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####------------------------------------------------------------------------ ### Origianlly follows Gregor Gojanc's example in CRAN package 'gdata' ### and the corresponding section in the R Wiki: ### http://wiki.r-project.org/rwiki/doku.php?id=developers:runit ### MM: Vastly changed: This should also be "runnable" for *installed* ## package which has no ./tests/ ## ----> put the bulk of the code e.g. in ../inst/unitTests/runTests.R : if(require("RUnit", quietly=TRUE)) { ## --- Setup --- wd <- getwd() pkg <- sub("\\.Rcheck$", '', basename(dirname(wd))) library(package=pkg, character.only=TRUE) path <- system.file("unitTests", package = pkg) stopifnot(file.exists(path), file.info(path.expand(path))$isdir) source(file.path(path, "runTests.R"), echo = TRUE) }fBasics/src/0000755000176000001440000000000012140021554012435 5ustar ripleyusersfBasics/src/nig.c0000644000176000001440000003410412140021554013360 0ustar ripleyusers //////////////////////////////////////////////////////////////////////////////// // // pnigC and qnigC Code // // Author: Kjersti Aas, 2000 // Diethelm Wuertz, added to fBasics // Nikolai Eurich, bug fixed which appeared in R 2.7 // Yohan Chalabi, changed XMAX for 64 bit compatibility // and removed unused headers // //////////////////////////////////////////////////////////////////////////////// #include #include // #include #include // #include // #include // #include // #include // #include #include #include #define SIGN(a,b) ((b) >= 0.0 ? fabs(a) : -fabs(a)) #define MIN(a, b) ((a) < (b) ? (a) : (b)) #define MAX(a, b) ((a) > (b) ? (a) : (b)) #define pi 3.141593 #define EPS 1e-12 #define XLEAST 2.23e-308 #define XSMALL 1.11e-16 #define XINF 1.79e308 #define XMAX 704.78 // log(XINF) - 5 #define MAX_ITER 5000 #define ITMAX 100 //////////////////////////////////////////////////////////////////////////////// // // Print error text and return to R // //////////////////////////////////////////////////////////////////////////////// void printError(char *text, char filename[200]) { error("%s: %s !!!\n", text, filename); } //////////////////////////////////////////////////////////////////////////////// // // FUNCTIONNAME: heap_sort // AUTHOR : Kjersti Aas, NR // DATE : March 2000 // ARGUMENTS : n Number of elements in the vector to be sorted // x Vector to be sorted (n x 1) // order Order of sorted values (n x 1) // DESCRIPTION: Returns order of sorted values in index array. // //////////////////////////////////////////////////////////////////////////////// void heapSort(int n, double *x, int *order) { int i,j,l; int ir, ordert; double q; for(j = 0; j < n; j++) order[j] = j; if(n <= 1) return; l = (n >> 1) + 1; /* shift n one bit to the right, i.e divide it by two.*/ ir = n; for(;;) /* Indefinite loop */ { if(l > 1) { /* Decrement l before evaluating the expression */ q = x[(ordert = order[--l-1])]; } else { q = x[(ordert = order[ir-1])]; order[ir-1] = order[0]; /* Decrement ir before its value is used */ if(--ir == 1) { order[0] = ordert; return; } } i = l; j = l << 1; while(j <= ir) { if((j < ir) && (x[order[j-1]] > x[order[j]])) j++; if(q > x[order[j-1]]) { order[i-1] = order[j-1]; j += (i=j); } else j = ir + 1; } order[i-1] = ordert; } } //////////////////////////////////////////////////////////////////////////////// // // Evaluates the Bessel function of the third order, index nu=1 // //////////////////////////////////////////////////////////////////////////////// double bessk1(double x) { int i; double y, k1; double sump, sumq, sumf, sumg; static double p[5] = { 4.8127070456878442310e-1, 9.9991373567429309922e+1, 7.1885382604084798576e+3, 1.7733324035147015630e+5, 7.1938920065420586101e+5 }; static double q[3] = { -2.8143915754538725829e+2, 3.7264298672067697862e+4, -2.2149374878243304548e+6 }; static double f[5] = { -2.2795590826955002390e-1,-5.3103913335180275253e+1, -4.5051623763436087023e+3,-1.4758069205414222471e+5, -1.3531161492785421328e+6 }; static double g[3] = { -3.0507151578787595807e+2, 4.3117653211351080007e+4, -2.7062322985570842656e+6 }; static double pp[11] = { 6.4257745859173138767e-2, 7.5584584631176030810e+0, 1.3182609918569941308e+2, 8.1094256146537402173e+2, 2.3123742209168871550e+3, 3.4540675585544584407e+3, 2.8590657697910288226e+3, 1.3319486433183221990e+3, 3.4122953486801312910e+2, 4.4137176114230414036e+1, 2.2196792496874548962e+0 }; static double qq[9] = { 3.6001069306861518855e+1, 3.3031020088765390854e+2, 1.2082692316002348638e+3, 2.1181000487171943810e+3, 1.9448440788918006154e+3, 9.6929165726802648634e+2, 2.5951223655579051357e+2, 3.4552228452758912848e+1, 1.7710478032601086579e+0 }; if(x < XLEAST) // Case: x < XLEAST { k1 = XINF; } else if(x <= 1.0) // Case: XLEAST <= x < XSMALL { if(x < XSMALL) { k1 = 1.0/x; } else // Case: XSMALL <= x < 1.0 { y = x*x; sump = ((((p[0]*y + p[1])*y + p[2])*y + p[3])*y + p[4])*y + q[2]; sumq = ((y + q[0])*y + q[1])*y + q[2]; sumf = (((f[0]*y + f[1])*y + f[2])*y + f[3])*y + f[4]; sumg = ((y + g[0])*y + g[1])*y + g[2]; k1 = (y*log(x)*sumf/sumg + sump/sumq)/x; } } else if(x > XMAX) // Case: x > XMAX { k1 = 0.0; } else // Case: 1.0 < x <= XMAX { y = 1.0/x; sump = pp[0]; for(i = 1; i < 11; i++) { sump = sump*y + pp[i]; } sumq = y; for(i = 0; i < 8; i++) { sumq = (sumq + qq[i]) * y; } sumq = sumq + qq[8]; k1 = sump/sumq/sqrt(x)*exp(-x); } return(k1); } //////////////////////////////////////////////////////////////////////////////// // // Return density of NIG distribution evaluated at given points // //////////////////////////////////////////////////////////////////////////////// // "FIXME": is only used from fdNIG() below ... could simplify // *or* call from R's dnig void dNIG(double* x, double* mu, double* delta, double* alpha, double* beta, int* n, double* d) { for(int i = 0; i < *n; i++) { double sdx = sqrt(pow(*delta,2.0)+pow((x[i]-*mu),2.0)), xarg = *alpha * sdx, exparg = *delta*sqrt(pow(*alpha,2.0)-pow(*beta,2.0))+*beta*(x[i]-*mu); if(exparg < -XMAX) exparg = -XMAX; if(exparg > XMAX) exparg = XMAX; d[i] = (*alpha*(*delta)/pi)*exp(exparg)*bessk1(xarg)/ sdx; } } //////////////////////////////////////////////////////////////////////////////// // // Return density of NIG distribution evaluated at a given point // (made for integration) // //////////////////////////////////////////////////////////////////////////////// double fdNIG(double x, double mu, double delta, double alpha, double beta) { int i = 1; double f; dNIG(&x, &mu, &delta, &alpha, &beta, &i, &f); return(f); } ///////////////////////////////////////////////////////////////////////// // // DE-Quadrature // Numerical Automatic Integrator for Improper Integral // method : Double Exponential (DE) Transformation // dimension : one // table : not use functions // intde : integrator of f(x) over (a,b). // intdei : integrator of f(x) over (a,infinity), f(x) is non oscillatory function. // intdeo : integrator of f(x) over (a,infinity), f(x) is oscillatory function. // /////////////////////////////////////////////////////////////////////////// void intdei(double a, double mu, double delta, double alpha, double beta, double *i, double *err) { /* ---- adjustable parameters ---- */ int mmax = 512; double efs = 0.1, hoff = 11.0; /* ------------------------------ */ int m; double pi4, epsln, epsh, h0, ehp, ehm, epst, ir, h; double iback, irback, t, ep, em, xp, xm, fp, fm, errt, errh, errd; pi4 = atan(1.0); epsln = 1 - log(efs * EPS); epsh = sqrt(efs * EPS); h0 = hoff / epsln; ehp = exp(h0); ehm = 1 / ehp; epst = exp(-ehm * epsln); ir = fdNIG(a + 1, mu, delta, alpha, beta); *i = ir * (2 * pi4); *err = fabs(*i) * epst; h = 2 * h0; m = 1; errh = 0; do { iback = *i; irback = ir; t = h * 0.5; do { em = exp(t); ep = pi4 * em; em = pi4 / em; do { xp = exp(ep - em); xm = 1 / xp; fp = fdNIG(a + xp, mu, delta, alpha, beta) * xp; fm = fdNIG(a + xm, mu, delta, alpha, beta) * xm; ir += fp + fm; *i += (fp + fm) * (ep + em); errt = (fabs(fp) + fabs(fm)) * (ep + em); if (m == 1) *err += errt * epst; ep *= ehp; em *= ehm; } while (errt > *err || xm > epsh); t += h; } while (t < h0); if (m == 1) { errh = (*err / epst) * epsh * h0; errd = 1 + 2 * errh; } else { errd = h * (fabs(*i - 2 * iback) + 4 * fabs(ir - 2 * irback)); } h *= 0.5; m *= 2; } while (errd > errh && m < mmax); *i *= h; if (errd > errh) { *err = -errd * m; } else { *err = errh * epsh * m / (2 * efs); } } //////////////////////////////////////////////////////////////////////////////// // // Return cumulative NIG distribution function evaluated at given points // //////////////////////////////////////////////////////////////////////////////// void pNIG(double *x, double *mu, double *delta, double *alpha, double *beta, int *n, double *p) { int i; double err, v; for(i = 0; i < *n; i++) { // Special cases (-Inf, Inf) if(x[i] <= -XINF) { p[i] = 0.0; } else if(x[i] >= XINF) { p[i] = 1.0; } // Integrates density from x to Inf else { // printf("intdei\n"); intdei(x[i], *mu, *delta, *alpha, *beta, &v, &err); // Check that 0 <= v <= 1 if(v < 0.0) v = 0.0; if(v > 1.0) v = 1.0; // Computes integral from -Inf to x p[i] = 1.0-v; } } } //////////////////////////////////////////////////////////////////////////////// // // Return cdf of NIG distribution evaluated at a given point, with the // probability p substracted (made for root finding) // //////////////////////////////////////////////////////////////////////////////// double fpNIG(double x, double mu, double delta, double alpha, double beta, double sp) { double f; int i = 1; pNIG(&x, &mu, &delta, &alpha, &beta, &i, &f); f -= sp; return(f); } //////////////////////////////////////////////////////////////////////////////// // // Computes the root of fpNIG // //////////////////////////////////////////////////////////////////////////////// double zbrent(double x1, double x2, double mu, double delta, double alpha, double beta, double sp) { int iter; double a, b, c, d, e, min1, min2; double fa, fb, fc, p, q, r, s, tol1, xm; d = 0; e = 0; a = x1; b = x2; c = x2; fa = fpNIG(a,mu,delta,alpha,beta,sp); fb = fpNIG(b,mu,delta,alpha,beta,sp); // if((fa > 0.0 && fb > 0.0)||(fa < 0.0 && fb < 0.0)) // printError("Root must be bracketed in zbrent", ""); fc = fb; for(iter = 1; iter <= ITMAX; iter++) { if((fb > 0.0 && fc > 0.0)||(fb < 0.0 && fc < 0.0)) { c = a; fc = fa; e = d = b-a; } if(fabs(fc) < fabs(fb)) { a = b; b = c; c = a; fa = fb; fb = fc; fc = fa; } tol1 = 2.0*EPS*fabs(b)+0.5*EPS; xm = 0.5*(c-b); if(fabs(xm) <= tol1 || fb == 0.0) return b; if(fabs(e) >= tol1 && fabs(fa) > fabs(fb)) { s = fb/fa; if(a == c) { p = 2.0*xm*s; q = 1.0-s; } else { q = fa/fc; r = fb/fc; p = s*(2.0*xm*q*(q-r)-(b-a)*(r-1.0)); q = (q-1.0)*(r-1.0)*(s-1.0); } if(p > 0.0) q = -q; p = fabs(p); min1 = 3.0*xm*q-fabs(tol1*q); min2 = fabs(e*q); if(2.0*p < (min1 < min2 ? min1 : min2)) { e = d; d = p/q; } else { d = xm; e = d; } } else { d = xm; e = d; } a = b; fa = fb; if(fabs(d) > tol1) b += d; else b += SIGN(tol1, xm); fb = fpNIG(b, mu, delta, alpha, beta, sp); } // printError("Maximum number of iterations exceeded in zbrent", ""); return 0.0; } //////////////////////////////////////////////////////////////////////////////// // // Function that computes inverse cumulative NIG distribution function, // evaluated at given points. // INPUT: // p Probabilities at which to evaluate cdf^-1 (nx1) // i_mu First parameter of NIG distribution // i_delta Second parameter of NIG distribution // i_alpha Third parameter of NIG distribution // i_beta Fourth parameter of NIG distribution // i_n Number of quantiles // q Computed quantiles // //////////////////////////////////////////////////////////////////////////////// void qNIG(double* p, double* i_mu, double* i_delta, double* i_alpha, double* i_beta, int* i_n, double* q) { int i, n, counter, *pOrder; double mu, delta, alpha, beta, qTmp, sp, lpiv, rpiv, mpiv, div, lpivVal, rpivVal; n=*i_n; mu=*i_mu; delta=*i_delta; alpha=*i_alpha; beta=*i_beta; // Expectation and standard deviation of NIG distribution, // used for setting lpiv and rpiv mpiv = mu+delta*beta/sqrt(alpha*alpha-beta*beta); div = sqrt(delta*alpha*alpha/pow(alpha*alpha-beta*beta,1.5)); //Allocagte space: pOrder = malloc(n*sizeof(int)); //Find descending order of probabilities:0.5, heapSort(n, p, pOrder); for(i=0;i 0) { lpiv = MAX(lpiv, q[pOrder[n-i]]); // Make sure that upper boundary is > than lower if(rpiv <= lpiv) { while(rpiv <= lpiv) { rpiv += 2*div; } } } lpivVal = fpNIG(lpiv,mu,delta,alpha,beta,sp); rpivVal = fpNIG(rpiv,mu,delta,alpha,beta,sp); // Search for a quantile q that fulfills |fpNIG(q)-p| < eps if(lpivVal*rpivVal >= 0.0) { while(lpivVal*rpivVal >= 0.0) { counter++; lpiv = lpiv-pow(2.0, (double)counter)*div; rpiv = rpiv+pow(2.0, (double)counter)*div; lpivVal = fpNIG(lpiv,mu,delta,alpha,beta,sp); rpivVal = fpNIG(rpiv,mu,delta,alpha,beta,sp); } } qTmp = zbrent(lpiv, rpiv, mu, delta, alpha, beta, sp); q[pOrder[n-i-1]] = qTmp; } } // Free space: free(pOrder); } //////////////////////////////////////////////////////////////////////////////// fBasics/src/gld.c0000644000176000001440000003451012140021554013352 0ustar ripleyusers/* gld.fm5.fx.c - Part of Robert King's gld package for the R statistical * language. * * Copyright (C) Robert King 1993,2000,2001,2005,2006 * robert.king@newcastle.edu.au * http://tolstoy.newcastle.edu.au/~rking/publ/software.html * * 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, write to the * Free Software Foundation, Inc., 59 Temple Place, Suite 330, * Boston, MA 02111-1307 USA * * These functions calculate F(x) for the five parameter version of the * FMKL paramterisation of the gld. * This parameterisation is a variation of the 5 parameter version of the * RSparameterisation. * * This is not very good C. It is based on gld.rs.fx.c, which is based on * code written in 1993, as my first real program. I still haven't tidied * it up very much for release with the R package. * However, it has been used for some time and works fine. * * This is code is loaded into R. * The two include lines are handled by R, but need to be here to keep * the compile step happy * * $Id$ */ #include #include /* proto */ void fm5_funcd( double , double , double *, double *, double *, double *, double *, double *, double *); /* This needs a comment for explaining the arguments */ void gl_fm5_distfunc( double *pa,double *pb,double *pc,double *pd, double *pe, double *pu1,double *pu2,double *pxacc, int *max_it, double *ecks, double *u, int *lengthofdata) { /* pa to pe: pointers to the values of the parameters of the gld (fm5 param) * pu1: minimum value of u, should be zero * pu2: maximum value of u, should be 1 * pxacc: desired accuracy of the calculation * max_it: maximum iterations for N-R root finder * ecks: the quantiles of the gld given * u: array to put the calculated depths * pl: length of the data */ double u1, u2, xacc; int i,j; double df,dx,dxold,f,fh,fl; double x; double temp,xh,xl,rts; /* trying initialising things */ i=0; j=0; df=0.0;dx=0.0;dxold=0.0;f=0.0;fh=0.0;fl=0.0; x=0.0; temp=0.0;xh=0.0;xl=0.0;rts=0.0; u1 = *pu1; u2 = *pu2; xacc = *pxacc; /* If we are trying to do things outside the range, lets set them at almost 0 or almost 1 */ if (*pc < 0) { if (u1 == 0) { u1 = xacc; } if (u2 == 1) { u2 = 1-xacc; } } if (*pd < 0) { if (u1 == 0) { u1 = xacc; } if (u2 == 1) { u2 = 1-xacc; } } for (i=0;i<*lengthofdata;i++) { x = ecks[i]; u[i] = 0.0; fm5_funcd(u1,x,&fl,&df,pa,pb,pc,pd,pe); fm5_funcd(u2,x,&fh,&df,pa,pb,pc,pd,pe); if (fl*fh >= 0.0) { REprintf("Program aborted at parameter values %f, %f, %f, %f %f\n", *pa, *pb, *pc, *pd, *pe); REprintf("The data value being investigated was index %d",i); REprintf(" value: %f\n",x); error("C code failure - see error message printed above"); } if (fl < 0.0) { xl = u1; xh = u2; } else { xh = u1; xl = u2; } rts = 0.5*(u1+u2); dxold = fabs(u2-u1); dx = dxold; fm5_funcd(rts,x,&f,&df,pa,pb,pc,pd,pe); for (j=1;j<=*max_it;j++) { if ((((rts - xh)*df - f)* ( (rts-xl)*df - f) >= 0.0 ) || ( fabs(2.0*f) > fabs (dxold*df))) { dxold = dx; dx = .5* (xh - xl); rts = xl +dx; if (xl == rts ) { u[i] = rts; break; } } else { dxold = dx; dx = f/df; temp = rts; rts -= dx; if (temp == rts) { u[i] = rts; break; } } if (fabs(dx) < xacc) { u[i] = rts; break; } fm5_funcd(rts,x,&f,&df,pa,pb,pc,pd,pe); if (f < 0.0) xl =rts; else xh =rts; } } } void fm5_funcd( double u, double x, double *F, double *dFdu, double *pa, double *pb, double *pc, double *pd, double *pe) { /* *F is the gld F-1(u) */ /* *dFdu is dF-1(u)/du */ /* NOTE: *dFdu is 1/f(x) a.k.a. 1/f(F-1(u)) - the opposite of what's required for the pdf */ if ( *pc == 0 ) { if ( *pd == 0 ) { /* Both l3 and l4 zero */ *F = *pa + ( (1 - *pe) * log(u) - (1 + *pe) * (log(1.0-u)) )/ *pb - x; /* need the derivatives for each case */ *dFdu = ( (1 - *pe)/u + (1 + *pe)/(1-u)) / *pb ; } else { /* l3 zero, l4 non-zero */ *F = *pa + ((1 - *pe) * log(u) - ((1 + *pe)*( pow((1-u),*pd)-1)/ *pd ) ) / *pb - x; *dFdu = ((1 - *pe)/u + (1 + *pe)*pow((1-u), *pd-1) )/ *pb ; } } else { if ( *pd == 0 ) { /* l4 zero, l3 non-zero */ *F = *pa + ( (1 - *pe)*(( pow(u,*pc)- 1)/ *pc) - (1 + *pe)* log(1-u) ) / *pb - x; *dFdu = ( (1 - *pe) * pow(u, *pc-1) + (1 + *pe)/(1-u) ) / *pb; } else { /* l3 non-zero, l4 non-zero */ *F = ((1 - *pe)* ( pow((u),*pc) -1 )/ *pc - (1 + *pe)* (pow((1.0-u),*pd) -1 )/ *pd )/ *pb + *pa - x; *dFdu = ((1 - *pe)* ( pow((u),(*pc-1.0)) ) + (1 + *pe)* ( pow( (1.0-u),(*pd-1.0)) ) )/ *pb ; } } } /* gld.fmkl.fx.c - Part of Robert King's gld package for the R statistical * language. * * Copyright (C) Robert King 1993,2000,2001,2006 * robert.king@newcastle.edu.au * http://tolstoy.newcastle.edu.au/~rking/publ/software.html * * 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, write to the * Free Software Foundation, Inc., 59 Temple Place, Suite 330, * Boston, MA 02111-1307 USA * * These functions calculate F(x) for the Freimer, Mudholkar, Kollia and Lin parameterisation * of the gld. It is adapted from gld.rs.fx.c (also in this R distribution). * Because of that, this is not very good C. The original code was my first * real program in C and I haven't tidied it up very much for release with the R package. * However, it has been used for some time and works fine. * * This is code is loaded into R. * The two include lines are handled by R, but need to be here to keep the compile step happy * $Id$ */ #include #include #include /* proto */ void fmkl_funcd( double , double , double *, double *, double *, double *, double *, double *); /* the function that finds the root */ void gl_fmkl_distfunc( double *pa,double *pb,double *pc,double *pd, double *pu1,double *pu2,double *pxacc, int *max_it, double *ecks, double *u, int *lengthofdata) { /* pa to pd: pointers to the values of the parameters of the gld (rs param) * pu1: minimum value of u, should be zero * pu2: maximum value of u, should be 1 * pxacc: desired accuracy of the calculation * max_it: maximum iterations for N-R root finder * ecks: the quantiles of the gld given * u: array to put the calculated depths * pl: length of the data */ double u1, u2, xacc; int i,j; double df,dx,dxold,f,fh,fl; double x; double temp,xh,xl,rts; /* trying initialising things */ i=0; j=0; df=0.0;dx=0.0;dxold=0.0;f=0.0;fh=0.0;fl=0.0; x=0.0; temp=0.0;xh=0.0;xl=0.0;rts=0.0; u1 = *pu1; u2 = *pu2; xacc = *pxacc; if (*pc < 0) { if (u1 == 0) { u1 = xacc; } if (u2 == 1) { u2 = 1-xacc; } } if (*pd < 0) { if (u1 == 0) { u1 = xacc; } if (u2 == 1) { u2 = 1-xacc; } } for (i=0;i<*lengthofdata;i++) { x = ecks[i]; u[i] = 0.0; fmkl_funcd(u1,x,&fl,&df,pa,pb,pc,pd); fmkl_funcd(u2,x,&fh,&df,pa,pb,pc,pd); if (fl*fh >= 0.0) { /* This is suggested in writing R extensions, but still gives the warning */ /* error("Program aborted at parameter values %f, %f, %f, %f\n The data value being investigated was index %d, value: %f\n", *pa, *pb, *pc, *pd, i, x); */ REprintf("C code aborted at parameter values %f, %f, %f, %f\n", *pa, *pb, *pc, *pd); REprintf("The data value being investigated was index %d",i); REprintf(" value: %f\n",x); error("C code numerical failure"); } if (fl < 0.0) { xl = u1; xh = u2; } else { xh = u1; xl = u2; } rts = 0.5*(u1+u2); dxold = fabs(u2-u1); dx = dxold; fmkl_funcd(rts,x,&f,&df,pa,pb,pc,pd); for (j=1;j<=*max_it;j++) { if ((((rts - xh)*df - f)* ( (rts-xl)*df - f) >= 0.0 ) || ( fabs(2.0*f) > fabs (dxold*df))) { dxold = dx; dx = .5* (xh - xl); rts = xl +dx; if (xl == rts ) { u[i] = rts; break; } } else { dxold = dx; dx = f/df; temp = rts; rts -= dx; if (temp == rts) { u[i] = rts; break; } } if (fabs(dx) < xacc) { u[i] = rts; break; } fmkl_funcd(rts,x,&f,&df,pa,pb,pc,pd); if (f < 0.0) xl =rts; else xh =rts; } } } void fmkl_funcd( double u, double x, double *F, double *dFdu, double *pa, double *pb, double *pc, double *pd) { /* *F is the gld F-1(u) */ /* *dFdu is dF-1(u)/du */ /* NOTE: *dFdu is 1/f(x) a.k.a. 1/f(F-1(u)) - the opposite of what's required for the pdf */ if ( *pc == 0 ) { if ( *pd == 0 ) { /* Both l3 and l4 zero */ *F = *pa + (log(u) - log(1.0-u))/ *pb - x; *dFdu = ( 1 /(u * (1-u))) / *pb ; /* correct but confusing, should be 1/u + 1/(1-u) */ } else { /* l3 zero, l4 non-zero */ *F = *pa + ( log(u) - (( pow((1-u),*pd)-1)/ *pd ) ) / *pb - x; *dFdu = (1/u + pow((1-u), *pd-1) )/ *pb ; } } else { if ( *pd == 0 ) { /* l4 zero, l3 non-zero */ *F = *pa + ( (( pow(u,*pc)- 1)/ *pc) - log(1-u) ) / *pb - x; *dFdu = (pow(u, *pc-1) + 1/(1-u) ) / *pb; } else { /* l3 non-zero, l4 non-zero */ *F = ( ( pow((u),*pc) -1 )/ *pc - (pow((1.0-u),*pd) -1 )/ *pd )/ *pb + *pa - x; *dFdu = ( ( pow((u),(*pc-1.0)) ) + ( pow( (1.0-u),(*pd-1.0)) ) )/ *pb ; } } } /* gld.rs.fx.c - Part of Robert King's gld package for the R statistical * language. * * Copyright (C) Robert King 1993,2000,2001 * robert.king@newcastle.edu.au * http://maths.newcastle.edu.au/~rking/publ/software.html * * 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, write to the * Free Software Foundation, Inc., 59 Temple Place, Suite 330, * Boston, MA 02111-1307 USA * * These functions calculate F(x) for the rs paramterisation for the gld * This is not very good C. I wrote this in 1993, as my first real program * and I haven't tidied it up very much for release with the R package. * However, it has been used for some time and works fine. * * This is code is loaded into R. It doesn't need stdio.h * It needs math.h, which is loaded automagically by R. */ #include #include #include static double la, lb, lc, ld, x; /* the function that finds the root */ void gl_rs_distfunc( double *pa,double *pb,double *pc,double *pd, double *px1,double *px2,double *pxacc, int *max_it, double *ecks, double *u, int *pl) { /* pa to pd: pointers to the values of the parameters of the gld (rs param) * px1: minimum value of u, should be zero * px2: maximum value of u, should be 1 * pxacc: desired accuracy of the calculation * max_it: maximum iterations for N-R root finder * ecks: the quantiles of the gld given * u: array to put the calculated depths * pl: length of the data */ double x1, x2, xacc; double a, b, c, d; int l; int i,j; double df,dx,dxold,f,fh,fl; double temp,xh,xl,rts; void funcd(); x1 = *px1; x2 = *px2; xacc = *pxacc; a = *pa; b = *pb; c = *pc; d = *pd; l = *pl; la = a; lb = b; lc = c; ld = d; /* The C version has something here to force the limits to be xacc and 1-xacc rather than 0 and 1 if lambda3 and lambda4 are negative. I can't see why, so I'm leaving it out.*/ for (i=0;i= 0.0) { REprintf("Program aborted during calculation of F(x)"); REprintf("at parameter values %f, %f, %f, %f\n", *pa, *pb, *pc, *pd); REprintf("The x value being investigated was index: %d",i); REprintf(" value: %f\n",x); error("C code numerical failure"); } if (fl < 0.0) { xl = x1; xh = x2; } else { xh = x1; xl = x2; } rts = 0.5*(x1+x2); dxold = fabs(x2-x1); dx = dxold; funcd(rts,&f,&df); for (j=1;j<= *max_it;j++) { if ((((rts - xh)*df - f)* ( (rts-xl)*df - f) >= 0.0 ) || ( fabs(2.0*f) > fabs (dxold*df))) { dxold = dx; dx = .5* (xh - xl); rts = xl +dx; if (xl == rts ) { u[i] = rts; break; } } else { dxold = dx; dx = f/df; temp = rts; rts -= dx; if (temp == rts) { u[i] = rts; break; } } if (fabs(dx) < xacc) { u[i] = rts; break; } funcd(rts,&f,&df); if (f < 0.0) xl =rts; else xh =rts; } } } void funcd(u,a,b) double u,*a,*b; { /* function is the gld F-1(u) */ /* df_du is dF-1(u)/du */ double function, df_du; if ( lc == 0 ) { if ( ld == 0 ) { /* Both l3 and l4 zero */ function = la - x; df_du = 0; } else { /* l3 zero, l4 non-zero */ function = la + ( 1 - pow((1-u),ld) )/ lb - x; df_du = (ld) * ( pow((1.0-u),(ld - 1.0)) / lb); } } else { if ( ld == 0 ) { /* l4 zero, l3 non-zero */ function = la + ( pow(u,lc) - 1 ) / lb - x; df_du = lc * ( pow(u,(lc- 1.0)) ) / lb; } else { /* l3 non-zero, l4 non-zero */ function = ( pow((u),lc) - pow((1.0-u),ld) )/ lb + la - x; df_du = (lc * (pow((u),(lc-1.0))) + ld * ( pow((1.0-u),(ld-1.0)) ))/ lb; } } *a = function; *b = df_du; } fBasics/src/Makevars0000644000176000001440000000003711370220775014143 0ustar ripleyusersPKG_LIBS=$(BLAS_LIBS) $(FLIBS) fBasics/man/0000755000176000001440000000000012027076511012430 5ustar ripleyusersfBasics/man/utils-symbolTable.Rd0000644000176000001440000000151211370220776016335 0ustar ripleyusers\name{symbolTable} \alias{symbolTable} \title{Table of Symbols} \description{ Displays a Table of plot characters and symbols. } \usage{ symbolTable(font = par('font'), cex = 0.7) } \arguments{ \item{cex}{ a numeric value, determines the character size, the default size is 0.7. } \item{font}{ an integer value, the number of the \code{font}, by default font number 1. } } \value{ \code{symbolTable} displays a table with the plot characters and symbols numbered from 0 to 255 and returns invisible the name of the font. } \seealso{ \code{link{characterTable}}, \code{link{colorTable}}. } \examples{ ## symbolTable - # Default Symbol Table: symbolTable() } \keyword{programming} fBasics/man/utils-randLCG.Rd0000644000176000001440000000472011370220776015336 0ustar ripleyusers\name{lcg} \alias{lcg} \alias{set.lcgseed} \alias{get.lcgseed} \alias{runif.lcg} \alias{rnorm.lcg} \alias{rt.lcg} \title{Generator for Portable Random Innovations} \description{ Functions to generate portable random innovations. The functions run under R and S-Plus and generate the same sequence of random numbers. Supported are uniform, normal and Student-t distributed random numbers. \cr The functions are: \tabular{ll}{ \code{set.lcgseed} \tab Set initial random seed, \cr \code{get.lcgseed} \tab Get the current valus of the random seed, \cr \code{runif.lcg} \tab Uniform linear congruational generator, \cr \code{rnorm.lcg} \tab Normal linear congruational generator, \cr \code{rt.lcg} \tab Student-t linear congruential generator. } } \usage{ set.lcgseed(seed = 4711) get.lcgseed() runif.lcg(n, min = 0, max = 1) rnorm.lcg(n, mean = 0, sd = 1) rt.lcg(n, df) } \arguments{ \item{df}{ number of degrees of freedom, a positive integer, maybe non-integer. } \item{mean, sd}{ means and standard deviation of the normal distributed innovations. } \item{min, max}{ lower and upper limits of the uniform distributed innovations. } \item{seed}{ an integer value, the random number seed. } \item{n}{ an integer, the number of random innovations to be generated. } } \details{ A simple portable random number generator for use in R and SPlus. We recommend to use this generator only for comparisons of calculations in R and Splus. The generator is a linear congruential generator with parameters \code{LCG(a=13445, c=0, m=2^31-1, X=0)}. It is a simple random number generator which passes the bitwise randomness test. } \value{ A vector of generated random innovations. The value of the current seed is stored in the variable \code{lcg.seed}. } \references{ Altman, N.S. (1988); \emph{Bitwise Behavior of Random Number Generators}, SIAM J. Sci. Stat. Comput., 9(5), September, 941--949. } \examples{ ## set.lcgseed - set.lcgseed(seed = 65890) ## runif.lcg - rnorm.lcg - rt.lcg - cbind(runif.lcg(10), rnorm.lcg(10), rt.lcg(10, df = 4)) ## get.lcgseed - get.lcgseed() ## Note, to overwrite rnorm, use # rnorm = rnorm.lcg # Going back to rnorm # rm(rnorm) } \keyword{programming} fBasics/man/utils-printControl.Rd0000644000176000001440000000111311370220776016552 0ustar ripleyusers\name{print} \alias{print} \alias{print.control} \title{Print Control} \description{ Unlists and prints a control object. } \usage{ \method{print}{control}(x, \dots) } \arguments{ \item{x}{ the object to be printed. } \item{\dots}{ arguments to be passed. } } \value{ \code{print.control} prints control. } \examples{ ## print - control = list(n = 211, seed = 54, name = "generator") print(control) class(control) = "control" print(control) } \keyword{programming} fBasics/man/utils-listIndex.Rd0000644000176000001440000000125011370220776016022 0ustar ripleyusers\name{listIndex} \alias{listIndex} \title{Index File Listing} \description{ Lists the content of an index file. } \usage{ listIndex(package, character.only = FALSE) } \arguments{ \item{package}{ a literal character string or a character string denoting the name of the package to be listed. } \item{character.only}{ a logical indicating whether 'package' can be assumed to be character strings. } } \value{ prints the index file. } \seealso{ \code{\link{listDescription}}, \code{\link{listIndex}}. } \examples{ ## listIndex - listIndex("fBasics") } \keyword{programming} fBasics/man/utils-listFunctions.Rd0000644000176000001440000000150111370220776016722 0ustar ripleyusers\name{listFunctions} \alias{listFunctions} \alias{countFunctions} \title{Functions Listing} \description{ Lists and counts functions from packages. } \usage{ listFunctions(package, character.only = FALSE) countFunctions(package, character.only = FALSE) } \arguments{ \item{package}{ a literal character or a character string denoting the name of the package to