEVA() {
$aArgs = PHPExcel_Calculation_Functions::flattenArrayIndexed(func_get_args());
// Return value
$returnValue = null;
$aMean = self::AVERAGEA($aArgs);
if (!is_null($aMean)) {
$aCount = -1;
foreach ($aArgs as $k => $arg) {
if (is_bool($arg) && !PHPExcel_Calculation_Functions::isMatrixValue($k)) {
}
else {
// Is it a numeric value?
if (is_numeric($arg) || is_bool($arg) || is_string($arg) & $arg != '') {
if (is_bool($arg)) {
$arg = (int) $arg;
}
elseif (is_string($arg)) {
$arg = 0;
}
if (is_null($returnValue)) {
$returnValue = pow($arg - $aMean, 2);
}
else {
$returnValue += pow($arg - $aMean, 2);
}
++$aCount;
}
}
}
// Return
if ($aCount > 0 && $returnValue >= 0) {
return sqrt($returnValue / $aCount);
}
}
return PHPExcel_Calculation_Functions::DIV0();
}
// function STDEVA()
/**
* STDEVP
*
* Calculates standard deviation based on the entire population
*
* Excel Function:
* STDEVP(value1[,value2[, ...]])
*
* @access public
* @category Statistical Functions
* @param mixed $arg,... Data values
* @return float
*/
public static function STDEVP() {
$aArgs = PHPExcel_Calculation_Functions::flattenArrayIndexed(func_get_args());
// Return value
$returnValue = null;
$aMean = self::AVERAGE($aArgs);
if (!is_null($aMean)) {
$aCount = 0;
foreach ($aArgs as $k => $arg) {
if (is_bool($arg) && (!PHPExcel_Calculation_Functions::isCellValue($k) || PHPExcel_Calculation_Functions::getCompatibilityMode() == PHPExcel_Calculation_Functions::COMPATIBILITY_OPENOFFICE)) {
$arg = (int) $arg;
}
// Is it a numeric value?
if (is_numeric($arg) && !is_string($arg)) {
if (is_null($returnValue)) {
$returnValue = pow($arg - $aMean, 2);
}
else {
$returnValue += pow($arg - $aMean, 2);
}
++$aCount;
}
}
// Return
if ($aCount > 0 && $returnValue >= 0) {
return sqrt($returnValue / $aCount);
}
}
return PHPExcel_Calculation_Functions::DIV0();
}
// function STDEVP()
/**
* STDEVPA
*
* Calculates standard deviation based on the entire population, including numbers, text, and logical values
*
* Excel Function:
* STDEVPA(value1[,value2[, ...]])
*
* @access public
* @category Statistical Functions
* @param mixed $arg,... Data values
* @return float
*/
public static function STDEVPA() {
$aArgs = PHPExcel_Calculation_Functions::flattenArrayIndexed(func_get_args());
// Return value
$returnValue = null;
$aMean = self::AVERAGEA($aArgs);
if (!is_null($aMean)) {
$aCount = 0;
foreach ($aArgs as $k => $arg) {
if (is_bool($arg) && !PHPExcel_Calculation_Functions::isMatrixValue($k)) {
}
else {
// Is it a numeric value?
if (is_numeric($arg) || is_bool($arg) || is_string($arg) & $arg != '') {
if (is_bool($arg)) {
$arg = (int) $arg;
}
elseif (is_string($arg)) {
$arg = 0;
}
if (is_null($returnValue)) {
$returnValue = pow($arg - $aMean, 2);
}
else {
$returnValue += pow($arg - $aMean, 2);
}
++$aCount;
}
}
}
// Return
if ($aCount > 0 && $returnValue >= 0) {
return sqrt($returnValue / $aCount);
}
}
return PHPExcel_Calculation_Functions::DIV0();
}
// function STDEVPA()
/**
* STEYX
*
* Returns the standard error of the predicted y-value for each x in the regression.
