Math PHP is the only library you need to integrate mathematical functions into your applications. It is a self-contained library in pure PHP with no external dependencies.
It is actively under development with development (0.y.z) releases.
- Algebra
- Functions
- Linear Algebra
- Numerical Analysis
- Probability
- Combinatorics
- Distributions
- Sequences
- Set Theory
- Statistics
Add the library to your composer.json file in your project:
{
"require": {
"markrogoyski/math-php": "0.*"
}
}Use composer to install the library:
$ php composer.phar installComposer will install Math PHP inside your vendor folder. Then you can add the following to your .php files to use the library with Autoloading.
require_once(__DIR__ . '/vendor/autoload.php');- PHP 7
use MathPHP\Algebra;
// Greatest common divisor (GCD)
$gcd = Algebra::gcd(8, 12);
// Extended greatest common divisor - gcd(a, b) = a*a' + b*b'
$gcd = Algebra::extendedGCD(12, 8); // returns array [gcd, a', b']
// Least common multiple (LCM)
$lcm = Algebra::lcm(5, 2);
// Factors of an integer
$factors = Algebra::factors(12); // returns [1, 2, 3, 4, 6, 12]use MathPHP\Functions\Map\Single;
$x = [1, 2, 3, 4];
$sums = Single::add($x, 2); // [3, 4, 5, 6]
$differences = Single::subtract($x, 1); // [0, 1, 2, 3]
$products = Single::multiply($x, 5); // [5, 10, 15, 20]
$quotients = Single::divide($x, 2); // [0.5, 1, 1.5, 2]
$x² = Single::square($x); // [1, 4, 9, 16]
$x³ = Single::cube($x); // [1, 8, 27, 64]
$x⁴ = Single::pow($x, 4); // [1, 16, 81, 256]
$√x = Single::sqrt($x); // [1, 1.414, 1.732, 2]
$∣x∣ = Single::abs($x); // [1, 2, 3, 4]
$maxes = Single::max($x, 3); // [3, 3, 3, 4]
$mins = Single::min($x, 3); // [1, 2, 3, 3]use MathPHP\Functions\Map\Multi;
$x = [10, 10, 10, 10];
$y = [1, 2, 5, 10];
// Map function against elements of two or more arrays, item by item (by item ...)
$sums = Multi::add($x, $y); // [11, 12, 15, 20]
$differences = Multi::subtract($x, $y); // [9, 8, 5, 0]
$products = Multi::multiply($x, $y); // [10, 20, 50, 100]
$quotients = Multi::divide($x, $y); // [10, 5, 2, 1]
$maxes = Multi::max($x, $y); // [10, 10, 10, 10]
$mins = Multi::mins($x, $y); // [1, 2, 5, 10]
// All functions work on multiple arrays; not limited to just two
$x = [10, 10, 10, 10];
$y = [1, 2, 5, 10];
$z = [4, 5, 6, 7];
$sums = Multi::add($x, $y, $z); // [15, 17, 21, 27]use MathPHP\Functions\Special;
// Gamma function Γ(z)
$z = 4;
$Γ = Special::gamma($z); // Uses gamma definition for integers and half integers; uses Lanczos approximation for real numbers
$Γ = Special::gammaLanczos($z); // Lanczos approximation
$Γ = Special::gammaStirling($z); // Stirling approximation
// Incomplete gamma functions - γ(s,t), Γ(s,x)
list($x, $s) = [1, 2];
$γ = Special::lowerIncompleteGamma($x, $s); // same as γ
$γ = Special::γ($x, $s); // same as lowerIncompleteGamma
$Γ = Special::upperIncompleteGamma($x, $s);
// Beta function
list($x, $y) = [1, 2];
$β = Special::beta($x, $y);
// Incomplete beta functions
list($x, $a, $b) = [0.4, 2, 3];
$B = Special::incompleteBeta($x, $a, $b);
$Iₓ = Special::regularizedIncompleteBeta($x, $a, $b);
// Error function (Gauss error function)
$error = Special::errorFunction(2); // same as erf
$error = Special::erf(2); // same as errorFunction
$error = Special::complementaryErrorFunction(2); // same as erfc
$error = Special::erfc(2); // same as complementaryErrorFunction
// Hypergeometric functions
$pFq = Special::generalizedHypergeometric($p, $q, $a, $b, $c, $z);
$₁F₁ = Special::confluentHypergeometric($a, $b, $z);
$₂F₁ = Special::hypergeometric($a, $b, $c, $z);
// Sign function (also known as signum or sgn)
$x = 4;
$sign = Special::signum($x); // same as sgn
$sign = Special::sgn($x); // same as signum
// Logistic function (logistic sigmoid function)
$x₀ = 2; // x-value of the sigmoid's midpoint
$L = 3; // the curve's maximum value
$k = 4; // the steepness of the curve
$x = 5;
$logistic = Special::logistic($x₀, $L, $k, $x);
// Sigmoid function
$t = 2;
$sigmoid = Special::sigmoid($t);use MathPHP\LinearAlgebra\Matrix;
$matrix = [
[1, 2, 3],
[4, 5, 6],
[7, 8, 9],
];
// Matrix factory creates most appropriate matrix
$A = MatrixFactory::create($matrix);
$B = MatrixFactory::create($matrix);
// Can also directly instantiate desired matrix class
$A = new Matrix($matrix);
$B = new SquareMatrix($matrix);
// Basic matrix data
$array = $A->getMatrix();
$rows = $A->getM(); // number of rows
$cols = $A->getN(); // number of columns
// Basic matrix elements (zero-based indexing)
$row = $A->getRow(2);
$col = $A->getColumn(2);
$item = $A->get(2, 2);
// Row operations
list($mᵢ, $mⱼ, $k) = [1, 2, 5];
$R = $A->rowInterchange($mᵢ, $mⱼ);
$R = $A->rowMultiply($mᵢ, $k); // Multiply row mᵢ by k
$R = $A->rowAdd($mᵢ, $mⱼ, $k); // Add k * row mᵢ to row mⱼ
$R = $A->rowExclude($mᵢ); // Exclude row $mᵢ
// Column operations
list($nᵢ, $nⱼ, $k) = [1, 2, 5];
$R = $A->columnInterchange($nᵢ, $nⱼ);
$R = $A->columnMultiply($nᵢ, $k); // Multiply column nᵢ by k
$R = $A->columnAdd($nᵢ, $nⱼ, $k); // Add k * column nᵢ to column nⱼ
$R = $A->columnExclude($nᵢ); // Exclude column $nᵢ
// Matrix operations - return a new Matrix
$A＋B = $A->add($B);
$A⊕B = $A->directSum($B);
$A−B = $A->subtract($B);
$AB = $A->multiply($B);
$２A = $A->scalarMultiply(2);
$A∘B = $A->hadamardProduct($B);
$A⊗B = $A->kroneckerProduct($B);
$Aᵀ = $A->transpose();
$D = $A->diagonal();
$⟮A∣B⟯ = $A->augment($B);
$⟮A∣I⟯ = $A->augmentIdentity(); // Augment with the identity matrix
$⟮A∣B⟯ = $A->augmentBelow($B);
$rref = $A->rref(); // Reduced row echelon form
$A⁻¹ = $A->inverse();
$Mᵢⱼ = $A->minorMatrix($mᵢ, $nⱼ); // Square matrix with row mᵢ and column nⱼ removed
$CM = $A->cofactorMatrix();
