/* * Mathlib : A C Library of Special Functions * Copyright (C) 1998 Ross Ihaka * Copyright (C) 2000-2013 The R Core Team * Copyright (C) 2003-2013 The R Foundation * * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, a copy is available at * https://www.R-project.org/Licenses/ * * DESCRIPTION * * The "Student" t distribution quantile function. * * NOTES * * This is a C translation of the Fortran routine given in: * Hill, G.W (1970) "Algorithm 396: Student's t-quantiles" * CACM 13(10), 619-620. * * Supplemented by inversion for 0 < ndf < 1. * * ADDITIONS: * - lower_tail, log_p * - using expm1() : takes care of Lozy (1979) "Remark on Algo.", TOMS * - Apply 2-term Taylor expansion as in * Hill, G.W (1981) "Remark on Algo.396", ACM TOMS 7, 250-1 * - Improve the formula decision for 1 < df < 2 */ #include "nmath.h" #include "dpq.h" double qt(double p, double ndf, int lower_tail, int log_p) { const static double eps = 1.e-12; double P, q; #ifdef IEEE_754 if (ISNAN(p) || ISNAN(ndf)) return p + ndf; #endif R_Q_P01_boundaries(p, ML_NEGINF, ML_POSINF); if (ndf <= 0) ML_WARN_return_NAN; if (ndf < 1) { /* based on qnt */ const static double accu = 1e-13; const static double Eps = 1e-11; /* must be > accu */ double ux, lx, nx, pp; int iter = 0; p = R_DT_qIv(p); /* Invert pt(.) : * 1. finding an upper and lower bound */ if(p > 1 - DBL_EPSILON) return ML_POSINF; pp = fmin2(1 - DBL_EPSILON, p * (1 + Eps)); for(ux = 1.; ux < DBL_MAX && pt(ux, ndf, TRUE, FALSE) < pp; ux *= 2); pp = p * (1 - Eps); for(lx =-1.; lx > -DBL_MAX && pt(lx, ndf, TRUE, FALSE) > pp; lx *= 2); /* 2. interval (lx,ux) halving regula falsi failed on qt(0.1, 0.1) */ do { nx = 0.5 * (lx + ux); if (pt(nx, ndf, TRUE, FALSE) > p) ux = nx; else lx = nx; } while ((ux - lx) / fabs(nx) > accu && ++iter < 1000); if(iter >= 1000) ML_WARNING(ME_PRECISION, "qt"); return 0.5 * (lx + ux); } /* Old comment: * FIXME: "This test should depend on ndf AND p !! * ----- and in fact should be replaced by * something like Abramowitz & Stegun 26.7.5 (p.949)" * * That would say that if the qnorm value is x then * the result is about x + (x^3+x)/4df + (5x^5+16x^3+3x)/96df^2 * The differences are tiny even if x ~ 1e5, and qnorm is not * that accurate in the extreme tails. */ if (ndf > 1e20) return qnorm(p, 0., 1., lower_tail, log_p); P = R_D_qIv(p); /* if exp(p) underflows, we fix below */ Rboolean neg = (!lower_tail || P < 0.5) && (lower_tail || P > 0.5), is_neg_lower = (lower_tail == neg); /* both TRUE or FALSE == !xor */ if(neg) P = 2 * (log_p ? (lower_tail ? P : -expm1(p)) : R_D_Lval(p)); else P = 2 * (log_p ? (lower_tail ? -expm1(p) : P) : R_D_Cval(p)); /* 0 <= P <= 1 ; P = 2*min(P', 1 - P') in all cases */ if (fabs(ndf - 