*
* @param array of mixed Data Series Y
* @param array of mixed Data Series X
* @return float
*/
public static function STEYX($yValues, $xValues) {
if (!self::_checkTrendArrays($yValues, $xValues)) {
return PHPExcel_Calculation_Functions::VALUE();
}
$yValueCount = count($yValues);
$xValueCount = count($xValues);
if ($yValueCount == 0 || $yValueCount != $xValueCount) {
return PHPExcel_Calculation_Functions::NA();
}
elseif ($yValueCount == 1) {
return PHPExcel_Calculation_Functions::DIV0();
}
$bestFitLinear = trendClass::calculate(trendClass::TREND_LINEAR, $yValues, $xValues);
return $bestFitLinear
->getStdevOfResiduals();
}
// function STEYX()
/**
* TDIST
*
* Returns the probability of Student's T distribution.
*
* @param float $value Value for the function
* @param float $degrees degrees of freedom
* @param float $tails number of tails (1 or 2)
* @return float
*/
public static function TDIST($value, $degrees, $tails) {
$value = PHPExcel_Calculation_Functions::flattenSingleValue($value);
$degrees = floor(PHPExcel_Calculation_Functions::flattenSingleValue($degrees));
$tails = floor(PHPExcel_Calculation_Functions::flattenSingleValue($tails));
if (is_numeric($value) && is_numeric($degrees) && is_numeric($tails)) {
if ($value < 0 || $degrees < 1 || $tails < 1 || $tails > 2) {
return PHPExcel_Calculation_Functions::NaN();
}
// tdist, which finds the probability that corresponds to a given value
// of t with k degrees of freedom. This algorithm is translated from a
// pascal function on p81 of "Statistical Computing in Pascal" by D
// Cooke, A H Craven & G M Clark (1985: Edward Arnold (Pubs.) Ltd:
// London). The above Pascal algorithm is itself a translation of the
// fortran algoritm "AS 3" by B E Cooper of the Atlas Computer
// Laboratory as reported in (among other places) "Applied Statistics
// Algorithms", editied by P Griffiths and I D Hill (1985; Ellis
// Horwood Ltd.; W. Sussex, England).
$tterm = $degrees;
$ttheta = atan2($value, sqrt($tterm));
$tc = cos($ttheta);
$ts = sin($ttheta);
$tsum = 0;
if ($degrees % 2 == 1) {
$ti = 3;
$tterm = $tc;
}
else {
$ti = 2;
$tterm = 1;
}
$tsum = $tterm;
while ($ti < $degrees) {
$tterm *= $tc * $tc * ($ti - 1) / $ti;
$tsum += $tterm;
$ti += 2;
}
$tsum *= $ts;
if ($degrees % 2 == 1) {
$tsum = M_2DIVPI * ($tsum + $ttheta);
}
$tValue = 0.5 * (1 + $tsum);
if ($tails == 1) {
return 1 - abs($tValue);
}
else {
return 1 - abs(1 - $tValue - $tValue);
}
}
return PHPExcel_Calculation_Functions::VALUE();
}
// function TDIST()
/**
* TINV
*
* Returns the one-tailed probability of the chi-squared distribution.
*
* @param float $probability Probability for the function
* @param float $degrees degrees of freedom
* @return float
*/
public static function TINV($probability, $degrees) {
$probability = PHPExcel_Calculation_Functions::flattenSingleValue($probability);
$degrees = floor(PHPExcel_Calculation_Functions::flattenSingleValue($degrees));
if (is_numeric($probability) && is_numeric($degrees)) {
$xLo = 100;
$xHi = 0;
$x = $xNew = 1;
$dx = 1;
$i = 0;
while (abs($dx) > PRECISION && $i++ < MAX_ITERATIONS) {
// Apply Newton-Raphson step
$result = self::TDIST($x, $degrees, 2);
$error = $result - $probability;
if ($error == 0.0) {
$dx = 0;
}
elseif ($error < 0.0) {
$xLo = $x;
}
else {
$xHi = $x;
}
// Avoid division by zero
if ($result != 0.0) {
$dx = $error / $result;
$xNew = $x - $dx;
}
// If the NR fails to converge (which for example may be the
// case if the initial guess is too rough) we apply a bisection
// step to determine a more narrow interval around the root.
if ($xNew < $xLo || $xNew > $xHi || $result == 0.0) {
$xNew = ($xLo + $xHi) / 2;
$dx = $xNew - $x;
}
$x = $xNew;
}
if ($i == MAX_ITERATIONS) {
return PHPExcel_Calculation_Functions::NA();
}
return round($x, 12);
}
return PHPExcel_Calculation_Functions::VALUE();
}
// function TINV()
/**
* TREND
*
* Returns values along a linear trend
*
* @param array of mixed Data Series Y
* @param array of mixed Data Series X
* @param array of mixed Values of X for which we want to find Y
* @param boolean A logical value specifying whether to force the intersect to equal 0.