// Matrix operations - return a value
$tr⟮A⟯ = $A->trace();
$|A| = $a->det(); // Determinant
$Mᵢⱼ = $A->minor($mᵢ, $nⱼ); // First minor
$Cᵢⱼ = $A->cofactor($mᵢ, $nⱼ);
// Matrix norms - return a value
$‖A‖₁ = $A->oneNorm();
$‖A‖F = $A->frobeniusNorm(); // Hilbert–Schmidt norm
$‖A‖∞ = $A->infinityNorm();
$max = $A->maxNorm();
// Matrix properties - return a bool
$bool = $A->isSquare();
$bool = $A->isSymmetric();
// Matrix decomposition
$PLU = $A->LUDecomposition(); // returns array of Matrices [L, U, P, A]; P is permutation matrix
// Solve a linear system of equations: Ax = b
$b = new Vector(1, 2, 3);
$x = $A->solve($b);
// Map a function over each element of the Matrix
$func = function($x) {
return $x * 2;
};
$R = $A->map($func);
// Print a matrix
print($A);
/*
[1, 2, 3]
[2, 3, 4]
[3, 4, 5]
*/
// Specialized matrices
list($m, $n, $k) = [4, 4, 2];
$identity_matrix = MatrixFactory::identity($n); // Ones on the main diagonal
$zero_matrix = MatrixFactory::zero($m, $n); // All zeros
$ones_matrix = MatrixFactory::one($m, $n); // All ones
$eye_matrix = MatrixFactory::eye($m, $n, $k); // Ones (or other value) on the k-th diagonal
// Vandermonde matrix
$V = MatrixFactory::create([1, 2, 3], 4); // 4 x 3 Vandermonde matrix
$V = new VandermondeMatrix([1, 2, 3], 4); // Same as using MatrixFactory
// Diagonal matrix
$D = MatrixFactory::create([1, 2, 3]); // 3 x 3 diagonal matrix with zeros above and below the diagonal
$D = new DiagonalMatrix([1, 2, 3]); // Same as using MatrixFactory
// PHP Predefined Interfaces
$json = json_encode($A); // JsonSerializable
$Aᵢⱼ = $A[$mᵢ][$nⱼ]; // ArrayAccessuse MathPHP\LinearAlgebra\Vector;
// Vector
$A = new Vector([1, 2]);
$B = new Vector([2, 4]);
// Basic vector data
$array = $A->getVector();
$n = $A->getN(); // number of elements
$M = $A->asColumnMatrix(); // Vector as an nx1 matrix
$M = $A->asRowMatrix(); // Vector as a 1xn matrix
// Basic vector elements (zero-based indexing)
$item = $A->get(1);
// Vector operations - return a value
$sum = $A->sum();
$│A│ = $A->length(); // same as l2Norm
$A⋅B = $A->dotProduct($B); // same as innerProduct
$A⋅B = $A->innerProduct($B); // same as dotProduct
$A⊥⋅B = $A->perpDotProduct($B);
// Vector operations - return a Vector or Matrix
$kA = $A->scalarMultiply($k);
$A＋B = $A->add($B);
$A−B = $A->subtract($B);
$A／k = $A->scalarDivide($k);
$A⨂B = $A->outerProduct($B);
$AxB = $A->crossProduct($B);
$AB = $A->directProduct($B);
$Â = $A->normalize();
$A⊥ = $A->perpendicular();
$projᵇA = $A->projection($B); // projection of A onto B
$perpᵇA = $A->perp($B); // perpendicular of A on B
// Vector norms - return a value
$l₁norm = $A->l1Norm();
$l²norm = $A->l2Norm();
$pnorm = $A->pNorm();
$max = $A->maxNorm();
// Print a vector
print($A); // [1, 2]
// PHP Predefined Interfaces
$n = count($A); // Countable
$json = json_encode($A); // JsonSerializable
$Aᵢ = $A[$i]; // ArrayAccessuse MathPHP\NumericalAnalysis\Interpolation;
// Interpolation is a method of constructing new data points with the range
// of a discrete set of known data points.
// Each integration method can take input in two ways:
// 1) As a set of points (inputs and outputs of a function)
// 2) As a callback function, and the number of function evaluations to
// perform on an interval between a start and end point.
// Input as a set of points
$points = [[0, 1], [1, 4], [2, 9], [3, 16]];
// Input as a callback function
$f⟮x⟯ = function ($x) {
return $x**2 + 2 * $x + 1;
};
list($start, $end, $n) = [0, 3, 4];
// Lagrange Polynomial
// Returns a function p(x) of x
$p = Interpolation\LagrangePolynomial::interpolate($points); // input as a set of points
$p = Interpolation\LagrangePolynomial::interpolate($f⟮x⟯, $start, $end, $n); // input as a callback function
$p(0) // 1
$p(3) // 16
// Nevilles Method
// More accurate than Lagrange Polynomial Interpolation given the same input
// Returns the evaluation of the interpolating polynomial at the $target point
$target = 2;
$result = Interpolation\NevillesMethod::interpolate($target, $points); // input as a set of points
$result = Interpolation\NevillesMethod::interpolate($target, $f⟮x⟯, $start, $end, $n); // input as a callback function
// Newton Polynomial (Forward)
// Returns a function p(x) of x
$p = Interpolation\NewtonPolynomialForward::interpolate($points); // input as a set of points
$p = Interpolation\NewtonPolynomialForward::interpolate($f⟮x⟯, $start, $end, $n); // input as a callback function
$p(0) // 1
$p(3) // 16
// Natural Cubic Spline
// Returns a piecewise polynomial p(x)
$p = Interpolation\NaturalCubicSpline::interpolate($points); // input as a set of points
$p = Interpolation\NaturalCubicSpline::interpolate($f⟮x⟯, $start, $end, $n); // input as a callback function
$p(0) // 1
$p(3) // 16
// Clamped Cubic Spline
// Returns a piecewise polynomial p(x)
// Input as a set of points
$points = [[0, 1, 0], [1, 4, -1], [2, 9, 4], [3, 16, 0]];
// Input as a callback function
$f⟮x⟯ = function ($x) {
return $x**2 + 2 * $x + 1;
};
$f’⟮x⟯ = function ($x) {
return 2*$x + 2;
};
list($start, $end, $n) = [0, 3, 4];
$p = Interpolation\ClampedCubicSpline::interpolate($points); // input as a set of points
$p = Interpolation\ClampedCubicSpline::interpolate($f⟮x⟯, $f’⟮x⟯, $start, $end, $n); // input as a callback function
$p(0) // 1
$p(3) // 16use MathPHP\NumericalAnalysis\NumericalDifferentiation;
// Numerical Differentiation approximates the derivative of a function.
// Each Differentiation method can take input in two ways:
// 1) As a set of points (inputs and outputs of a function)
// 2) As a callback function, and the number of function evaluations to
// perform on an interval between a start and end point.