be listed. } \item{character.only}{ a logical indicating whether 'package' can be assumed to be character strings. } } \value{ prints a list and counts of functions. } \seealso{ \code{\link{listFunctions}}, \code{\link{listIndex}}. } \examples{ ## listFunctions - listFunctions("fBasics") ## countFunctions - countFunctions("fBasics") } \keyword{programming} fBasics/man/utils-listDescription.Rd0000644000176000001440000000131611370220776017241 0ustar ripleyusers\name{listDescription} \alias{listDescription} \title{Description File Listing} \description{ Lists the content of a description file. } \usage{ listDescription(package, character.only = FALSE) } \arguments{ \item{package}{ a literal character or character string denoting the name of the package to be listed. } \item{character.only}{ a logical indicating whether 'package' can be assumed to be character strings. } } \value{ prints the description file. } \seealso{ \code{\link{listFunctions}}, \code{\link{listIndex}}. } \examples{ ## listDescription - listDescription("fBasics") } \keyword{programming} fBasics/man/utils-interactivePlot.Rd0000644000176000001440000000363311370220776017242 0ustar ripleyusers\name{interactivePlot} \alias{interactivePlot} \title{Interactive Plot Utility} \description{ Plots with emphasis on interactive plots. } \usage{ interactivePlot(x, choices = paste("Plot", 1:9), plotFUN = paste("plot.", 1:9, sep = ""), which = "all", \dots) } \arguments{ \item{choices}{ a vector of character strings for the choice menu. By Default \code{"Plot 1"} ... \code{"Plot 9"} allowing for 9 plots at maximum. } \item{plotFUN}{ a vector of character strings naming the plot functions. By Default \code{"plot.1"} ... \code{"plot.9"} allowing for 9 plots at maximum. } \item{which}{ plot selection, which graph should be displayed? If \code{"which"} is a character string named "ask" the user is interactively asked which to plot, if a logical vector of length \code{N}, those plots which are set \code{TRUE} are displayed, if a character string named \code{"all"} all plots are displayed. } \item{x}{ an object to be plotted. } \item{\dots}{ additional arguments passed to the FUN or plot function. } } \examples{ ## Test Plot Function: testPlot = function(x, which = "all", ...) { # Plot Function and Addons: plot.1 <<- function(x, ...) plot(x, ...) plot.2 <<- function(x, ...) acf(x, ...) plot.3 <<- function(x, ...) hist(x, ...) plot.4 <<- function(x, ...) qqnorm(x, ...) # Plot: interactivePlot(x, choices = c("Series Plot", "ACF", "Histogram", "QQ Plot"), plotFUN = c("plot.1", "plot.2", "plot.3", "plot.4"), which = which, ...) # Return Value: invisible() } # Plot: par(mfrow = c(2, 2), cex = 0.7) testPlot(rnorm(500)) # Try: # par(mfrow = c(1,1)) # testPlot(rnorm(500), which = "ask") } \keyword{hplot} fBasics/man/utils-hessian.Rd0000644000176000001440000000072111613257743015517 0ustar ripleyusers\name{tsHessian} \alias{tsHessian} \title{Two sided approximated Hessian} \description{ Computes two sided (TS) approximated Hessian. } \usage{ tsHessian(x, fun, ...) } \arguments{ \item{x}{arugment to be passed to function} \item{fun}{function} \item{\dots}{additional paramters to be passed to function} } \author{ A function borrowed from Kevin Sheppard's Matlab garch toolbox as implemented by Alexios Ghalanos in his \pkg{rgarch} package } fBasics/man/utils-getS4.Rd0000644000176000001440000000501611732460032015042 0ustar ripleyusers\name{getS4} \alias{getS4} \alias{getCall} \alias{getCall,ANY-method} \alias{getModel} \alias{getTitle} \alias{getDescription} \alias{getSlot} \alias{getArgs} \title{General S4 Class Extractor Functions} \description{ A collection and description of functions to extract slots from S4 class objects. \cr The extractor functions are: \tabular{ll}{ % \code{getCall} \tab Extracts the call slot from a S4 object, \cr \code{getModel} \tab Extracts the model slot from a S4 object, \cr \code{getTitle} \tab Extracts the title slot from a S4 object, \cr \code{getDescription} \tab Extracts the description slot from a S4 object, \cr \code{getSlot} \tab Extracts a specified slot from a S4 object, \cr \code{getArgs} \tab Shows the arguments of a S4 function. } Since \R version 2.14.0, a generic \code{getCall()} is part of \R; for earlier versions, we had provided a simple version for S4 objects. } \usage{ % getCall(x, \dots) getModel(object) getTitle(object) getDescription(object) getSlot(object, slotName) getArgs(f, signature) } \arguments{ \item{f}{a generic function or the character-string name of one.} \item{object}{ an object of class S4. % (For \code{getCall()} and \R 2.14.0 and newer: \dQuote{any} object.) } \item{signature}{ the signature of classes to match to the arguments of \code{f} } \item{slotName}{ a character string, the name of the slot to be extracted from the S4 object. } \item{\dots}{Further arguments to be passed to function.} } \value{ % \code{getCall}\cr \code{getModel}\cr \code{getTitle}\cr \code{getDescription}\cr \code{getSlot}\cr return the content of the slot. \code{getArgs} returns the names of the arguments. } \examples{ ## Example S4 Representation: # Hyothesis Testing with Control Settings setClass("hypTest", representation( call = "call", data = "numeric", test = "list", description = "character") ) ## Shapiro Wilk Normaility Test swTest = function(x, description = "") { ans = shapiro.test(x) class(ans) = "list" new("hypTest", call = match.call(), data = x, test = ans, description = description) } test = swTest(x = rnorm(500), description = "500 RVs") ## Extractor Functions: isS4(test) getCall(test) getDescription(test) ## get arguments args(returns) getArgs(returns) getArgs("returns") getArgs(returns, "timeSeries") getArgs("returns", "timeSeries") } \keyword{programming} fBasics/man/utils-decor.Rd0000644000176000001440000000266011370220776015161 0ustar ripleyusers\name{decor} \alias{decor} \alias{hgrid} \alias{vgrid} \alias{boxL} \alias{box_} \alias{copyright} \title{Decor Functions} \description{ Functions for decorating plots. \cr The plot utility functions are: \tabular{ll}{ \code{decor} \tab simple decoration function, \cr \code{hgrid} \tab creates horizontal grid lines, \cr \code{vgrid} \tab creates vertical grid lines, \cr \code{boxL} \tab creates a L-shaped box, \cr \code{box_} \tab creates a bogttom line box, \cr \code{copyright} \tab adds Rmetrics copyright to a plot. } } \usage{ decor() hgrid(ny = NULL, ...) vgrid(nx = NULL, ...) boxL(col = "white") box_(col = c("white", "black")) copyright() } \arguments{ \item{col}{ the color of the background, \code{"black"} and foreground \code{"white"} lines of the box. } \item{nx, ny}{ number of cells of the grid in x or y direction. When \code{NULL}, as per default, the grid aligns with the tick marks on the corresponding default axis (i.e., tickmarks as computed by axTicks). } \item{\dots}{ additional arguments passed to the \code{grid()} function. } } \examples{ ## Test Plot Function: plot(x = rnorm(100), type = "l", col = "red", xlab = "", ylab = "Variates", las = 1) title("Normal Deviates", adj = 0) hgrid() boxL() copyright() } \keyword{hplot} fBasics/man/utils-colorTable.Rd0000644000176000001440000000121411370220776016145 0ustar ripleyusers\name{colorTable} \alias{colorTable} \title{Table of Colors} \description{ Displays a Table of color codes and plots the colors themselves. } \usage{ colorTable(cex = 0.7) } \arguments{ \item{cex}{ a numeric value, determines the character size in the color plot, the default size is 0.7. } } \value{ \code{colorTable} returns a table of plot plot colors with the associated color numbers. } \seealso{ \code{link{characterTable}}, \code{link{symbolTable}}. } \examples{ ## Color Table: colorTable() } \keyword{programming} fBasics/man/utils-colorPalette.Rd0000644000176000001440000002153311370220776016522 0ustar ripleyusers\name{colorPalette} \alias{colorPalette} \alias{rainbowPalette} \alias{heatPalette} \alias{terrainPalette} \alias{topoPalette} \alias{cmPalette} \alias{greyPalette} \alias{timPalette} \alias{rampPalette} \alias{seqPalette} \alias{divPalette} \alias{qualiPalette} \alias{focusPalette} \alias{monoPalette} \title{Color Palettes} \description{ Functions to create color palettes. \cr The functions are: \tabular{ll}{ \code{rainbowPalette} \tab Contiguous rainbow color palette, \cr \code{heatPalette} \tab Contiguous heat color palette, \cr \code{terrainPalette} \tab Contiguous terrain color palette, \cr \code{topoPalette} \tab Contiguous topo color palette, \cr \code{cmPalette} \tab Contiguous cm color palette, \cr \code{greyPalette} \tab R's gamma-corrected gray palette, \cr \code{timPalette} \tab Tim's Matlab like color palette, \cr \code{rampPalette} \tab Color ramp palettes, \cr \code{seqPalette} \tab Sequential color brewer palettes, \cr \code{divPalette} \tab Diverging color brewer palettes, \cr \code{qualiPalette} \tab Qualified color brewer palettes, \cr \code{focusPalette} \tab Red, green blue focus palettes, \cr \code{monoPalette} \tab Red, green blue mono palettes. } } \usage{ rainbowPalette(n = 64, \dots) heatPalette(n = 64, \dots) terrainPalette(n = 64, \dots) topoPalette(n = 64, \dots) cmPalette(n = 64, \dots) greyPalette(n = 64, \dots) timPalette(n = 64) rampPalette(n, name = c("blue2red", "green2red", "blue2green", "purple2green", "blue2yellow", "cyan2magenta")) seqPalette(n, name = c( "Blues", "BuGn", "BuPu", "GnBu", "Greens", "Greys", "Oranges", "OrRd", "PuBu", "PuBuGn", "PuRd", "Purples", "RdPu", "Reds", "YlGn", "YlGnBu", "YlOrBr", "YlOrRd")) divPalette(n, name = c( "BrBG", "PiYG", "PRGn", "PuOr", "RdBu", "RdGy", "RdYlBu", "RdYlGn", "Spectral")) qualiPalette(n, name = c( "Accent", "Dark2", "Paired", "Pastel1", "Pastel2", "Set1", "Set2", "Set3")) focusPalette(n, name = c("redfocus", "greenfocus", "bluefocus")) monoPalette(n, name = c("redmono", "greenmono", "bluemono")) } \arguments{ \item{n}{ an integer, giving the number of greys or colors to be constructed. } \item{name}{ a character string, the name of the color set. } \item{\dots}{ arguments to be passed, see the details section } } \value{ returns a character string of color strings. } \details{ All Rmetrics' color sets are named as \code{fooPalette} where the prefix \code{foo} denotes the name of the underlying color set. \cr \bold{R's Contiguous Color Palettes:}\cr \cr Palettes for \code{n} contiguous colors are implemented in the \code{grDevices} package. To be conform with Rmetrics' naming convention for color palettes we have build a wrapper around the underlying functions. These are the \code{rainbowPalette}, \code{heatPalette}, \code{terrainPalette}, \code{topoPalette}, and the \code{cmPalette}. Conceptually, all of these functions actually use (parts of) a line cut out of the 3-dimensional color space, parametrized by the function \code{hsv(h,s,v,gamma)}, where \code{gamma=1} for the \code{fooPalette} function, and hence, equispaced hues in RGB space tend to cluster at the red, green and blue primaries. Some applications such as contouring require a palette of colors which do not wrap around to give a final color close to the starting one. To pass additional arguments to the underlying functions we refer to consult \code{help(rainbow)}. With rainbow, the parameters start and end can be used to specify particular subranges of hues. Synonyme function calls are \code{rainbow}, \code{heat.colors},\code{terrain.colors}, \code{topo.colors}, and the \code{cm.colors}. \cr \bold{R's Gamma-Corrected Gray Palette:}\cr \cr The function \code{grayPalette} chooses a series of \code{n} gamma-corrected gray levels. The range of the gray levels can be optionally monitored through the \code{\dots} arguments, for details \code{help(gray.colors)}, which is a synonyme function call in the \code{grDevices} package. \cr \bold{Tim's Matlab like Color Palette:}\cr \cr The function \code{timPalette} creates a color set ranging from blue to red, and passes through the colors cyan, yellow, and orange. It comes from the Matlab software, originally used in fluid dynamics simulations. The function here is a copy from R's contributed package \code{fields} doing a spline interpolation on \code{n=64} color points. \cr \bold{Color Ramp Palettes:}\cr \cr The function \code{rampPalette} creates several color ramps. The function is implemented from Tim Keitt's contributed R package \code{colorRamps}. Supported through the argument \code{name} are the following color ramps: \code{"blue2red"}, \code{"green2red"}, \code{"blue2green"}, \code{"purple2green"}, \code{"blue2yellow"}, \code{"cyan2magenta"}. \cr \bold{Color Brewer Palettes:}\cr \cr The functions \code{seqPalette}, \code{divPalette}, and \code{qualiPalette} create color sets according to R's contributed \code{RColorBrewer} package. The first letter in the function name denotes the type of the color set: "s" for sequential palettes, 'd" for diverging palettes, and "q" for qualitative palettes. \cr \emph{Sequential palettes} are suited to ordered data that progress from low to high. Lightness steps dominate the look of these schemes, with light colors for low data values to dark colors for high data values. The sequential palettes names are: Blues, BuGn, BuPu, GnBu, Greens, Greys, Oranges, OrRd, PuBu, PuBuGn, PuRd, Purples, RdPu, Reds, YlGn, YlGnBu, YlOrBr, YlOrRd. \cr \emph{Diverging palettes} put equal emphasis on mid-range critical values and extremes at both ends of the data range. The critical class or break in the middle of the legend is emphasized with light colors and low and high extremes are emphasized with dark colors that have contrasting hues. The diverging palettes names are: BrBG, PiYG, PRGn, PuOr, RdBu, RdGy, RdYlBu, RdYlGn, Spectral. \cr \emph{Qualitative palettes} do not imply magnitude differences between legend classes, and hues are used to create the primary visual differences between classes. Qualitative schemes are best suited to representing nominal or categorical data. The qualitative palettes names are: Accent, Dark2, Paired, Pastel1, Pastel2, Set1, Set2, Set3. \cr In contrast to the original color brewer palettes, the palettes here are created by spline interpolation from the color variation with the most different values, i.e for the sequential palettes these are 9 values, for the diverging palettes these are 11 values, and for the qualitative palettes these are between 8 and 12 values dependeing on the color set. \cr \bold{Graph Color Palettes:}\cr \cr The function \code{perfanPalette} creates color sets inspired by R's cotributed package \code{Performance Analytics}. These color palettes have been designed to create readable, comparable line and bar graphs with specific objectives. \cr \emph{Focused Color Palettes:} Color sets designed to provide focus to the data graphed as the first element. This palette is best used when there is clearly an important data set for the viewer to focus on, with the remaining data being secondary, tertiary, etc. Later elements graphed in diminishing values of gray. \cr \emph{Monchrome Color Palettes:} These include color sets for monochrome color displays. } \note{ The palettes are wrapper functions provided in several contributed R packages. These include: Cynthia Brewer and Mark Harrower for the brewer palettes, \cr Peter Carl and Brian G. Peterson for the "PerformanceAnalytics" package, \cr Tim Keitt for the "colorRamps" package, \cr Ross Ihaka for the "colorspace" package, \cr Tomas Aragon for the "epitools" package, \cr Doug Nychka for the "fields" package, \cr Erich Neuwirth for the "RColorBrewer" package. \cr Additional undocumented hidden functions: \tabular{ll}{ % RGB CONVERTER: \code{.asRGB} \tab Converts any R color to RGB (red/green/blue), \cr % CONVERSION HEXIMAL/DECIMAL: \code{.chcode} \tab Changes from one to another number system, \cr \code{.hex.to.dec} \tab Converts heximal numbers do decimal numbers, \cr \code{.dec.to.hex} \tab Converts decimal numbers do heximal numbers.} } \examples{ ## GreyPalette: greyPalette() } \keyword{programming} fBasics/man/utils-colorLocator.Rd0000644000176000001440000000244611720123756016530 0ustar ripleyusers\name{colorLocator} \alias{colorLocator} \alias{colorMatrix} \title{Color Selection} \description{ Displays \R's 657 named colors for selection and returns optionally \R's color names. } \usage{ colorLocator(locator = FALSE, cex.axis = 0.7) colorMatrix() } \arguments{ \item{locator}{logical, if true, \code{\link{locator}} is used for interactive selection of color names, default is \code{FALSE}.} \item{cex.axis}{size of axis labels.} } \value{ Color Locator: \code{colorsLocator()} generates a plot with R colors and, when \code{locator} is true, returns matrix with graph coordinates and names of colors selected. \code{colorsMatrix()} quietly returns the matrix of names. } \details{ Color Locator: The \code{colorLocator} function plots R's 657 named colors. If \code{locator=TRUE} then you can interactively point and click to select the colors for which you want names. To end selection, right click on the mouse and select 'Stop', then R returns the selected color names. The functions used here are wrappers to the functions provided by Tomas Aragon in the contributed R package. \code{epitools}. } \seealso{ \code{\link{colorPalette}}, \code{\link{colorTable}}. } \examples{ colorLocator() } \keyword{programming} fBasics/man/utils-colIds.Rd0000644000176000001440000000161011370220776015274 0ustar ripleyusers\name{Ids} \alias{Ids} \alias{colIds} \alias{rowIds} \alias{colIds<-} \alias{rowIds<-} \title{Set and Retrieve Column/Row Names} \description{ Sets and retrieves column and row names. The functions are for compatibility with SPlus. } \usage{ colIds(x, \dots) rowIds(x, \dots) } \arguments{ \item{x}{ a numeric matrix. } \item{\dots}{ arguments to be passed. } } \details{ Usualli in R the functions \code{colnames}, and \code{rownames} are used to retrieve and set the names of matrices. The functions \code{rowIds} and \code{colIds}, are S-Plus like synonyms. } \examples{ ## pascal - # Create Pascal Matrix: P = pascal(3) P ## rownames - rownames(P) <- letters[1:3] P ## colIds<- - colIds(P) <- as.character(1:3) P } \keyword{math} fBasics/man/utils-characterTable.Rd0000644000176000001440000000202411370220776016763 0ustar ripleyusers\name{characterTable} \alias{characterTable} \title{Table of Characters} \description{ Displays a table of numerical equivalents to Latin characters. } \usage{ characterTable(font = 1, cex = 0.7) } \arguments{ \item{cex}{ a numeric value, determines the character size, the default size is 0.7. } \item{font}{ an integer value, the number of the \code{font}, by default font number 1. } } \value{ \code{characterTable} displays a table with the characters of the requested font. The character on line "xy" and column "z" of the table has code \code{"\xyz"}, e.g \code{cat("\\126")} prints: V for font number 1. These codes can be used as any other characters. } \seealso{ \code{link{colorTable}}, \code{link{symbolTable}}. } \examples{ ## Character Table for Font 2: characterTable(font = 1) } \keyword{programming} fBasics/man/utils-Heaviside.Rd0000644000176000001440000000446211370220776015770 0ustar ripleyusers\name{Heaviside} \alias{Heaviside} \alias{Sign} \alias{Delta} \alias{Boxcar} \alias{Ramp} \title{Haviside and Related Functions} \description{ Functions which compute the Heaviside and related functions. These include the sign function, the delta function, the boxcar function, and the ramp function. \cr The functions are: \tabular{ll}{ \code{Heaviside} \tab Computes Heaviside unit step function, \cr \code{Sign} \tab Just another signum function, \cr \code{Delta} \tab Computes delta function, \cr \code{Boxcar} \tab Computes boxcar function, \cr \code{Ramp} \tab Computes ramp function. } } \usage{ Heaviside(x, a = 0) Sign(x, a = 0) Delta(x, a = 0) Boxcar(x, a = 0.5) Ramp(x, a = 0) } \arguments{ \item{a}{ a numeric value, the location of the break.\cr } \item{x}{ a numeric vector. } } \details{ The Heaviside step function \code{Heaviside} is 1 for \code{x>a}, \code{1/2} for \code{x=a}, and \code{0} for \code{xa}, \code{0} for \code{x=a}, and \code{-1} for \code{x infinity samples = 1000 # Range of expected Drawdons xlim = c(0, 5)*sqrt(horizon) # Plot Histogram of Simulated Max Drawdowns: r = rmaxdd(n = samples, mean = 0, sd = 1, horizon = horizon) hist(x = r, n = 40, probability = TRUE, xlim = xlim, col = "steelblue4", border = "white", main = "Max. Drawdown Density") points(r, rep(0, samples), pch = 20, col = "orange", cex = 0.7) ## dmaxdd - x = seq(0, xlim[2], length = 200) d = dmaxdd(x = x, sd = 1, horizon = horizon, N = 1000) lines(x, d, lwd = 2) ## pmaxdd - # Count Frequencies of Drawdowns Greater or Equal to "h": n = 50 x = seq(0, xlim[2], length = n) g = rep(0, times = n) for (i in 1:n) g[i] = length (r[r > x[i]]) / samples plot(x, g, type ="h", lwd = 3, xlab = "q", main = "Max. Drawdown Probability") # Compare with True Probability "G_D(h)": x = seq(0, xlim[2], length = 5*n) p = pmaxdd(q = x, sd = 1, horizon = horizon, N = 5000) lines(x, p, lwd = 2, col="steelblue4") ## maxddStats - # Compute expectation Value E[D]: maxddStats(mean = -0.5, sd = 1, horizon = 10^(1:4)) maxddStats(mean = 0.0, sd = 1, horizon = 10^(1:4)) maxddStats(mean = 0.5, sd = 1, horizon = 10^(1:4)) } \keyword{distribution} fBasics/man/stats-interpLinear.Rd0000644000176000001440000000450111370220776016513 0ustar ripleyusers\name{linearInterp} \alias{linearInterp} \alias{linearInterpp} \title{Bivariate Linear Interpolation} \description{ Bivariate Linear Interpolation. Two options are available gridded and pointwise interpolation. } \usage{ linearInterp(x, y = NULL, z = NULL, gridPoints = 21, xo = seq(min(x), max(x), length = gridPoints), yo = seq(min(y), max(y), length = gridPoints)) linearInterpp(x, y = NULL, z = NULL, xo, yo) } \arguments{ \item{x, y, z}{ for \code{linearInterp} the arguments \code{x} and \code{y} are two numeric vectors of grid pounts, and \code{z} is a numeric matrix or any other rectangular object which can be transformed by the function \code{as.matrix} into a matrix object. For \code{linearInterpp} we consider either three numeric vectors of equal length or if \code{y} and \code{z} are NULL, a list with entries \code{x}, \code{y}, \code{z}, or named data frame with \code{x} in the first, \code{y} in the second, and \code{z} in the third column. } \item{gridPoints}{ an integer value specifying the number of grid points in \code{x} and \code{y} direction. } \item{xo, yo}{ for \code{linearInterp} two numeric vectors of data points spanning the grid, and for \code{linearInterpp} two numeric vectors of data points building pairs for pointwise interpolation. } } \value{ \code{linearInterp} returns a list with at least three entries, \code{x}, \code{y} and \code{z}. Note, that the returned values, can be directly used by the \code{persp} and \code{contour} 3D plotting methods. \code{linearInterpp} returns a data.frame with columns \code{"x"}, \code{"y"}, and \code{"z"}. } \seealso{ \code{\link{akimaInterp}}, and \code{\link{krigeInterp}}. } \examples{ ## linearInterp - # Linear Interpolation: if (require(akima)) { set.seed(1953) x = runif(999) - 0.5 y = runif(999) - 0.5 z = cos(2*pi*(x^2+y^2)) ans = linearInterp(x, y, z, gridPoints = 41) persp(ans, theta = -40, phi = 30, col = "steelblue", xlab = "x", ylab = "y", zlab = "z") contour(ans) } } \keyword{programming} fBasics/man/stats-interpKrige.Rd0000644000176000001440000000354011370220776016344 0ustar ripleyusers\name{krigeInterp} \alias{krigeInterp} \title{Bivariate Krige Interpolation} \description{ Bivariate Krige Interpolation. } \usage{ krigeInterp(x, y = NULL, z = NULL, gridPoints = 21, xo = seq(min(x), max(x), length = gridPoints), yo = seq(min(y), max(y), length = gridPoints), extrap = FALSE, polDegree = 6) } \arguments{ \item{x, y, z}{ the arguments \code{x} and \code{y} are two numeric vectors of grid pounts, and \code{z} is a numeric matrix or any other rectangular object which can be transformed by the function \code{as.matrix} into a matrix object. } \item{gridPoints}{ an integer value specifying the number of grid points in \code{x} and \code{y} direction. } \item{xo, yo}{ two numeric vectors of data points spanning the grid. } \item{extrap}{ a logical, if \code{TRUE} then the data points are extrapolated. } \item{polDegree}{ the polynomial krige degree, an integer ranging between 1 and 6. } } \note{ The function \code{krigeInterp} requires loading of the R package \code{spatial}. } \value{ \code{krigeInterp} returns a list with at least three entries, \code{x}, \code{y} and \code{z}. Note, that the returned values, can be directly used by the \code{persp} and \code{contour} 3D plotting methods. } \seealso{ \code{\link{akimaInterp}}, \code{\link{linearInterp}}. } \examples{ ## krigeInterp - # Kriging: set.seed(1953) x = runif(999) - 0.5 y = runif(999) - 0.5 z = cos(2*pi*(x^2+y^2)) ans = krigeInterp(x, y, z, extrap = FALSE) persp(ans, theta = -40, phi = 30, col = "steelblue", xlab = "x", ylab = "y", zlab = "z") contour(ans) } \keyword{programming} fBasics/man/stats-interpAkima.Rd0000644000176000001440000000710211370220776016323 0ustar ripleyusers\name{akimaInterp} \alias{akimaInterp} \alias{akimaInterpp} \title{Bivariate Spline Interpolation} \description{ Interpolates bivariate data sets using Akima spline interpolation. } \usage{ akimaInterp(x, y = NULL, z = NULL, gridPoints = 21, xo = seq(min(x), max(x), length = gridPoints), yo = seq(min(y), max(y), length = gridPoints), extrap = FALSE) akimaInterpp(x, y = NULL, z = NULL, xo, yo, extrap = FALSE) } \arguments{ \item{x, y, z}{ for \code{akimaInterp} the arguments \code{x} and \code{y} are two numeric vectors of grid pounts, and \code{z} is a numeric matrix or any other rectangular object which can be transformed by the function \code{as.matrix} into a matrix object. For \code{akimaInterpp} we consider either three numeric vectors of equal length or if \code{y} and \code{z} are NULL, a list with entries \code{x}, \code{y}, \code{z}, or named data frame with \code{x} in the first, \code{y} in the second, and \code{z} in the third column. } \item{gridPoints}{ an integer value specifying the number of grid points in \code{x} and \code{y} direction. } \item{xo, yo}{ for \code{akimaInterp} two numeric vectors of data points spanning the grid, and for \code{akimaInterpp} two numeric vectors of data points building pairs for pointwise interpolation. } \item{extrap}{ a logical, if \code{TRUE} then the data points are extrapolated. } } \details{ Two options are available gridded and pointwise interpolation. \code{akimaInterp} is a function wrapper to the \code{interp} function provided by the contributed R package \code{akima}. The Fortran code of the Akima spline interpolation routine was written by H. Akima. Linear surface fitting and krige surface fitting are provided by the functions \code{linearInterp} and \code{krigeInterp}. } \note{ IMPORTANT: The contributed package \code{akima} is not in the dependence list of the DESCRIPTION file due to license conditions. The Rmetrics user has to load this package from the CRAN server on his own responsibility, please check the license conditions. } \value{ \code{akimaInterp} returns a list with at least three entries, \code{x}, \code{y} and \code{z}. Note, that the returned values, can be directly used by the \code{persp} and \code{contour} 3D plotting methods. \cr \code{akimaInterpp} returns a data.frame with columns \code{"x"}, \code{"y"}, and \code{"z"}. } \seealso{ \code{\link{linearInterp}}, \code{\link{krigeInterp}}. } \references{ Akima H., 1978, \emph{A Method of Bivariate Interpolation and Smooth Surface Fitting for Irregularly Distributed Data Points}, ACM Transactions on Mathematical Software 4, 149-164. Akima H., 1996, \emph{Algorithm 761: Scattered-Data Surface Fitting that has the Accuracy of a Cubic Polynomial}, ACM Transactions on Mathematical Software 22, 362-371. } \examples{ ## akimaInterp - # Akima Interpolation: if (require(akima)) { set.seed(1953) x = runif(999) - 0.5 y = runif(999) - 0.5 z = cos(2*pi*(x^2+y^2)) ans = akimaInterp(x, y, z, gridPoints = 41, extrap = FALSE) persp(ans, theta = -40, phi = 30, col = "steelblue", xlab = "x", ylab = "y", zlab = "z") contour(ans) } } \keyword{programming} fBasics/man/stats-basicStats.Rd0000644000176000001440000000230211370220776016154 0ustar ripleyusers\name{BasicStatistics} \alias{basicStats} \title{Basic Time Series Statistics} \description{ Computes basic financial time series statististics. \cr List of Functions: \tabular{ll}{ \code{basicStats} \tab Computes an overview of basic statistical values.} } \usage{ basicStats(x, ci = 0.95) } \arguments{ \item{ci}{ confidence interval, a numeric value, by default 0.95, i.e. 95 percent. } \item{x}{ an object of class \code{"timeSeries"} or any other object which can be transformed by the function \code{as.timeSeries} into an object of class \code{timeSeries}. The latter case, other then \code{timeSeries} objects, is more or less untested. } } \value{ \code{basicsStats} \cr returns a data frame with the following entries and row names: nobs, NAs, Minimum, Maximum , 1. Quartile, 3. Quartile, Mean, Median, Sum, SE Mean, LCL Mean, UCL Mean, Variance, Stdev, Skewness, Kurtosis. } \examples{ ## basicStats - # Simulated Monthly Return Data: tS = timeSeries(matrix(rnorm(12)), timeCalendar()) basicStats(tS) } \keyword{programming} fBasics/man/stableSlider.Rd0000644000176000001440000000134111616001524015326 0ustar ripleyusers\name{StableSlider} \title{Slider GUI for Stable Distribution} \alias{stableSlider} \description{ The \code{stableSlider()} function provides interactive displays of density and probabilities of stable distributions. } \usage{ stableSlider(col= "steelblue", col.med = "gray30") } \arguments{ \item{col, col.med}{optional arguments for the slider.