From f1ab2f022fdc780aca0944d90e9a0e844a0820d7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Anton=20Luka=20=C5=A0ijanec?= Date: Mon, 27 May 2024 13:12:17 +0200 Subject: =?UTF-8?q?2024-02-19:=20popravljen=20(prej=C5=A1nji=20commit=20je?= =?UTF-8?q?=20napa=C4=8Den)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../excel/PHPExcel/Calculation/Statistical.php | 3644 -------------------- 1 file changed, 3644 deletions(-) delete mode 100644 admin/survey/excel/PHPExcel/Calculation/Statistical.php (limited to 'admin/survey/excel/PHPExcel/Calculation/Statistical.php') diff --git a/admin/survey/excel/PHPExcel/Calculation/Statistical.php b/admin/survey/excel/PHPExcel/Calculation/Statistical.php deleted file mode 100644 index b2a4f43..0000000 --- a/admin/survey/excel/PHPExcel/Calculation/Statistical.php +++ /dev/null @@ -1,3644 +0,0 @@ - $value) { - if ((is_bool($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.
- * Based on public domain NETLIB (Fortran) code by W. J. Cody and L. Stoltz
- * Applied Mathematics Division
- * Argonne National Laboratory
- * Argonne, IL 60439
- *

- * References: - *

    - *
  1. W. J. Cody and K. E. Hillstrom, 'Chebyshev Approximations for the Natural - * Logarithm of the Gamma Function,' Math. Comp. 21, 1967, pp. 198-203.
    2. - *
  2. K. E. Hillstrom, ANL/AMD Program ANLC366S, DGAMMA/DLGAMA, May, 1969.
    3. - *
  3. Hart, Et. Al., Computer Approximations, Wiley and sons, New York, 1968.
    4. - *
- *

- *

- * From the original documentation: - *

- *

- * 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. - *

- *

- * Error returns:
- * 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. - *