* @return array of float
*/
public static function TREND($yValues, $xValues = array(), $newValues = array(), $const = True) {
$yValues = PHPExcel_Calculation_Functions::flattenArray($yValues);
$xValues = PHPExcel_Calculation_Functions::flattenArray($xValues);
$newValues = PHPExcel_Calculation_Functions::flattenArray($newValues);
$const = is_null($const) ? True : (bool) PHPExcel_Calculation_Functions::flattenSingleValue($const);
$bestFitLinear = trendClass::calculate(trendClass::TREND_LINEAR, $yValues, $xValues, $const);
if (empty($newValues)) {
$newValues = $bestFitLinear
->getXValues();
}
$returnArray = array();
foreach ($newValues as $xValue) {
$returnArray[0][] = $bestFitLinear
->getValueOfYForX($xValue);
}
return $returnArray;
}
// function TREND()
/**
* TRIMMEAN
*
* Returns the mean of the interior of a data set. TRIMMEAN calculates the mean
* taken by excluding a percentage of data points from the top and bottom tails
* of a data set.
*
* Excel Function:
* TRIMEAN(value1[,value2[, ...]],$discard)
*
* @access public
* @category Statistical Functions
* @param mixed $arg,... Data values
* @param float $discard Percentage to discard
* @return float
*/
public static function TRIMMEAN() {
$aArgs = PHPExcel_Calculation_Functions::flattenArray(func_get_args());
// Calculate
$percent = array_pop($aArgs);
if (is_numeric($percent) && !is_string($percent)) {
if ($percent < 0 || $percent > 1) {
return PHPExcel_Calculation_Functions::NaN();
}
$mArgs = array();
foreach ($aArgs as $arg) {
// Is it a numeric value?
if (is_numeric($arg) && !is_string($arg)) {
$mArgs[] = $arg;
}
}
$discard = floor(self::COUNT($mArgs) * $percent / 2);
sort($mArgs);
for ($i = 0; $i < $discard; ++$i) {
array_pop($mArgs);
array_shift($mArgs);
}
return self::AVERAGE($mArgs);
}
return PHPExcel_Calculation_Functions::VALUE();
}
// function TRIMMEAN()
/**
* VARFunc
*
* Estimates variance based on a sample.
*
* Excel Function:
* VAR(value1[,value2[, ...]])
*
* @access public
* @category Statistical Functions
* @param mixed $arg,... Data values
* @return float
*/
public static function VARFunc() {
// Return value
$returnValue = PHPExcel_Calculation_Functions::DIV0();
$summerA = $summerB = 0;
// Loop through arguments
$aArgs = PHPExcel_Calculation_Functions::flattenArray(func_get_args());
$aCount = 0;
foreach ($aArgs as $arg) {
if (is_bool($arg)) {
$arg = (int) $arg;
}
// Is it a numeric value?
if (is_numeric($arg) && !is_string($arg)) {
$summerA += $arg * $arg;
$summerB += $arg;
++$aCount;
}
}
// Return
if ($aCount > 1) {
$summerA *= $aCount;
$summerB *= $summerB;
$returnValue = ($summerA - $summerB) / ($aCount * ($aCount - 1));
}
return $returnValue;
}
// function VARFunc()
/**
* VARA
*
* Estimates variance based on a sample, including numbers, text, and logical values
*
* Excel Function:
* VARA(value1[,value2[, ...]])