} } \value{ The \code{stableSlider()} function displays densities and probabilities of the skew stable distribution, for educational purposes. } \author{ Diethelm Wuertz for the Rmetrics \R-port. } \references{ see those in \code{\link[stabledist]{dstable}}, in package \pkg{stabledist}. } \examples{ if(dev.interactive()) stableSlider() } \keyword{distribution} fBasics/man/plot-seriesPlot.Rd0000644000176000001440000001020011370220776016021 0ustar ripleyusers\name{TimeSeriesPlots} \alias{seriesPlot} \alias{returnPlot} \alias{cumulatedPlot} \alias{drawdownPlot} \title{Financial Time Series Plots} \description{ Returns an index/price, a return, or a drawdown plot. \cr List of Functions: \tabular{ll}{ \code{seriesPlot} \tab Returns a tailored return series plot, \cr \code{cumulatedPlot} \tab Displays a cumulated series given the returns, \cr \code{returnPlot} \tab Displays returns given the cumulated series, \cr \code{drawdownPlot} \tab Displays drawdowns given the return series.} } \usage{ seriesPlot(x, labels = TRUE, type = "l", col = "steelblue", title = TRUE, grid = TRUE, box = TRUE, rug = TRUE, \dots) cumulatedPlot(x, index = 100, labels = TRUE, type = "l", col = "steelblue", title = TRUE, grid = TRUE, box = TRUE, rug = TRUE, \dots) returnPlot(x, labels = TRUE, type = "l", col = "steelblue", title = TRUE, grid = TRUE, box = TRUE, rug = TRUE, \dots) drawdownPlot(x, labels = TRUE, type = "l", col = "steelblue", title = TRUE, grid = TRUE, box = TRUE, rug = TRUE, \dots) } \arguments{ \item{box}{ a logical flag, should a box be added to the plot? By default \code{TRUE}. } \item{col}{ the color for the series. In the univariate case use just a color name like the default, \code{col="steelblue"}, in the multivariate case we recommend to select the colors from a color palette, e.g. \code{col=heat.colors(ncol(x))}. } \item{grid}{ a logical flag, should a grid be added to the plot? By default \code{TRUE}. } \item{index}{ a numeric value, by default 100. The function cumulates column by colum the returns and multiplies the result with the index value: \code{index*exp(colCumsums(x))}. } \item{labels}{ a logical flag, should the plot be returned with default labels and decorated in an automated way? By default \code{TRUE}. } \item{rug}{ a logical flag, by default TRUE. Should a rug representation of the data added to the plot? } \item{title}{ a logical flag, by default TRUE. Should a default title added to the plot? } \item{type}{ what type of plot should be drawn? By default we use a line plot, \code{type="l"}. An alternative plot style which produces nice figures is for example \code{type="h"}. } \item{x}{ an object of class \code{"timeSeries"} or any other object which can be transformed by the function \code{as.timeSeries} into an object of class \code{timeSeries}. The latter case, other then \code{timeSeries} objects, is more or less untested. } \item{\dots}{ optional arguments to be passed. } } \details{ The plot functions can be used to plot univariate and multivariate time series of class \code{timeSeries}. The graphical parameters \code{type} and \code{col} can be set by the values specified through the argument list. In the case of multivariate time series \code{col} can be specified by the values returned by a color palette. Automated titles including main title, x- and y-lables, grid lines, box style and rug represenatations cann be selected by setting these arguments to \code{TRUE} which is the default. If the title flag is unset, then the main title, x-, and y-labels are empty strings. This allows to set user defined labels with the function \code{title} after the plot is drawn. Beside \code{type}, \code{col}, \code{main}, \code{xlab} and \code{ylab}, all other \code{par} arguments can be passed to the \code{plot} function. If the \code{labels} flag is unset to \code{FALSE}, then no decorations will be added tothe plot, and the plot can be fully decorated by the user. } \value{ displays a time series plot. } \examples{ ## seriesPlot - tS = as.timeSeries(data(LPP2005REC)) seriesPlot(tS) } \keyword{programming} fBasics/man/plot-scalinglawPlot.Rd0000644000176000001440000000525711370220776016673 0ustar ripleyusers\name{ScalingLawPlot} \alias{scalinglawPlot} \alias{scalinglawPlot} \title{Scaling Law Bahaviour} \description{ Evaluates the scaling exponent of a financial return series and plots the scaling law. } \usage{ scalinglawPlot(x, span = ceiling(log(length(x)/252)/log(2)), doplot = TRUE, labels = TRUE, trace = TRUE, \dots) } \arguments{ \item{doplot}{ a logical value. Should a plot be displayed? } \item{labels}{ a logical value. Whether or not x- and y-axes should be automatically labeled and a default main title should be added to the plot. By default \code{TRUE}. } \item{span}{ an integer value, determines for the \code{qqgaussPlot} the plot range, by default 5, and for the \code{scalingPlot} a reasonable number of of points for the scaling range, by default daily data with 252 business days per year are assumed. } \item{trace}{ a logical value. Should the computation be traced? } \item{x}{ an uni- or multivariate return series of class \code{timeSeries} or any other object which can be transformed by the function \code{as.timeSeries()} into an object of class \code{timeSeries}. } \item{\dots}{ arguments to be passed. } } \value{ returns a list with the following components: \code{Intercept}, \code{Exponent} the scaling exponent, and \code{InverseExponent} its inverse value. } \details{ \bold{Scaling Behavior:} \cr\cr The function \code{scalingPlot} plots the scaling law of financial time series under aggregation and returns an estimate for the scaling exponent. The scaling behavior is a very striking effect of the foreign exchange market and also other markets expressing a regular structure for the volatility. Considering the average absolute return over individual data periods one finds a scaling power law which relates the mean volatility over given time intervals to the size of these intervals. The power law is in many cases valid over several orders of magnitude in time. Its exponent usually deviates significantly from a Gaussian random walk model which implies 1/2. } \references{ Taylor S.J. (1986); \emph{Modeling Financial Time Series}, John Wiley and Sons, Chichester. } \author{ Diethelm Wuertz for the Rmetrics \R-port. } \examples{ ## data - # require(MASS) plot(SP500, type = "l", col = "steelblue", main = "SP500") abline(h = 0, col = "grey") ## scalinglawPlot - # Taylor Effect: scalinglawPlot(SP500) } \keyword{hplot} fBasics/man/plot-returnSeriesGUI.Rd0000644000176000001440000000166611370220775016746 0ustar ripleyusers\name{ReturnSeriesGUI} \alias{returnSeriesGUI} \title{Return Series Plots} \description{ A graphical user interface to display finanical time series plots. \cr List of Functions: \tabular{ll}{ \code{returnSeriesGUI} \tab Opens a GUI for return series plots.} } \usage{ returnSeriesGUI(x) } \arguments{ \item{x}{ an object of class \code{"timeSeries"} or any other object which can be transformed by the function \code{as.timeSeries} into an object of class \code{timeSeries}. The latter case, other then \code{timeSeries} objects, is more or less untested. } } \value{ \code{returnSeriesGUI} \cr For the \code{returnSeriesGUI} function, beside the graphical user interface no values are returned.\cr } \author{ Diethelm Wuertz for the Rmetrics \R-port. } \examples{ ## } \keyword{programming} fBasics/man/plot-qqPlot.Rd0000644000176000001440000000626211370220776015165 0ustar ripleyusers\name{QuantileQuantilePlots} \alias{qqnormPlot} \alias{qqnigPlot} \alias{qqghtPlot} \alias{qqgldPlot} \title{Quantile-Quantile Plots} \description{ Returns quantile-quantile plots for the normal, the normal inverse Gaussian, the generalized hyperbolic Student-t and the generalized lambda distribution. \cr List of Functions: \tabular{ll}{ \code{qqnormPlot} \tab Returns a tailored Normal quantile-quantile plot, \cr \code{qqnigPlot} \tab Returns a tailored NIG quantile-quantile plot, \cr \code{qqghtPlot} \tab Returns a tailored GHT quantile-quantile plot, \cr \code{qqgldPlot} \tab Returns a tailored GLD quantile-quantile plot.} } \usage{ qqnormPlot(x, labels = TRUE, col = "steelblue", pch = 19, title = TRUE, mtext = TRUE, grid = FALSE, rug = TRUE, scale = TRUE, \dots) qqnigPlot(x, labels = TRUE, col = "steelblue", pch = 19, title = TRUE, mtext = TRUE, grid = FALSE, rug = TRUE, scale = TRUE, \dots) qqghtPlot(x, labels = TRUE, col = "steelblue", pch = 19, title = TRUE, mtext = TRUE, grid = FALSE, rug = TRUE, scale = TRUE, \dots) qqgldPlot(x, labels = TRUE, col = "steelblue", pch = 19, title = TRUE, mtext = TRUE, grid = FALSE, rug = TRUE, scale = TRUE, \dots) } \arguments{ \item{x}{ an object of class \code{"timeSeries"} or any other object which can be transformed by the function \code{as.timeSeries} into an object of class \code{timeSeries}. The latter case, other then \code{timeSeries} objects, is more or less untested. } \item{labels}{ a logical flag, should the plot be returned with default labels and decorated in an automated way? By default \code{TRUE}. } \item{col}{ the color for the series. In the univariate case use just a color name like the default, \code{col="steelblue"}, in the multivariate case we recommend to select the colors from a color palette, e.g. \code{col=heat.colors(ncol(x))}. } \item{pch}{ an integer value, by default 19. Which plot character should be used in the plot? } \item{title}{ a logical flag, by default TRUE. Should a default title added to the plot? } \item{mtext}{ a logical flag, by default TRUE. Should a marginal text be printed on the third site of the graph? } \item{grid}{ a logical flag, should a grid be added to the plot? By default \code{TRUE}. To plot a horizontal lines only use \code{grid="h"} and for vertical lines use \code{grid="h"}, respectively. } \item{rug}{ a logical flag, by default TRUE. Should a rug representation of the data added to the plot? } \item{scale}{ a logical flag, by default TRUE. Should the time series be scaled for the investigation? } \item{\dots}{ optional arguments to be passed. } } \value{ displays a quantile-quantile plot. } \author{ Diethelm Wuertz for the Rmetrics \R-port. } \examples{ ## qqPlot - qqnormPlot(rnorm(100)) } \keyword{programming} fBasics/man/plot-histPlot.Rd0000644000176000001440000000540511370220776015511 0ustar ripleyusers\name{HistogramPlot} \alias{histPlot} \alias{densityPlot} \alias{logDensityPlot} \title{Histogram and Density Plots} \description{ Returns a histogram, a density, or a logarithmic density plot. \cr List of Functions: \tabular{ll}{ \code{histPlot} \tab Returns a tailored histogram plot, \cr \code{densityPlot} \tab Returns a tailored kernel density estimate plot, \cr \code{logDensityPlot} \tab Returns a tailored log kernel density estimate plot.} } \usage{ histPlot(x, labels = TRUE, col = "steelblue", fit = TRUE, title = TRUE, grid = TRUE, rug = TRUE, skip = FALSE, \dots) densityPlot(x, labels = TRUE, col = "steelblue", fit = TRUE, hist = TRUE, title = TRUE, grid = TRUE, rug = TRUE, skip = FALSE, \dots) logDensityPlot(x, labels = TRUE, col = "steelblue", robust = TRUE, title = TRUE, grid = TRUE, rug = TRUE, skip = FALSE, \dots) } \arguments{ \item{col}{ the color for the series. In the univariate case use just a color name like the default, \code{col="steelblue"}, in the multivariate case we recommend to select the colors from a color palette, e.g. \code{col=heat.colors(ncol(x))}. } \item{fit}{ a logical flag, should a fit added to the Plot? } \item{grid}{ a logical flag, should a grid be added to the plot? By default \code{TRUE}. To plot a horizontal lines only use \code{grid="h"} and for vertical lines use \code{grid="h"}, respectively. } \item{hist}{ a logical flag, by default TRUE. Should a histogram to be underlaid to the plot? } \item{labels}{ a logical flag, should the plot be returned with default labels and decorated in an automated way? By default \code{TRUE}. } \item{rug}{ a logical flag, by default TRUE. Should a rug representation of the data added to the plot? } \item{skip}{ a logical flag, should zeros be skipped in the return Series? } \item{robust}{ a logical flag, by default TRUE. Should a robust fit added to the plot? } \item{title}{ a logical flag, by default TRUE. Should a default title added to the plot? } \item{x}{ an object of class \code{"timeSeries"} or any other object which can be transformed by the function \code{as.timeSeries} into an object of class \code{timeSeries}. The latter case, other then \code{timeSeries} objects, is more or less untested. } \item{\dots}{ optional arguments to be passed. } } \value{ displays a time series plot. } \examples{ ## histPlot } \keyword{programming} fBasics/man/plot-boxPlot.Rd0000644000176000001440000000260611370220776015332 0ustar ripleyusers\name{BoxPlot} \alias{boxPlot} \alias{boxPercentilePlot} \title{Time Series Box Plots} \description{ Returns a box or a box percentile plot. \cr List of Functions: \tabular{ll}{ \code{boxPlot} \tab Returns a side-by-side standard box plot, \cr \code{boxPercentilePlot} \tab Returns a side-by-side box-percentile plot.} } \usage{ boxPlot(x, col = "steelblue", title = TRUE, \dots) boxPercentilePlot(x, col = "steelblue", title = TRUE, \dots) } \arguments{ \item{col}{ the color for the series. In the univariate case use just a color name like the default, \code{col="steelblue"}, in the multivariate case we recommend to select the colors from a color palette, e.g. \code{col=heat.colors(ncol(x))}. } \item{title}{ a logical flag, by default TRUE. Should a default title added to the plot? } \item{x}{ an object of class \code{"timeSeries"} or any other object which can be transformed by the function \code{as.timeSeries} into an object of class \code{timeSeries}. The latter case, other then \code{timeSeries} objects, is more or less untested. } \item{\dots}{ optional arguments to be passed. } } \value{ displays a time series plot. } \examples{ ## boxplot - } \keyword{programming} fBasics/man/plot-acfPlot.Rd0000644000176000001440000001100111370220776015260 0ustar ripleyusers\name{acfPlot} \alias{acfPlot} \alias{pacfPlot} \alias{teffectPlot} \alias{lacfPlot} \title{Autocorrelation Function Plots} \description{ Returns plots of autocorrelations including the autocorrelation function ACF, the partial ACF, the lagged ACF, and the Taylor effect plot. \cr The functions to display stylized facts are: \tabular{ll}{ \code{acfPlot} \tab autocorrelation function plot, \cr \code{pacfPlot} \tab partial autocorrelation function plot, \cr \code{lacfPlot} \tab lagged autocorrelation function plot, \cr \code{teffectPlot} \tab Taylor effect plot.} } \usage{ acfPlot(x, labels = TRUE, \dots) pacfPlot(x, labels = TRUE, \dots) lacfPlot(x, n = 12, lag.max = 20, type = c("returns", "values"), labels = TRUE, \dots) teffectPlot(x, deltas = seq(from = 0.2, to = 3, by = 0.2), lag.max = 10, ymax = NA, standardize = TRUE, labels = TRUE, \dots) } \arguments{ \item{deltas}{ the exponents, a numeric vector, by default ranging from 0.2 to 3.0 in steps of 0.2. } \item{labels}{ a logical value. Whether or not x- and y-axes should be automatically labeled and a default main title should be added to the plot. By default \code{TRUE}. } \item{lag.max}{ maximum lag for which the autocorrelation should be calculated, an integer. } \item{n}{ an integer value, the number of lags. } \item{standardize}{ a logical value. Should the vector \code{x} be standardized? } \item{type}{ [lacf] - \cr a character string which specifies the type of the input series, either "returns" or series "values". In the case of a return series as input, the required value series is computed by cumulating the financial returns: \code{exp(colCumsums(x))} } \item{x}{ an uni- or multivariate return series of class \code{timeSeries} or any other object which can be transformed by the function \code{as.timeSeries()} into an object of class \code{timeSeries}. } \item{ymax}{ maximum y-axis value on plot, \code{is.na(ymax)} TRUE, then the value is selected automatically. } \item{\dots}{ arguments to be passed. } } \value{ \code{acfPlot}, \code{pacfplot}, \cr return an object of class \code{"acf"}, see \code{\link{acf}}. \cr \code{lacfPlot} returns a list with the following two elements: \code{Rho}, the autocorrelation function, \code{lagged}, the lagged correlations. \cr \code{teffectPlot} \cr returns a numeric matrix of order \code{deltas} by \code{max.lag} with the values of the autocorrelations. } \details{ \bold{Autocorrelation Functions:} \cr\cr The functions \code{acfPlot} and \code{pacfPlot}, plot and estimate autocorrelation and partial autocorrelation function. The functions allow to get a first view on correlations within the time series. The functions are synonyme function calls for R's \code{acf} and \code{pacf} from the the \code{ts} package. \cr \bold{Taylor Effect:} \cr\cr The "Taylor Effect" describes the fact that absolute returns of speculative assets have significant serial correlation over long lags. Even more, autocorrelations of absolute returns are typically greater than those of squared returns. From these observations the Taylor effect states, that that the autocorrelations of absolute returns to the the power of \code{delta}, \code{abs(x-mean(x))^delta} reach their maximum at \code{delta=1}. The function \code{teffect} explores this behaviour. A plot is created which shows for each lag (from 1 to \code{max.lag}) the autocorrelations as a function of the exponent \code{delta}. In the case that the above formulated hypothesis is supported, all the curves should peak at the same value around \code{delta=1}. } \references{ Taylor S.J. (1986); \emph{Modeling Financial Time Series}, John Wiley and Sons, Chichester. Ding Z., Granger C.W.J., Engle R.F. (1993); \emph{A long memory property of stock market returns and a new model}, Journal of Empirical Finance 1, 83. } \examples{ ## data - # require(MASS) plot(SP500, type = "l", col = "steelblue", main = "SP500") abline(h = 0, col = "grey") ## teffectPlot - # Taylor Effect: teffectPlot(SP500) } \keyword{hplot} fBasics/man/matrix-vech.Rd0000644000176000001440000000162011370220776015151 0ustar ripleyusers\name{vec} \alias{vec} \alias{vech} \title{Stacking Vectors and Matrixes} \description{ Stacks either a lower triangle matrix or a matrix. } \usage{ vec(x) vech(x) } \arguments{ \item{x}{ a numeric matrix. } } \details{ The function \code{vec} implements the operator that stacks a matrix as a column vector, to be more precise in a matrix with one column. vec(X) = (X11, X21, ..., XN1, X12, X22, ..., XNN). The function \code{vech} implements the operator that stacks the lower triangle of a NxN matrix as an N(N+1)/2x1 vector: vech(X) =(X11, X21, X22, X31, ..., XNN), to be more precise in a matrix with one row. } \examples{ ## Create Pascal Matrix: P = pascal(3) ## Stack a matrix vec(P) ## Stack the lower triangle vech(P) } \keyword{math} fBasics/man/matrix-tslag.Rd0000644000176000001440000000142111370220776015335 0ustar ripleyusers\name{tslag} \alias{tslag} \title{Lagged or Leading Vector/Matrix} \description{ Creates a lagged or leading vector/matrix of selected order(s). } \usage{ tslag(x, k = 1, trim = FALSE) } \arguments{ \item{k}{ an integer value, the number of positions the new series is to lag or to lead the input series. } \item{x}{ a numeric vector or matrix, missing values are allowed. } \item{trim}{ a logical flag, if TRUE, the missing values at the beginning ans/or end of the returned series will be trimmed. The default value is FALSE. } } \seealso{ \code{\link{pdl}}. } \examples{ ## tslag - # } \keyword{math} fBasics/man/matrix-triang.Rd0000644000176000001440000000360711370220776015517 0ustar ripleyusers\name{triang} \alias{triang} \alias{Triang} \title{Upper and Lower Triangular Matrixes} \description{ Extracs the pper or lower tridiagonal part from a matrix. } \usage{ triang(x) Triang(x) } \arguments{ \item{x}{ a numeric matrix. } } \details{ The functions \code{triang} and \code{Triang} allow to transform a square matrix to a lower or upper triangular form. A triangular matrix is either an upper triangular matrix or lower triangular matrix. For the first case all matrix elements \code{a[i,j]} of matrix \code{A} are zero for \code{i>j}, whereas in the second case we have just the opposite situation. A lower triangular matrix is sometimes also called left triangular. In fact, triangular matrices are so useful that much computational linear algebra begins with factoring or decomposing a general matrix or matrices into triangular form. Some matrix factorization methods are the Cholesky factorization and the LU-factorization. Even including the factorization step, enough later operations are typically avoided to yield an overall time savings. Triangular matrices have the following properties: the inverse of a triangular matrix is a triangular matrix, the product of two triangular matrices is a triangular matrix, the determinant of a triangular matrix is the product of the diagonal elements, the eigenvalues of a triangular matrix are the diagonal elements. } \references{ Higham, N.J., (2002); \emph{Accuracy and Stability of Numerical Algorithms}, 2nd ed., SIAM. Golub, van Loan, (1996); \emph{Matrix Computations}, 3rd edition. Johns Hopkins University Press. } \examples{ ## Create Pascal Matrix: P = pascal(3) P ## Create lower triangle matrix L = triang(P) L } \keyword{math} fBasics/man/matrix-tr.Rd0000644000176000001440000000117711370220776014660 0ustar ripleyusers\name{tr} \alias{tr} \title{Trace of a Matrix} \description{ Returns trace of a matrix. } \usage{ tr(x) } \arguments{ \item{x}{ a numeric matrix. } } \details{ The function \code{tr} computes the trace of a square matrix which is the sum of the diagonal elements of the matrix under consideration. } \references{ Golub, van Loan, (1996); \emph{Matrix Computations}, 3rd edition. Johns Hopkins University Press. } \examples{ ## Create Pascal Matrix: P = pascal(3) P ## Trace: tr(P) } \keyword{math} fBasics/man/matrix-rk.Rd0000644000176000001440000000213411370220775014640 0ustar ripleyusers\name{rk} \alias{rk} \title{The Rank of a Matrix} \description{ Returns the rank of a matrix. } \usage{ rk(x, method = c("qr", "chol")) } \arguments{ \item{x}{ a numeric matrix. } \item{method}{ a character string. For \code{method = "qr"} the rank is computed as \code{qr(x)\$rank}, or alternatively for \code{method="chol"} the rank is computed as \code{attr(chol(x, pivot=TRUE), "rank")}. } } \details{ The function \code{rk} computes the rank of a matrix which is the dimension of the range of the matrix corresponding to the number of linearly independent rows or columns of the matrix, or to the number of nonzero singular values. The rank of a matrix is also named inear map. } \references{ Golub, van Loan, (1996); \emph{Matrix Computations}, 3rd edition. Johns Hopkins University Press. } \examples{ ## Create Pascal Matrix: P = pascal(5) P ## Compute the Rank: rk(P) rk(P, "chol") } \keyword{math} fBasics/man/matrix-posDefinite.Rd0000644000176000001440000000146011370220776016477 0ustar ripleyusers\name{positiveDefinite} \alias{positiveDefinite} \alias{isPositiveDefinite} \alias{makePositiveDefinite} \title{Positive Definite Matrixes} \description{ Checks if a matrix is positive definite and/or forces a matrix to be positive definite. } \usage{ isPositiveDefinite(x) makePositiveDefinite(x) } \arguments{ \item{x}{ a square numeric matrix. } } \details{ The function \code{isPositiveDefinite} checks if a square matrix is positive definite. The function \code{makePositiveDefinite} forces a matrix to be positive definite. } \author{ Korbinian Strimmer. } \examples{ ## isPositiveDefinite - # the 3x3 Pascal Matrix is positive define isPositiveDefinite(pascal(3)) } \keyword{math} fBasics/man/matrix-pdl.Rd0000644000176000001440000000145411370220775015007 0ustar ripleyusers\name{pdl} \alias{pdl} \title{Polynomial Distributed Lags} \description{ Returns a regressor matrix for polynomial distributed lags. } \usage{ pdl(x, d = 2, q = 3, trim = FALSE) } \arguments{ \item{x}{ a numeric vector. } \item{d}{ an integer specifying the order of the polynomial. } \item{q}{ an integer specifying the number of lags to use in creating polynomial distributed lags. This must be greater than d. } \item{trim}{ a logical flag; if TRUE, the missing values at the beginning of the returned matrix will be trimmed. } } \seealso{ \code{\link{tslag}}. } \examples{ ## pdl - # } \keyword{math} fBasics/man/matrix-pascal.Rd0000644000176000001440000000224111370220776015467 0ustar ripleyusers\name{pascal} \alias{pascal} \title{Pascal Matrix} \description{ Creates a Pascal matrix. } \usage{ pascal(n) } \arguments{ \item{n}{ an integer value, the dimension of the square matrix. } } \details{ The function \code{pascal} generates a Pascal matrix of order \code{n} which is a symmetric, positive, definite matrix with integer entries made up from Pascal's triangle. The determinant of a Pascal matrix is 1. The inverse of a Pascal matrix has integer entries. If \code{lambda} is an eigenvalue of a Pascal matrix, then \code{1/lambda} is also an eigenvalue of the matrix. Pascal matrices are ill-conditioned. } \references{ Call G.S., Velleman D.J., (1993); \emph{Pascal's matrices}, American Mathematical Monthly 100, 372--376. Edelman A., Strang G., (2004); \emph{Pascal Matrices}, American Mathematical Monthly 111, 361--385. } \examples{ ## Create Pascal Matrix: P = pascal(5) P ## Determinant det(pascal(5)) det(pascal(10)) det(pascal(15)) det(pascal(20)) } \keyword{math} fBasics/man/matrix-norm.Rd0000644000176000001440000000351311370220775015201 0ustar ripleyusers\name{norm} \alias{norm} \title{Matrix Norm} \description{ Returns the norm of a matrix. } \usage{ norm(x, p = 2) } \arguments{ \item{x}{ a numeric matrix. } \item{p}{ an integer value, \code{1}, \code{2} or \code{Inf}. \code{p=1} - The maximum absolute column sum norm which is defined as the maximum of the sum of the absolute valued elements of columns of the matrix. \code{p=2} - The spectral norm is "the norm" of a matrix \code{X}. This value is computed as the square root of the maximum eigenvalue of \code{CX} where \code{C} is the conjugate transpose. \code{p=Inf} - The maximum absolute row sum norm is defined as the maximum of the sum of the absolute valued elements of rows of the matrix. } } \details{ The function \code{norm} computes the norm of a matrix. Three choices are possible: \code{p=1} - The maximum absolute column sum norm which is defined as the maximum of the sum of the absolute valued elements of columns of the matrix. \code{p=2} - The spectral norm is "the norm" of a matrix \code{X}. This value is computed as the square root of the maximum eigenvalue of \code{CX} where \code{C} is the conjugate transpose. \code{p=Inf} - The maximum absolute row sum norm is defined as the maximum of the sum of the absolute valued elements of rows of the matrix. } \references{ Golub, van Loan, (1996); \emph{Matrix Computations}, 3rd edition. Johns Hopkins University Press. } \examples{ ## Create Pascal Matrix: P = pascal(5) P ## Return the Norm of the Matrix: norm(P) } \keyword{math} fBasics/man/matrix-kron.Rd0000644000176000001440000000146311370220775015201 0ustar ripleyusers\name{kron} \alias{kron} \title{Kronecker Product} \description{ Returns the Kronecker product. } \usage{ kron(x, y) } \arguments{ \item{x, y}{ two numeric matrixes. } } \details{ The \emph{Kronecker product} can be computed using the operator \code{\%x\%} or alternatively using the function \code{kron} for SPlus compatibility. } \note{ \code{kron} is a synonyme to \code{\%x\%}. } \references{ Golub, van Loan, (1996); \emph{Matrix Computations}, 3rd edition. Johns Hopkins University Press. } \examples{ ## Create Pascal Matrix: P = pascal(3) P ## Return the Kronecker Product kron(P, diag(3)) P %x% diag(3) } \keyword{math} fBasics/man/matrix-inv.Rd0000644000176000001440000000133211370220776015020 0ustar ripleyusers\name{inv} \alias{inv} \title{The Inverse of a Matrix} \description{ Returns the inverse of a matrix. } \usage{ inv(x) } \arguments{ \item{x}{ a numeric matrix. } } \note{ The function \code{inv} is a synonyme to the function \code{solve}. } \value{ returns the inverse matrix. } \references{ Golub, van Loan, (1996); \emph{Matrix Computations}, 3rd edition. Johns Hopkins University Press. } \examples{ ## Create Pascal Matrix: P = pascal(5) P ## Compute the Inverse Matrix: inv(P) ## Check: inv(P) %*% P ## Alternatives: chol2inv(chol(P)) solve(P) } \keyword{math} fBasics/man/matrix-hilbert.Rd0000644000176000001440000000301611370220776015656 0ustar ripleyusers\name{hilbert} \alias{hilbert} \title{Hilbert Matrix} \description{ Creates a Hilbert matrix. } \usage{ hilbert(n) } \arguments{ \item{n}{ an integer value, the dimension of the square matrix. } } \value{ \code{hilbert} generates a Hilbert matrix of order \code{n}. } \details{ In linear algebra, a Hilbert matrix is a matrix with the unit fraction elements. The Hilbert matrices are canonical examples of ill-conditioned matrices, making them notoriously difficult to use in numerical computation. For example, the 2-norm condition number of a 5x5 Hilbert matrix above is about 4.8e5. The Hilbert matrix is symmetric and positive definite. } \references{ Hilbert D., \emph{Collected papers}, vol. II, article 21. Beckermann B, (2000); \emph{The condition number of real Vandermonde, Krylov and positive definite Hankel matrices}, Numerische Mathematik 85, 553--577, 2000. Choi, M.D., (1983); \emph{Tricks or Treats with the Hilbert Matrix}, American Mathematical Monthly 90, 301--312, 1983. Todd, J., (1954); \emph{The