// Input as a callback function
$f⟮x⟯ = function ($x) {
return $x**2 + 2 * $x + 1;
};
// Three Point Formula
// Returns an approximation for the derivative of our input at our target
// Input as a set of points
$points = [[0, 1], [1, 4], [2, 9]];
$target = 0;
list($start, $end, $n) = [0, 2, 3];
$derivative = NumericalDifferentiation\ThreePointFormula::differentiate($target, $points); // input as a set of points
$derivative = NumericalDifferentiation\ThreePointFormula::differentiate($target, $f⟮x⟯, $start, $end, $n); // input as a callback function
// Five Point Formula
// Returns an approximation for the derivative of our input at our target
// Input as a set of points
$points = [[0, 1], [1, 4], [2, 9], [3, 16], [4, 25]];
$target = 0;
list($start, $end, $n) = [0, 4, 5];
$derivative = NumericalDifferentiation\FivePointFormula::differentiate($target, $points); // input as a set of points
$derivative = NumericalDifferentiation\FivePointFormula::differentiate($target, $f⟮x⟯, $start, $end, $n); // input as a callback function
// Second Derivative Midpoint Formula
// Returns an approximation for the second derivative of our input at our target
// Input as a set of points
$points = [[0, 1], [1, 4], [2, 9];
$target = 1;
list($start, $end, $n) = [0, 2, 3];
$derivative = NumericalDifferentiation\SecondDerivativeMidpointFormula::differentiate($target, $points); // input as a set of points
$derivative = NumericalDifferentiation\SecondDerivativeMidpointFormula::differentiate($target, $f⟮x⟯, $start, $end, $n); // input as a callback functionuse MathPHP\NumericalAnalysis\NumericalIntegration;
// Numerical integration approximates the definite integral of a function.
// Each integration method can take input in two ways:
// 1) As a set of points (inputs and outputs of a function)
// 2) As a callback function, and the number of function evaluations to
// perform on an interval between a start and end point.
// Trapezoidal Rule (closed Newton-Cotes formula)
$points = [[0, 1], [1, 4], [2, 9], [3, 16]];
$∫f⟮x⟯dx = NumericalIntegration\TrapezoidalRule::approximate($points); // input as a set of points
$f⟮x⟯ = function ($x) {
return $x**2 + 2 * $x + 1;
};
list($start, $end, $n) = [0, 3, 4];
$∫f⟮x⟯dx = NumericalIntegration\TrapezoidalRule::approximate($f⟮x⟯, $start, $end, $n); // input as a callback function
// Simpsons Rule (closed Newton-Cotes formula)
$points = [[0, 1], [1, 4], [2, 9], [3, 16], [4,3]];
$∫f⟮x⟯dx = NumericalIntegration\SimpsonsRule::approximate($points); // input as a set of points
$f⟮x⟯ = function ($x) {
return $x**2 + 2 * $x + 1;
};
list($start, $end, $n) = [0, 3, 5];
$∫f⟮x⟯dx = NumericalIntegration\SimpsonsRule::approximate($f⟮x⟯, $start, $end, $n); // input as a callback function
// Simpsons 3/8 Rule (closed Newton-Cotes formula)
$points = [[0, 1], [1, 4], [2, 9], [3, 16]];
$∫f⟮x⟯dx = NumericalIntegration\SimpsonsThreeEighthsRule::approximate($points); // input as a set of points
$f⟮x⟯ = function ($x) {
return $x**2 + 2 * $x + 1;
};
list($start, $end, $n) = [0, 3, 5];
$∫f⟮x⟯dx = NumericalIntegration\SimpsonsThreeEighthsRule::approximate($f⟮x⟯, $start, $end, $n); // input as a callback function
// Booles Rule (closed Newton-Cotes formula)
$points = [[0, 1], [1, 4], [2, 9], [3, 16], [4, 25]];
$∫f⟮x⟯dx = NumericalIntegration\BoolesRule::approximate($points); // input as a set of points
$f⟮x⟯ = function ($x) {
return $x**3 + 2 * $x + 1;
};
list($start, $end, $n) = [0, 4, 5];
$∫f⟮x⟯dx = NumericalIntegration\BoolesRuleRule::approximate($f⟮x⟯, $start, $end, $n); // input as a callback function
// Rectangle Method (open Newton-Cotes formula)
$points = [[0, 1], [1, 4], [2, 9], [3, 16]];
$∫f⟮x⟯dx = NumericalIntegration\RectangleMethod::approximate($points); // input as a set of points
$f⟮x⟯ = function ($x) {
return $x**2 + 2 * $x + 1;
};
list($start, $end, $n) = [0, 3, 4];
$∫f⟮x⟯dx = NumericalIntegration\RectangleMethod::approximate($f⟮x⟯, $start, $end, $n); // input as a callback function
// Midpoint Rule (open Newton-Cotes formula)
$points = [[0, 1], [1, 4], [2, 9], [3, 16]];
$∫f⟮x⟯dx = NumericalIntegration\MidpointRule::approximate($points); // input as a set of points
$f⟮x⟯ = function ($x) {
return $x**2 + 2 * $x + 1;
};
list($start, $end, $n) = [0, 3, 4];
$∫f⟮x⟯dx = NumericalIntegration\MidpointRule::approximate($f⟮x⟯, $start, $end, $n); // input as a callback functionuse MathPHP\NumericalAnalysis\RootFinding;
// Root-finding methods solve for a root of a polynomial.
// f(x) = x⁴ + 8x³ -13x² -92x + 96
$f⟮x⟯ = function($x) {
return $x**4 + 8 * $x**3 - 13 * $x**2 - 92 * $x + 96;
};
// Newton's Method
$args = [-4.1]; // Parameters to pass to callback function (initial guess, other parameters)
$target = 0; // Value of f(x) we a trying to solve for
$tol = 0.00001; // Tolerance; how close to the actual solution we would like
$position = 0; // Which element in the $args array will be changed; also serves as initial guess. Defaults to 0.