*
* @access public
* @category Statistical Functions
* @param mixed $arg,... Data values
* @return float
*/
public static function VARA() {
// Return value
$returnValue = PHPExcel_Calculation_Functions::DIV0();
$summerA = $summerB = 0;
// Loop through arguments
$aArgs = PHPExcel_Calculation_Functions::flattenArrayIndexed(func_get_args());
$aCount = 0;
foreach ($aArgs as $k => $arg) {
if (is_string($arg) && PHPExcel_Calculation_Functions::isValue($k)) {
return PHPExcel_Calculation_Functions::VALUE();
}
elseif (is_string($arg) && !PHPExcel_Calculation_Functions::isMatrixValue($k)) {
}
else {
// Is it a numeric value?
if (is_numeric($arg) || is_bool($arg) || is_string($arg) & $arg != '') {
if (is_bool($arg)) {
$arg = (int) $arg;
}
elseif (is_string($arg)) {
$arg = 0;
}
$summerA += $arg * $arg;
$summerB += $arg;
++$aCount;
}
}
}
// Return
if ($aCount > 1) {
$summerA *= $aCount;
$summerB *= $summerB;
$returnValue = ($summerA - $summerB) / ($aCount * ($aCount - 1));
}
return $returnValue;
}
// function VARA()
/**
* VARP
*
* Calculates variance based on the entire population
*
* Excel Function:
* VARP(value1[,value2[, ...]])
*
* @access public
* @category Statistical Functions
* @param mixed $arg,... Data values
* @return float
*/
public static function VARP() {
// Return value
$returnValue = PHPExcel_Calculation_Functions::DIV0();
$summerA = $summerB = 0;
// Loop through arguments
$aArgs = PHPExcel_Calculation_Functions::flattenArray(func_get_args());
$aCount = 0;
foreach ($aArgs as $arg) {
if (is_bool($arg)) {
$arg = (int) $arg;
}
// Is it a numeric value?
if (is_numeric($arg) && !is_string($arg)) {
$summerA += $arg * $arg;
$summerB += $arg;
++$aCount;
}
}
// Return
if ($aCount > 0) {
$summerA *= $aCount;
$summerB *= $summerB;
$returnValue = ($summerA - $summerB) / ($aCount * $aCount);
}
return $returnValue;
}
// function VARP()
/**
* VARPA
*
* Calculates variance based on the entire population, including numbers, text, and logical values
*
* Excel Function:
* VARPA(value1[,value2[, ...]])
*
* @access public
* @category Statistical Functions
* @param mixed $arg,... Data values
* @return float
*/
public static function VARPA() {
// Return value
$returnValue = PHPExcel_Calculation_Functions::DIV0();
$summerA = $summerB = 0;
// Loop through arguments
$aArgs = PHPExcel_Calculation_Functions::flattenArrayIndexed(func_get_args());
$aCount = 0;
foreach ($aArgs as $k => $arg) {
if (is_string($arg) && PHPExcel_Calculation_Functions::isValue($k)) {
return PHPExcel_Calculation_Functions::VALUE();
}
elseif (is_string($arg) && !PHPExcel_Calculation_Functions::isMatrixValue($k)) {
}
else {
// Is it a numeric value?
if (is_numeric($arg) || is_bool($arg) || is_string($arg) & $arg != '') {
if (is_bool($arg)) {
$arg = (int) $arg;
}
elseif (is_string($arg)) {
$arg = 0;
}
$summerA += $arg * $arg;
$summerB += $arg;
++$aCount;
}
}
}
// Return
if ($aCount > 0) {
$summerA *= $aCount;
$summerB *= $summerB;
$returnValue = ($summerA - $summerB) / ($aCount * $aCount);
}
return $returnValue;
}
// function VARPA()
/**
* WEIBULL
*
* Returns the Weibull distribution. Use this distribution in reliability
* analysis, such as calculating a device's mean time to failure.