Condition Number of the Finite Segment of the Hilbert Matrix}, National Bureau of Standards, Applied Mathematics Series 39, 109--116. Wilf, H.S., (1970); \emph{Finite Sections of Some Classical Inequalities}, Heidelberg, Springer. } \examples{ ## Create a Hilbert Matrix: H = hilbert(5) H } \keyword{math} fBasics/man/matrix-gridVector.Rd0000644000176000001440000000132411370220776016335 0ustar ripleyusers\name{gridVector} \alias{gridVector} \title{Grid Vector Coordinates} \description{ Creates from two vectors rectangular grid coordinates.. } \usage{ gridVector(x, y = NULL)} \arguments{ \item{x, y}{ two numeric vectors of length m and n which span the rectangular grid of size m times n. If \code{y} takes the default value, \code{NULL}, then \code{y=x}. } } \value{ returns a list with two entries named \code{$X} and \code{$Y}, giving the coordinates which span the bivariate grid. } \seealso{ \code{\link{expand.grid}}. } \examples{ ## gridVector - gridVector((0:10)/10) gridVector((0:10)/10, (0:10)/10) } \keyword{hplot} fBasics/man/matrix-colVec.Rd0000644000176000001440000000126411370220776015443 0ustar ripleyusers\name{colVec} \alias{colVec} \alias{rowVec} \title{Column and Row Vectors} \description{ Creates a column or row vector from a numeric vector. } \usage{ colVec(x) rowVec(x) } \arguments{ \item{x}{ a numeric vector. } } \details{ The functions \code{colVec} and \code{rowVec} transform a vector into a column and row vector, respectively. A column vector is a matrix object with one column, and a row vector is a matrix object with one row. } \examples{ ## Create a numeric Vector: x = rnorm(5) ## Column and Row Vectors: colVec(x) rowVec(x) } \keyword{math} fBasics/man/fBasics-deprecated.Rd0000644000176000001440000000210012027076511016360 0ustar ripleyusers% Part of the Rmetrics project, http://R-metrics.org/ % Copyright 2005-2011 Rmetrics Core Team % Distributed under GPL 2 or later \name{fBasics-deprecated} %------ NOTE: ../R/fBasics-deprecated.R must be synchronized with this! \alias{fBasics-deprecated} %% \alias{dstable} %% \alias{pstable} %% \alias{qstable} %% \alias{rstable} %% \alias{stableMode} \title{Deprecated Functions in Package fBasics} \description{ These functions are provided for compatibility with older versions of the package only, and may be defunct as soon as of the next release. There are none currently. \code{\link[stabledist]{dstable}} etc now are defunct, as they have been available from \pkg{stabledist} since early 2011. %% dstable(x, alpha, beta, gamma = 1, delta = 0, pm = 0) %% pstable(q, alpha, beta, gamma = 1, delta = 0, pm = 0) %% qstable(p, alpha, beta, gamma = 1, delta = 0, pm = 0) %% rstable(n, alpha, beta, gamma = 1, delta = 0, pm = 0) %% stableMode(alpha, beta) } %% \usage{ %% } %% \arguments{ %% } \seealso{ \code{\link{Deprecated}}, \code{\link{Defunct}} } \keyword{misc} fBasics/man/dist-ssdFit.Rd0000644000176000001440000000226211370220776015122 0ustar ripleyusers\name{ssdFit} \alias{ssdFit} \title{Fit Density Using Smoothing Splines } \description{ Estimates the parameters of a density function using smoothing splines. } \usage{ ssdFit(x) } \arguments{ \item{x}{ a numeric vector. } } \value{ The function \code{ssdFit}, \code{hypFit} returns an object of class \code{ssden}. The returned object can be used to evaluate density, probabilities and quantiles. } \author{ Diethelm Wuertz, Chong Gu for the underlying \code{gss} package. } \references{ Gu, C. (2002), \emph{Smoothing Spline ANOVA Models}, New York Springer--Verlag. Gu, C. and Wang, J. (2003), \emph{Penalized likelihood density estimation: Direct cross-validation and scalable approximation}, Statistica Sinica, 13, 811--826. } \examples{ ## ssdFit - set.seed(1953) r = rnorm(500) hist(r, breaks = "FD", probability = TRUE, col = "steelblue", border = "white") ## ssdFit - param = ssdFit(r) ## dssd - u = seq(min(r), max(r), len = 301) v = dssd(u, param) lines(u, v, col = "orange", lwd = 2) } \keyword{distribution} fBasics/man/dist-ssd.Rd0000644000176000001440000000350711370220776014462 0ustar ripleyusers\name{ssd} \alias{ssd} \alias{dssd} \alias{pssd} \alias{qssd} \alias{rssd} \title{Spline Smoothed Distribution} \description{ Density, distribution function, quantile function and random generation from smoothing spline estimates. } \usage{ dssd(x, param, log = FALSE) pssd(q, param) qssd(p, param) rssd(n, param) } \arguments{ \item{param}{ an object as returned by the function \code{ssdFit}.. } \item{log}{ a logical flag by default \code{FALSE}. Should labels and a main title drawn to the plot? } \item{n}{ number of observations. } \item{p}{ a numeric vector of probabilities. } \item{x, q}{ a numeric vector of quantiles. } } \value{ All values for the \code{*ssd} functions are numeric vectors: \code{d*} returns the density, \code{p*} returns the distribution function, \code{q*} returns the quantile function, and \code{r*} generates random deviates. All values have attributes named \code{"param"} listing the values of the distributional parameters. } \author{ Diethelm Wuertz, Chong Gu for the underlying \code{gss} package. } \references{ Gu, C. (2002), \emph{Smoothing Spline ANOVA Models}, New York Springer--Verlag. Gu, C. and Wang, J. (2003), \emph{Penalized likelihood density estimation: Direct cross-validation and scalable approximation}, Statistica Sinica, 13, 811--826. } \examples{ ## ssdFit - set.seed(1953) r = rnorm(500) hist(r, breaks = "FD", probability = TRUE, col = "steelblue", border = "white") ## ssdFit - param = ssdFit(r) ## dssd - u = seq(min(r), max(r), len = 301) v = dssd(u, param) lines(u, v, col = "orange", lwd = 2) } \keyword{distribution} fBasics/man/dist-snigFit.Rd0000644000176000001440000000621111370220776015267 0ustar ripleyusers\name{snigFit} \alias{snigFit} \title{Fit of a Stndardized NIG Distribution} \description{ Estimates the parameters of a standardized normal inverse Gaussian distribution. } \usage{ snigFit(x, zeta = 1, rho = 0, scale = TRUE, doplot = TRUE, span = "auto", trace = TRUE, title = NULL, description = NULL, \dots) } \arguments{ \item{zeta, rho}{ shape parameter \code{zeta} is positive, skewness parameter \code{rho} is in the range (-1, 1). } \item{description}{ a character string which allows for a brief description. } \item{doplot}{ a logical flag. Should a plot be displayed? } \item{scale}{ a logical flag, by default \code{TRUE}. Should the time series be scaled by its standard deviation to achieve a more stable optimization? } \item{span}{ x-coordinates for the plot, by default 100 values automatically selected and ranging between the 0.001, and 0.999 quantiles. Alternatively, you can specify the range by an expression like \code{span=seq(min, max, times = n)}, where, \code{min} and \code{max} are the left and right endpoints of the range, and \code{n} gives the number of the intermediate points. } \item{title}{ a character string which allows for a project title. } \item{trace}{ a logical flag. Should the parameter estimation process be traced? } \item{x}{ a numeric vector. } \item{\dots}{ parameters to be parsed. } } \value{ The function \code{snigFit} returns a list with the following components: \item{estimate}{ the point at which the maximum value of the log liklihood function is obtained. } \item{minimum}{ the value of the estimated maximum, i.e. the value of the log liklihood function. } \item{code}{ an integer indicating why the optimization process terminated.\cr 1: relative gradient is close to zero, current iterate is probably solution; \cr 2: successive iterates within tolerance, current iterate is probably solution; \cr 3: last global step failed to locate a point lower than \code{estimate}. Either \code{estimate} is an approximate local minimum of the function or \code{steptol} is too small; \cr 4: iteration limit exceeded; \cr 5: maximum step size \code{stepmax} exceeded five consecutive times. Either the function is unbounded below, becomes asymptotic to a finite value from above in some direction or \code{stepmax} is too small. } \item{gradient}{ the gradient at the estimated maximum. } \item{steps}{ number of function calls. } } \examples{ ## snigFit - # Simulate Random Variates: set.seed(1953) s = rsnig(n = 2000, zeta = 0.7, rho = 0.5) ## snigFit - # Fit Parameters: snigFit(s, zeta = 1, rho = 0, doplot = TRUE) } \keyword{distribution} fBasics/man/dist-snig.Rd0000644000176000001440000000414211370220776014625 0ustar ripleyusers\name{snig} \alias{snig} \alias{dsnig} \alias{psnig} \alias{qsnig} \alias{rsnig} \title{Standardized Normal Inverse Gaussian Distribution} \description{ Density, distribution function, quantile function and random generation for the standardized normal inverse Gaussian distribution. } \usage{ dsnig(x, zeta = 1, rho = 0, log = FALSE) psnig(q, zeta = 1, rho = 0) qsnig(p, zeta = 1, rho = 0) rsnig(n, zeta = 1, rho = 0) } \arguments{ \item{zeta, rho}{ shape parameter \code{zeta} is positive, skewness parameter \code{rho} is in the range (-1, 1). } \item{log}{ a logical flag by default \code{FALSE}. If TRUE, log values are returned. } \item{n}{ number of observations. } \item{p}{ a numeric vector of probabilities. } \item{x, q}{ a numeric vector of quantiles. } } \value{ All values for the \code{*snig} functions are numeric vectors: \code{d*} returns the density, \code{p*} returns the distribution function, \code{q*} returns the quantile function, and \code{r*} generates random deviates. All values have attributes named \code{"param"} listing the values of the distributional parameters. } \details{ The random deviates are calculated with the method described by Raible (2000). } \author{ Diethelm Wuertz. } \examples{ ## snig - set.seed(1953) r = rsnig(5000, zeta = 1, rho = 0.5) plot(r, type = "l", col = "steelblue", main = "snig: zeta=1 rho=0.5") ## snig - # Plot empirical density and compare with true density: hist(r, n = 50, probability = TRUE, border = "white", col = "steelblue") x = seq(-5, 5, length = 501) lines(x, dsnig(x, zeta = 1, rho = 0.5)) ## snig - # Plot df and compare with true df: plot(sort(r), (1:5000/5000), main = "Probability", col = "steelblue") lines(x, psnig(x, zeta = 1, rho = 0.5)) ## snig - # Compute Quantiles: qsnig(psnig(seq(-5, 5, 1), zeta = 1, rho = 0.5), zeta = 1, rho = 0.5) } \keyword{distribution} fBasics/man/dist-sght.Rd0000644000176000001440000000463611613257743014646 0ustar ripleyusers\name{sght} \alias{sght} \alias{dsght} \alias{psght} \alias{qsght} \alias{rsght} \title{Standardized generalized hyperbolic Student-t Distribution} \description{ Density, distribution function, quantile function and random generation for the standardized generalized hyperbolic distribution. } \usage{ dsght(x, beta = 0.1, delta = 1, mu = 0, nu = 10, log = FALSE) psght(q, beta = 0.1, delta = 1, mu = 0, nu = 10) qsght(p, beta = 0.1, delta = 1, mu = 0, nu = 10) rsght(n, beta = 0.1, delta = 1, mu = 0, nu = 10) } \arguments{ \item{beta, delta, mu}{ numeric values. \code{beta} is the skewness parameter in the range \code{(0, alpha)}; \code{delta} is the scale parameter, must be zero or positive; \code{mu} is the location parameter, by default 0. These are the parameters in the first parameterization. } \item{nu}{ a numeric value, the number of degrees of freedom. Note, \code{alpha} takes the limit of \code{abs(beta)}, and \code{lambda=-nu/2}. } \item{x, q}{ a numeric vector of quantiles. } \item{p}{ a numeric vector of probabilities. } \item{n}{ number of observations. } \item{log}{ a logical, if TRUE, probabilities \code{p} are given as \code{log(p)}. } } \value{ All values for the \code{*sght} functions are numeric vectors: \code{d*} returns the density, \code{p*} returns the distribution function, \code{q*} returns the quantile function, and \code{r*} generates random deviates. All values have attributes named \code{"param"} listing the values of the distributional parameters. } \author{ Diethelm Wuertz. } \examples{ ## rsght - set.seed(1953) r = rsght(5000, beta = 0.1, delta = 1, mu = 0, nu = 10) plot(r, type = "l", col = "steelblue", main = "gh: zeta=1 rho=0.5 lambda=1") ## dsght - # Plot empirical density and compare with true density: hist(r, n = 50, probability = TRUE, border = "white", col = "steelblue") x = seq(-5, 5, length = 501) lines(x, dsght(x, beta = 0.1, delta = 1, mu = 0, nu = 10)) ## psght - # Plot df and compare with true df: plot(sort(r), (1:5000/5000), main = "Probability", col = "steelblue") lines(x, psght(x, beta = 0.1, delta = 1, mu = 0, nu = 10)) ## qsght - # Compute Quantiles: round(qsght(psght(seq(-5, 5, 1), beta = 0.1, delta = 1, mu = 0, nu =10), beta = 0.1, delta = 1, mu = 0, nu = 10), 4) } \keyword{distribution} fBasics/man/dist-sghFit.Rd0000644000176000001440000000671511370220776015121 0ustar ripleyusers\name{sghFit} \alias{sghFit} \title{Standardized GH Distribution Fit} \description{ Estimates the distrinbutional parameters for a standardized generalized hyperbolic distribution. } \usage{ sghFit(x, zeta = 1, rho = 0, lambda = 1, include.lambda = TRUE, scale = TRUE, doplot = TRUE, span = "auto", trace = TRUE, title = NULL, description = NULL, \dots) } \arguments{ \item{x}{ a numeric vector. } \item{zeta, rho, lambda}{ shape parameter \code{zeta} is positive, skewness parameter \code{rho} is in the range (-1, 1). and index parameter \code{lambda}, by default 1. } \item{include.lambda}{ a logical flag, by default \code{TRUE}. Should the index parameter \code{lambda} included in the parameter estimate? } \item{scale}{ a logical flag, by default \code{TRUE}. Should the time series be scaled by its standard deviation to achieve a more stable optimization? } \item{doplot}{ a logical flag. Should a plot be displayed? } \item{span}{ x-coordinates for the plot, by default 100 values automatically selected and ranging between the 0.001, and 0.999 quantiles. Alternatively, you can specify the range by an expression like \code{span=seq(min, max, times = n)}, where, \code{min} and \code{max} are the left and right endpoints of the range, and \code{n} gives the number of the intermediate points. } \item{trace}{ a logical flag. Should the parameter estimation process be traced? } \item{title}{ a character string which allows for a project title. } \item{description}{ a character string which allows for a brief description. } \item{\dots}{ parameters to be parsed. } } \value{ returns a list with the following components: \item{estimate}{ the point at which the maximum value of the log liklihood function is obtained. } \item{minimum}{ the value of the estimated maximum, i.e. the value of the log liklihood function. } \item{code}{ an integer indicating why the optimization process terminated.\cr 1: relative gradient is close to zero, current iterate is probably solution; \cr 2: successive iterates within tolerance, current iterate is probably solution; \cr 3: last global step failed to locate a point lower than \code{estimate}. Either \code{estimate} is an approximate local minimum of the function or \code{steptol} is too small; \cr 4: iteration limit exceeded; \cr 5: maximum step size \code{stepmax} exceeded five consecutive times. Either the function is unbounded below, becomes asymptotic to a finite value from above in some direction or \code{stepmax} is too small. } \item{gradient}{ the gradient at the estimated maximum. } \item{steps}{ number of function calls. } } \examples{ ## sghFit - # Simulate Random Variates: set.seed(1953) s = rsgh(n = 2000, zeta = 0.7, rho = 0.5, lambda = 0) ## sghFit - # Fit Parameters: sghFit(s, zeta = 1, rho = 0, lambda = 1, include.lambda = TRUE, doplot = TRUE) } \keyword{distribution} fBasics/man/dist-sgh.Rd0000644000176000001440000000431411370220776014447 0ustar ripleyusers\name{sgh} \alias{sgh} \alias{dsgh} \alias{psgh} \alias{qsgh} \alias{rsgh} \title{Standardized Generalized Hyperbolic Distribution} \description{ Density, distribution function, quantile function and random generation for the standardized generalized hyperbolic distribution. } \usage{ dsgh(x, zeta = 1, rho = 0, lambda = 1, log = FALSE) psgh(q, zeta = 1, rho = 0, lambda = 1) qsgh(p, zeta = 1, rho = 0, lambda = 1) rsgh(n, zeta = 1, rho = 0, lambda = 1) } \arguments{ \item{zeta, rho, lambda}{ shape parameter \code{zeta} is positive, skewness parameter \code{rho} is in the range (-1, 1). } \item{log}{ a logical flag by default \code{FALSE}. If TRUE, log values are returned. } \item{n}{ number of observations. } \item{p}{ a numeric vector of probabilities. } \item{x, q}{ a numeric vector of quantiles. } } \value{ All values for the \code{*sgh} functions are numeric vectors: \code{d*} returns the density, \code{p*} returns the distribution function, \code{q*} returns the quantile function, and \code{r*} generates random deviates. All values have attributes named \code{"param"} listing the values of the distributional parameters. } \details{ The generator \code{rsgh} is based on the GH algorithm given by Scott (2004). } \author{ Diethelm Wuertz. } \examples{ ## rsgh - set.seed(1953) r = rsgh(5000, zeta = 1, rho = 0.5, lambda = 1) plot(r, type = "l", col = "steelblue", main = "gh: zeta=1 rho=0.5 lambda=1") ## dsgh - # Plot empirical density and compare with true density: hist(r, n = 50, probability = TRUE, border = "white", col = "steelblue", ylim = c(0, 0.6)) x = seq(-5, 5, length = 501) lines(x, dsgh(x, zeta = 1, rho = 0.5, lambda = 1)) ## psgh - # Plot df and compare with true df: plot(sort(r), (1:5000/5000), main = "Probability", col = "steelblue") lines(x, psgh(x, zeta = 1, rho = 0.5, lambda = 1)) ## qsgh - # Compute Quantiles: round(qsgh(psgh(seq(-5, 5, 1), zeta = 1, rho = 0.5), zeta = 1, rho = 0.5), 4) } \keyword{distribution} fBasics/man/dist-normRobMoments.Rd0000644000176000001440000000223711613257743016655 0ustar ripleyusers\name{normRobMoments} \alias{normRobMoments} \alias{normMED} \alias{normIQR} \alias{normSKEW} \alias{normKURT} \title{Robust Moments for the NORM} \description{ Computes the first four robust moments for the Normal Distribution. } \usage{ normMED(mean = 0, sd = 1) normIQR(mean = 0, sd = 1) normSKEW(mean = 0, sd = 1) normKURT(mean = 0, sd = 1) } \arguments{ \item{mean}{locaiton parameter} \item{sd}{scale parameter} } \value{ All values for the \code{*norm} functions are numeric vectors: \code{d*} returns the density, \code{p*} returns the distribution function, \code{q*} returns the quantile function, and \code{r*} generates random deviates. All values have attributes named \code{"param"} listing the values of the distributional parameters. } %\references{ % go here ... %} \author{ Diethelm Wuertz. } \examples{ ## normMED - # Median: normMED(mean = 0, sd = 1) ## normIQR - # Inter-quartile Range: normIQR(mean = 0, sd = 1) ## normSKEW - # Robust Skewness: normSKEW(mean = 0, sd = 1) ## normKURT - # Robust Kurtosis: normKURT(mean = 0, sd = 1) } \keyword{distribution} fBasics/man/dist-nigSlider.Rd0000644000176000001440000000100611370220776015601 0ustar ripleyusers\name{nigSlider} \alias{nigSlider} \title{nigerbolic Distribution Slider} \description{ Displays interactively the dependence of the nigerbolic distribution on its parameters. } \usage{ nigSlider() } \value{ a tcl/tk based graphical user interface. This is a nice display for educational purposes to investigate the densities and probabilities of the invetrse Gaussian distribution. } \examples{ ## nigSlider - # nigSlider() } \keyword{distribution} fBasics/man/dist-nigShapeTriangle.Rd0000644000176000001440000000337111370220776017114 0ustar ripleyusers\name{nigShapeTriangle} \alias{nigShapeTriangle} \title{NIG Shape Triangle} \description{ Plots the normal inverse Gaussian Shape Triangle. } \usage{ nigShapeTriangle(object, add = FALSE, labels = TRUE, \dots) } \arguments{ \item{object}{ an object of class \code{"fDISTFIT"} as returned by the function \code{nigFit}. } \item{add}{ a logical value. Should another point added to the NIG shape triangle? By default FALSE, a new plot will be created. } \item{labels}{ a logical flag by default \code{TRUE}. Should the logarithm of the density be returned? } \item{\dots}{ arguments to be passed to the function \code{integrate}. } } \value{ displays the parameters of fitted distributions in the NIG shape triangle. } \author{ David Scott for code implemented from \R's contributed package \code{HyperbolicDist}. } \references{ Atkinson, A.C. (1982); \emph{The simulation of generalized inverse Gaussian and hyperbolic random variables}, SIAM J. Sci. Stat. Comput. 3, 502--515. Barndorff-Nielsen O. (1977); \emph{Exponentially decreasing distributions for the logarithm of particle size}, Proc. Roy. Soc. Lond., A353, 401--419. Barndorff-Nielsen O., Blaesild, P. (1983); \emph{Hyperbolic distributions. In Encyclopedia of Statistical Sciences}, Eds., Johnson N.L., Kotz S. and Read C.B., Vol. 3, pp. 700--707. New York: Wiley. Raible S. (2000); \emph{Levy Processes in Finance: Theory, Numerics and Empirical Facts}, PhD Thesis, University of Freiburg, Germany, 161 pages. } \examples{ ## nigShapeTriangle - # } \keyword{distribution} fBasics/man/dist-nigRobMoments.Rd0000644000176000001440000000304211613257743016452 0ustar ripleyusers\name{nigRobMoments} \alias{nigRobMoments} \alias{nigMED} \alias{nigIQR} \alias{nigSKEW} \alias{nigKURT} \title{Robust Moments for the NIG} \description{ Computes the first four robust moments for the Normal Inverse Gaussian Distribution. } \usage{ nigMED(alpha = 1, beta = 0, delta = 1, mu = 0) nigIQR(alpha = 1, beta = 0, delta = 1, mu = 0) nigSKEW(alpha = 1, beta = 0, delta = 1, mu = 0) nigKURT(alpha = 1, beta = 0, delta = 1, mu = 0) } \arguments{ \item{alpha, beta, delta, mu}{ are numeric values where \code{alpha} is the location parameter, \code{beta} is the location parameter, \code{delta} is the first shape parameter, and \code{mu} is the second shape parameter. } } \value{ All values for the \code{*nig} functions are numeric vectors: \code{d*} returns the density, \code{p*} returns the distribution function, \code{q*} returns the quantile function, and \code{r*} generates random deviates. All values have attributes named \code{"param"} listing the values of the distributional parameters. } %\references{ % go here ... %} \author{ Diethelm Wuertz. } \examples{ ## nigMED - # Median: nigMED(alpha = 1, beta = 0, delta = 1, mu = 0) ## nigIQR - # Inter-quartile Range: nigIQR(alpha = 1, beta = 0, delta = 1, mu = 0) ## nigSKEW - # Robust Skewness: nigSKEW(alpha = 1, beta = 0, delta = 1, mu = 0) ## nigKURT - # Robust Kurtosis: nigKURT(alpha = 1, beta = 0, delta = 1, mu = 0) } \keyword{distribution} fBasics/man/dist-nigMoments.Rd0000644000176000001440000000327011370220776016006 0ustar ripleyusers\name{nigMoments} \alias{nigMoments} \alias{nigMean} \alias{nigVar} \alias{nigSkew} \alias{nigKurt} \title{Moments for the Normal Inverse Gaussian} \description{ Computes the first four moments for the normal inverse Gaussian distribution. } \usage{ nigMean(alpha = 1, beta = 0, delta = 1, mu = 0) nigVar(alpha = 1, beta = 0, delta = 1, mu = 0) nigSkew(alpha = 1, beta = 0, delta = 1, mu = 0) nigKurt(alpha = 1, beta = 0, delta = 1, mu = 0) } \arguments{ \item{alpha, beta, delta, mu}{ are numeric values where \code{alpha} is the location parameter, \code{beta} is the location parameter, \code{delta} is the first shape parameter, and \code{mu} is the second shape parameter. } } \value{ All values for the \code{*nig} functions are numeric vectors: \code{d*} returns the density, \code{p*} returns the distribution function, \code{q*} returns the quantile function, and \code{r*} generates random deviates. All values have attributes named \code{"param"} listing the values of the distributional parameters. } \references{ Scott, D. J., Wuertz, D. and Tran, T. T. (2008) \emph{Moments of the Generalized Hyperbolic Distribution}. Preprint. } \author{ Diethelm Wuertz. } \examples{ ## nigMean - # Median: nigMean(alpha = 1, beta = 0, delta = 1, mu = 0) ## nigVar - # Inter-quartile Range: nigVar(alpha = 1, beta = 0, delta = 1, mu = 0) ## nigSKEW - # Robust Skewness: nigSkew(alpha = 1, beta = 0, delta = 1, mu = 0) ## nigKurt - # Robust Kurtosis: nigKurt(alpha = 1, beta = 0, delta = 1, mu = 0) } \keyword{distribution} fBasics/man/dist-nigMode.Rd0000644000176000001440000000300111370220776015240 0ustar ripleyusers\name{nigMode} \alias{nigMode} \title{Normal Inverse Gaussian Mode} \description{ Computes the mode of the norm inverse Gaussian distribution. } \usage{ nigMode(alpha = 1, beta = 0, delta = 1, mu = 0) } \arguments{ \item{alpha, beta, delta, mu}{ shape parameter \code{alpha}; skewness parameter \code{beta}, \code{abs(beta)} is in the range (0, alpha); scale parameter \code{delta}, \code{delta} must be zero or positive; location parameter \code{mu}, by default 0. These are the parameters in the first parameterization. } } \value{ returns the mode for the normal inverse Gaussian distribution. A numeric value. } \references{ Atkinson, A.C. (1982); \emph{The simulation of generalized inverse Gaussian and hyperbolic random variables}, SIAM J. Sci. Stat. Comput. 3, 502--515. Barndorff-Nielsen O. (1977); \emph{Exponentially decreasing distributions for the logarithm of particle size}, Proc. Roy. Soc. Lond., A353, 401--419. Barndorff-Nielsen O., Blaesild, P. (1983); \emph{Hyperbolic distributions. In Encyclopedia of Statistical Sciences}, Eds., Johnson N.L., Kotz S. and Read C.B., Vol. 3, pp. 700--707. New York: Wiley. Raible S. (2000); \emph{Levy Processes in Finance: Theory, Numerics and Empirical Facts}, PhD Thesis, University of Freiburg, Germany, 161 pages. } \examples{ ## nigMode - nigMode() } \keyword{distribution} fBasics/man/dist-nigFit.Rd0000644000176000001440000001104011370220775015077 0ustar ripleyusers\name{nigFit} \alias{nigFit} \title{Fit of a Normal Inverse Gaussian Distribution} \description{ Estimates the parameters of a normal inverse Gaussian distribution. } \usage{ nigFit(x, alpha = 1, beta = 0, delta = 1, mu = 0, method = c("mle", "gmm", "mps", "vmps"), scale = TRUE, doplot = TRUE, span = "auto", trace = TRUE, title = NULL, description = NULL, \dots) } \arguments{ \item{alpha, beta, delta, mu}{ The parameters are \code{alpha}, \code{beta}, \code{delta}, and \code{mu}:\cr shape parameter \code{alpha}; skewness parameter \code{beta}, \code{abs(beta)} is in the range (0, alpha); scale parameter \code{delta}, \code{delta} must be zero or positive; location parameter \code{mu}, by default 0. These is the meaning of the parameters in the first parameterization \code{pm=1} which is the default parameterization selection. In the second parameterization, \code{pm=2} \code{alpha} and \code{beta} take the meaning of the shape parameters (usually named) \code{zeta} and \code{rho}. In the third parameterization, \code{pm=3} \code{alpha} and \code{beta} take the meaning of the shape parameters (usually named) \code{xi} and \code{chi}. In the fourth parameterization, \code{pm=4} \code{alpha} and \code{beta} take the meaning of the shape parameters (usually named) \code{a.bar} and \code{b.bar}. } \item{description}{ a character string which allows for a brief description. } \item{doplot}{ a logical flag. Should a plot be displayed? } \item{method}{ a character string. Either \code{"mle"}, Maximum Likelihood Estimation, the default, \code{"gmm"} Gemeralized Method of Moments Estimation, \code{"mps"} Maximum Product Spacings Estimation, or \code{"vmps"} Minimum Variance Product Spacings Estimation. } \item{scale}{ a logical flag, by default \code{TRUE}. Should the time series be scaled by its standard deviation to achieve a more stable optimization? } \item{span}{ x-coordinates for the plot, by default 100 values automatically selected and ranging between the 0.001, and 0.999 quantiles. Alternatively, you can specify the range by an expression like \code{span=seq(min, max, times = n)}, where, \code{min} and \code{max} are the left and right endpoints of the range, and \code{n} gives the number of the intermediate points. } \item{title}{ a character string which allows for a project title. } \item{trace}{ a logical flag. Should the parameter estimation process be traced? } \item{x}{ a numeric vector. } \item{\dots}{ parameters to be parsed. } } \value{ The functions \code{tFit}, \code{hypFit} and \code{nigFit} return a list with the following components: \item{estimate}{ the point at which the maximum value of the log liklihood function is obtained. } \item{minimum}{ the value of the estimated maximum, i.e. the value of the log liklihood function. } \item{code}{ an integer indicating why the optimization process terminated.