$x = RootFinding\NewtonsMethod::solve($f⟮x⟯, $args, $target, $tol, $position); // Solve for x where f(x) = $target
// Secant Method
$p₀ = -1; // First initial approximation
$p₁ = 2; // Second initial approximation
$tol = 0.00001; // Tolerance; how close to the actual solution we would like
$x = RootFinding\SecantMethod::solve($f⟮x⟯, $p₀, $p₁, $tol); // Solve for x where f(x) = 0
// Bisection Method
$a = 2; // The start of the interval which contains a root
$b = 5; // The end of the interval which contains a root
$tol = 0.00001; // Tolerance; how close to the actual solution we would like
$x = RootFinding\BisectionMethod::solve($f⟮x⟯, $a, $b, $tol); // Solve for x where f(x) = 0
// Fixed-Point Iteration
// f(x) = x⁴ + 8x³ -13x² -92x + 96
// Rewrite f(x) = 0 as (x⁴ + 8x³ -13x² + 96)/92 = x
// Thus, g(x) = (x⁴ + 8x³ -13x² + 96)/92
$g⟮x⟯ = function($x) {
return ($x**4 + 8 * $x**3 - 13 * $x**2 + 96)/92;
};
$a = 0; // The start of the interval which contains a root
$b = 2; // The end of the interval which contains a root
$p = 0; // The initial guess for our root
$tol = 0.00001; // Tolerance; how close to the actual solution we would like
$x = RootFinding\FixedPointIteration::solve($g⟮x⟯, $a, $b, $p, $tol); // Solve for x where f(x) = 0use MathPHP\Probability\Combinatorics;
list($n, $x, $k) = [10, 3, 4];
// Factorials
$n！ = Combinatorics::factorial($n);
$n‼︎ = Combinatorics::doubleFactorial($n);
$x⁽ⁿ⁾ = Combinatorics::risingFactorial($x, $n);
$x₍ᵢ₎ = Combinatorics::fallingFactorial($x, $n);
$！n = Combinatorics::subfactorial($n);
// Permutations
$nPn = Combinatorics::permutations($n); // Permutations of n things, taken n at a time (same as factorial)
$nPk = Combinatorics::permutations($n, $k); // Permutations of n things, taking only k of them
// Combinations
$nCk = Combinatorics::combinations($n, $k); // n choose k without repetition
$nC′k = Combinatorics::combinations($n, $k, Combinatorics::REPETITION); // n choose k with repetition (REPETITION const = true)
// Central binomial coefficient
$cbc = Combinatorics::centralBinomialCoefficient($n);
// Catalan number
$Cn = Combinatorics::catalanNumber($n);
// Lah number
$L⟮n、k⟯ = Combinatorics::lahNumber($n, $k)
// Multinomial coefficient
$groups = [5, 2, 3];
$divisions = Combinatorics::multinomial($groups);use MathPHP\Probability\Distribution\Continuous;
// Beta distribution
$α = 1; // shape parameter
$β = 1; // shape parameter
$x = 2;
$pdf = Beta::PDF($α, $β, $x);
$cdf = Beta::CDF($α, $β, $x);
$μ = Beta::mean($α, $β);
// Cauchy distribution
$x = 1;
$x₀ = 2; // location parameter
$γ = 3; // scale parameter
$pdf = Cauchy::PDF(x, x₀, γ);
$cdf = Cauchy::CDF(x, x₀, γ);
// χ²-distribution (Chi-Squared)
$x = 1;
$k = 2; // degrees of freedom
$pdf = ChiSquared::PDF($x, $k);
$cdf = ChiSquared::CDF($x, $k);
// Exponential distribution
$x = 2; // random variable
$λ = 1; // rate parameter
$pdf = Exponential::PDF($x, $λ);
$cdf = Exponential::CDF($x, $λ);
$μ = Exponential::mean($λ);
// F-distribution
$x = 2;
$d₁ = 3; // degree of freedom v1
$d₂ = 4; // degree of freedom v2
$pdf = F::PDF($x, $d₁, $d₂);
$cdf = F::CDF($x, $d₁, $d₂);
$μ = F::mean($d₁, $d₂);
// Laplace distribution
$x = 1;
$μ = 1; // location parameter
$b = 1.5; // scale parameter (diversity)
$pdf = Laplace::PDF($x, $μ, $b);
$cdf = Laplace::CDF($x, $μ, $b);
// Logistic distribution
$x = 3;
$μ = 2; // location parameter
$s = 1.5; // scale parameter
$pdf = Logistic::PDF($x, $μ, $s);
$cdf = Logistic::CDF($x, $μ, $s);
// Log-logistic distribution (Fisk distribution)
$x = 2;
$α = 1; // scale parameter
$β = 1; // shape parameter
$pdf = LogLogistic::PDF($x, $α, $β);
$cdf = LogLogistic::CDF($x, $α, $β);
$μ = LogLogistic::mean($α, $β);
// Log-normal distribution
$x = 4.3;
$μ = 6; // scale parameter
$σ = 2; // location parameter
$pdf = LogNormal::PDF($x, $μ, $σ);
$cdf = LogNormal::CDF($x, $μ, $σ);
$mean = LogNormal::mean($μ, $σ);
// Normal distribution
list($x, $σ, $μ) = [2, 1, 0];
$pdf = Normal::PDF($x, $μ, $σ);
$cdf = Normal::CDF($x, $μ, $σ);
// Noncentral T distribution
list($x, $ν, $μ) = [8, 50, 10];
$pdf = NoncentralT::PDF($x, $ν, $μ);
$cdf = NoncentralT::CDF($x, $ν, $μ);
$mean = NoncentralT::mean($ν, $μ);
// Pareto distribution
$x = 2;
$a = 1; // shape parameter
$b = 1; // scale parameter
$pdf = Pareto::PDF($x, $a, $b);
$cdf = Pareto::CDF($x, $a, $b);
$μ = Pareto::mean($a, $b);
// Standard normal distribution
$z = 2;
$pdf = StandardNormal::PDF($z);
$cdf = StandardNormal::CDF($z);
// Student's t-distribution
$x = 2;
$ν = 3; // degrees of freedom
$p = 0.4; // proportion of area
$pdf = StudentT::PDF($x, $ν);
$cdf = StudentT::CDF($x, $ν);
$t = StudentT::inverse2Tails($p, $ν); // t such that the area greater than t and the area beneath -t is p
// Uniform distribution
$a = 1; // lower boundary of the distribution
$b = 4; // upper boundary of the distribution
$x = 2;
$pdf = Uniform::PDF($a, $b, $x);
$cdf = Uniform::CDF($a, $b, $x);
$μ = Uniform::mean($a, $b);
// Weibull distribution
$x = 2;
$k = 1; // shape parameter
$λ = 2; // scale parameter
$pdf = Weibull::PDF($x, $k, $λ);
$cdf = Weibull::CDF($x, $k, $λ);
$μ = Weibull::mean($k, $λ);
// Other CDFs - All continuous distributions (...params will be distribution-specific)
// Replace 'DistributionName' with desired distribution.
$inv_cdf = DistributionName::inverse($target, ...$params); // Inverse CDF of the distribution
$between = DistributionName::between($x₁, $x₂, ...$params); // Probability of being between two points, x₁ and x₂
$outside = DistributionName::outside($x₁, $x₂, ...$params); // Probability of being between below x₁ and above x₂
$above = DistributionName::above($x, ...$params); // Probability of being above x to ∞
// Random Number Generator
$random = DistributionName::rand(...$params); // A random number with a given distributionuse MathPHP\Probability\Distribution\Discrete;
// Binomial distribution
$n = 2; // number of events
$r = 1; // number of successful events
$P = 0.5; // probability of success
$pmf = Binomial::PMF($n, $r, $P);
$cdf = Binomial::CDF($n, $r, $P);
// Bernoulli distribution (special case of binomial where n = 1)
$pmf = Bernoulli::PMF($r, $P);
$cdf = Bernoulli::CDF($r, $P);
// Geometric distribution (failures before the first success)
$k = 2; // number of trials
$p = 0.5; // success probability
$pmf = Geometric::PMF($k, $p);
$cdf = Geometric::CDF($k, $p);
// Multinomial distribution
$frequencies = [7, 2, 3];
$probabilities = [0.40, 0.35, 0.25];
$pmf = Multinomial::PMF($frequencies, $probabilities);
// Negative binomial distribution (Pascal)
$x = 2; // number of trials required to produce r successes
$r = 1; // number of successful events
$P = 0.5; // probability of success on an individual trial
$pmf = NegativeBinomial::PMF($x, $r, $P); // same as Pascal::PMF
$pmf = Pascal::PMF($x, $r, $P); // same as NegativeBinomial::PMF
// Poisson distribution
$k = 3; // events in the interval
$λ = 2; // average number of successful events per interval
$pmf = Poisson::PMF($k, $λ);
$cdf = Poisson::CDF($k, $λ);
// Shifted geometric distribution (probability to get one success)
$k = 2; // number of trials
$p = 0.5; // success probability
$pmf = ShiftedGeometric::PMF($k, $p);
$cdf = ShiftedGeometric::CDF($k, $p);use MathPHP\Probability\Distribution\Table;
// Provided solely for completeness' sake.