*
* @param float $value
* @param float $alpha Alpha Parameter
* @param float $beta Beta Parameter
* @param boolean $cumulative
* @return float
*
*/
public static function WEIBULL($value, $alpha, $beta, $cumulative) {
$value = PHPExcel_Calculation_Functions::flattenSingleValue($value);
$alpha = PHPExcel_Calculation_Functions::flattenSingleValue($alpha);
$beta = PHPExcel_Calculation_Functions::flattenSingleValue($beta);
if (is_numeric($value) && is_numeric($alpha) && is_numeric($beta)) {
if ($value < 0 || $alpha <= 0 || $beta <= 0) {
return PHPExcel_Calculation_Functions::NaN();
}
if (is_numeric($cumulative) || is_bool($cumulative)) {
if ($cumulative) {
return 1 - exp(0 - pow($value / $beta, $alpha));
}
else {
return $alpha / pow($beta, $alpha) * pow($value, $alpha - 1) * exp(0 - pow($value / $beta, $alpha));
}
}
}
return PHPExcel_Calculation_Functions::VALUE();
}
// function WEIBULL()
/**
* ZTEST
*
* Returns the Weibull distribution. Use this distribution in reliability
* analysis, such as calculating a device's mean time to failure.
*
* @param float $dataSet
* @param float $m0 Alpha Parameter
* @param float $sigma Beta Parameter
* @param boolean $cumulative
* @return float
*
*/
public static function ZTEST($dataSet, $m0, $sigma = NULL) {
$dataSet = PHPExcel_Calculation_Functions::flattenArrayIndexed($dataSet);
$m0 = PHPExcel_Calculation_Functions::flattenSingleValue($m0);
$sigma = PHPExcel_Calculation_Functions::flattenSingleValue($sigma);
if (is_null($sigma)) {
$sigma = self::STDEV($dataSet);
}
$n = count($dataSet);
return 1 - self::NORMSDIST((self::AVERAGE($dataSet) - $m0) / ($sigma / SQRT($n)));
}
}Members
Name |
Modifiers | Type | Description | Overrides |
|---|---|---|---|---|
|
PHPExcel_Calculation_Statistical:: |
private static | property | ||
|
PHPExcel_Calculation_Statistical:: |
private static | property | ||
|
PHPExcel_Calculation_Statistical:: |
private static | property | ||
|
PHPExcel_Calculation_Statistical:: |
private static | property | ||
|
PHPExcel_Calculation_Statistical:: |
private static | property | ||
|
PHPExcel_Calculation_Statistical:: |
public static | function | * AVEDEV * * Returns the average of the absolute deviations of data points from their mean. * AVEDEV is a measure of the variability in a data set. * * Excel Function: * AVEDEV(value1[,value2[, ...]]) * * @access public * @category… | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * AVERAGE * * Returns the average (arithmetic mean) of the arguments * * Excel Function: * AVERAGE(value1[,value2[, ...]]) * * @access public * @category Statistical Functions * | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * AVERAGEA * * Returns the average of its arguments, including numbers, text, and logical values * * Excel Function: * AVERAGEA(value1[,value2[, ...]]) * * @access public * @category Statistical Functions * | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * AVERAGEIF * * Returns the average value from a range of cells that contain numbers within the list of arguments * * Excel Function: * AVERAGEIF(value1[,value2[, ...]],condition) * * @access public * @category Mathematical and… | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * BETADIST * * Returns the beta distribution. * * | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * BETAINV * * Returns the inverse of the beta distribution. * * | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * BINOMDIST * * Returns the individual term binomial distribution probability. Use BINOMDIST in problems with * a fixed number of tests or trials, when the outcomes of any trial are only success or failure, * when trials are independent, and… | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * CHIDIST * * Returns the one-tailed probability of the chi-squared distribution. * * | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * CHIINV * * Returns the one-tailed probability of the chi-squared distribution. * * | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * CONFIDENCE * * Returns the confidence interval for a population mean * * | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * CORREL * * Returns covariance, the average of the products of deviations for each data point pair. * * | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * COUNT * * Counts the number of cells that contain numbers within the list of arguments * * Excel Function: * COUNT(value1[,value2[, ...]]) * * @access public * @category Statistical Functions * | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * COUNTA * * Counts the number of cells that are not empty within the list of arguments * * Excel Function: * COUNTA(value1[,value2[, ...]]) * * @access public * @category Statistical Functions * | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * COUNTBLANK * * Counts the number of empty cells within the list of arguments * * Excel Function: * COUNTBLANK(value1[,value2[, ...]]) * * @access public * @category Statistical Functions * | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * COUNTIF * * Counts the number of cells that contain numbers within the list of arguments * * Excel Function: * COUNTIF(value1[,value2[, ...]],condition) * * @access public * @category Statistical Functions * | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * COVAR * * Returns covariance, the average of the products of deviations for each data point pair. * * | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * CRITBINOM * * Returns the smallest value for which the