\cr 1: relative gradient is close to zero, current iterate is probably solution; \cr 2: successive iterates within tolerance, current iterate is probably solution; \cr 3: last global step failed to locate a point lower than \code{estimate}. Either \code{estimate} is an approximate local minimum of the function or \code{steptol} is too small; \cr 4: iteration limit exceeded; \cr 5: maximum step size \code{stepmax} exceeded five consecutive times. Either the function is unbounded below, becomes asymptotic to a finite value from above in some direction or \code{stepmax} is too small. } \item{gradient}{ the gradient at the estimated maximum. } \item{steps}{ number of function calls. } } \examples{ ## nigFit - # Simulate Random Variates: set.seed(1953) s = rnig(n = 1000, alpha = 1.5, beta = 0.3, delta = 0.5, mu = -1.0) ## nigFit - # Fit Parameters: nigFit(s, alpha = 1, beta = 0, delta = 1, mu = mean(s), doplot = TRUE) } \keyword{distribution} fBasics/man/dist-nig.Rd0000644000176000001440000000635611370220775014452 0ustar ripleyusers\name{nig} \alias{nig} \alias{dnig} \alias{pnig} \alias{qnig} \alias{rnig} \title{Normal Inverse Gaussian Distribution} \description{ Density, distribution function, quantile function and random generation for the normal inverse Gaussian distribution. } \usage{ dnig(x, alpha = 1, beta = 0, delta = 1, mu = 0, log = FALSE) pnig(q, alpha = 1, beta = 0, delta = 1, mu = 0) qnig(p, alpha = 1, beta = 0, delta = 1, mu = 0) rnig(n, alpha = 1, beta = 0, delta = 1, mu = 0) } \arguments{ \item{alpha, beta, delta, mu}{ shape parameter \code{alpha}; skewness parameter \code{beta}, \code{abs(beta)} is in the range (0, alpha); scale parameter \code{delta}, \code{delta} must be zero or positive; location parameter \code{mu}, by default 0. These are the parameters in the first parameterization. } \item{log}{ a logical flag by default \code{FALSE}. Should labels and a main title drawn to the plot? } \item{n}{ number of observations. } \item{p}{ a numeric vector of probabilities. } \item{x, q}{ a numeric vector of quantiles. } } \value{ All values for the \code{*nig} functions are numeric vectors: \code{d*} returns the density, \code{p*} returns the distribution function, \code{q*} returns the quantile function, and \code{r*} generates random deviates. All values have attributes named \code{"param"} listing the values of the distributional parameters. } \details{ The random deviates are calculated with the method described by Raible (2000). } \author{ David Scott for code implemented from \R's contributed package \code{HyperbolicDist}. } \references{ Atkinson, A.C. (1982); \emph{The simulation of generalized inverse Gaussian and hyperbolic random variables}, SIAM J. Sci. Stat. Comput. 3, 502--515. Barndorff-Nielsen O. (1977); \emph{Exponentially decreasing distributions for the logarithm of particle size}, Proc. Roy. Soc. Lond., A353, 401--419. Barndorff-Nielsen O., Blaesild, P. (1983); \emph{Hyperbolic distributions. In Encyclopedia of Statistical Sciences}, Eds., Johnson N.L., Kotz S. and Read C.B., Vol. 3, pp. 700--707. New York: Wiley. Raible S. (2000); \emph{Levy Processes in Finance: Theory, Numerics and Empirical Facts}, PhD Thesis, University of Freiburg, Germany, 161 pages. } \examples{ ## nig - set.seed(1953) r = rnig(5000, alpha = 1, beta = 0.3, delta = 1) plot(r, type = "l", col = "steelblue", main = "nig: alpha=1 beta=0.3 delta=1") ## nig - # Plot empirical density and compare with true density: hist(r, n = 25, probability = TRUE, border = "white", col = "steelblue") x = seq(-5, 5, 0.25) lines(x, dnig(x, alpha = 1, beta = 0.3, delta = 1)) ## nig - # Plot df and compare with true df: plot(sort(r), (1:5000/5000), main = "Probability", col = "steelblue") lines(x, pnig(x, alpha = 1, beta = 0.3, delta = 1)) ## nig - # Compute Quantiles: qnig(pnig(seq(-5, 5, 1), alpha = 1, beta = 0.3, delta = 1), alpha = 1, beta = 0.3, delta = 1) } \keyword{distribution} fBasics/man/dist-hypSlider.Rd0000644000176000001440000000075111370220776015632 0ustar ripleyusers\name{hypSlider} \alias{hypSlider} \title{Hyperbolic Distribution Slider} \description{ Displays interactively the dependence of the hyperbolic distribution on its parameters. } \usage{ hypSlider() } \value{ a tcl/tk based graphical user interface. This is a nice display for educational purposes to investigate the densities and probabilities of the hyperbolic distribution. } \examples{ ## hypSlider - # } \keyword{distribution} fBasics/man/dist-hypRobMoments.Rd0000644000176000001440000000422111613257743016475 0ustar ripleyusers\name{hypRobMoments} \alias{hypRobMoments} \alias{hypMED} \alias{hypIQR} \alias{hypSKEW} \alias{hypKURT} \title{Robust Moments for the HYP} \description{ Computes the first four robust moments for the hyperbolic distribution. } \usage{ hypMED(alpha = 1, beta = 0, delta = 1, mu = 0) hypIQR(alpha = 1, beta = 0, delta = 1, mu = 0) hypSKEW(alpha = 1, beta = 0, delta = 1, mu = 0) hypKURT(alpha = 1, beta = 0, delta = 1, mu = 0) } \arguments{ \item{alpha, beta, delta, mu}{ shape parameter \code{alpha}; skewness parameter \code{beta}, \code{abs(beta)} is in the range (0, alpha); scale parameter \code{delta}, \code{delta} must be zero or positive; location parameter \code{mu}, by default 0. These is the meaning of the parameters in the first parameterization \code{pm=1} which is the default parameterization selection. In the second parameterization, \code{pm=2} \code{alpha} and \code{beta} take the meaning of the shape parameters (usually named) \code{zeta} and \code{rho}. In the third parameterization, \code{pm=3} \code{alpha} and \code{beta} take the meaning of the shape parameters (usually named) \code{xi} and \code{chi}. In the fourth parameterization, \code{pm=4} \code{alpha} and \code{beta} take the meaning of the shape parameters (usually named) \code{a.bar} and \code{b.bar}. } } \value{ All values for the \code{*hyp} functions are numeric vectors: \code{d*} returns the density, \code{p*} returns the distribution function, \code{q*} returns the quantile function, and \code{r*} generates random deviates. All values have attributes named \code{"param"} listing the values of the distributional parameters. } %\references{ % go here ... %} \author{ Diethelm Wuertz. } \examples{ ## hypMED - # Median: hypMED(alpha = 1, beta = 0, delta = 1, mu = 0) ## hypIQR - # Inter-quartile Range: hypIQR(alpha = 1, beta = 0, delta = 1, mu = 0) ## hypSKEW - # Robust Skewness: hypSKEW(alpha = 1, beta = 0, delta = 1, mu = 0) ## hypKURT - # Robust Kurtosis: hypKURT(alpha = 1, beta = 0, delta = 1, mu = 0) } \keyword{distribution} fBasics/man/dist-hypMoments.Rd0000644000176000001440000000334411370220776016033 0ustar ripleyusers\name{hypMoments} \alias{hypMoments} \alias{hypMean} \alias{hypVar} \alias{hypSkew} \alias{hypKurt} \alias{hypMoments} \title{Hyperbolic Distribution Moments} \description{ Calculates moments of the hyperbbolic distribution function } \usage{ hypMean(alpha=1, beta=0, delta=1, mu=0) hypVar(alpha=1, beta=0, delta=1, mu=0) hypSkew(alpha=1, beta=0, delta=1, mu=0) hypKurt(alpha=1, beta=0, delta=1, mu=0) hypMoments(order, type = c("raw", "central", "mu"), alpha=1, beta=0, delta=1, mu=0) } \arguments{ \item{alpha, beta, delta, mu}{ numeric values. \code{alpha} is the first shape parameter; \code{beta} is the second shape parameter in the range \code{(0, alpha)}; \code{delta} is the scale parameter, must be zero or positive; \code{mu} is the location parameter, by default 0. } \item{order}{ an integer value, the order of the moment. } \item{type}{ a character value, \code{"raw"} returns the moments about zero, \code{"central"} returns the central moments about the mean, and \code{"mu"} returns the moments about the location parameter \code{mu}. } } \value{ a numerical value. } \references{ Scott, D. J., Wuertz, D. and Tran, T. T. (2008) \emph{Moments of the Generalized Hyperbolic Distribution}. Preprint. } \author{ Diethelm Wuertz. } \examples{ ## hypMean - hypMean(alpha=1.1, beta=0.1, delta=0.8, mu=-0.3) ## ghKurt - hypKurt(alpha=1.1, beta=0.1, delta=0.8, mu=-0.3) ## hypMoments - hypMoments(4, alpha=1.1, beta=0.1, delta=0.8, mu=-0.3) hypMoments(4, "central", alpha=1.1, beta=0.1, delta=0.8, mu=-0.3) } \keyword{distribution} fBasics/man/dist-hypMode.Rd0000644000176000001440000000467411370220776015304 0ustar ripleyusers\name{hypMode} \alias{hypMode} \title{Hyperbolic Mode} \description{ Computes the mode of the hyperbolic function. } \usage{ hypMode(alpha = 1, beta = 0, delta = 1, mu = 0, pm = c(1, 2, 3, 4)) } \arguments{ \item{alpha, beta, delta, mu}{ shape parameter \code{alpha}; skewness parameter \code{beta}, \code{abs(beta)} is in the range (0, alpha); scale parameter \code{delta}, \code{delta} must be zero or positive; location parameter \code{mu}, by default 0. These is the meaning of the parameters in the first parameterization \code{pm=1} which is the default parameterization selection. In the second parameterization, \code{pm=2} \code{alpha} and \code{beta} take the meaning of the shape parameters (usually named) \code{zeta} and \code{rho}. In the third parameterization, \code{pm=3} \code{alpha} and \code{beta} take the meaning of the shape parameters (usually named) \code{xi} and \code{chi}. In the fourth parameterization, \code{pm=4} \code{alpha} and \code{beta} take the meaning of the shape parameters (usually named) \code{a.bar} and \code{b.bar}. } \item{pm}{ an integer value between \code{1} and \code{4} for the selection of the parameterization. The default takes the first parameterization. } } \value{ returns the mode in the appropriate parameterization for the hyperbolic distribution. A numeric value. } \references{ Atkinson, A.C. (1982); \emph{The simulation of generalized inverse Gaussian and hyperbolic random variables}, SIAM J. Sci. Stat. Comput. 3, 502--515. Barndorff-Nielsen O. (1977); \emph{Exponentially decreasing distributions for the logarithm of particle size}, Proc. Roy. Soc. Lond., A353, 401--419. Barndorff-Nielsen O., Blaesild, P. (1983); \emph{Hyperbolic distributions. In Encyclopedia of Statistical Sciences}, Eds., Johnson N.L., Kotz S. and Read C.B., Vol. 3, pp. 700--707. New York: Wiley. Raible S. (2000); \emph{Levy Processes in Finance: Theory, Numerics and Empirical Facts}, PhD Thesis, University of Freiburg, Germany, 161 pages. } \author{ David Scott for code implemented from \R's contributed package \code{HyperbolicDist}. } \examples{ ## hypMode - hypMode() } \keyword{distribution} fBasics/man/dist-hypFit.Rd0000644000176000001440000001052111370220776015126 0ustar ripleyusers\name{hypFit} \alias{hypFit} \title{Fit of a Hyperbolic Distribution} \description{ Estimates the parameters of a hyperbolic distribution. } \usage{ hypFit(x, alpha = 1, beta = 0, delta = 1, mu = 0, scale = TRUE, doplot = TRUE, span = "auto", trace = TRUE, title = NULL, description = NULL, \dots) } \arguments{ \item{alpha, beta, delta, mu}{ \code{alpha} is a shape parameter by default 1, \code{beta} is a skewness parameter by default 0, note \code{abs(beta)} is in the range (0, alpha), \code{delta} is a scale parameter by default 1, note, \code{delta} must be zero or positive, and \code{mu} is a location parameter, by default 0. These is the meaning of the parameters in the first parameterization \code{pm=1} which is the default parameterization selection. In the second parameterization, \code{pm=2} \code{alpha} and \code{beta} take the meaning of the shape parameters (usually named) \code{zeta} and \code{rho}. In the third parameterization, \code{pm=3} \code{alpha} and \code{beta} take the meaning of the shape parameters (usually named) \code{xi} and \code{chi}. In the fourth parameterization, \code{pm=4} \code{alpha} and \code{beta} take the meaning of the shape parameters (usually named) \code{a.bar} and \code{b.bar}. } \item{description}{ a character string which allows for a brief description. } \item{doplot}{ a logical flag. Should a plot be displayed? } \item{scale}{ a logical flag, by default \code{TRUE}. Should the time series be scaled by its standard deviation to achieve a more stable optimization? } \item{span}{ x-coordinates for the plot, by default 100 values automatically selected and ranging between the 0.001, and 0.999 quantiles. Alternatively, you can specify the range by an expression like \code{span=seq(min, max, times = n)}, where, \code{min} and \code{max} are the left and right endpoints of the range, and \code{n} gives the number of the intermediate points. } \item{title}{ a character string which allows for a project title. } \item{trace}{ a logical flag. Should the parameter estimation process be traced? } \item{x}{ a numeric vector. } \item{\dots}{ parameters to be parsed. } } \value{ The functions \code{tFit}, \code{hypFit} and \code{nigFit} return a list with the following components: \item{estimate}{ the point at which the maximum value of the log liklihood function is obtained. } \item{minimum}{ the value of the estimated maximum, i.e. the value of the log liklihood function. } \item{code}{ an integer indicating why the optimization process terminated.\cr 1: relative gradient is close to zero, current iterate is probably solution; \cr 2: successive iterates within tolerance, current iterate is probably solution; \cr 3: last global step failed to locate a point lower than \code{estimate}. Either \code{estimate} is an approximate local minimum of the function or \code{steptol} is too small; \cr 4: iteration limit exceeded; \cr 5: maximum step size \code{stepmax} exceeded five consecutive times. Either the function is unbounded below, becomes asymptotic to a finite value from above in some direction or \code{stepmax} is too small. } \item{gradient}{ the gradient at the estimated maximum. } \item{steps}{ number of function calls. } } \details{ The function \code{\link{nlm}} is used to minimize the "negative" maximum log-likelihood function. \code{nlm} carries out a minimization using a Newton-type algorithm. } \examples{ ## rhyp - # Simulate Random Variates: set.seed(1953) s = rhyp(n = 1000, alpha = 1.5, beta = 0.3, delta = 0.5, mu = -1.0) ## hypFit - # Fit Parameters: hypFit(s, alpha = 1, beta = 0, delta = 1, mu = mean(s), doplot = TRUE) } \keyword{distribution} fBasics/man/dist-hyp.Rd0000644000176000001440000001036011370220776014464 0ustar ripleyusers\name{hyp} \alias{hyp} \alias{dhyp} \alias{phyp} \alias{qhyp} \alias{rhyp} \title{Hyperbolic Distribution} \description{ Density, distribution function, quantile function and random generation for the hyperbolic distribution. } \usage{ dhyp(x, alpha = 1, beta = 0, delta = 1, mu = 0, pm = c("1", "2", "3", "4"), log = FALSE) phyp(q, alpha = 1, beta = 0, delta = 1, mu = 0, pm = c("1", "2", "3", "4"), \dots) qhyp(p, alpha = 1, beta = 0, delta = 1, mu = 0, pm = c("1", "2", "3", "4"), \dots) rhyp(n, alpha = 1, beta = 0, delta = 1, mu = 0, pm = c("1", "2", "3", "4")) } \arguments{ \item{alpha, beta, delta, mu}{ shape parameter \code{alpha}; skewness parameter \code{beta}, \code{abs(beta)} is in the range (0, alpha); scale parameter \code{delta}, \code{delta} must be zero or positive; location parameter \code{mu}, by default 0. These is the meaning of the parameters in the first parameterization \code{pm=1} which is the default parameterization selection. In the second parameterization, \code{pm=2} \code{alpha} and \code{beta} take the meaning of the shape parameters (usually named) \code{zeta} and \code{rho}. In the third parameterization, \code{pm=3} \code{alpha} and \code{beta} take the meaning of the shape parameters (usually named) \code{xi} and \code{chi}. In the fourth parameterization, \code{pm=4} \code{alpha} and \code{beta} take the meaning of the shape parameters (usually named) \code{a.bar} and \code{b.bar}. } \item{n}{ number of observations. } \item{p}{ a numeric vector of probabilities. } \item{pm}{ an integer value between \code{1} and \code{4} for the selection of the parameterization. The default takes the first parameterization. } \item{x, q}{ a numeric vector of quantiles. } \item{log}{ a logical, if TRUE, probabilities \code{p} are given as \code{log(p)}. } \item{\dots}{ arguments to be passed to the function \code{integrate}. } } \value{ All values for the \code{*hyp} functions are numeric vectors: \code{d*} returns the density, \code{p*} returns the distribution function, \code{q*} returns the quantile function, and \code{r*} generates random deviates. All values have attributes named \code{"param"} listing the values of the distributional parameters. } \details{ The generator \code{rhyp} is based on the HYP algorithm given by Atkinson (1982). } \author{ David Scott for code implemented from \R's contributed package \code{HyperbolicDist}. } \references{ Atkinson, A.C. (1982); \emph{The simulation of generalized inverse Gaussian and hyperbolic random variables}, SIAM J. Sci. Stat. Comput. 3, 502--515. Barndorff-Nielsen O. (1977); \emph{Exponentially decreasing distributions for the logarithm of particle size}, Proc. Roy. Soc. Lond., A353, 401--419. Barndorff-Nielsen O., Blaesild, P. (1983); \emph{Hyperbolic distributions. In Encyclopedia of Statistical Sciences}, Eds., Johnson N.L., Kotz S. and Read C.B., Vol. 3, pp. 700--707. New York: Wiley. Raible S. (2000); \emph{Levy Processes in Finance: Theory, Numerics and Empirical Facts}, PhD Thesis, University of Freiburg, Germany, 161 pages. } \examples{ ## hyp - set.seed(1953) r = rhyp(5000, alpha = 1, beta = 0.3, delta = 1) plot(r, type = "l", col = "steelblue", main = "hyp: alpha=1 beta=0.3 delta=1") ## hyp - # Plot empirical density and compare with true density: hist(r, n = 25, probability = TRUE, border = "white", col = "steelblue") x = seq(-5, 5, 0.25) lines(x, dhyp(x, alpha = 1, beta = 0.3, delta = 1)) ## hyp - # Plot df and compare with true df: plot(sort(r), (1:5000/5000), main = "Probability", col = "steelblue") lines(x, phyp(x, alpha = 1, beta = 0.3, delta = 1)) ## hyp - # Compute Quantiles: qhyp(phyp(seq(-5, 5, 1), alpha = 1, beta = 0.3, delta = 1), alpha = 1, beta = 0.3, delta = 1) } \keyword{distribution} fBasics/man/dist-gldRobMoments.Rd0000644000176000001440000000336211370220776016444 0ustar ripleyusers\name{gldRobMoments} \alias{gldRobMoments} \alias{gldMED} \alias{gldIQR} \alias{gldSKEW} \alias{gldKURT} \title{Robust Moments for the GLD} \description{ Computes the first four robust moments for the Generalized Lambda Distribution. } \usage{ gldMED(lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8) gldIQR(lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8) gldSKEW(lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8) gldKURT(lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8) } \arguments{ \item{lambda1, lambda2, lambda3, lambda4}{ are numeric values where \code{lambda1} is the location parameter, \code{lambda2} is the location parameter, \code{lambda3} is the first shape parameter, and \code{lambda4} is the second shape parameter. } } \value{ All values for the \code{*gld} functions are numeric vectors: \code{d*} returns the density, \code{p*} returns the distribution function, \code{q*} returns the quantile function, and \code{r*} generates random deviates. All values have attributes named \code{"param"} listing the values of the distributional parameters. } %\references{ % go here ... %} \author{ Diethelm Wuertz. } \examples{ ## gldMED - # Median: gldMED(lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8) ## gldIQR - # Inter-quartile Range: gldIQR(lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8) ## gldSKEW - # Robust Skewness: gldSKEW(lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8) ## gldKURT - # Robust Kurtosis: gldKURT(lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8) } \keyword{distribution} fBasics/man/dist-gldMode.Rd0000644000176000001440000000107211613257743015243 0ustar ripleyusers\name{gldMode} \alias{gldMode} \title{Generalized Lambda Distribution Mode} \description{ Computes the mode of the generalized lambda distribution. } \usage{ gldMode(lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8) } \arguments{ \item{lambda1, lambda2, lambda3, lambda4}{ are numeric values where \code{lambda1} is the location parameter, \code{lambda2} is the location parameter, \code{lambda3} is the first shape parameter, and \code{lambda4} is the second shape parameter. } } \author{ Implemented by Diethelm Wuertz } fBasics/man/dist-gldFit.Rd0000644000176000001440000001060311370220776015075 0ustar ripleyusers\name{gldFit} \alias{gldFit} \title{GH Distribution Fit} \description{ Estimates the distrinbutional parameters for a generalized lambda distribution. } \usage{ gldFit(x, lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8, method = c("mle", "mps", "gof", "hist", "rob"), scale = NA, doplot = TRUE, add = FALSE, span = "auto", trace = TRUE, title = NULL, description = NULL, \dots) } \arguments{ \item{x}{ a numeric vector. } \item{lambda1, lambda2, lambda3, lambda4}{ are numeric values where \code{lambda1} is the location parameter, \code{lambda2} is the location parameter, \code{lambda3} is the first shape parameter, and \code{lambda4} is the second shape parameter. } \item{method}{ a character string, the estimation approach to fit the distributional parameters, see details. } \item{scale}{ not used. %a logical flag, by default \code{TRUE}. Should the time series %be scaled by its standard deviation to achieve a more stable %optimization? } \item{doplot}{ a logical flag. Should a plot be displayed? } \item{add}{ a logical flag. Should a new fit added to an existing plot? } \item{span}{ x-coordinates for the plot, by default 100 values automatically selected and ranging between the 0.001, and 0.999 quantiles. Alternatively, you can specify the range by an expression like \code{span=seq(min, max, times = n)}, where, \code{min} and \code{max} are the left and rigldt endpoints of the range, and \code{n} gives the number of the intermediate points. } \item{trace}{ a logical flag. Should the parameter estimation process be traced? } \item{title}{ a character string which allows for a project title. } \item{description}{ a character string which allows for a brief description. } \item{\dots}{ parameters to be parsed. } } \value{ returns a list with the following components: \item{estimate}{ the point at which the maximum value of the log liklihood function is obtained. } \item{minimum}{ the value of the estimated maximum, i.e. the value of the log liklihood function. } \item{code}{ an integer indicating why the optimization process terminated.\cr 1: relative gradient is close to zero, current iterate is probably solution; \cr 2: successive iterates within tolerance, current iterate is probably solution; \cr 3: last global step failed to locate a point lower than \code{estimate}. Either \code{estimate} is an approximate local minimum of the function or \code{steptol} is too small; \cr 4: iteration limit exceeded; \cr 5: maximum step size \code{stepmax} exceeded five consecutive times. Either the function is unbounded below, becomes asymptotic to a finite value from above in some direction or \code{stepmax} is too small. } \item{gradient}{ the gradient at the estimated maximum. } \item{steps}{ number of function calls. } } \details{ The function \code{\link{nlminb}} is used to minimize the objective function. The following approaches have been implemented: \code{"mle"}, maximimum log likelihoo estimation. \code{"mps"}, maximum product spacing estimation. \code{"gof"}, goodness of fit approaches, \code{type="ad"} Anderson-Darling, \code{type="cvm"} Cramer-vonMise, \code{type="ks"} Kolmogorov-Smirnov. \code{"hist"}, histogram binning approaches, \code{"fd"} Freedman-Diaconis binning, \code{"scott"}, Scott histogram binning, \code{"sturges"}, Sturges histogram binning. \code{"rob"}, robust moment matching. } \examples{ ## gldFit - # Simulate Random Variates: set.seed(1953) s = rgld(n = 1000, lambda1=0, lambda2=-1, lambda3=-1/8, lambda4=-1/8) ## gldFit - # Fit Parameters: gldFit(s, lambda1=0, lambda2=-1, lambda3=-1/8, lambda4=-1/8, doplot = TRUE, trace = TRUE) } \keyword{distribution} fBasics/man/dist-gld.Rd0000644000176000001440000000504611370220776014437 0ustar ripleyusers\name{gld} \alias{gld} \alias{dgld} \alias{pgld} \alias{qgld} \alias{rgld} \title{Generalized Lambda Distribution} \description{ Density, distribution function, quantile function and random generation for the generalized lambda distribution. } \usage{ dgld(x, lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8, log = FALSE) pgld(q, lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8) qgld(p, lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8) rgld(n, lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8) } \arguments{ \item{lambda1, lambda2, lambda3, lambda4}{ are numeric values where \code{lambda1} is the location parameter, \code{lambda2} is the location parameter, \code{lambda3} is the first shape parameter, and \code{lambda4} is the second shape parameter. } \item{n}{ number of observations. } \item{p}{ a numeric vector of probabilities. } \item{x, q}{ a numeric vector of quantiles. } \item{log}{ a logical, if TRUE, probabilities \code{p} are given as \code{log(p)}. } } \value{ All values for the \code{*gld} functions are numeric vectors: \code{d*} returns the density, \code{p*} returns the distribution function, \code{q*} returns the quantile function, and \code{r*} generates random deviates. All values have attributes named \code{"param"} listing the values of the distributional parameters. } %\references{ % go here ... %} \author{ Chong Gu for code implemented from \R's contributed package \code{gld}. } \examples{ ## rgld - set.seed(1953) r = rgld(500, lambda1=0, lambda2=-1, lambda3=-1/8, lambda4=-1/8) plot(r, type = "l", col = "steelblue", main = "gld: lambda1=0 lambda2=-1 lambda3/4=-1/8") ## dgld - # Plot empirical density and compare with true density: hist(r, n = 25, probability = TRUE, border = "white", col = "steelblue") x = seq(-5, 5, 0.25) lines(x, dgld(x, lambda1=0, lambda2=-1, lambda3=-1/8, lambda4=-1/8)) ## pgld - # Plot df and compare with true df: plot(sort(r), ((1:500)-0.5)/500, main = "Probability", col = "steelblue") lines(x, pgld(x, lambda1=0, lambda2=-1, lambda3=-1/8, lambda4=-1/8)) ## qgld - # Compute Quantiles: qgld(pgld(seq(-5, 5, 1), lambda1=0, lambda2=-1, lambda3=-1/8, lambda4=-1/8), lambda1=0, lambda2=-1, lambda3=-1/8, lambda4=-1/8) } \keyword{distribution} fBasics/man/dist-ghtRobMoments.Rd0000644000176000001440000000340611613257743016463 0ustar ripleyusers\name{ghtRobMoments} \alias{ghtRobMoments} \alias{ghtMED} \alias{ghtIQR} \alias{ghtSKEW} \alias{ghtKURT} \title{Robust Moments for the GHT} \description{ Computes the first four robust moments for the generalized hyperbolic Student-t. } \usage{ ghtMED(beta = 0.1, delta = 1, mu = 0, nu = 10) ghtIQR(beta = 0.1, delta = 1, mu = 0, nu = 10) ghtSKEW(beta = 0.1, delta = 1, mu = 0, nu = 10) ghtKURT(beta = 0.1, delta = 1, mu = 0, nu = 10) } \arguments{ \item{beta, delta, mu}{ numeric values. \code{beta} is the skewness parameter in the range \code{(0, alpha)}; \code{delta} is the scale parameter, must be zero or positive; \code{mu} is the location parameter, by default 0. These are the parameters in the first parameterization. } \item{nu}{ a numeric value, the number of degrees of freedom. Note, \code{alpha} takes the limit of \code{abs(beta)}, and \code{lambda=-nu/2}. } } \value{ All values for the \code{*ght} functions are numeric vectors: \code{d*} returns the density, \code{p*} returns the distribution function, \code{q*} returns the quantile function, and \code{r*} generates random deviates. All values have attributes named \code{"param"} listing the values of the distributional parameters. } %\references{ % go here ... %} \author{ Diethelm Wuertz. } \examples{ ## ghtMED - # Median: ghtMED(beta = 0.1, delta = 1, mu = 0, nu = 10) ## ghtIQR - # Inter-quartile Range: ghtIQR(beta = 0.1, delta = 1, mu = 0, nu = 10) ## ghtSKEW - # Robust Skewness: ghtSKEW(beta = 0.1, delta = 1, mu = 0, nu = 10) ## ghtKURT - # Robust Kurtosis: ghtKURT(beta = 0.1, delta = 1, mu = 0, nu = 10) } \keyword{distribution} fBasics/man/dist-ghtMoments.Rd0000644000176000001440000000366111370220776016017 0ustar ripleyusers\name{ghtMoments} \alias{ghtMoments} \alias{ghtMean} \alias{ghtVar} \alias{ghtSkew} \alias{ghtKurt} \alias{ghtMoments} \title{Generalized Hyperbolic Student-t Moments} \description{ Calculates moments of the generalized hyperbbolic Student-t distribution function. } \usage{ ghtMean(beta=0.1, delta=1, mu=0, nu=10) ghtVar(beta=0.1, delta=1, mu=0, nu=10) ghtSkew(beta=0.1, delta=1, mu=0, nu=10) ghtKurt(beta=0.1, delta=1, mu=0, nu=10) ghtMoments(order, type = c("raw", "central", "mu"), beta=0.1, delta=1, mu=0, nu=10) } \arguments{ \item{beta, delta, mu}{ numeric values. \code{beta} is the skewness parameter in the range \code{(0, alpha)}; \code{delta} is the scale parameter, must be zero or positive; \code{mu} is the location parameter, by default 0. These are the parameters in the first parameterization. } \item{nu}{ a numeric value, the number of degrees of freedom. Note, \code{alpha} takes the limit of \code{abs(beta)}, and \code{lambda=-nu/2}. } \item{order}{ an integer value, the order of the moment. } \item{type}{ a character value, \code{"raw"} returns the moments about zero, \code{"central"} returns the central moments about the mean, and \code{"mu"} returns the moments about the location parameter \code{mu}. } } \value{ a numerical value. } \references{ Scott, D.J., Wuertz, D. and Tran, T.T. (2008) \emph{Moments of the Generalized Hyperbolic Distribution}. Preprint. } \author{ Diethelm Wuertz. } \examples{ ## ghtMean - ghtMean(beta=0.2, delta=1.2, mu=-0.5, nu=4) ## ghtKurt - ghtKurt(beta=0.2, delta=1.2, mu=-0.5, nu=4) ## ghtMoments - ghtMoments(4, beta=0.2, delta=1.2, mu=-0.5, nu=4) ghtMoments(4, "central", beta=0.2, delta=1.2, mu=-0.5, nu=4) } \keyword{distribution} fBasics/man/dist-ghtMode.Rd0000644000176000001440000000324711370220776015261 0ustar ripleyusers\name{ghtMode} \alias{ghtMode} \title{Generalized Hyperbolic Student-t Mode} \description{ Computes the mode of the generalized hyperbolic Student-t distribution. } \usage{ ghtMode(beta = 0.1, delta = 1, mu = 0, nu = 10) } \arguments{ \item{beta, delta, mu}{ numeric values. \code{beta} is the skewness parameter in the range \code{(0, alpha)}; \code{delta} is the scale parameter, must be zero or positive; \code{mu} is the location parameter, by default 0. These are the parameters in the first parameterization. } \item{nu}{ a numeric value, the number of degrees of freedom. Note, \code{alpha} takes the limit of \code{abs(beta)}, and \code{lambda=-nu/2}. } } \value{ returns the mode for the generalized hyperbolic Student-t distribution. A numeric value. } \references{ Atkinson, A.C. (1982); \emph{The simulation of generalized inverse Gaussian and hyperbolic random variables}, SIAM J. Sci. Stat. Comput. 