// It is statistics tradition to provide these tables.
// Math PHP has dynamic distribution CDF functions you can use instead.
// Standard Normal Table (Z Table)
$table = Table\StandardNormal::Z_SCORES;
$probability = $table[1.5][0]; // Value for Z of 1.50
// t Distribution Tables
$table = Table\TDistribution::ONE_SIDED_CONFIDENCE_LEVEL;
$table = Table\TDistribution::TWO_SIDED_CONFIDENCE_LEVEL;
$ν = 5; // degrees of freedom
$cl = 99; // confidence level
$t = $table[$ν][$cl];
// t Distribution Tables
$table = Table\TDistribution::ONE_SIDED_ALPHA;
$table = Table\TDistribution::TWO_SIDED_ALPHA;
$ν = 5; // degrees of freedom
$α = 0.001; // alpha value
$t = $table[$ν][$α];
// χ² Distribution Table
$table = Table\ChiSquared::CHI_SQUARED_SCORES;
$df = 2; // degrees of freedom
$p = 0.05; // P value
$χ² = $table[$df][$p];use MathPHP\Sequence\Basic;
$n = 5; // Number of elements in the sequence
// Arithmetic progression
$d = 2; // Difference between the elements of the sequence
$a₁ = 1; // Starting number for the sequence
$progression = Basic::arithmeticProgression($n, $d, $a₁);
// [1, 3, 5, 7, 9] - Indexed from 1
// Geometric progression (arⁿ⁻¹)
$a = 2; // Scalar value
$r = 3; // Common ratio
$progression = Basic::geometricProgression($n, $a, $r);
// [2(3)⁰, 2(3)¹, 2(3)², 2(3)³] = [2, 6, 18, 54] - Indexed from 1
// Square numbers (n²)
$squares = Basic::squareNumber($n);
// [0², 1², 2², 3², 4²] = [0, 1, 4, 9, 16] - Indexed from 0
// Cubic numbers (n³)
$cubes = Basic::cubicNumber($n);
// [0³, 1³, 2³, 3³, 4³] = [0, 1, 8, 27, 64] - Indexed from 0
// Powers of 2 (2ⁿ)
$po2 = Basic::powersOfTwo($n);
// [2⁰, 2¹, 2², 2³, 2⁴] = [1, 2, 4, 8, 16] - Indexed from 0
// Powers of 10 (10ⁿ)
$po10 = Basic::powersOfTen($n);
// [10⁰, 10¹, 10², 10³, 10⁴] = [1, 10, 100, 1000, 10000] - Indexed from 0
// Factorial (n!)
$fact = Basic::factorial($n);
// [0!, 1!, 2!, 3!, 4!] = [1, 1, 2, 6, 24] - Indexed from 0use MathPHP\Sequence\Advanced;
$n = 6; // Number of elements in the sequence
// Fibonacci (Fᵢ = Fᵢ₋₁ + Fᵢ₋₂)
$fib = Advanced::fibonacci($n);
// [0, 1, 1, 2, 3, 5] - Indexed from 0
// Lucas numbers
$lucas = Advanced::lucasNumber($n);
// [2, 1, 3, 4, 7, 11] - Indexed from 0
// Pell numbers
$pell = Advanced::pellNumber($n);
// [0, 1, 2, 5, 12, 29] - Indexed from 0
// Triangular numbers (figurate number)
$triangles = Advanced::triangularNumber($n);
// [1, 3, 6, 10, 15, 21] - Indexed from 1
// Pentagonal numbers (figurate number)
$pentagons = Advanced::pentagonalNumber($n);
// [1, 5, 12, 22, 35, 51] - Indexed from 1
// Hexagonal numbers (figurate number)
$hexagons = Advanced::hexagonalNumber($n);
// [1, 6, 15, 28, 45, 66] - Indexed from 1
// Heptagonal numbers (figurate number)
$hexagons = Advanced::heptagonalNumber($n)
// [1, 4, 7, 13, 18, 27] - Indexed from 1use MathPHP\SetTheory\Set;
use MathPHP\SetTheory\ImmutableSet;
// Sets and immutable sets
$A = new Set([1, 2, 3]); // Can add and remove members
$B = new ImmutableSet([3, 4, 5]); // Cannot modify set once created
// Basic set data
$set = $A->asArray();
$cardinality = $A->length();
$bool = $A->isEmpty();
// Set membership
$true = $A->isMember(2);
$true = $A->isNotMember(8);
// Add and remove members
$A->add(4);
$A->add(new Set(['a', 'b']));
$A->addMulti([5, 6, 7]);
$A->remove(7);
$A->removeMulti([5, 6]);
$A->clear();
// Set properties against other sets - return boolean
$bool = $A->isDisjoint($B);
$bool = $A->isSubset($B); // A ⊆ B
$bool = $A->isProperSubset($B); // A ⊆ B & A ≠ B
$bool = $A->isSuperset($B); // A ⊇ B
$bool = $A->isProperSuperset($B); // A ⊇ B & A ≠ B
// Set operations with other sets - return a new Set
$A∪B = $A->union($B);
$A∩B = $A->intersect($B);
$A＼B = $A->difference($B); // relative complement
$AΔB = $A->symmetricDifference($B);
$A×B = $A->cartesianProduct($B);
// Other set operations
$P⟮A⟯ = $A->powerSet();
$C = $A->copy();
// Print a set
print($A); // Set{1, 2, 3, 4, Set{a, b}}
// PHP Interfaces
$n = count($A); // Countable
foreach ($A as $member) { ... } // Iterator
// Fluent interface
$A->add(5)->add(6)->remove(4)->addMulti([7, 8, 9]);use MathPHP\Statistics\ANOVA;
// One-way ANOVA
$sample1 = [1, 2, 3];
$sample2 = [3, 4, 5];
$sample3 = [5, 6, 7];
⋮ ⋮
$anova = ANOVA::oneWay($sample1, $sample2, $sample3);
print_r($anova);
/* Array (
[ANOVA] => Array ( // ANOVA hypothesis test summary data
[treatment] => Array (
[SS] => 24 // Sum of squares (between)
[df] => 2 // Degrees of freedom
[MS] => 12 // Mean squares
[F] => 12 // Test statistic
[P] => 0.008 // P value
)
[error] => Array (
[SS] => 6 // Sum of squares (within)
[df] => 6 // Degrees of freedom
[MS] => 1 // Mean squares
)
[total] => Array (
[SS] => 30 // Sum of squares (total)
[df] => 8 // Degrees of freedom
)
)
[total_summary] => Array ( // Total summary data
[n] => 9
[sum] => 36
[mean] => 4
[SS] => 174
[variance] => 3.75
[sd] => 1.9364916731037
[sem] => 0.6454972243679
)
[data_summary] => Array ( // Data summary (each input sample)
[0] => Array ([n] => 3 [sum] => 6 [mean] => 2 [SS] => 14 [variance] => 1 [sd] => 1 [sem] => 0.57735026918963)
[1] => Array ([n] => 3 [sum] => 12 [mean] => 4 [SS] => 50 [variance] => 1 [sd] => 1 [sem] => 0.57735026918963)
[2] => Array ([n] => 3 [sum] => 18 [mean] => 6 [SS] => 110 [variance] => 1 [sd] => 1 [sem] => 0.57735026918963)
)
) */
// Two-way ANOVA
/* | Factor B₁ | Factor B₂ | Factor B₃ | ⋯
Factor A₁ | 4, 6, 8 | 6, 6, 9 | 8, 9, 13 | ⋯
Factor A₂ | 4, 8, 9 | 7, 10, 13 | 12, 14, 16| ⋯
⋮ ⋮ ⋮ ⋮ */
$factorA₁ = [
[4, 6, 8], // Factor B₁
[6, 6, 9], // Factor B₂
[8, 9, 13], // Factor B₃
];
$factorA₂ = [
[4, 8, 9], // Factor B₁
[7, 10, 13], // Factor B₂
[12, 14, 16], // Factor B₃
];
⋮
$anova = ANOVA::twoWay($factorA₁, $factorA₂);
print_r($anova);
/* Array (
[ANOVA] => Array ( // ANOVA hypothesis test summary data
[factorA] => Array (
[SS] => 32 // Sum of squares
[df] => 1 // Degrees of freedom
[MS] => 32 // Mean squares
[F] => 5.6470588235294 // Test statistic