- * @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 = -0.5772156649015328605195174; - static $lg_d2 = 0.4227843350984671393993777; - static $lg_d4 = 1.791759469228055000094023; - - static $lg_p1 = array( 4.945235359296727046734888, - 201.8112620856775083915565, - 2290.838373831346393026739, - 11319.67205903380828685045, - 28557.24635671635335736389, - 38484.96228443793359990269, - 26377.48787624195437963534, - 7225.813979700288197698961 ); - static $lg_p2 = array( 4.974607845568932035012064, - 542.4138599891070494101986, - 15506.93864978364947665077, - 184793.2904445632425417223, - 1088204.76946882876749847, - 3338152.967987029735917223, - 5106661.678927352456275255, - 3074109.054850539556250927 ); - static $lg_p4 = array( 14745.02166059939948905062, - 2426813.369486704502836312, - 121475557.4045093227939592, - 2663432449.630976949898078, - 29403789566.34553899906876, - 170266573776.5398868392998, - 492612579337.743088758812, - 560625185622.3951465078242 ); - - static $lg_q1 = array( 67.48212550303777196073036, - 1113.332393857199323513008, - 7738.757056935398733233834, - 27639.87074403340708898585, - 54993.10206226157329794414, - 61611.22180066002127833352, - 36351.27591501940507276287, - 8785.536302431013170870835 ); - static $lg_q2 = array( 183.0328399370592604055942, - 7765.049321445005871323047, - 133190.3827966074194402448, - 1136705.821321969608938755, - 5267964.117437946917577538, - 13467014.54311101692290052, - 17827365.30353274213975932, - 9533095.591844353613395747 ); - static $lg_q4 = array( 2690.530175870899333379843, - 639388.5654300092398984238, - 41355999.30241388052042842, - 1120872109.61614794137657, - 14886137286.78813811542398, - 101680358627.2438228077304, - 341747634550.7377132798597, - 446315818741.9713286462081 ); - - static $lg_c = array( -0.001910444077728, - 8.4171387781295e-4, - -5.952379913043012e-4, - 7.93650793500350248e-4, - -0.002777777777777681622553, - 0.08333333333333333331554247, - 0.0057083835261 ); - - // Rough estimate of the fourth root of logGamma_xBig - static $lg_frtbig = 2.25e76; - 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 => -86.50532032941677, - 3 => 24.01409824083091, - 4 => -1.231739572450155, - 5 => 1.208650973866179e-3, - 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 => -3.969683028665376e+01, - 2 => 2.209460984245205e+02, - 3 => -2.759285104469687e+02, - 4 => 1.383577518672690e+02, - 5 => -3.066479806614716e+01, - 6 => 2.506628277459239e+00 - ); - - static $b = array( 1 => -5.447609879822406e+01, - 2 => 1.615858368580409e+02, - 3 => -1.556989798598866e+02, - 4 => 6.680131188771972e+01, - 5 => -1.328068155288572e+01 - ); - - static $c = array( 1 => -7.784894002430293e-03, - 2 => -3.223964580411365e-01, - 3 => -2.400758277161838e+00, - 4 => -2.549732539343734e+00, - 5 => 4.374664141464968e+00, - 6 => 2.938163982698783e+00 - ); - - static $d = array( 1 => 7.784695709041462e-03, - 2 => 3.224671290700398e-01, - 3 => 2.445134137142996e+00, - 4 => 3.754408661907416e+00 - ); - - // 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 = 2.76438810333863E-02; - $c5 = 3.8405729373609E-03; - $c6 = 3.951896511919E-04; - $c7 = 3.21767881768E-05; - $c8 = 2.888167364E-07; - $c9 = 3.960315187E-07; - - $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 = 3.3871328727963666080; - $a1 = 1.3314166789178437745E+2; - $a2 = 1.9715909503065514427E+3; - $a3 = 1.3731693765509461125E+4; - $a4 = 4.5921953931549871457E+4; - $a5 = 6.7265770927008700853E+4; - $a6 = 3.3430575583588128105E+4; - $a7 = 2.5090809287301226727E+3; - - $b1 = 4.2313330701600911252E+1; - $b2 = 6.8718700749205790830E+2; - $b3 = 5.3941960214247511077E+3; - $b4 = 2.1213794301586595867E+4; - $b5 = 3.9307895800092710610E+4; - $b6 = 2.8729085735721942674E+4; - $b7 = 5.2264952788528545610E+3; - - // coefficients for p not close to 0, 0.5 or 1. - $c0 = 1.42343711074968357734; - $c1 = 4.63033784615654529590; - $c2 = 5.76949722146069140550; - $c3 = 3.64784832476320460504; - $c4 = 1.27045825245236838258; - $c5 = 2.41780725177450611770E-1; - $c6 = 2.27238449892691845833E-2; - $c7 = 7.74545014278341407640E-4; - - $d1 = 2.05319162663775882187; - $d2 = 1.67638483018380384940; - $d3 = 6.89767334985100004550E-1; - $d4 = 1.48103976427480074590E-1; - $d5 = 1.51986665636164571966E-2; - $d6 = 5.47593808499534494600E-4; - $d7 = 1.05075007164441684324E-9; - - // coefficients for p near 0 or 1. - $e0 = 6.65790464350110377720; - $e1 = 5.46378491116411436990; - $e2 = 1.78482653991729133580; - $e3 = 2.96560571828504891230E-1; - $e4 = 2.65321895265761230930E-2; - $e5 = 1.24266094738807843860E-3; - $e6 = 2.71155556874348757815E-5; - $e7 = 2.01033439929228813265E-7; - - $f1 = 5.99832206555887937690E-1; - $f2 = 1.36929880922735805310E-1; - $f3 = 1.48753612908506148525E-2; - $f4 = 7.86869131145613259100E-4; - $f5 = 1.84631831751005468180E-5; - $f6 = 1.42151175831644588870E-7; - $f7 = 2.04426310338993978564E-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 = (integer) $arg; - } - // Is it a numeric value? - if ((is_numeric($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 = (integer) $arg; - } - // Is it a numeric value? - if ((is_numeric($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 = (integer) $arg; - } elseif (is_string($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 $condition The criteria that defines which cells will be checked. - * @return float - */ - public static function AVERAGEIF($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 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 = (integer) $arg; - } - // Is it a numeric value? - if ((is_numeric($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 = 10e-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 = (integer) $arg; - } - if ((is_numeric($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 : (boolean) 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 : (boolean) PHPExcel_Calculation_Functions::flattenSingleValue($const); - $stats = (is_null($stats)) ? False : (boolean) 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 : (boolean) PHPExcel_Calculation_Functions::flattenSingleValue($const); - $stats = (is_null($stats)) ? False : (boolean) 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 $value - * @return float - * - * @todo Try implementing P J Acklam's refinement algorithm for greater - * accuracy if I can get my head round the mathematics - * (as described at) http://home.online.no/~pjacklam/notes/invnorm/ - */ - public static function LOGINV($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 $value - * @return float - */ - public static function LOGNORMDIST($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 = (integer) $arg; - } elseif (is_string($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 = (integer) $arg; - } elseif (is_string($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 : (integer) 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 : (integer) 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 = (integer) $arg; - } - // Is it a numeric value? - if ((is_numeric($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 = (integer) $arg; - } elseif (is_string($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 = (integer) $arg; - } - // Is it a numeric value? - if ((is_numeric($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 = (integer) $arg; - } elseif (is_string($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 : (boolean) 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 = (integer) $arg; } - // Is it a numeric value? - if ((is_numeric($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 = (integer) $arg; - } elseif (is_string($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 = (integer) $arg; } - // Is it a numeric value? - if ((is_numeric($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 = (integer) $arg; - } elseif (is_string($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 $value - * @param float $alpha Alpha Parameter - * @param float $beta 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))); - } // function ZTEST() - -} // class PHPExcel_Calculation_Statistical -- cgit v1.2.3