3, 502--515. Barndorff-Nielsen O. (1977); \emph{Exponentially decreasing distributions for the logarithm of particle size}, Proc. Roy. Soc. Lond., A353, 401--419. Barndorff-Nielsen O., Blaesild, P. (1983); \emph{Hyperbolic distributions. In Encyclopedia of Statistical Sciences}, Eds., Johnson N.L., Kotz S. and Read C.B., Vol. 3, pp. 700--707. New York: Wiley. Raible S. (2000); \emph{Levy Processes in Finance: Theory, Numerics and Empirical Facts}, PhD Thesis, University of Freiburg, Germany, 161 pages. } \examples{ ## ghtMode - ghtMode() } \keyword{distribution} fBasics/man/dist-ghtFit.Rd0000644000176000001440000000717211370220776015120 0ustar ripleyusers\name{ghtFit} \alias{ghtFit} \alias{ghtFit} \title{GHT Distribution Fit} \description{ Estimates the distributional parameters for a generalized hyperbolic Student-t distribution. } \usage{ ghtFit(x, beta = 0.1, delta = 1, mu = 0, nu = 10, scale = TRUE, doplot = TRUE, span = "auto", trace = TRUE, title = NULL, description = NULL, \dots) } \arguments{ \item{beta, delta, mu}{ numeric values. \code{beta} is the skewness parameter in the range \code{(0, alpha)}; \code{delta} is the scale parameter, must be zero or positive; \code{mu} is the location parameter, by default 0. These are the parameters in the first parameterization. } \item{nu}{ defines the number of degrees of freedom. Note, \code{alpha} takes the limit of \code{abs(beta)}, and \code{lambda=-nu/2}. } \item{x}{ a numeric vector. } \item{scale}{ a logical flag, by default \code{TRUE}. Should the time series be scaled by its standard deviation to achieve a more stable optimization? } \item{doplot}{ a logical flag. Should a plot be displayed? } \item{span}{ x-coordinates for the plot, by default 100 values automatically selected and ranging between the 0.001, and 0.999 quantiles. Alternatively, you can specify the range by an expression like \code{span=seq(min, max, times = n)}, where, \code{min} and \code{max} are the left and right endpoints of the range, and \code{n} gives the number of the intermediate points. } \item{trace}{ a logical flag. Should the parameter estimation process be traced? } \item{title}{ a character string which allows for a project title. } \item{description}{ a character string which allows for a brief description. } \item{\dots}{ parameters to be parsed. } } \value{ returns a list with the following components: \item{estimate}{ the point at which the maximum value of the log liklihood function is obtained. } \item{minimum}{ the value of the estimated maximum, i.e. the value of the log liklihood function. } \item{code}{ an integer indicating why the optimization process terminated.\cr 1: relative gradient is close to zero, current iterate is probably solution; \cr 2: successive iterates within tolerance, current iterate is probably solution; \cr 3: last global step failed to locate a point lower than \code{estimate}. Either \code{estimate} is an approximate local minimum of the function or \code{steptol} is too small; \cr 4: iteration limit exceeded; \cr 5: maximum step size \code{stepmax} exceeded five consecutive times. Either the function is unbounded below, becomes asymptotic to a finite value from above in some direction or \code{stepmax} is too small. } \item{gradient}{ the gradient at the estimated maximum. } \item{steps}{ number of function calls. } } \details{ The function \code{\link{nlm}} is used to minimize the "negative" maximum log-likelihood function. \code{nlm} carries out a minimization using a Newton-type algorithm. } \examples{ ## ghtFit - # Simulate Random Variates: set.seed(1953) ## ghtFit - # Fit Parameters: } \keyword{distribution} fBasics/man/dist-ght.Rd0000644000176000001440000000464711370220776014461 0ustar ripleyusers\name{ght} \alias{dght} \alias{pght} \alias{qght} \alias{rght} \title{Generalized Hyperbolic Student-t} \description{ Density, distribution function, quantile function and random generation for the hyperbolic distribution. } \usage{ dght(x, beta = 0.1, delta = 1, mu = 0, nu = 10, log = FALSE) pght(q, beta = 0.1, delta = 1, mu = 0, nu = 10) qght(p, beta = 0.1, delta = 1, mu = 0, nu = 10) rght(n, beta = 0.1, delta = 1, mu = 0, nu = 10) } \arguments{ \item{beta, delta, mu}{ numeric values. \code{beta} is the skewness parameter in the range \code{(0, alpha)}; \code{delta} is the scale parameter, must be zero or positive; \code{mu} is the location parameter, by default 0. These are the parameters in the first parameterization. } \item{nu}{ a numeric value, the number of degrees of freedom. Note, \code{alpha} takes the limit of \code{abs(beta)}, and \code{lambda=-nu/2}. } \item{x, q}{ a numeric vector of quantiles. } \item{p}{ a numeric vector of probabilities. } \item{n}{ number of observations. } \item{log}{ a logical, if TRUE, probabilities \code{p} are given as \code{log(p)}. } } \value{ All values for the \code{*ght} functions are numeric vectors: \code{d*} returns the density, \code{p*} returns the distribution function, \code{q*} returns the quantile function, and \code{r*} generates random deviates. All values have attributes named \code{"param"} listing the values of the distributional parameters. } \references{ Atkinson, A.C. (1982); \emph{The simulation of generalized inverse Gaussian and hyperbolic random variables}, SIAM J. Sci. Stat. Comput. 3, 502--515. Barndorff-Nielsen O. (1977); \emph{Exponentially decreasing distributions for the logarithm of particle size}, Proc. Roy. Soc. Lond., A353, 401--419. Barndorff-Nielsen O., Blaesild, P. (1983); \emph{Hyperbolic distributions. In Encyclopedia of Statistical Sciences}, Eds., Johnson N.L., Kotz S. and Read C.B., Vol. 3, pp. 700--707. New York: Wiley. Raible S. (2000); \emph{Levy Processes in Finance: Theory, Numerics and Empirical Facts}, PhD Thesis, University of Freiburg, Germany, 161 pages. } \examples{ ## ght - # } \keyword{distribution} fBasics/man/dist-ghSlider.Rd0000644000176000001440000000103111370220776015420 0ustar ripleyusers\name{ghSlider} \alias{ghSlider} \title{Generalized Hyperbolic Distribution Slider} \description{ Displays interactively the dependence of the generalized hyperbolic distribution on its parameters. } \usage{ ghSlider() } \value{ a tcl/tk based graphical user interface. This is a nice display for educational purposes to investigate the densities and probabilities of the generalized hyperbolic distribution. } \examples{ ## ghSlider - # ghSlider() } \keyword{distribution} fBasics/man/dist-ghRobMoments.Rd0000644000176000001440000000452411613257743016301 0ustar ripleyusers\name{ghRobMoments} \alias{ghRobMoments} \alias{ghMED} \alias{ghIQR} \alias{ghSKEW} \alias{ghKURT} \title{Robust Moments for the GH} \description{ Computes the first four robust moments for the generalized hyperbolic distribution.. } \usage{ ghMED(alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) ghIQR(alpha= 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) ghSKEW(alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) ghKURT(alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) } \arguments{ \item{alpha, beta, delta, mu, lambda}{ numeric values. \code{alpha} is the first shape parameter; \code{beta} is the second shape parameter in the range \code{(0, alpha)}; \code{delta} is the scale parameter, must be zero or positive; \code{mu} is the location parameter, by default 0; and \code{lambda} defines the sublclass, by default -1/2. These are the meanings of the parameters in the first parameterization \code{pm=1} which is the default parameterization. In the second parameterization, \code{pm=2} \code{alpha} and \code{beta} take the meaning of the shape parameters (usually named) \code{zeta} and \code{rho}. In the third parameterization, \code{pm=3} \code{alpha} and \code{beta} take the meaning of the shape parameters (usually named) \code{xi} and \code{chi}. In the fourth parameterization, \code{pm=4} \code{alpha} and \code{beta} take the meaning of the shape parameters (usually named) \code{a.bar} and \code{b.bar}. } } \value{ All values for the \code{*gh} functions are numeric vectors: \code{d*} returns the density, \code{p*} returns the distribution function, \code{q*} returns the quantile function, and \code{r*} generates random deviates. All values have attributes named \code{"param"} listing the values of the distributional parameters. } %\references{ % go here ... %} \author{ Diethelm Wuertz. } \examples{ ## ghMED - # Median: ghMED(alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) ## ghIQR - # Inter-quartile Range: ghIQR(alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) ## ghSKEW - # Robust Skewness: ghSKEW(alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) ## ghKURT - # Robust Kurtosis: ghKURT(alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) } \keyword{distribution} fBasics/man/dist-ghMoments.Rd0000644000176000001440000000365111370220776015632 0ustar ripleyusers\name{ghMoments} \alias{ghMoments} \alias{ghMean} \alias{ghVar} \alias{ghSkew} \alias{ghKurt} \alias{ghMoments} \title{Generalized Hyperbolic Distribution Moments} \description{ Calculates moments of the generalized hyperbbolic distribution function } \usage{ ghMean(alpha=1, beta=0, delta=1, mu=0, lambda=-1/2) ghVar(alpha=1, beta=0, delta=1, mu=0, lambda=-1/2) ghSkew(alpha=1, beta=0, delta=1, mu=0, lambda=-1/2) ghKurt(alpha=1, beta=0, delta=1, mu=0, lambda=-1/2) ghMoments(order, type = c("raw", "central", "mu"), alpha = 1, beta=0, delta=1, mu=0, lambda=-1/2) } \arguments{ \item{alpha, beta, delta, mu, lambda}{ numeric values. \code{alpha} is the first shape parameter; \code{beta} is the second shape parameter in the range \code{(0, alpha)}; \code{delta} is the scale parameter, must be zero or positive; \code{mu} is the location parameter, by default 0; and \code{lambda} defines the sublclass, by default -1/2. } \item{order}{ an integer value, the order of the moment. } \item{type}{ a character value, \code{"raw"} returns the moments about zero, \code{"central"} returns the central moments about the mean, and \code{"mu"} returns the moments about the location parameter \code{mu}. } } \value{ a numerical value. } \references{ Scott, D. J., Wuertz, D. and Tran, T. T. (2008) \emph{Moments of the Generalized Hyperbolic Distribution}. Preprint. } \author{ Diethelm Wuertz. } \examples{ ## ghMean - ghMean(alpha=1.1, beta=0.1, delta=0.8, mu=-0.3, lambda=1) ## ghKurt - ghKurt(alpha=1.1, beta=0.1, delta=0.8, mu=-0.3, lambda=1) ## ghMoments - ghMoments(4, alpha=1.1, beta=0.1, delta=0.8, mu=-0.3, lambda=1) ghMoments(4, "central", alpha=1.1, beta=0.1, delta=0.8, mu=-0.3, lambda=1) } \keyword{distribution} fBasics/man/dist-ghMode.Rd0000644000176000001440000000421011370220776015064 0ustar ripleyusers\name{ghMode} \alias{ghMode} \title{Generalized Hyperbolic Mode} \description{ Computes the mode of the generalized hyperbolic function. } \usage{ ghMode(alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) } \arguments{ \item{alpha, beta, delta, mu, lambda}{ shape parameter \code{alpha}; skewness parameter \code{beta}, \code{abs(beta)} is in the range (0, alpha); scale parameter \code{delta}, \code{delta} must be zero or positive; location parameter \code{mu}, by default 0. These is the meaning of the parameters in the first parameterization \code{pm=1} which is the default parameterization selection. In the second parameterization, \code{pm=2} \code{alpha} and \code{beta} take the meaning of the shape parameters (usually named) \code{zeta} and \code{rho}. In the third parameterization, \code{pm=3} \code{alpha} and \code{beta} take the meaning of the shape parameters (usually named) \code{xi} and \code{chi}. In the fourth parameterization, \code{pm=4} \code{alpha} and \code{beta} take the meaning of the shape parameters (usually named) \code{a.bar} and \code{b.bar}. } } \value{ returns the mode for the generalized hyperbolic distribution. A numeric value. } \references{ Atkinson, A.C. (1982); \emph{The simulation of generalized inverse Gaussian and hyperbolic random variables}, SIAM J. Sci. Stat. Comput. 3, 502--515. Barndorff-Nielsen O. (1977); \emph{Exponentially decreasing distributions for the logarithm of particle size}, Proc. Roy. Soc. Lond., A353, 401--419. Barndorff-Nielsen O., Blaesild, P. (1983); \emph{Hyperbolic distributions. In Encyclopedia of Statistical Sciences}, Eds., Johnson N.L., Kotz S. and Read C.B., Vol. 3, pp. 700--707. New York: Wiley. Raible S. (2000); \emph{Levy Processes in Finance: Theory, Numerics and Empirical Facts}, PhD Thesis, University of Freiburg, Germany, 161 pages. } \examples{ ## ghMode - ghMode() } \keyword{distribution} fBasics/man/dist-ghFit.Rd0000644000176000001440000000735411370220776014736 0ustar ripleyusers\name{ghFit} \alias{ghFit} \title{GH Distribution Fit} \description{ Estimates the distrinbutional parameters for a generalized hyperbolic distribution. } \usage{ ghFit(x, alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2, scale = TRUE, doplot = TRUE, span = "auto", trace = TRUE, title = NULL, description = NULL, \dots) } \arguments{ \item{x}{ a numeric vector. } \item{alpha, beta, delta, mu, lambda}{ The parameters are \code{alpha}, \code{beta}, \code{delta}, \code{mu}, and and \code{lambda}:\cr shape parameter \code{alpha}; skewness parameter \code{beta}, \code{abs(beta)} is in the range (0, alpha); scale parameter \code{delta}, \code{delta} must be zero or positive; location parameter \code{mu}, by default 0; and lambda parameter \code{lambda}, by default -1/2. } \item{scale}{ a logical flag, by default \code{TRUE}. Should the time series be scaled by its standard deviation to achieve a more stable optimization? } \item{doplot}{ a logical flag. Should a plot be displayed? } \item{span}{ x-coordinates for the plot, by default 100 values automatically selected and ranging between the 0.001, and 0.999 quantiles. Alternatively, you can specify the range by an expression like \code{span=seq(min, max, times = n)}, where, \code{min} and \code{max} are the left and right endpoints of the range, and \code{n} gives the number of the intermediate points. } \item{trace}{ a logical flag. Should the parameter estimation process be traced? } \item{title}{ a character string which allows for a project title. } \item{description}{ a character string which allows for a brief description. } \item{\dots}{ parameters to be parsed. } } \value{ returns a list with the following components: \item{estimate}{ the point at which the maximum value of the log liklihood function is obtained. } \item{minimum}{ the value of the estimated maximum, i.e. the value of the log liklihood function. } \item{code}{ an integer indicating why the optimization process terminated.\cr 1: relative gradient is close to zero, current iterate is probably solution; \cr 2: successive iterates within tolerance, current iterate is probably solution; \cr 3: last global step failed to locate a point lower than \code{estimate}. Either \code{estimate} is an approximate local minimum of the function or \code{steptol} is too small; \cr 4: iteration limit exceeded; \cr 5: maximum step size \code{stepmax} exceeded five consecutive times. Either the function is unbounded below, becomes asymptotic to a finite value from above in some direction or \code{stepmax} is too small. } \item{gradient}{ the gradient at the estimated maximum. } \item{steps}{ number of function calls. } } \details{ The function \code{\link{nlm}} is used to minimize the "negative" maximum log-likelihood function. \code{nlm} carries out a minimization using a Newton-type algorithm. } \examples{ ## ghFit - # Simulate Random Variates: set.seed(1953) s = rgh(n = 1000, alpha = 1.5, beta = 0.3, delta = 0.5, mu = -1.0) ## ghFit - # Fit Parameters: ghFit(s, alpha = 1, beta = 0, delta = 1, mu = mean(s), doplot = TRUE) } \keyword{distribution} fBasics/man/dist-gh.Rd0000644000176000001440000001005011370220776014256 0ustar ripleyusers\name{gh} \alias{gh} \alias{dgh} \alias{pgh} \alias{qgh} \alias{rgh} \title{Generalized Hyperbolic Distribution} \description{ Calculates moments of the generalized hyperbbolic distribution function. } \usage{ dgh(x, alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2, log = FALSE) pgh(q, alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) qgh(p, alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) rgh(n, alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) } \arguments{ \item{alpha, beta, delta, mu, lambda}{ numeric values. \code{alpha} is the first shape parameter; \code{beta} is the second shape parameter in the range \code{(0, alpha)}; \code{delta} is the scale parameter, must be zero or positive; \code{mu} is the location parameter, by default 0; and \code{lambda} defines the sublclass, by default -1/2. These are the meanings of the parameters in the first parameterization \code{pm=1} which is the default parameterization. In the second parameterization, \code{pm=2} \code{alpha} and \code{beta} take the meaning of the shape parameters (usually named) \code{zeta} and \code{rho}. In the third parameterization, \code{pm=3} \code{alpha} and \code{beta} take the meaning of the shape parameters (usually named) \code{xi} and \code{chi}. In the fourth parameterization, \code{pm=4} \code{alpha} and \code{beta} take the meaning of the shape parameters (usually named) \code{a.bar} and \code{b.bar}. } \item{log}{ a logical flag by default \code{FALSE}. Should labels and a main title drawn to the plot? } \item{n}{ number of observations. } \item{p}{ a numeric vector of probabilities. } \item{x, q}{ a numeric vector of quantiles. } \item{\dots}{ arguments to be passed to the function \code{integrate}. } } \value{ All values for the \code{*gh} functions are numeric vectors: \code{d*} returns the density, \code{p*} returns the distribution function, \code{q*} returns the quantile function, and \code{r*} generates random deviates. All values have attributes named \code{"param"} listing the values of the distributional parameters. } \details{ The generator \code{rgh} is based on the GH algorithm given by Scott (2004). } \references{ Atkinson, A.C. (1982); \emph{The simulation of generalized inverse Gaussian and hyperbolic random variables}, SIAM J. Sci. Stat. Comput. 3, 502--515. Barndorff-Nielsen O. (1977); \emph{Exponentially decreasing distributions for the logarithm of particle size}, Proc. Roy. Soc. Lond., A353, 401--419. Barndorff-Nielsen O., Blaesild, P. (1983); \emph{Hyperbolic distributions. In Encyclopedia of Statistical Sciences}, Eds., Johnson N.L., Kotz S. and Read C.B., Vol. 3, pp. 700--707. New York: Wiley. Raible S. (2000); \emph{Levy Processes in Finance: Theory, Numerics and Empirical Facts}, PhD Thesis, University of Freiburg, Germany, 161 pages. } \author{ David Scott for code implemented from \R's contributed package \code{HyperbolicDist}. } \examples{ ## rgh - set.seed(1953) r = rgh(5000, alpha = 1, beta = 0.3, delta = 1) plot(r, type = "l", col = "steelblue", main = "gh: alpha=1 beta=0.3 delta=1") ## dgh - # Plot empirical density and compare with true density: hist(r, n = 25, probability = TRUE, border = "white", col = "steelblue") x = seq(-5, 5, 0.25) lines(x, dgh(x, alpha = 1, beta = 0.3, delta = 1)) ## pgh - # Plot df and compare with true df: plot(sort(r), (1:5000/5000), main = "Probability", col = "steelblue") lines(x, pgh(x, alpha = 1, beta = 0.3, delta = 1)) ## qgh - # Compute Quantiles: qgh(pgh(seq(-5, 5, 1), alpha = 1, beta = 0.3, delta = 1), alpha = 1, beta = 0.3, delta = 1) } \keyword{distribution} fBasics/man/dist-distCheck.Rd0000644000176000001440000000173311613257743015575 0ustar ripleyusers\name{distCheck} \alias{distCheck} \title{Distribution Check} \description{ Tests properties of an \R implementation of a distribution, i.e. of all four of its \dQuote{dpqr} functions. } \usage{ distCheck(fun = "norm", n = 1000, robust = TRUE, subdivisions = 100, \dots) } \arguments{ \item{fun}{a character string denoting the name of the distribution.} \item{n}{an integer specifying the number of random variates to be generated.} \item{robust}{logical flag, should robust estimates be used? By default \code{TRUE}.} \item{subdivisions}{integer specifying the numbers of subdivisions in integration.} \item{\dots}{the distributional parameters.} } \examples{ distCheck("norm", mean = 1, sd = 1) distCheck("lnorm", meanlog = 0.5, sdlog = 2, robust=FALSE) ## here, true E(X) = exp(mu + 1/2 sigma^2) = exp(.5 + 2) = exp(2.5) = 12.182 ## and Var(X) = exp(2*mu + sigma^2)*(exp(sigma^2) - 1) = 7954.67 } \keyword{distribution} fBasics/man/dist-DistributionFits.Rd0000644000176000001440000001072611370220776017177 0ustar ripleyusers\name{DistributionFits} \alias{DistributionFits} \alias{fDISTFIT} \alias{fDISTFIT-class} \alias{show,fDISTFIT-method} \alias{nFit} \alias{tFit} \alias{stableFit} \alias{print.fDISTFIT} \title{Parameter Fit of a Distribution} \description{ A collection and description of moment and maximum likelihood estimators to fit the parameters of a distribution. \cr The functions are: \tabular{ll}{ \code{nFit} \tab MLE parameter fit for a normal distribution, \cr \code{tFit} \tab MLE parameter fit for a Student t-distribution, \cr \code{stableFit} \tab MLE and Quantile Method stable parameter fit. } } \usage{ nFit(x, doplot = TRUE, span = "auto", title = NULL, description = NULL, \dots) tFit(x, df = 4, doplot = TRUE, span = "auto", trace = FALSE, title = NULL, description = NULL, \dots) stableFit(x, alpha = 1.75, beta = 0, gamma = 1, delta = 0, type = c("q", "mle"), doplot = TRUE, control = list(), trace = FALSE, title = NULL, description = NULL) \S4method{show}{fDISTFIT}(object) } \arguments{ \item{control}{ [stableFit] - \cr a list of control parameters, see function \code{nlminb}. } \item{alpha, beta, gamma, delta}{ [stable] - \cr The parameters are \code{alpha}, \code{beta}, \code{gamma}, and \code{delta}:\cr value of the index parameter \code{alpha} with \code{alpha = (0,2]}; skewness parameter \code{beta}, in the range [-1, 1]; scale parameter \code{gamma}; and shift parameter \code{delta}. } \item{description}{ a character string which allows for a brief description. } \item{df}{ the number of degrees of freedom for the Student distribution, \code{df > 2}, maybe non-integer. By default a value of 4 is assumed. } \item{object}{ [show] - \cr an S4 class object as returned from the fitting functions. } \item{doplot}{ a logical flag. Should a plot be displayed? } \item{span}{ x-coordinates for the plot, by default 100 values automatically selected and ranging between the 0.001, and 0.999 quantiles. Alternatively, you can specify the range by an expression like \code{span=seq(min, max, times = n)}, where, \code{min} and \code{max} are the left and right endpoints of the range, and \code{n} gives the number of the intermediate points. } \item{title}{ a character string which allows for a project title. } \item{trace}{ a logical flag. Should the parameter estimation process be traced? } \item{type}{ a character string which allows to select the method for parameter estimation: \code{"mle"}, the maximum log likelihood approach, or \code{"qm"}, McCulloch's quantile method. } \item{x}{ a numeric vector. } \item{\dots}{ parameters to be parsed. } } \value{ The functions \code{tFit}, \code{hypFit} and \code{nigFit} return a list with the following components: \item{estimate}{ the point at which the maximum value of the log liklihood function is obtained. } \item{minimum}{ the value of the estimated maximum, i.e. the value of the log liklihood function. } \item{code}{ an integer indicating why the optimization process terminated. } \item{gradient}{ the gradient at the estimated maximum. } Remark: The parameter estimation for the stable distribution via the maximum Log-Likelihood approach may take a quite long time. } \details{ \bold{Stable Parameter Estimation:} Estimation techniques based on the quantiles of an empirical sample were first suggested by Fama and Roll [1971]. However their technique was limited to symmetric distributions and suffered from a small asymptotic bias. McCulloch [1986] developed a technique that uses five quantiles from a sample to estimate \code{alpha} and \code{beta} without asymptotic bias. Unfortunately, the estimators provided by McCulloch have restriction \code{alpha>0.6}. } \examples{ ## nFit - # Simulate random normal variates N(0.5, 2.0): set.seed(1953) s = rnorm(n = 1000, 0.5, 2) ## nigFit - # Fit Parameters: nFit(s, doplot = TRUE) } \keyword{distribution} fBasics/man/baseMethods.Rd0000644000176000001440000000342011370220776015160 0ustar ripleyusers\name{baseMethods} \alias{baseMethods} \alias{volatility} \alias{volatility.default} \alias{termPlot} \alias{termPlot.default} \alias{stdev} \alias{stdev.default} \title{Generic Functions Extensions} \description{ Basic extensions which which add and/or modify additional functionality which is not available in R's basic packages. \cr Added and/or modified functions: \tabular{ll}{ \code{attach} \tab extends attach function, \cr \code{rank} \tab extends rank function, \cr \code{stdev} \tab adds stdev function, \cr \code{termPlot} \tab adds term plot function, \cr \code{volatility} \tab adds volatility function. } } \usage{ \method{stdev}{default}(x, na.rm = FALSE) \method{termPlot}{default}(model, \dots) \method{volatility}{default}(object, \dots) } \arguments{ \item{na.rm}{ an logical value - should the NA values be removed. } \item{model}{ a fitted model object. } \item{object}{ an object from which to extract the volatility. } \item{x}{ [align] - \cr x-coordinates of the points to be aligned.\cr [log][sort][var] - \cr first argument.\cr [print.control] - cr prints an unlisted object of class control.\cr [as.matrix.ts][as.matrix.mts] - \cr an univariate or multivariate time series object of class \code{"ts"} or \code{"mts"} which will be transformed into an one-column or multi-column rectangular object of class \code{"matrix"}.\cr [as.POSIXlt] - \cr an object to be converted. } \item{\dots}{ arguments to be passed. } } \details{ For details we refer to the original help pages. } \keyword{programming} fBasics/man/00fBasics-package.Rd0000644000176000001440000011324611717226247016041 0ustar ripleyusers\name{fBasics-package} \alias{fBasics} \docType{package} \title{Portfolio Modelling, Optimization and Backtesting} \description{ The Rmetrics "fBasics" package is a collection of functions to explore and to investigate basic properties of financial returns and related quantities. The covered fields include techniques of explorative data analysis and the investigation of distributional properties, including parameter estimation and hypothesis testing. Evenmore there are several utility functions for data handling and managemnet. } \details{ \tabular{ll}{ Package: \tab fBasics\cr Type: \tab Package\cr Version: \tab 270.73\cr Date: \tab 2009\cr License: \tab GPL Version 2 or later\cr Copyright: \tab (c) 1999-2010 Rmetrics Assiciation\cr URL: \tab \url{http://www.rmetrics.org} } } \section{A. Overview:}{ 1. Basic Statistics Functions 2. Financial Return Distributions 3. Hypothesis Testing 4. Plotting Routines 5. Matrix Computations and Linear Algebra 6. Utility Functions for Data Management 7. Other Utility Functions } \section{1. Basic Statistics Functions}{ \bold{Financial Return Statistics:} \tabular{ll}{ \emph{Function ________________} \tab Description _________________________________________\cr \code{basicStats} \tab Returns a basic statistics summary. \cr \tab \emph{File: stats-basicStats.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{dmaxdd} \tab Density function of mean Max-Drawdowns \cr \code{pmaxdd} \tab Probability function of mean Max-Drawdowns \cr \code{rmaxdd} \tab Random Variates of mean Max-Drawdowns \cr \code{maxddStats} \tab Expectation of Drawdowns for BM with drift. \cr \tab \emph{File: stats-maxdd.R} } \bold{Calculation of Sample Moments:} \tabular{ll}{ \emph{Function ________________} \tab Description _________________________________________\cr \code{sampleLmoments} \tab Computes sample L-moments. \cr \tab \emph{File: stats-sampleLMoments.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{sampleMED} \tab Returns sample median \cr \code{sampleIQR} \tab returns sample inter quartal range \cr \code{sampleSKEW} \tab returns robust sample skewness \cr \code{sampleKURT} \tab returns robust sample kurtosis. \cr \tab \emph{File: stats-sampleRobMoments.R} } \bold{Bivariate Interpolation:} \tabular{ll}{ \emph{Function ________________} \tab Description _________________________________________\cr \code{akimaInterp} \tab Interpolates irregularly spaced points \cr \code{akimaInterpp} \tab Interpolates and smoothes pointwise. \cr \tab \emph{File: stats-interpAkima.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{krigeInterp} \tab Kriges irregularly spaced data points. \cr \tab \emph{File: stats-interpKrige.