cumulative binomial distribution is greater * than or equal to a criterion value * * See http://support.microsoft.com/kb/828117/ for details of the algorithm used * * | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * DEVSQ * * Returns the sum of squares of deviations of data points from their sample mean. * * Excel Function: * DEVSQ(value1[,value2[, ...]]) * * @access public * @category Statistical Functions * | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * EXPONDIST * * Returns the exponential distribution. Use EXPONDIST to model the time between events, * such as how long an automated bank teller takes to deliver cash. For example, you can * use EXPONDIST to determine the probability that… | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * FISHER * * Returns the Fisher transformation at x. This transformation produces a function that * is normally distributed rather than skewed. Use this function to perform hypothesis * testing on the correlation coefficient. * * | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * FISHERINV * * Returns the inverse of the Fisher transformation. Use this transformation when * analyzing correlations between ranges or arrays of data. If y = FISHER(x), then * FISHERINV(y) = x. * * | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * FORECAST * * Calculates, or predicts, a future value by using existing values. The predicted value is a y-value for a given x-value. * * | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * GAMMADIST * * Returns the gamma distribution. * * | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * GAMMAINV * * Returns the inverse of the beta distribution. * * | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * GAMMALN * * Returns the natural logarithm of the gamma function. * * | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * GEOMEAN * * Returns the geometric mean of an array or range of positive data. For example, you * can use GEOMEAN to calculate average growth rate given compound interest with * variable rates. * * Excel Function: … | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * GROWTH * * Returns values along a predicted emponential trend * * | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * HARMEAN * * Returns the harmonic mean of a data set. The harmonic mean is the reciprocal of the * arithmetic mean of reciprocals. * * Excel Function: * HARMEAN(value1[,value2[, ...]]) * * @access public * @category Statistical… | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * HYPGEOMDIST * * Returns the hypergeometric distribution. HYPGEOMDIST returns the probability of a given number of * sample successes, given the sample size, population successes, and population size. * * | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * INTERCEPT * * Calculates the point at which a line will intersect the y-axis by using existing x-values and y-values. * * | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * KURT * * Returns the kurtosis of a data set. Kurtosis characterizes the relative peakedness * or flatness of a distribution compared with the normal distribution. Positive * kurtosis indicates a relatively peaked distribution. Negative… | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * LARGE * * Returns the nth largest value in a data set. You can use this function to * select a value based on its relative standing. * * Excel Function: * LARGE(value1[,value2[, ...]],entry) * * @access public * @category… | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * LINEST * * Calculates the statistics for a line by using the "least squares" method to calculate a straight line that best fits your data, * and then returns an array that describes the line. * * | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * LOGEST * * Calculates an exponential curve that best fits the X and Y data series, * and then returns an array that describes the line. * * | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * LOGINV * * Returns the inverse of the normal cumulative distribution * * | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * LOGNORMDIST * * Returns the cumulative lognormal distribution of x, where ln(x) is normally distributed * with parameters mean and standard_dev. * * | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * MAX * * MAX returns the value of the element of the values passed that has the highest value, * with negative numbers considered smaller than positive numbers. * * Excel Function: * MAX(value1[,value2[, ...]]) * * @access public … | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * MAXA * * Returns the greatest value in a list of arguments, including numbers, text, and logical values * * Excel Function: * MAXA(value1[,value2[, ...]]) * * @access public * @category Statistical Functions * | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * MAXIF * * Counts the maximum value within a range of cells that contain numbers within the list of arguments * * Excel Function: * MAXIF(value1[,value2[, ...]],condition) * * @access public * @category Mathematical and Trigonometric… | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * MEDIAN * * Returns the median of the given numbers. The median is the number in the middle of a set of numbers. * * Excel Function: * MEDIAN(value1[,value2[, ...]]) * * @access public * @category Statistical Functions * | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * MIN * * MIN returns the value of the element of the values passed