[P] => 0.034994350619895 // P value
)
[factorB] => Array (
[SS] => 93 // Sum of squares
[df] => 2 // Degrees of freedom
[MS] => 46.5 // Mean squares
[F] => 8.2058823529412 // Test statistic
[P] => 0.0056767297582031 // P value
)
[interaction] => Array (
[SS] => 7 // Sum of squares
[df] => 2 // Degrees of freedom
[MS] => 3.5 // Mean squares
[F] => 0.61764705882353 // Test statistic
[P] => 0.5555023440712 // P value
)
[error] => Array (
[SS] => 68 // Sum of squares (within)
[df] => 12 // Degrees of freedom
[MS] => 5.6666666666667 // Mean squares
)
[total] => Array (
[SS] => 200 // Sum of squares (total)
[df] => 17 // Degrees of freedom
)
)
[total_summary] => Array ( // Total summary data
[n] => 18
[sum] => 162
[mean] => 9
[SS] => 1658
[variance] => 11.764705882353
[sd] => 3.4299717028502
[sem] => 0.80845208345444
)
[summary_factorA] => Array ( ... ) // Summary data of factor A
[summary_factorB] => Array ( ... ) // Summary data of factor B
[summary_interaction] => Array ( ... ) // Summary data of interactions of factors A and B
) */use MathPHP\Statistics\Average;
$numbers = [13, 18, 13, 14, 13, 16, 14, 21, 13];
// Mean, median, mode
$mean = Average::mean($numbers);
$median = Average::median($numbers);
$mode = Average::mode($numbers); // Returns an array — may be multimodal
// Other means of a list of numbers
$geometric_mean = Average::geometricMean($numbers);
$harmonic_mean = Average::harmonicMean($numbers);
$contraharmonic_mean = Average::contraharmonicMean($numbers);
$quadratic_mean = Average::quadraticMean($numbers); // same as rootMeanSquare
$root_mean_square = Average::rootMeanSquare($numbers); // same as quadraticMean
$trimean = Average::trimean($numbers);
$interquartile_mean = Average::interquartileMean($numbers); // same as iqm
$interquartile_mean = Average::iqm($numbers); // same as interquartileMean
$cubic_mean = Average::cubicMean($numbers);
// Truncated mean (trimmed mean)
$trim_percent = 25;
$truncated_mean = Average::truncatedMean($numbers, $trim_percent);
// Generalized mean (power mean)
$p = 2;
$generalized_mean = Average::generalizedMean($numbers, $p); // same as powerMean
$power_mean = Average::powerMean($numbers, $p); // same as generalizedMean
// Lehmer mean
$p = 3;
$lehmer_mean = Average::lehmerMean($numbers, $p);
// Moving averages
$n = 3;
$weights = [3, 2, 1];
$SMA = Average::simpleMovingAverage($numbers, $n); // 3 n-point moving average
$CMA = Average::cumulativeMovingAverage($numbers);
$WMA = Average::weightedMovingAverage($numbers, $n, $weights);
$EPA = Average::exponentialMovingAverage($numbers, $n);
// Means of two numbers
list($x, $y) = [24, 6];
$agm = Average::arithmeticGeometricMean($x, $y); // same as agm
$agm = Average::agm($x, $y); // same as arithmeticGeometricMean
$log_mean = Average::logarithmicMean($x, $y);
$heronian_mean = Average::heronianMean($x, $y);
$identric_mean = Average::identricMean($x, $y);
// Averages report
$averages = Average::describe($numbers);
print_r($averages);
/* Array (
[mean] => 15
[median] => 14
[mode] => Array ( [0] => 13 )
[geometric_mean] => 14.789726414533
[harmonic_mean] => 14.605077399381
[contraharmonic_mean] => 15.474074074074
[quadratic_mean] => 15.235193176035
[trimean] => 14.5
[iqm] => 14
[cubic_mean] => 15.492307432707
) */use MathPHP\Statistics\Correlation;
$X = [1, 2, 3, 4, 5];
$Y = [2, 3, 4, 4, 6];
// Covariance
$σxy = Correlation::covariance($X, $Y); // Has optional parameter to set population (defaults to sample covariance)
// r - Pearson product-moment correlation coefficient (Pearson's r)
$r = Correlation::r($X, $Y); // Has optional parameter to set population (defaults to sample correlation coefficient)
// R² - Coefficient of determination
$R² = Correlation::R2($X, $Y); // Has optional parameter to set population (defaults to sample coefficient of determination)
// τ - Kendall rank correlation coefficient (Kendall's tau)
$τ = Correlation::kendallsTau($X, $Y);
// ρ - Spearman's rank correlation coefficient (Spearman's rho)
$ρ = Correlation::spearmansRho($X, $Y);
// Descriptive correlation report
$stats = Correlation::describe($X, $Y);
print_r($stats);
/* Array (
[cov] => 2.25
[r] => 0.95940322360025
[R2] => 0.92045454545455
[tau] => 0.94868329805051
[rho] => 0.975
) */use MathPHP\Statistics\Descriptive;
$numbers = [13, 18, 13, 14, 13, 16, 14, 21, 13];
// Range and midrange
$range = Descriptive::range($numbers);
$midrange = Descriptive::midrange($numbers);
// Variance (population and sample)
$σ² = Descriptive::populationVariance($numbers); // n degrees of freedom
$S² = Descriptive::sampleVariance($numbers); // n - 1 degrees of freedom
// Variance (Custom degrees of freedom)
$df = 5; // degrees of freedom
$S² = Descriptive::variance($numbers, $df); // can specify custom degrees of freedom
// Standard deviation (Uses population variance)
$σ = Descriptive::sd($numbers); // same as standardDeviation;
$σ = Descriptive::standardDeviation($numbers); // same as sd;
// SD+ (Standard deviation for a sample; uses sample variance)
$SD＋ = Descriptive::sd($numbers, Descriptive::SAMPLE); // SAMPLE constant = true
$SD＋ = Descriptive::standardDeviation($numbers, true); // same as sd with SAMPLE constant
// Coefficient of variation (cᵥ)
$cᵥ = Descriptive::coefficientOfVariation($numbers);
// MAD - mean/median absolute deviations
$mean_mad = Descriptive::meanAbsoluteDeviation($numbers);
$median_mad = Descriptive::medianAbsoluteDeviation($numbers);
// Quartiles (inclusive and exclusive methods)
// [0% => 13, Q1 => 13, Q2 => 14, Q3 => 17, 100% => 21, IQR => 4]
$quartiles = Descriptive::quartiles($numbers); // Has optional parameter to specify method. Default is Exclusive
$quartiles = Descriptive::quartilesExclusive($numbers);
$quartiles = Descriptive::quartilesInclusive($numbers);
// IQR - Interquartile range
$IQR = Descriptive::interquartileRange($numbers); // Same as IQR; has optional parameter to specify quartile method.