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{linearInterp} \tab Interpolates irregularly spaced points \cr \code{linearInterpp} \tab Interpolates linearly pointwise. \cr \tab \emph{File: stats-interpLinear.R} } \bold{Utility Functions:} \tabular{ll}{ \emph{Function ________________} \tab Description _________________________________________\cr \code{colStats} \tab Computes sample statistics by col \cr \code{colSums} \tab Computes sums of values in each col \cr \code{colMeans} \tab Computes means of values in each col \cr \code{colSds} \tab Computes standard deviation of each col \cr \code{colVars} \tab Computes sample variance by col \cr \code{colSkewness} \tab Computes sample skewness by col \cr \code{colKurtosis} \tab Computes sample kurtosis by col \cr \code{colMaxs} \tab Computes maximum values in each col \cr \code{colMins} \tab Computes minimum values in each col \cr \code{colProds} \tab Computes product of values in each col \cr \code{colQuantiles} \tab Computes product of values in each col. \cr \tab \emph{File: has been moved tp package timeSeries} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{rowStats} \tab Computes sample statistics by row \cr \code{rowSums} \tab Computes sums of values in each row \cr \code{rowMeans} \tab Computes means of values in each row \cr \code{rowSds} \tab Computes standard deviation of each row \cr \code{rowVars} \tab Computes sample variance by row \cr \code{rowSkewness} \tab Computes sample skewness by row \cr \code{rowKurtosis} \tab Computes sample kurtosis by row \cr \code{rowMaxs} \tab Computes maximum values in each row \cr \code{rowMins} \tab Computes minimum values in each row \cr \code{rowProds} \tab Computes product of values in each row \cr \code{rowQuantiles} \tab Computes product of values in each row. \cr \tab \emph{File: stats-rowStats.R} } } \section{2. Financial Return Distributions}{ \bold{Generalized Hyperbolic Distribution:} \tabular{ll}{ \emph{Function ________________} \tab Description _________________________________________\cr \code{dgh} \tab Returns density for the GH distribution \cr \code{pgh} \tab returns probability for the GH distribution \cr \code{qgh} \tab returns quantiles for the GH distribution \cr \code{rgh} \tab returns random variates for the GH distribution. \cr \tab \emph{File: dist-gh.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{ghFit} \tab Fits parameters of the GH distribution. \cr \tab \emph{File: dist-ghFit.R} \cr \tab \cr \emph{Function: ________________} \tab Description _________________________________________\cr \code{ghMode} \tab Computes mode of the GH distribution. \cr \tab \emph{File: dist-ghMode.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{ghMean} \tab Returns true mean of the GH distribution \cr \code{ghVar} \tab returns true variance of the GH distribution \cr \code{ghSkew} \tab returns true skewness of the GH distribution \cr \code{ghKurt} \tab returns true kurtosis of the GH distribution \cr \code{ghMoments} \tab returns true n-th moment of the GH distribution. \cr \tab \emph{File: dist-ghMoments.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{ghMED} \tab Returns true median of te GH distribution \cr \code{ghIQR} \tab returns true inter quartal range of te GH \cr \code{ghSKEW} \tab returns true robust skewness of te GH \cr \code{ghKURT} \tab returns true robust kurtosis of te GH. \cr \tab \emph{File: dist-ghRobMoments.R} } \bold{Hyperbolic Distribution:} \tabular{ll}{ \emph{Function ________________} \tab Description _________________________________________\cr \code{dhyp} \tab Returns density for the HYP distribution \cr \code{phyp} \tab returns probability for the HYP distribution \cr \code{qhyp} \tab returns quantiles for the HYP distribution \cr \code{rhyp} \tab returns random variates for the HYP distribution. \cr \tab \emph{File: dist-hyp.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{hypFit} \tab Fits parameters of the HYP distribution.\cr \tab \emph{File: dist-hypFit.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{hypMode} \tab Computes mode of the HYP distribution.\cr \tab \emph{File: dist-hypMode.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{hypMean} \tab Returns true mean of the HYP distribution \cr \code{hypVar} \tab returns true variance of the HYP distribution \cr \code{hypSkew} \tab returns true skewness of the HYP distribution \cr \code{hypKurt} \tab returns true kurtosis of the HYP distribution \cr \code{hypMoments} \tab returns true n-th moment of the HYP distribution.\cr \tab \emph{File: dist-hypMoments.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{hypMED} \tab Returns true median of the HYP distribution \cr \code{hypIQR} \tab returns true inter quartal range of the HYP \cr \code{hypSKEW} \tab returns true robust skewness of the HYP \cr \code{hypKURT} \tab returns true robust kurtosis of the HYP.\cr \tab \emph{File: dist-hypRobMoments.R} } \bold{Normal Inverse Gaussian:} \tabular{ll}{ \emph{Function ________________} \tab Description _________________________________________\cr \code{dnig} \tab Returns density for the NIG distribution \cr \code{pnig} \tab returns probability for the NIG distribution \cr \code{qnig} \tab returns quantiles for the NIG distribution \cr \code{rnig} \tab returns random variates for the NIG distribution \cr \code{.pnigC} \tab fast C implementation of function pnig() \cr \code{.qnigC} \tab fast C implementation of function qnig().\cr \tab \emph{File: dist-nig.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{nigFit} \tab Fits parameters of a NIG distribution \cr \code{.nigFit.mle} \tab uses max Log-likelihood estimation \cr \code{.nigFit.gmm} \tab uses generalized method of moments \cr \code{.nigFit.mps} \tab maximum product spacings estimation \cr \code{.nigFit.vmps} \tab minimum variance mps estimation. \cr \tab \emph{File: dist-nigFit.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{nigMode} \tab Computes mode of the NIG distribution.\cr \tab \emph{File: dist-nigMode.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{nigMean} \tab Returns true mean of the NIG distribution \cr \code{nigVar} \tab returns true variance of the NIG distribution \cr \code{nigSkew} \tab returns true skewness of the NIG distribution \cr \code{nigKurt} \tab returns true kurtosis of the NIG distribution \cr \code{nigMoments} \tab returns true n-th moment of the NIG distribution.\cr \tab \emph{File: dist-nigMoments.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{nigMED} \tab Returns true median of the NIG distribution \cr \code{nigIQR} \tab returns true inter quartal range of the NIG \cr \code{nigSKEW} \tab returns true robust skewness of the NIG \cr \code{nigKURT} \tab returns true robust kurtosis of the NIG.\cr \tab \emph{File: dist-nigRobMoments.R} } \bold{Generalized Hyperbolic Student-t Distribution:} \tabular{ll}{ \emph{Function ________________} \tab Description _________________________________________\cr \code{dght} \tab Returns density for the GHT distribution \cr \code{pght} \tab returns probability for the GHT distribution \cr \code{qght} \tab returns quantiles for the GHT distribution \cr \code{rght} \tab returns random variates for the GHT distribution.\cr \tab \emph{File: dist-ght.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{ghtFit} \tab Fits parameters of the GHT distribution.\cr \tab \emph{File: dist-ghtFit.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{ghtMode} \tab Computes mode of the GHT distribution.\cr \tab \emph{File: dist-ghtMode.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{ghtMean} \tab Returns true mean of the NIG distribution \cr \code{ghtVar} \tab returns true variance of the GHT distribution \cr \code{ghtSkew} \tab returns true skewness of the GHT distribution \cr \code{ghtKurt} \tab returns true kurtosis of the GHT distribution \cr \code{ghtMoments} \tab returns true n-th moment of the GHT distribution.\cr \tab \emph{File: dist-ghtMoments.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{ghtMED} \tab Returns true median of the GHT distribution \cr \code{ghtIQR} \tab returns true inter quartal range of the GHT \cr \code{ghtSKEW} \tab returns true robust skewness of the GHT \cr \code{ghtKURT} \tab returns true robust kurtosis of the GHT.\cr \tab \emph{File: dist-ghtRobMoments.R} } \bold{Stable Distribution:} \tabular{ll}{ \emph{Function ________________} \tab Description _________________________________________\cr \code{dstable} \tab Returns density for the stable distribution \cr \code{pstable} \tab returns probability for the stable distribution \cr \code{qstable} \tab returns quantiles for the stable distribution \cr \code{rstable} \tab returns random variates for the dtsble distribution.\cr \tab \emph{File: dist-stable.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{stableFit} \tab Fits parameters of a the stable distribution \cr \code{.phiStable} \tab Creates contour table for McCulloch estimators \cr \code{.PhiStable} \tab Contour table created by function .phiStable() \cr \code{.qStableFit} \tab Estimates parameters by McCulloch's approach \cr \code{.mleStableFit} \tab Estimates stable parameters by MLE approach \cr \code{.stablePlot} \tab Plots results of stable parameter estimates.\cr \tab \emph{File: dist-stableFit.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{stableMode} \tab Computes mode of the stable distribution.\cr \tab \emph{File: dist-stableMode.R} } \bold{Generalized Lambda Distribution:} \tabular{ll}{ \emph{Function ________________} \tab Description _________________________________________\cr \code{dgld} \tab Returns density for the GLD distribution \cr \code{pgld} \tab returns probability for the GLD distribution \cr \code{qgld} \tab returns quantiles for the GLD distribution \cr \code{rgld} \tab returns random variates for the GLD distribution.\cr \tab \emph{File: dist-gld.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{gldFit} \tab Fits parameters of the GLD distribution \cr \code{.gldFit.mle} \tab fits GLD using maximum log-likelihood \cr \code{.gldFit.mps} \tab fits GLD using maximum product spacings \cr \code{.gldFit.gof} \tab fits GLD using Goodness of Fit statistics \cr %\code{.ksGLD} \tab Kolmogorov-Smirnov Statistics \cr %\code{.cvmGLD} \tab Cramer-vonMise Statistics \cr %\code{.adGLD} \tab Anderson-Darling Statistics \cr \code{.gldFit.hist} \tab fits GLD using a histogram fit \cr \code{.gldFit.rob} \tab fits GLD using robust moments fit.\cr \tab \emph{File: dist-gldFit.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{gldMode} \tab Computes mode of the GLD distribution.\cr \tab \emph{File: dist-gldMode.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{gldMED} \tab Returns true median of the GLD distribution \cr \code{gldIQR} \tab returns true inter quartal range of the GLD \cr \code{gldSKEW} \tab returns true robust skewness of the GLD \cr \code{gldKURT} \tab returns true robust kurtosis of the GLD.\cr \tab \emph{File: dist-gldRobMoments.R} } \bold{Spline Smoothed Distribution:} \tabular{ll}{ \emph{Function ________________} \tab Description _________________________________________\cr \code{dssd} \tab Returns spline smoothed density function \cr \code{pssd} \tab returns spline smoothed probability function \cr \code{qssd} \tab returns spline smoothed quantile function \cr \code{rssd} \tab returns spline smoothed random variates.\cr \tab \emph{File: dist-ssd.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{ssdFit} \tab Fits parameters for a spline smoothed distribution.\cr \tab \emph{File: dist-ssdFit.R} } } \section{3. Hypthesis Testing}{ \bold{One Sample Tests:} \tabular{ll}{ \emph{Function ________________} \tab Description _________________________________________\cr \code{ksnormTest} \tab One sample Kolmogorov-Smirnov normality test \cr \code{shapiroTest} \tab Shapiro-Wilk normality test \cr \code{jarqueberaTest} \tab Jarque-Bera normality test \cr \code{normalTest} \tab Normality tests S-Plus compatible call \cr \code{dagoTest} \tab D'Agostino normality test \cr \code{adTest} \tab Anderson-Darling normality test \cr \code{cvmTest} \tab Cramer-von Mises normality test \cr \code{lillieTest} \tab Lilliefors (KS) normality test \cr \code{pchiTest} \tab Pearson chi-square normality test \cr \code{sfTest} \tab Shapiro-Francia normality test \cr \code{jbTest} \tab Finite sample adjusted JB LM and ALM test \cr \tab \emph{File: test-normalTest.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{locationTest} \tab Performs locations tests on two samples \cr \code{.tTest} \tab Unpaired t test for differences in mean \cr \code{.kw2Test} \tab Kruskal-Wallis test for differences in locations \cr \tab \emph{File: test-locationTest.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{scaleTest} \tab Performs scale tests on two samples \cr \code{.ansariTest} \tab Ansari-Bradley test for differences in scale \cr \code{.moodTest} \tab Mood test for differences in scale \cr \tab \emph{File: test-scaleTest.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{varianceTest} \tab Performs variance tests on two samples \cr \code{.varfTest} \tab F test for differences in variances \cr \code{.bartlett2Test} \tab Bartlett's test for differences in variances \cr \code{.fligner2Test} \tab Fligner-Killeen test for differences in variances \cr \tab \emph{File: test-variance Test.R} } \bold{Two Sample Tests:} \tabular{ll}{ \emph{Function ________________} \tab Description _________________________________________\cr \code{ks2Test} \tab Performs a two sample Kolmogorov-Smirnov test \cr \code{correlationTest} \tab Performs correlation tests on two samples \cr \code{pearsonTest} \tab Pearson product moment correlation coefficient \cr \code{kendallTest} \tab Kendall's tau correlation test \cr \code{spearmanTest} \tab Spearman's rho correlation test \cr \tab \emph{File: test-ks2Test.R} } \bold{Test Utilities:} \tabular{ll}{ \emph{Function ________________} \tab Description _________________________________________\cr \code{'fHTEST'} \tab S4 Class Representation \cr \code{show.fHTEST} \tab S4 Print Method \cr \code{.jbALM} \tab Jarque Bera Augmented Lagrange Multiplier Data \cr \code{.jbLM} \tab Jarque-Bera Lagrange Multiplier Data \cr \code{.jbTable} \tab Finite sample p values for the Jarque Bera test \cr \code{.jbPlot} \tab Plots probabilities \cr \code{.pjb} \tab Returns probabilities for JB given quantiles \cr \code{.qjb} \tab Returns quantiles for JB given probabilities \cr \tab \emph{File: test-fHTEST.R} } } \section{4. Plotting Routines}{ \bold{Financial Time Series Plots:} \tabular{ll}{ \emph{Function ________________} \tab Description _________________________________________\cr \code{seriesPlot} \tab Dispalys a time series plot \cr \code{cumulatedPlot} \tab displays cumulated series give returns \cr \code{returnPlot} \tab displays returns given cumulated series \cr \code{drawdownPlot} \tab displays drawdown series from returns.\cr \tab \emph{File: plot-seriesPlot.R} } \bold{Correlation Plots:} \tabular{ll}{ \emph{Function ________________} \tab Description _________________________________________\cr \code{acfPlot} \tab Displays tailored ACF plot \cr \code{pacfPlot} \tab displays tailored partial ACF plot \cr \code{teffectPlot} \tab displays the Taylor effect \cr \code{lacfPlot} \tab displays lagged autocorrelations.\cr \tab \emph{File: plot-acfPlot.R} } \bold{Distribution Plots:} \tabular{ll}{ \emph{Function ________________} \tab Description _________________________________________\cr \code{histPlot} \tab Returns tailored histogram plot \cr \code{densityPlot} \tab returns tailored density plot \cr \code{logDensityPlot} \tab returns tailored log density plot.\cr \tab \emph{File: plot-histPlot.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{boxPlot} \tab Returns side-by-side standard box plot \cr \code{boxPercentilePlot} \tab returns box-percentile plot.\cr \tab \emph{File: plot-boxPlot.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{qqnormPlot} \tab Returns normal quantile-quantile plot \cr \code{qqnigPlot} \tab returns NIG quantile-quantile plot \cr \code{qqghtPlot} \tab returns GHT quantile-quantile plot \cr \code{qqgldPlot} \tab returns GLD quantile-quantile plot.\cr \tab \emph{File: plot-qqPlot.R} } \bold{Aggregation Plots:} \tabular{ll}{ \emph{Function ________________} \tab Description _________________________________________\cr \code{scalinglawPlot} \tab displays scaling law behavior.\cr \tab \emph{File: plot-scalinglawPlot.R} } } \section{5. Matrix Computations and Linear Algebra}{ \bold{Matrix Operations:} \tabular{ll}{ \emph{Function ________________} \tab Description _________________________________________\cr \code{kron} \tab Returns the Kronecker product.\cr \tab \emph{File: matrix-kron.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{vec} \tab Stacks a matrix as column vector \cr \code{vech} \tab stacks a lower triangle matrix.\cr \tab \emph{File: matrix-vech.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{pdl} \tab Returns regressor matrix for polynomial lags.\cr \tab \emph{File: matrix-pdl.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{tslag} \tab Returns Lagged/leading vector/matrix.\cr \tab \emph{File: matrix-tslag.R} } \bold{Linear Algebra:} \tabular{ll}{ \emph{Function ________________} \tab Description _________________________________________\cr \code{inv} \tab Returns the inverse of a matrix.\cr \tab \emph{File: matrix-inv.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{norm} \tab Returns the norm of a matrix.\cr \tab \emph{File: matrix-norm.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{rk} \tab Returns the rank of a matrix.\cr \tab \emph{File: matrix-rk.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{tr} \tab Returns the trace of a matrix.\cr \tab \emph{File: matrix-tr.R} } \bold{Matrix Utilities:} \tabular{ll}{ \emph{Function ________________} \tab Description _________________________________________\cr \code{isPositiveDefinite} \tab Checks if a matrix is positive definite \cr \code{makePositiveDefinite} \tab forces a matrix to be positive definite.\cr \tab \emph{File: matrix-posDefinite.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{colVec} \tab Creates a column vector from a data vector \cr \code{rowVec} \tab creates a row vector from a data vector.\cr \tab \emph{File: matrix-colVec.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{gridVector} \tab Creates from two vectors rectangular grid.\cr \tab \emph{File: matrix-gridVector.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{triang} \tab Extracs lower tridiagonal part from a matrix \cr \code{Triang} \tab Extracs upper tridiagonal part from a matrix.\cr \tab \emph{File: matrix-triang.R} } \bold{Matrix Examples:} \tabular{ll}{ \emph{Function ________________} \tab Description _________________________________________\cr \code{hilbert} \tab Creates a Hilbert matrix.\cr \tab \emph{File: matrix-hilbert.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{pascal} \tab Creates a Pascal matrix.\cr \tab \emph{File: matrix-pascal.R} } } \section{6. Utility Functions}{ \bold{Color Utilities:} \tabular{ll}{ \emph{Function ________________} \tab Description _________________________________________\cr \code{colorLocator} \tab Plots Rs 657 named colors for selection \cr \code{colorMatrix} \tab returns matrix of R's color names.\cr \tab \emph{File: utils-colorLocator.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{colorTable} \tab Table of Color Codes and Plot Colors itself.\cr \tab \emph{File: utils-colorTable.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{rainbowPalette} \tab Contiguous rainbow color palette \cr \code{heatPalette} \tab Contiguous heat color palette \cr \code{terrainPalette} \tab Contiguous terrain color palette \cr \code{topoPalette} \tab Contiguous topo color palette \cr \code{cmPalette} \tab Contiguous cm color palette \cr \code{greyPalette} \tab R's gamma-corrected gray palette \cr \code{timPalette} \tab Tim's Matlab like color palette \cr \code{rampPalette} \tab Color ramp palettes \cr \code{seqPalette} \tab Sequential color brewer palettes \cr \code{divPalette} \tab Diverging color brewer palettes \cr \code{qualiPalette} \tab Qualified color brewer palettes \cr \code{focusPalette} \tab Red, green blue focus palettes \cr \code{monoPalette} \tab Red, green blue mono palettes.\cr \tab \emph{File: utils-colorPalette.R} } \bold{Plot Utilities:} \tabular{ll}{ \emph{Function ________________} \tab Description _________________________________________\cr \code{symbolTable} \tab Shows a table of plot symbols.\cr \tab \emph{File: utils-symbolTable.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{characterTable} \tab Shows a table of character codes.\cr \tab \emph{File: utils-characterTable.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{decor} \tab Adds horizontal grid and L shaped box \cr \code{hgrid} \tab adds horizontal grid lines \cr \code{vgrid} \tab adds vertical grid lines \cr \code{boxL} \tab adds L-shaped box \cr \code{box_} \tab adds unterlined box \cr \code{.xrug} \tab adds rugs on x axis \cr \code{.yrug} \tab adds rugs on y axis \cr \code{copyright} \tab adds copyright notice.\cr \tab \emph{File: utils-decor.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{interactivePlot} \tab Plots several graphs interactively.\cr \tab \emph{File: utils-interactivePlot.R} } \bold{Special Function Utilities:} \tabular{ll}{ \emph{Function ________________} \tab Description _________________________________________\cr \code{Heaviside} \tab Computes Heaviside unit step function \cr \code{Sign} \tab another signum function \cr \code{Delta} \tab computes delta function \cr \code{Boxcar} \tab computes boxcar function \cr \code{Ramp} \tab computes ramp function.\cr \tab \emph{File: utils-heaviside.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr \code{tsHessian} \tab Computes Two Sided Hessian matrix.\cr \tab \emph{File: utils-hessian.R} } \bold{Other Utilities:} \tabular{ll}{ \emph{Function ________________} \tab Description _________________________________________\cr \code{.unirootNA} \tab Computes zero of a function without error exit.\cr \tab \emph{File: utils-unitrootNA.R} \cr \tab \cr \emph{Function ________________} \tab Description _________________________________________\cr % in R % \code{getCall} \tab Extracts the call slot from a S4 object \cr \code{getModel} \tab extracts the model slot from a S4 object \cr \code{getTitle} \tab extracts the title slot from a S4 object \cr \code{getDescription} \tab extracts the description slot \cr \code{getSlot} \tab extracts a specified slot from a S4 object.\cr \tab \emph{File: utils-getS4.R} } } \section{B. Code and Function Dependencies:}{ 1. Dependences and Suggested Packages 2. Builtin Functions 3. Compiled Fortran and C Code } \section{1. Dependences and Suggested Packages:}{ Required packages include: \tabular{ll}{ \emph{Dependences _________________} \tab Description _________________________________________\cr \code{MASS} \tab R base package \cr \code{methods} \tab R base package \cr \code{timeDate} \tab Rmetrics package \cr \code{timeSeries} \tab Rmetrics package \cr \tab \cr \emph{Suggested ___________________} \tab Description _________________________________________\cr \code{akima} \tab contributed R package \cr \code{spatial} \tab contributed R package \cr \code{RUnit} \tab contributed R package \cr \code{tcltk} \tab contributed R package.} } \section{2. Builtin Functions:}{ Builtin functions are borrowed from contributed R packages and other sources. There are several reasons why we have modified and copied code from other sources and included in this package. * The builtin code is not available on Debian, so that Linux users have no easy acces to this code. * The code conflicts with other code from this package or conflicts with Rmetrics design objectives. * We only need a very small piece of functionality from the original package which may depend on other packages which are not needed. * The package from which we builtin the code is under current development, so that the functions often change and thus leads to unexpectect behavior in the Rmetrics packages * The package may be incompatible since it uses other time date and time series classes than the timDate and timeSeries objects and methods from Rmetrics. We put the code in script files named \emph{builtin-funPackage.R} where "fun" denotes the (optional) major function name, and "Package" the name of the contributed package from which we copied the original code. Builtin functions include: \tabular{ll}{ \emph{Builtin _________________} \tab Description _________________________________________\cr \code{gelGmm} \tab gll function from gmm package \cr \code{gmmGMM} \tab gmm function from gmm package \cr \code{kweightsSandwhich} \tab kweights from sandwhich package \cr \code{glGld} \tab glfunctions from gld package \cr \code{ssdenGss} \tab ssden from the gss package\cr \code{hypHyperbolicDist} \tab hyp from HyperbolicDist package.} } \section{3. Compiled Fortran and C Code:}{ \tabular{ll}{ \emph{Fortran/C _________________} \tab Description _________________________________________\cr \code{gld.c} \tab source code from gld package \cr \code{nig.c} \tab source code from Kersti Aas \cr \code{gss.f} \tab source code fromsandwhich package.} } \section{C. Copyright and Licencies:}{ Copyright Rmetrics Association } \section{D. Contact Address:}{ www.rmetrics.org Addresses: } \keyword{models} fBasics/inst/0000755000176000001440000000000011720123747012635 5ustar ripleyusersfBasics/inst/unitTests/0000755000176000001440000000000012030001147014617 5ustar ripleyusersfBasics/inst/unitTests/runit.unitrootNA.R0000644000176000001440000000302311370220776020223 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DESCRIPTION: # .unirootNA Computes zero without error exit ################################################################################ test.unitrootNA <- function() { NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.symbolTable.R0000644000176000001440000000316611370220776020406 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: CHARACTER, SYMBOL AND COLOR TABLES: # symbolTable Shows a table of plot symbols ################################################################################ test.symbolTable <- function() { # Symbol Table: args(symbolTable) par(mfrow = c(1, 1)) symbolTable() # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.subStars.R0000644000176000001440000000316611370220776017737 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DESCRIPTION: # .emptyPlot Creates an empty plot page # .subPlot Creates a sub plot # .cnvrt.coords Converts coordinates ################################################################################ test.subplot <- function() { # Plot: NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.subPlot.R0000644000176000001440000000316611370220776017561 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DESCRIPTION: # .emptyPlot Creates an empty plot page # .subPlot Creates a sub plot # .cnvrt.coords Converts coordinates ################################################################################ test.subplot <- function() { # Plot: NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.sliderMenu.R0000644000176000001440000000340411370220776020233 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: SLIDER MENU: # .sliderMenu Opens a teching demo slider menu # .tdSliderMenu Opens a teching demo slider and button menu ################################################################################ test.sliderMenu <- function() { NA # Return Value: return() } # ------------------------------------------------------------------------------ test.sliderMenu <- function() { NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.skewness.R0000644000176000001440000000432511370220776017771 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: SKEWNESS: # skewness.default Default method # skewness.data.frame Method for objects of class data.frame # skewness.POSIXct Method for objects of class POSIXct # skewness.POSIXlt Method for objects of class POSIXlt ################################################################################ test.skewness.default <- function() { NA # Return Value: return() } # ------------------------------------------------------------------------------ test.skewness.data.frame <- function() { NA # Return Value: return() } # ------------------------------------------------------------------------------ test.skewness.POSIXct <- function() { NA # Return Value: return() } # ------------------------------------------------------------------------------ test.skewness.POSIXlt <- function() { NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.rowStats.R0000644000176000001440000000446111370220776017756 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: ROW AND COLUMN STATISTICS: # rowStats Computes sample statistics by row # rowAvgs Computes sample mean by row # rowVars Computes sample variance by row # rowStdevs Computes sample variance by row # rowSkewness Computes sample skewness by row # rowKurtosis Computes sample kurtosis by row # rowCumsums Computes sample cumulated sums by row ################################################################################ test.colStats = function() { # colStats Computes sample statistics by column # colAvgs Computes sample mean by column # colVars Computes sample variance by column # colStdevs Computes sample variance by column # colSkewness Computes sample skewness by column # colKurtosis Computes sample kurtosis by column # colCumsums Computes sample cumulated sums by column # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.rgb.R0000644000176000001440000000440011370220776016673 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: UTILITIES: # .asRGB Converts any R color to RGB (red/green/blue) # .chcode Changes from one to another number system # .hex.to.dec Converts heximal numbers do decimal numbers # .dec.to.hex Converts decimal numbers do heximal numbers ################################################################################ test.hexCode <- function() { # .chcode Changes from one to another number system # .hex.to.dec Converts heximal numbers do decimal numbers # .dec.to.hex Converts decimal numbers do heximal numbers # Change from one to another number system # .chcode(b, base.in = 2, base.out = 10, digits="0123456789ABCDEF") # Convert heximal numbers do decimal numbers # .hex.to.dec(b) .hex.to.dec("AA") # Convert decimal numbers do heximal numbers # .dec.to.hex(b) .dec.to.hex(170) # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.print.R0000644000176000001440000000320511370220776017257 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: CONTROL ATTRIBUTES: # print.control Prints unlisted