/* fdtr.c * * F distribution * * * * SYNOPSIS: * * double df1, df2; * double x, y, fdtr(); * * y = fdtr( df1, df2, x ); * * DESCRIPTION: * * Returns the area from zero to x under the F density * function (also known as Snedcor's density or the * variance ratio density). This is the density * of x = (u1/df1)/(u2/df2), where u1 and u2 are random * variables having Chi square distributions with df1 * and df2 degrees of freedom, respectively. * * The incomplete beta integral is used, according to the * formula * * P(x) = incbet( df1/2, df2/2, (df1*x/(df2 + df1*x) ). * * * The arguments a and b are greater than zero, and x is * nonnegative. * * ACCURACY: * * Tested at random points (a,b,x). * * x a,b Relative error: * arithmetic domain domain # trials peak rms * IEEE 0,1 0,100 100000 9.8e-15 1.7e-15 * IEEE 1,5 0,100 100000 6.5e-15 3.5e-16 * IEEE 0,1 1,10000 100000 2.2e-11 3.3e-12 * IEEE 1,5 1,10000 100000 1.1e-11 1.7e-13 * See also incbet.c. * * * ERROR MESSAGES: * * message condition value returned * fdtr domain a<0, b<0, x<0 0.0 * */ /* fdtrc() * * Complemented F distribution * * * * SYNOPSIS: * * double df1, df2; * double x, y, fdtrc(); * * y = fdtrc( df1, df2, x ); * * DESCRIPTION: * * Returns the area from x to infinity under the F density * function (also known as Snedcor's density or the * variance ratio density). * * * inf. * - * 1 | | a-1 b-1 * 1-P(x) = ------ | t (1-t) dt * B(a,b) | | * - * x * * * The incomplete beta integral is used, according to the * formula * * P(x) = incbet( df2/2, df1/2, (df2/(df2 + df1*x) ). * * * ACCURACY: * * Tested at random points (a,b,x) in the indicated intervals. * x a,b Relative error: * arithmetic domain domain # trials peak rms * IEEE 0,1 1,100 100000 3.7e-14 5.9e-16 * IEEE 1,5 1,100 100000 8.0e-15 1.6e-15 * IEEE 0,1 1,10000 100000 1.8e-11 3.5e-13 * IEEE 1,5 1,10000 100000 2.0e-11 3.0e-12 * See also incbet.c. * * ERROR MESSAGES: * * message condition value returned * fdtrc domain a<0, b<0, x<0 0.0 * */ /* fdtri() * * Inverse of F distribution * * * * SYNOPSIS: * * double df1, df2; * double x, p, fdtri(); * * x = fdtri( df1, df2, p ); * * DESCRIPTION: * * Finds the F density argument x such that the integral * from -infinity to x of the F density is equal to the * given probability p. * * This is accomplished using the inverse beta integral * function and the relations * * z = incbi( df2/2, df1/2, p ) * x = df2 (1-z) / (df1 z). * * Note: the following relations hold for the inverse of * the uncomplemented F distribution: * * z = incbi( df1/2, df2/2, p ) * x = df2 z / (df1 (1-z)). * * ACCURACY: * * Tested at random points (a,b,p). * * a,b Relative error: * arithmetic domain # trials peak rms * For p between .001 and 1: * IEEE 1,100 100000 8.3e-15 4.7e-16 * IEEE 1,10000 100000 2.1e-11 1.4e-13 * For p between 10^-6 and 10^-3: * IEEE 1,100 50000 1.3e-12 8.4e-15 * IEEE 1,10000 50000 3.0e-12 4.8e-14 * See also fdtrc.c. * * ERROR MESSAGES: * * message condition value returned * fdtri domain p <= 0 or p > 1 NaN * v < 1 * */ /* * Cephes Math Library Release 2.3: March, 1995 * Copyright 1984, 1987, 1995 by Stephen L. Moshier */ #include "mconf.h" double fdtrc(double a, double b, double x) { double w; if ((a <= 0.0) || (b <= 0.0) || (x < 0.0)) { sf_error("fdtrc", SF_ERROR_DOMAIN, NULL); return NPY_NAN; } w = b / (b + a * x); return incbet(0.5 * b, 0.5 * a, w); } double fdtr(double a, double b, double x) { double w; if ((a <= 0.0) || (b <= 0.0) || (x < 0.0)) { sf_error("fdtr", SF_ERROR_DOMAIN, NULL); return NPY_NAN; } w = a * x; w = w / (b + w); return incbet(0.5 * a, 0.5 * b, w); } double fdtri(double a, double b, double y) { double w, x; if ((a <= 0.0) || (b <= 0.0) || (y <= 0.0) || (y > 1.0)) { sf_error("fdtri", SF_ERROR_DOMAIN, NULL); return NPY_NAN; } y = 1.0 - y; /* Compute probability for x = 0.5. */ w = incbet(0.5 * b, 0.5 * a, 0.5); /* If that is greater than y, then the solution w < .5. * Otherwise, solve at 1-y to remove cancellation in (b - b*w). */ if (w > y || y < 0.001) { w = incbi(0.5 * b, 0.5 * a, y); x = (b - b * w) / (a * w); } else { w = incbi(0.5 * a, 0.5 * b, 1.0 - y); x = b * w / (a * (1.0 - w)); } return x; }