$IQR = Descriptive::IQR($numbers); // Same as interquartileRange; has optional parameter to specify quartile method.
// Percentiles
$twentieth_percentile = Descriptive::percentile($numbers, 20);
$ninety_fifth_percentile = Descriptive::percentile($numbers, 95);
// Midhinge
$midhinge = Descriptive::midhinge($numbers);
// Describe a list of numbers - descriptive stats report
$stats = Descriptive::describe($numbers); // Has optional parameter to set population or sample calculations
print_r($stats);
/* Array (
[n] => 9
[min] => 13
[max] => 21
[mean] => 15
[median] => 14
[mode] => Array ( [0] => 13 )
[range] => 8
[midrange] => 17
[variance] => 8
[sd] => 2.8284271247462
[cv] => 0.18856180831641
[mean_mad] => 2.2222222222222
[median_mad] => 1
[quartiles] => Array (
[0%] => 13
[Q1] => 13
[Q2] => 14
[Q3] => 17
[100%] => 21
[IQR] => 4
)
[midhinge] => 15
[skewness] => 1.4915533665654
[ses] => 0.71713716560064
[kurtosis] => 0.1728515625
[sek] => 1.3997084244475
[sem] => 0.94280904158206
[ci_95] => Array (
[ci] => 1.8478680091392
[lower_bound] => 13.152131990861
[upper_bound] => 16.847868009139
)
[ci_99] => Array (
[ci] => 2.4285158135783
[lower_bound] => 12.571484186422
[upper_bound] => 17.428515813578
)
) */
// Five number summary - five most important sample percentiles
$summary = Descriptive::fiveNumberSummary($numbers);
// [min, Q1, median, Q3, max]use MathPHP\Statistics\Distribution;
$grades = ['A', 'A', 'B', 'B', 'B', 'B', 'C', 'C', 'D', 'F'];
// Frequency distributions (frequency and relative frequency)
$frequencies = Distribution::frequency($grades); // [ A => 2, B => 4, C => 2, D => 1, F => 1 ]
$relative_frequencies = Distribution::relativeFrequency($grades); // [ A => 0.2, B => 0.4, C => 0.2, D => 0.1, F => 0.1 ]
// Cumulative frequency distributions (cumulative and cumulative relative)
$cumulative_frequencies = Distribution::cumulativeFrequency($grades); // [ A => 2, B => 6, C => 8, D => 9, F => 10 ]
$cumulative_relative_frequencies = Distribution::cumulativeRelativeFrequency($grades); // [ A => 0.2, B => 0.6, C => 0.8, D => 0.9, F => 1 ]
// Stem and leaf plot
// Return value is array where keys are the stems, values are the leaves
$values = [44, 46, 47, 49, 63, 64, 66, 68, 68, 72, 72, 75, 76, 81, 84, 88, 106];
$stem_and_leaf_plot = Distribution::stemAndLeafPlot($values);
// [4 => [4, 6, 7, 9], 5 => [], 6 => [3, 4, 6, 8, 8], 7 => [2, 2, 5, 6], 8 => [1, 4, 8], 9 => [], 10 => [6]]
// Optional second parameter will print stem and leaf plot to STDOUT
Distribution::stemAndLeafPlot($values, Distribution::PRINT);
/*
4 | 4 6 7 9
5 |
6 | 3 4 6 8 8
7 | 2 2 5 6
8 | 1 4 8
9 |
10 | 6
*/use MathPHP\Statistics\Experiment;
$a = 28; // Exposed and event present
$b = 129; // Exposed and event absent
$c = 4; // Non-exposed and event present
$d = 133; // Non-exposed and event absent
// Risk ratio (relative risk) - RR
$RR = Experiment::riskRatio($a, $b, $c, $d);
// ['RR' => 6.1083, 'ci_lower_bound' => 2.1976, 'ci_upper_bound' => 16.9784, 'p' => 0.0005]
// Odds ratio (OR)
$OR = Experiment::oddsRatio($a, $b, $c, $d);
// ['OR' => 7.2171, 'ci_lower_bound' => 2.4624, 'ci_upper_bound' => 21.1522, 'p' => 0.0003]
// Likelihood ratios (positive and negative)
$LL = Experiment::likelihoodRatio($a, $b, $c, $d);
// ['LL+' => 7.4444, 'LL-' => 0.3626]
$sensitivity = 0.67;
$specificity = 0.91;
$LL = Experiment::likelihoodRatioSS($sensitivity, $specificity);use MathPHP\Statistics\RandomVariable;
$X = [1, 2, 3, 4];
$Y = [2, 3, 4, 5];
// Central moment (nth moment)
$second_central_moment = RandomVariable::centralMoment($X, 2);
$third_central_moment = RandomVariable::centralMoment($X, 3);
// Skewness (population and sample)
$skewness = RandomVariable::skewness($X); // general method of calculating skewness
$skewness = RandomVariable::populationSkewness($X); // similar to Excel's SKEW.P
$skewness = RandomVariable::sampleSkewness($X); // similar to Excel's SKEW
$SES = RandomVariable::SES(count($X)); // standard error of skewness
// Kurtosis (excess)
$kurtosis = RandomVariable::kurtosis($X);
$platykurtic = RandomVariable::isPlatykurtic($X); // true if kurtosis is less than zero
$leptokurtic = RandomVariable::isLeptokurtic($X); // true if kurtosis is greater than zero
$mesokurtic = RandomVariable::isMesokurtic($X); // true if kurtosis is zero
$SEK = RandomVariable::SEK(count($X)); // standard error of kurtosis
// Standard error of the mean (SEM)
$sem = RandomVariable::standardErrorOfTheMean($X); // same as sem
$sem = RandomVariable::sem($X); // same as standardErrorOfTheMean
// Confidence interval
$μ = 90; // sample mean
$n = 9; // sample size
$σ = 36; // standard deviation
$cl = 99; // confidence level
$ci = RandomVariable::confidenceInterval($μ, $n, $σ, $cl); // Array( [ci] => 30.91, [lower_bound] => 59.09, [upper_bound] => 120.91 )use MathPHP\Statistics\Regression;
$points = [[1,2], [2,3], [4,5], [5,7], [6,8]];
// Simple linear regression (least squares method)
$regression = new Regresion\Linear($points);
$parameters = $regression->getParameters(); // [m => 1.2209302325581, b => 0.6046511627907]
$equation = $regression->getEquation(); // y = 1.2209302325581x + 0.6046511627907
$y = $regression->evaluate(5); // Evaluate for y at x = 5 using regression equation
$ci = $regression->CI(5, 0.5); // Confidence interval for x = 5 with p-value of 0.5
$pi = $regression->PI(5, 0.5); // Prediction interval for x = 5 with p-value of 0.5; Optional number of trials parameter.