that has the smallest value, * with negative numbers considered smaller than positive numbers. * * Excel Function: * MIN(value1[,value2[, ...]]) * * @access public … | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * MINA * * Returns the smallest value in a list of arguments, including numbers, text, and logical values * * Excel Function: * MINA(value1[,value2[, ...]]) * * @access public * @category Statistical Functions * | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * MINIF * * Returns the minimum value within a range of cells that contain numbers within the list of arguments * * Excel Function: * MINIF(value1[,value2[, ...]],condition) * * @access public * @category Mathematical and… | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * MODE * * Returns the most frequently occurring, or repetitive, value in an array or range of data * * Excel Function: * MODE(value1[,value2[, ...]]) * * @access public * @category Statistical Functions * | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * NEGBINOMDIST * * Returns the negative binomial distribution. NEGBINOMDIST returns the probability that * there will be number_f failures before the number_s-th success, when the constant * probability of a success is probability_s. This… | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * NORMDIST * * Returns the normal distribution for the specified mean and standard deviation. This * function has a very wide range of applications in statistics, including hypothesis * testing. * * | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * NORMINV * * Returns the inverse of the normal cumulative distribution for the specified mean and standard deviation. * * | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * NORMSDIST * * Returns the standard normal cumulative distribution function. The distribution has * a mean of 0 (zero) and a standard deviation of one. Use this function in place of a * table of standard normal curve areas. * * | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * NORMSINV * * Returns the inverse of the standard normal cumulative distribution * * | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * PERCENTILE * * Returns the nth percentile of values in a range.. * * Excel Function: * PERCENTILE(value1[,value2[, ...]],entry) * * @access public * @category Statistical Functions * | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * PERCENTRANK * * Returns the rank of a value in a data set as a percentage of the data set. * * | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * PERMUT * * Returns the number of permutations for a given number of objects that can be * selected from number objects. A permutation is any set or subset of objects or * events where internal order is significant. Permutations are… | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * POISSON * * Returns the Poisson distribution. A common application of the Poisson distribution * is predicting the number of events over a specific time, such as the number of * cars arriving at a toll plaza in 1 minute. * * | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * QUARTILE * * Returns the quartile of a data set. * * Excel Function: * QUARTILE(value1[,value2[, ...]],entry) * * @access public * @category Statistical Functions * | |
|
PHPExcel_Calculation_Statistical:: |
public static | function | * RANK * * Returns the rank of a number in a list of numbers. * * | |
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PHPExcel_Calculation_Statistical:: |
public static | function | * RSQ * * Returns the square of the Pearson product moment correlation coefficient through data points in known_y's and known_x's. * * | |
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PHPExcel_Calculation_Statistical:: |
public static | function | * SKEW * * Returns the skewness of a distribution. Skewness characterizes the degree of asymmetry * of a distribution around its mean. Positive skewness indicates a distribution with an * asymmetric tail extending toward more positive values.… | |
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PHPExcel_Calculation_Statistical:: |
public static | function | * SLOPE * * Returns the slope of the linear regression line through data points in known_y's and known_x's. * * | |
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PHPExcel_Calculation_Statistical:: |
public static | function | * SMALL * * Returns the nth smallest value in a data set. You can use this function to * select a value based on its relative standing. * * Excel Function: * SMALL(value1[,value2[, ...]],entry) * * @access public * @category… | |
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PHPExcel_Calculation_Statistical:: |
public static | function | * STANDARDIZE * * Returns a normalized value from a distribution characterized by mean and standard_dev. * * | |
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PHPExcel_Calculation_Statistical:: |
public static | function | * STDEV * * Estimates standard deviation based on a sample. The standard deviation is a measure of how * widely values are dispersed from the average value (the mean). * * Excel Function: * STDEV(value1[,value2[, ...]]) * *… | |
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PHPExcel_Calculation_Statistical:: |
public static | function | * STDEVA * * Estimates standard deviation based on a sample, including numbers, text, and logical values * * Excel Function: * STDEVA(value1[,value2[, ...]]) * * @access public * @category Statistical Functions * | |