2) < eps) { /* df ~= 2 */ if(P > DBL_MIN) { if(3* P < DBL_EPSILON) /* P ~= 0 */ q = 1 / sqrt(P); else if (P > 0.9) /* P ~= 1 */ q = (1 - P) * sqrt(2 /(P * (2 - P))); else /* eps/3 <= P <= 0.9 */ q = sqrt(2 / (P * (2 - P)) - 2); } else { /* P << 1, q = 1/sqrt(P) = ... */ if(log_p) q = is_neg_lower ? exp(- p/2) / M_SQRT2 : 1/sqrt(-expm1(p)); else q = ML_POSINF; } } else if (ndf < 1 + eps) { /* df ~= 1 (df < 1 excluded above): Cauchy */ if(P == 1.) q = 0; // some versions of tanpi give Inf, some NaN else if(P > 0) q = 1/tanpi(P/2.);/* == - tan((P+1) * M_PI_2) -- suffers for P ~= 0 */ else { /* P = 0, but maybe = 2*exp(p) ! */ if(log_p) /* 1/tan(e) ~ 1/e */ q = is_neg_lower ? M_1_PI * exp(-p) : -1./(M_PI * expm1(p)); else q = ML_POSINF; } } else { /*-- usual case; including, e.g., df = 1.1 */ double x = 0., y, log_P2 = 0./* -Wall */, a = 1 / (ndf - 0.5), b = 48 / (a * a), c = ((20700 * a / b - 98) * a - 16) * a + 96.36, d = ((94.5 / (b + c) - 3) / b + 1) * sqrt(a * M_PI_2) * ndf; Rboolean P_ok1 = P > DBL_MIN || !log_p, P_ok = P_ok1; if(P_ok1) { y = pow(d * P, 2.0 / ndf); P_ok = (y >= DBL_EPSILON); } if(!P_ok) {// log.p && P very.small || (d*P)^(2/df) =: y < eps_c log_P2 = is_neg_lower ? R_D_log(p) : R_D_LExp(p); /* == log(P / 2) */ x = (log(d) + M_LN2 + log_P2) / ndf; y = exp(2 * x); } if ((ndf < 2.1 && P > 0.5) || y > 0.05 + a) { /* P > P0(df) */ /* Asymptotic inverse expansion about normal */ if(P_ok) x = qnorm(0.5 * P, 0., 1., /*lower_tail*/TRUE, /*log_p*/FALSE); else /* log_p && P underflowed */ x = qnorm(log_P2, 0., 1., lower_tail, /*log_p*/ TRUE); y = x * x; if (ndf < 5) c += 0.3 * (ndf - 4.5) * (x + 0.6); c = (((0.05 * d * x - 5) * x - 7) * x - 2) * x + b + c; y = (((((0.4 * y + 6.3) * y + 36) * y + 94.5) / c - y - 3) / b + 1) * x; y = expm1(a * y * y); q = sqrt(ndf * y); } else if(!P_ok && x < - M_LN2 * DBL_MANT_DIG) {/* 0.5* log(DBL_EPSILON) */ /* y above might have underflown */ q = sqrt(ndf) * exp(-x); } else { /* re-use 'y' from above */ y = ((1 / (((ndf + 6) / (ndf * y) - 0.089 * d - 0.822) * (ndf + 2) * 3) + 0.5 / (ndf + 4)) * y - 1) * (ndf + 1) / (ndf + 2) + 1 / y; q = sqrt(ndf * y); } /* Now apply 2-term Taylor expansion improvement (1-term = Newton): * as by Hill (1981) [ref.above] */ /* FIXME: This can be far from optimal when log_p = TRUE * but is still needed, e.g. for qt(-2, df=1.01, log=TRUE). * Probably also improvable when lower_tail = FALSE */ if(P_ok1) { int it=0; while(it++ < 10 && (y = dt(q, ndf, FALSE)) > 0 && R_FINITE(x = (pt(q, ndf, FALSE, FALSE) - P/2) / y) && fabs(x) > 1e-14*fabs(q)) /* Newton (=Taylor 1 term): * q += x; * Taylor 2-term : */ q += x * (1. + x * q * (ndf + 1) / (2 * (q * q + ndf))); } } if(neg) q = -q; return q; }