$Ŷ = $regression->yHat();
$r = $regression->r(); // same as correlationCoefficient
$r² = $regression->r2(); // same as coefficientOfDetermination
$se = $regression->standardErrors(); // [m => se(m), b => se(b)]
$t = $regression->tValues(); // [m => t, b => t]
$p = $regression->tProbability(); // [m => p, b => p]
$F = $regression->FStatistic();
$p = $regression->FProbability();
$h = $regression->leverages();
$e = $regression->residuals();
$D = $regression->cooksD();
$DFFITS = $regression->DFFITS();
$SStot = $regression->sumOfSquaresTotal();
$SSreg = $regression->sumOfSquaresRegression();
$SSres = $regression->sumOfSquaresResidual();
$MSR = $regression->meanSquareRegression();
$MSE = $regression->meanSquareResidual();
$MSTO = $regression->meanSquareTotal();
$error = $regression->errorSD(); // Standard error of the residuals
$V = $regression->regressionVariance();
$n = $regression->getSampleSize(); // 5
$points = $regression->getPoints(); // [[1,2], [2,3], [4,5], [5,7], [6,8]]
$xs = $regression->getXs(); // [1, 2, 4, 5, 6]
$ys = $regression->getYs(); // [2, 3, 5, 7, 8]
$ν = $regression->degreesOfFreedom();
// Linear regression through a fixed point (least squares method)
$force_point = [0,0];
$regression = new Regresion\LinearThroughPoint($points, $force_point);
$parameters = $regression->getParameters();
$equation = $regression->getEquation();
$y = $regression->evaluate(5);
$Ŷ = $regression->yHat();
$r = $regression->r();
$r² = $regression->r2();
⋮ ⋮
// Theil–Sen estimator (Sen's slope estimator, Kendall–Theil robust line)
$regression = new Regresion\TheilSen($points);
$parameters = $regression->getParameters();
$equation = $regression->getEquation();
$y = $regression->evaluate(5);
⋮ ⋮
// Use Lineweaver-Burk linearization to fit data to the Michaelis–Menten model: y = (V * x) / (K + x)
$regression = new Regresion\LineweaverBurk($points);
$parameters = $regression->getParameters(); // [V, K]
$equation = $regression->getEquation(); // y = Vx / (K + x)
$y = $regression->evaluate(5);
⋮ ⋮
// Use Hanes-Woolf linearization to fit data to the Michaelis–Menten model: y = (V * x) / (K + x)
$regression = new Regresion\HanesWoolf($points);
$parameters = $regression->getParameters(); // [V, K]
$equation = $regression->getEquation(); // y = Vx / (K + x)
$y = $regression->evaluate(5);
⋮ ⋮
// Power law regression - power curve (least squares fitting)
$regression = new Regresion\PowerLaw($points);
$parameters = $regression->getParameters(); // [a => 56.483375436574, b => 0.26415375648621]
$equation = $regression->getEquation(); // y = 56.483375436574x^0.26415375648621
$y = $regression->evaluate(5);
⋮ ⋮
// LOESS - Locally Weighted Scatterplot Smoothing (Local regression)
$α = 1/3; // Smoothness parameter
$λ = 1; // Order of the polynomial fit
$regression = new Regresion\LOESS($points, $α, $λ);
$y = $regression->evaluate(5);
$Ŷ = $regression->yHat();
⋮ ⋮use MathPHP\Statistics\Significance;
// Z test (z and p values)
$Hₐ = 20; // Alternate hypothesis (M Sample mean)
$n = 200; // Sample size
$H₀ = 19.2; // Null hypothesis (μ Population mean)
$σ = 6; // SD of population (Standard error of the mean)
$z = Significance:zTest($Hₐ, $n, $H₀, $σ);
/* [
'z' => 1.88562, // Z score
'p1' => 0.02938, // one-tailed p value
'p2' => 0.0593, // two-tailed p value
] */
// Z score
$M = 8; // Sample mean
$μ = 7; // Population mean
$σ = 1; // Population SD
$z = Significance::zScore($μ, $σ, $x);
// T test - One sample (t and p values)
$Hₐ = 280; //Alternate hypothesis (M Sample mean)
$s = 50; // SD of sample
$n = 15; // Sample size
$H₀ = 300; // Null hypothesis (μ₀ Population mean)
$t = Significance::tTestOneSample($Hₐ, $s, $n, $H);
/* [
't' => -1.549, // t score
'p1' => 0.0718, // one-tailed p value
'p2' => 0.1437, // two-tailed p value
] */
// T test - Two samples (t and p values)
$μ₁ = 42.14; // Sample mean of population 1
$μ₂ = 43.23; // Sample mean of population 2
$n₁ = 10; // Sample size of population 1
$n₂ = 10; // Sample size of population 2
$σ₁ = 0.683; // Standard deviation of sample mean 1
$σ₂ = 0.750; // Standard deviation of sample mean 2
$t = Significance::tTestTwoSample($μ₁, $μ₂, $n₁, $n₂, $σ₁, $σ₂);
/* [
't' => -3.3978, // t score
'p1' => 0.001604, // one-tailed p value
'p2' => 0.181947, // two-tailed p value
] */
// T score
$Hₐ = 280; //Alternate hypothesis (M Sample mean)
$s = 50; // SD of sample
$n = 15; // Sample size
$H₀ = 300; // Null hypothesis (μ₀ Population mean)
$t = Significance::tScore($Hₐ, $s, $n, $H);
// χ² test (chi-squared goodness of fit test)
$observed = [4, 6, 17, 16, 8, 9];
$expected = [10, 10, 10, 10, 10, 10];
$χ² = Significance::chiSquaredTest($observed, $expected);
// ['chi-square' => 14.2, 'p' => 0.014388]$ cd tests
$ phpunit
Math PHP conforms to the following standards:
- PSR-1 - Basic coding standard (http://www.php-fig.org/psr/psr-1/)
- PSR-2 - Coding style guide (http://www.php-fig.org/psr/psr-2/)
- PSR-4 - Autoloader (http://www.php-fig.org/psr/psr-4/)
Math PHP is licensed under the MIT License.