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PHPExcel_Calculation_Statistical:: |
public static | function | * STDEVP * * Calculates standard deviation based on the entire population * * Excel Function: * STDEVP(value1[,value2[, ...]]) * * @access public * @category Statistical Functions * | |
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PHPExcel_Calculation_Statistical:: |
public static | function | * STDEVPA * * Calculates standard deviation based on the entire population, including numbers, text, and logical values * * Excel Function: * STDEVPA(value1[,value2[, ...]]) * * @access public * @category Statistical Functions * | |
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PHPExcel_Calculation_Statistical:: |
public static | function | * STEYX * * Returns the standard error of the predicted y-value for each x in the regression. * * | |
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PHPExcel_Calculation_Statistical:: |
public static | function | * TDIST * * Returns the probability of Student's T distribution. * * | |
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PHPExcel_Calculation_Statistical:: |
public static | function | * TINV * * Returns the one-tailed probability of the chi-squared distribution. * * | |
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PHPExcel_Calculation_Statistical:: |
public static | function | * TREND * * Returns values along a linear trend * * | |
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PHPExcel_Calculation_Statistical:: |
public static | function | * TRIMMEAN * * Returns the mean of the interior of a data set. TRIMMEAN calculates the mean * taken by excluding a percentage of data points from the top and bottom tails * of a data set. * * Excel Function: * TRIMEAN(value1[,value2[,… | |
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PHPExcel_Calculation_Statistical:: |
public static | function | * VARA * * Estimates variance based on a sample, including numbers, text, and logical values * * Excel Function: * VARA(value1[,value2[, ...]]) * * @access public * @category Statistical Functions * | |
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PHPExcel_Calculation_Statistical:: |
public static | function | * VARFunc * * Estimates variance based on a sample. * * Excel Function: * VAR(value1[,value2[, ...]]) * * @access public * @category Statistical Functions * | |
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PHPExcel_Calculation_Statistical:: |
public static | function | * VARP * * Calculates variance based on the entire population * * Excel Function: * VARP(value1[,value2[, ...]]) * * @access public * @category Statistical Functions * | |
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PHPExcel_Calculation_Statistical:: |
public static | function | * VARPA * * Calculates variance based on the entire population, including numbers, text, and logical values * * Excel Function: * VARPA(value1[,value2[, ...]]) * * @access public * @category Statistical Functions * | |
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PHPExcel_Calculation_Statistical:: |
public static | function | * WEIBULL * * Returns the Weibull distribution. Use this distribution in reliability * analysis, such as calculating a device's mean time to failure. * * | |
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PHPExcel_Calculation_Statistical:: |
public static | function | * ZTEST * * Returns the Weibull distribution. Use this distribution in reliability * analysis, such as calculating a device's mean time to failure. * * | |
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PHPExcel_Calculation_Statistical:: |
private static | function | * Beta function. * * @author Jaco van Kooten * * | |
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PHPExcel_Calculation_Statistical:: |
private static | function | * Evaluates of continued fraction part of incomplete beta function. * Based on an idea from Numerical Recipes (W.H. Press et al, 1992). * @author Jaco van Kooten | |
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PHPExcel_Calculation_Statistical:: |
private static | function | ||
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PHPExcel_Calculation_Statistical:: |
private static | function | ||
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PHPExcel_Calculation_Statistical:: |
private static | function | * Incomplete beta function * * @author Jaco van Kooten * @author Paul Meagher * * The computation is based on formulas from Numerical Recipes, Chapter 6.4 (W.H. Press et al, 1992). * | |
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PHPExcel_Calculation_Statistical:: |
private static | function | ||
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PHPExcel_Calculation_Statistical:: |
private static | function | ||
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PHPExcel_Calculation_Statistical:: |
private static | function | ||
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PHPExcel_Calculation_Statistical:: |
private static | function | ||
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PHPExcel_Calculation_Statistical:: |
private static | function | * The natural logarithm of the beta function. * * | |
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PHPExcel_Calculation_Statistical:: |
private static | function | ||
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PHPExcel_Calculation_Statistical:: |
private static | function |