control attributes # FUNCTION: DESCRIPTION: # .print Used in regression package ################################################################################ test.print <- function() { # Print: NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.plot.R0000644000176000001440000000440411370220776017103 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: INTERNAL USED PLOT FUNCTIONS: # .residualsPlot Returns a residual series plot # .acfPlot Returns a autocorrelation function plot # .pacfPlot Returns a partial ACF plot # .mrlPlot Returns a mean residual life plot # FUNCTION: INTERNAL USED BIVARIATE PLOT FUNCTIONS: # .responsesPlot Returns a response series plot # .firePlot Returns a fitted values vs.residuals plot # FUNCTION: INTERNAL THREE-DIMENSIONAL PLOT UTILITIES: # .circlesPlot Returns a circles plot indexing 3rd variable # .perspPlot Returns a perspective plot in 2 dimensions # .contourPlot Returns a contour plot in 2 dimensions # .histStack Returns a stacked histogram plot ################################################################################ test.plot <- function() { # Plot: NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.matrixAddon.R0000644000176000001440000002344111370220776020401 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # GENERATION: DESCRIPTION: # matrix R Creates a matrix from the given set of values # diag R Creates a diagonal matrix or extracts diagonals # triang M Extracs the lower tridiagonal part from a matrix # Triang M Extracs the upper tridiagonal part from a matrix # pascal M Creates a Pascal matrix # hilbert M Creates a Hilbert matrix # colVec M Creates a column vector from a data vector # rowVec M Creates a row vector from a data vector # as.matrix R Attempts to turn its argument into a matrix # is.matrix R Tests if its argument is a (strict) matrix # isPositiveDefinite M Checks if the matrix X is positive definite # makePositiveDefinite M Forces the matrix x to be positive definite # dimnames R Retrieves or sets the dimnames of an object # colnames|rownames R Retrieves or sets the row or column names # colIds|rowIds M ... use alternatively # colIds<-|rowIds<- M ... for assignments # SUBSETS: DESCRIPTION: # dim R Returns the dimension of a matrix object # ncol|nrow R Counts columns|rows of a matrix object # length R Counts elements of a matrix object # "["|"[[" R Subsets a matrix object # (Arith) R Elementwise Arithmetic: + - * / # (Lops) R Elementwise logical Ops: > < >= <= == != # cbind|rbind R Augments a matrix object by columns|rows # na.omit R Removes NA from a matrix object # BASIC STATISTICS: DESCRIPTION: # var R Returns the variance matrix # cov R Returns the covariance matrix # col|rowStats B calculates column|row statistics # col|rowMeans R calculates column|row means # col|rowAvgs B calculates column|row averages # col|rowVars B calculates column|row variances # col|rowStdevs B calculates column|row standard deviations # col|rowSkewness B calculates column|row skewness # col|rowKurtosis B calculates column|row kurtosis # col|rowCumsums B calculates column|row cumulated sums # LINEAR ALGEBRA: DESCRIPTION: # t R Returns the transposed matrix # det R Returns the determinant of a matrix # inv M returns the inverse of a matrix, synonyme # chol2inv R Returns the inverse of a matrix # norm M returns the norm of a matrix # rk M returns the rank of a matrix # tr M returns the trace of a matrix # %*% R Returns the product of two matrices # %x% R Returns the Kronecker product # kron S returns the Kronecker product # vec M is the operator that stacks a matrix # vech M is the operator that stacks the lower triangle # MORE LINEAR ALGEBRA: DESCRIPTION: # chol R Returns the Cholesky factor matrix # eigen R Returns eigenvalues and eigenvectors # svd R Returns the singular value decomposition # kappa R Returns the condition number of a matrix # qr R Returns the QR decomposition of a matrix # solve R Solves a system of linear equations # backsolve R ... use when the matrix is upper triangular # forwardsolve R ... use when the matrix is lower triangular # TIME SERIES DESCRIPTION: # tslag R Lagged/leading vector/matrix of selected orders # .tslag1 Internal Function used by tslag # pdl R Regressor matrix for polynomial distributed lags # NOTES: WHERE YOU FIND THE FUCTIONS? # R Basic R Package # B Rmetrics fBasics Package # M This Rmetrics fMultivar Package ################################################################################ test.creation = function() { # Create Pascal Matrix: P = pascal(3) P # Create lower triangle matrix L = triang(P) L # Extract diagonal part diag(P) # Return Value: return() } # ------------------------------------------------------------------------------ test.mathOps = function() { # Create Pascal Matrix: P = pascal(3) P # Add/Subtract/Multiply/Divide: X = P # Multiply matrix with a constant 3 * X # Multiply two matrices elementwise X * P # Multiplies rows/columns of a matrix by a vector X %*% diag(P) diag(P) %*% X # Return Value: return() } # ------------------------------------------------------------------------------ test.subsets = function() { # Create Pascal Matrix: P = pascal(3) P # Operate on Subsets of a Matrix: n = 3 i = 2 j = 3 D = diag(1:3) # Return the dimension of a matrix dim(P) # Get the last colum of a matrix P[, ncol(P)] # Delete a column of a matrix P[, -i] # Permute the columns of a matrix P[c(3, 1, 2), ] # Augments matrix horizontally cbind(P, D) # Return Value: return() } # ------------------------------------------------------------------------------ test.apply = function() { # Apply a function to all Elements of a Matrix: # Create Pascal Matrix: P = pascal(3) P # Return square root for each element sqrt(P) # Exponentiate the matrix elementwise exp(P) # Compute the median of each column apply(P, 2, "median") # Test on all elements of a matrix all( P > 2 ) # test on any element in a matrix any( P > 2 ) # Return Value: return() } # ------------------------------------------------------------------------------ test.moreOperations = function() { # More Matrix Operations: # Create Pascal Matrix: P = pascal(3) P # Create Diagonal Matrix: D = diag(1:3) # Return the product of two matrices P %*% D # Return the Kronecker Product P %x% D # Return the transposed matrix t(P) # Return the inverse of a matrix inv(P) # Return the norm of a matrix norm(P) # Return the determinante of a matrix det(P) # Return the rank of a matrix rk(P) # Return trace of a matrix tr(P) # Return the variance matrix var(P) # Return the covariance matrix cov(P) # Stack a matrix vec(P) # Stack the lower triangle vech(P) # Return Value: return() } # ------------------------------------------------------------------------------ test.linearAlgebra = function() { # More Linear Algebra: # Create Pascal Matrix: P = pascal(3) P # Example Matrix and Vector X = P b = c(1, 2, 3) # Return the Cholesky factor matrix chol(X) # Return eigenvalues and eigenvectors eigen(X) # Return the singular value decomposition svd(X) # Return the condition number of a matrix kappa(X) # Return the QR decomposition of a matrix qr(X) # Solve a system of linear equations # ... use backsolve when the matrix is upper triangular # ... use forwardsolve when the matrix is lower triangular solve(X, b) backsolve(Triang(X), b) solve(Triang(X), b) forwardsolve(triang(X), b) solve(triang(X), b) # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.listIndex.R0000644000176000001440000000306511370220776020072 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DESCRIPTION: # listIndex Extracts R package index ################################################################################ test.listIndex <- function() { # List Functions: print(listIndex(package = "fBasics")) # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.listFunctions.R0000644000176000001440000000355711370220776021001 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DESCRIPTION: # listFunctionss Lists all functions in a package # countFunctions Counts all functions in a package ################################################################################ test.listFunctions <- function() { # List Functions: print(listFunctions("fUtilities")) # Return Value: return() } # ------------------------------------------------------------------------------ test.countFunctions <- function() { # Count Functions: print(countFunctions("fUtilities")) # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.listDescription.R0000644000176000001440000000310111370220776021275 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DESCRIPTION: # listDescription Extracts R package description ################################################################################ test.listDescription <- function() { # List Description: print(listDescription()) # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.linearInterp.R0000644000176000001440000000344011370220776020560 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: BIVARIATE GRIDDED INTERPOLATION: # linearInterp Interpolates linearly irregularly spaced data points # linearInterpp Interpolates linearly pointwise ################################################################################ test.linearInterp <- function() { NA # Return Value: return() } # ------------------------------------------------------------------------------ test.linearInterp <- function() { NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.lcg.R0000644000176000001440000000530711370220776016675 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: PORTABLE INNOVATIONS: # set.lcgseed Sets initial random seed # get.lcgseed Gets the current valus of the random seed # FUNCTION: DISTRIBUTIONS: # runif.lcg Generates portable uniform linear congruational rvs # rnorm.lcg Generates portable normal linear congruational rvs # rt.lcg Generates portable Student-t linear congruational rvs ################################################################################ test.portableInnovations <- function() { # set.lcgseed Sets initial random seed # get.lcgseed Gets the current valus of the random seed # Set initial random seed - set.lcgseed(seed = 4711) set.lcgseed() # Get the current valus of the random seed - get.lcgseed() get.lcgseed() # Return Value: return() } # ------------------------------------------------------------------------------ test.randomNumbers <- function() { # runif.lcg Generates portable uniform linear congruational rvs # rnorm.lcg Generates portable normal linear congruational rvs # rt.lcg Generates portable Student-t linear congruational rvs # Uniform: x = runif.lcg(n = 1000, min = 0, max = 1) hist(x) # Normal: x = rnorm.lcg(n = 1000, mean = 0, sd = 1) hist(x) # Student-t: x = rt.lcg(n = 1000, df = 4) hist(x) # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.kurtosis.R0000644000176000001440000000435011370220776020010 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DESCRIPTION: # kurtosis.default Default method # kurtosis.data.frame Method for objects of class data.frame # kurtosis.POSIXct Method for objects of class POSIXct # kurtosis.POSIXlt Method for objects of class POSIXlt ################################################################################ test.kurtosis.default <- function() { NA # Return Value: return() } # ------------------------------------------------------------------------------ test.kurtosis.data.frame <- function() { NA # Return Value: return() } # ------------------------------------------------------------------------------ test.kurtosis.POSIXct <- function() { NA # Return Value: return() } # ------------------------------------------------------------------------------ test.kurtosis.POSIXlt <- function() { NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.krigeInterp.R0000644000176000001440000000305611370220776020412 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: BIVARIATE GRIDDED INTERPOLATION: # krigeInterp Kriges irregularly distributed data points ################################################################################ test.krigeInterp <- function() { NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.julian.R0000644000176000001440000000335511370220776017413 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DATE FUNCTIONS: # .fjulian Transforms formatted dates to julian day numbers # .julian Implements SPlus like 'julian' # .isPOSIX Checks for an object of class POSIX # .by2seconds Converts 'by' string into numeric value of seconds ################################################################################ test.fjulian <- function() { NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.interactivePlot.R0000644000176000001440000000475211370220776021307 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: PLOT UTILITIES: # interactivePlot Plots several graphs interactively ################################################################################ test.interactivePlot <- function() { # interactivePlot(x, choices = paste("Plot", 1:9), # plotFUN = paste("plot.", 1:9, sep = ""), which = "all", ...) # Test Plot Function: testPlot = function(x, which = "all", ...) { # Plot Function and Addons: plot.1 <<- function(x, ...) plot(x, ...) plot.2 <<- function(x, ...) acf(x, ...) plot.3 <<- function(x, ...) hist(x, ...) plot.4 <<- function(x, ...) qqnorm(x, ...) # Plot: interactivePlot(x, choices = c("Series Plot", "ACF", "Histogram", "QQ Plot"), plotFUN = c("plot.1", "plot.2", "plot.3", "plot.4"), which = which, ...) # Return Value: invisible() } # Plot: par(mfrow = c(2, 2), cex = 0.7) testPlot(rnorm(500)) # Reorder 2: par(mfrow = c(2, 1), cex = 0.7) testPlot(rnorm(500), which = c(1, 2)) # Try: # par(mfrow = c(1,1)); testPlot(rnorm(500), which = "ask") # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.gridVector.R0000644000176000001440000000325211370220776020235 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DESCRIPTION: # gridVector Creates from two vectors rectangular grid coordinates ################################################################################ test.gridVector = function() { # Grid Vector - gridVector(x, y) gridVector(1:3, 1:3) as.data.frame(gridVector(1:3, 1:3)) as.matrix(as.data.frame(gridVector(1:3, 1:3))) # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.getS4.R0000644000176000001440000000360211370220776017112 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: GENERAL EXTRACTORS FOR S4: # isS4 Checks if a function is a S4 object # getCall Extracts the call slot from a S4 object # getModel Extracts the model slot from a S4 object # getTitle Extracts the title slot from a S4 object # getDescription Extracts the description slot from a S4 object # getSlot Extracts a specified slot from a S4 object ################################################################################ test.isS4 <- function() { NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.fHTEST.R0000644000176000001440000000314011370220776017156 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: NORMALITY TESTS: # 'fHTEST' S4 Class Representation # show.fHTEST S4 Print Method ################################################################################ test.fHTEST = function() { # Class: getClass("fHTEST") # Slots: getSlots("fHTEST") # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.distCheck.R0000644000176000001440000000302311370220776020022 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DESCRIPTION: # .distCheck Checks consistency of distributions ################################################################################ test.distCheck <- function() { NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.description.R0000644000176000001440000000302111370220776020442 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DESCRIPTION: # description Sets default description string ################################################################################ test.description <- function() { NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.decor.R0000644000176000001440000000277111370220776017226 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: # decor # hgrid # vgrid # boxL # box_ # xrug # yrug # copyright ################################################################################ test.decor <- function() { NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.colorTable.R0000644000176000001440000000312711370220776020214 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DESCRIPTION: # colorTable Shows a table of plot color codes ################################################################################ test.colorTable = function() { # Color Table: args(colorTable) par(mfrow = c(1, 1)) colorTable() # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.colorPalette.R0000644000176000001440000000452011370220776020561 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: COLOR PALETTES: # rainbowPalette Creates a rainbow color palette # heatPalette Creates a heat color palette # terrainPalette Creates a terrain color palette # topoPalette Creates a topo color palette # cmPalette Creates a cm color palette # greyPalette Creates a grey palette # timPalette Creates a cyan, yellow, to orange palette # FUNCTION: COLOR RAMPS: # rampPalette Creates a color ramp palette # seqPalette Creates a sequential color palette # divPalette Creates a diverging color palette # qualiPalette Creates a qualitative color palette # focusPalette Creates a focus color palette # monoPalette Creates a mono color palette ################################################################################ test.greyPalette <- function() { # Grey Palette: args(greyPalette) greyPalette() # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.colorLocator.R0000644000176000001440000000313211370220776020564 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: COLOR NAMES: # colorLocator Plots R's 657 named colors for selection # colorMatrix Returns matrix of R's color names ################################################################################ test.colorLocator <- function() { NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.colStats.R0000644000176000001440000000466411370220776017731 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: COLUMN STATISTICS: # colStats Computes sample statistics by column # colSums Computes sums of all values in each column # colMeans Computes means of all values in each column # colSds Computes standardard deviation of each column # colVars Computes sample variance by column # colSkewness Computes sample skewness by column # colKurtosis Computes sample kurtosis by column # colMaxs Computes maximum values in each colum # colMins Computes minimum values in each colum # colProds Computes product of all values in each colum # FUNCTION: NO LONGER USED: # colAvgs Computes sample mean by column # colStdevs Computes sample standard deviation by column # mean.timeSeries Computes sample means by column # var.timeSeries Computes sample variance by column ################################################################################ test.colStats <- function() { NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.characterTable.R0000644000176000001440000000315411370220776021032 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DESCRIPTION: # characterTable Shows a table of character codes ################################################################################ test.characterTable <- function() { # Character Table: args(characterTable) par(mfrow = c(1, 1)) characterTable() # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.baseMethods.R0000644000176000001440000000724011370220776020364 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: BASIC EXTENSIONS: # align.default align default method # atoms.default atoms default method # attach.default attach default method # colnames<-.default colnames<- default method # cor.default cor default method # cov.default var default method # log.default log default method # outlier.default outlier default method # rownames<-.default rownames<- default method # rank.default rank default method # sample.default sample default method # sort.default sort default method # stdev.default stdev default method # termPlot.default termPlot default method # var.default var default method # volatility.default volatility default method ################################################################################ test.as.align.default <- function() { NA # Return Value: return() } # ------------------------------------------------------------------------------ test.colnames.default = function() { # "colnames<-.default" colnames<- default method # Row and Column Names: m = matrix(1:8, ncol = 2) m # Set Names: colnames(m) <- c("A", "B") m # Get Names: colnames(m) # Return Value: return() } # ------------------------------------------------------------------------------ test.rownames.default = function() { # "rownames<-.default" rownames<- default methodd # Row and Column Names: m = matrix(1:8, ncol = 2) m # Set Names: rownames(m) = as.character(1:4) m # Get Names: rownames(m) # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.asPOSIXlt.R0000644000176000001440000000376711370220776017726 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DESCRIPTION: # as.POSIXlt.default Default Method ################################################################################ # IMPORTANT NOTE: # as.POSIXlt was introduced as generic function in R 2.7.0 # Preliminarily, keep the function here to ne downward # compatible with previous R Versions. DW as.POSIXlt.default <- function() { NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.as.matrix.ts.R0000644000176000001440000000407511370220776020464 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DESCRIPTION: # as.matrix.ts Converts univariate ts to 1-column matrix # as.matrix.mts Converts multivariate ts to matrix ################################################################################ test.as.matrix.ts <- function() { NA # Return Value: return() } # ------------------------------------------------------------------------------ as.matrix.mts <- function() { NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.akimaInterp.R0000644000176000001440000000344511370220776020375 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: BIVARIATE GRIDDED INTERPOLATION: # akimaInterp Interpolates and smoothes irregularly spaced points # akimaInterpp Interpolates and smoothes pointwise ################################################################################ test.akimaInterp <- function() { NA # Return Value: return() } # ------------------------------------------------------------------------------ test.akimaInterpp <- function() { NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.TwoSampleTests.R0000644000176000001440000001327711370220776021073 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2006, Diethelm Wuertz, GPL # Diethelm Wuertz # info@rmetrics.org # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DISTRIBUTIONAL TESTS: # ks2Test Performs a two sample Kolmogorov-Smirnov test # FUNCTION: LOCATION TESTS: # locationTest Performs locations tests on two samples # .tTest Unpaired t test for differences in mean # .kw2Test Kruskal-Wallis test for differences in locations # FUNCTION: VARIANCE TESTS: # varianceTest Performs variance tests on two samples # .varfTest F test for differences in variances # .bartlett2Test Bartlett's test for differences in variances # .fligner2Test Fligner-Killeen test for differences in variances # FUNCTION: SCALE TESTS: # scaleTest Performs scale tests on two samples # .ansariTest Ansari-Bradley test for differences in scale # .moodTest Mood test for differences in scale # dansariw Returns density of the Ansari W statistic # pansariw Returns probabilities of the Ansari W statistic # qansariw Returns quantiles of the Ansari W statistic # FUNCTION: CORRELATION TESTS: # correlationTest Performs correlation tests on two samples # pearsonTest Pearson product moment correlation coefficient # kendallTest Kendall's tau correlation test # spearmanTest Spearman's rho correlation test ################################################################################ test.distributionTest = function() { # Data: RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") set.seed(4711, kind = "Marsaglia-Multicarry") X = rnorm(100) Y = rt(50, df = 3) # Two Sample Kolmogorov-Smirnov Test: TEST = ks2Test(X, Y) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") # Return Value: return() } # ------------------------------------------------------------------------------ test.locationTests = function() { # Data: RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") set.seed(4711, kind = "Marsaglia-Multicarry") X = rnorm(100) Y = rt(50, df = 3) # Location t-Test: TEST = fBasics:::.tTest(X, Y) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") # Location kw2-Test: TEST = fBasics:::.kw2Test(X, Y) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") # Return Value: return() } # ------------------------------------------------------------------------------ test.varianceTests = function() { # Data: RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") set.seed(4711, kind = "Marsaglia-Multicarry") X = rnorm(100) Y = rt(50, df = 3) # Variance F-Test: TEST = fBasics:::.varfTest(X, Y) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") # Variance Bartlett-Test: TEST = fBasics:::.bartlett2Test(X, Y) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") # Variance Fligner-Test: TEST = fBasics:::.fligner2Test(X, Y) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") # Return Value: return() } # ------------------------------------------------------------------------------ test.scaleTests = function() { # Data: RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") set.seed(4711, kind = "Marsaglia-Multicarry") X = rnorm(100) Y = rt(50, df = 3) # Scale Ansari-Test: TEST = fBasics:::.ansariTest(X, Y) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") # Scale Mood-Test: TEST = fBasics:::.moodTest(X, Y) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") # Return Value: return() } # ------------------------------------------------------------------------------ test.correlationTests = function() { # Data: RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") set.seed(4711, kind = "Marsaglia-Multicarry") X = rnorm(100) Y = rt(100, df = 3) # Correlation Pearson-Test: TEST = pearsonTest(X, Y) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") # Correlation Kendall-Test: TEST = kendallTest(X, Y) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") # Correlation Spearman-Test: TEST = spearmanTest(X, Y) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.Sys.putenv.R0000644000176000001440000000354711370220776020232 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DESCRIPTION: # Sys.putenv depreciated after 2.4.1 # head.ts Adds head method ################################################################################ test.Sys.putenv <- function() { NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.StylizedFacts.R0000644000176000001440000001355011370220776020717 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2006, Diethelm Wuertz, GPL # Diethelm Wuertz # info@rmetrics.org # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DESCRIPTION: # acfPlot Displays autocorrelations function plot # pacfPlot Displays partial autocorrelation function plot # teffectPlot Estimates and plots the Taylor effect # lmacfPlot Estimates and plots the long memory ACF # lacfPlot Plots lagged autocorrelations # .logpdfPlot Returns a pdf plot on logarithmic scale(s) # .qqgaussPlot Returns a Gaussian quantile-quantile plot # scalinglawPlot Evaluates and plots scaling law behavior ################################################################################ test.acfPlot = function() { # MSFT Data: msft.dat = MSFT msft = msft.dat[, 1] msft.vol = msft.dat[ , 5]/10^6 msft.ret = returns(msft) # Graph Frame: par(mfrow = c(1, 1)) # acfPlot - acfPlot(x = msft.ret) # acfPlot - acfPlot(x = msft.ret, labels = FALSE) # Return Value: return() } # ------------------------------------------------------------------------------ test.pacfPlot = function() { # MSFT Data: msft.dat = MSFT msft = msft.dat[, 1] msft.vol = msft.dat[ , 5]/10^6 msft.ret = returns(msft) # Graph Frame: par(mfrow = c(1, 1)) # pacfPlot - pacfPlot(x = msft.ret) # pacfPlot - pacfPlot(x = msft.ret, labels = FALSE) # Return Value: return() } # ------------------------------------------------------------------------------ test.teffectPlot = function() { # MSFT Data: msft.dat = MSFT msft = msft.dat[, 1] msft.vol = msft.dat[ , 5]/10^6 msft.ret = returns(msft) # Graph Frame: par(mfrow = c(1, 1)) # teffectPlot - teffectPlot(x = msft.ret) # teffectPlot - teffectPlot(x = msft.ret, labels = FALSE) # Return Value: return() } # ------------------------------------------------------------------------------ test.lmacfPlot = function() { # MSFT Data: msft.dat = MSFT msft = msft.dat[, 1] msft.vol = msft.dat[ , 5]/10^6 msft.ret = returns(msft) # Graph Frame: par(mfrow = c(1, 1)) # lmacfPlot - ## lmacfPlot(x = abs(msft.ret), type = "acf") ## lmacfPlot(x = abs(msft.ret), type = "hurst") # ... CHECK ACF OF RETURNS # lmacfPlot - ## lacfPlot(x = msft, n = 4, type = "values") ## CHECK !!! # lmacfPlot - ## lmacfPlot(x = abs(msft.ret), type = "acf", labels = FALSE) ## lmacfPlot(x = abs(msft.ret), type = "hurst", labels = FALSE) # ... CHECK ACF OF RETURNS # lmacfPlot - ## lacfPlot(x = msft, n = 4, labels = FALSE, type = "values") ## CHECK !!! # Return Value: return() } # ------------------------------------------------------------------------------ test.lacfPlot = function() { # MSFT Data: msft.dat = MSFT msft = msft.dat[, 1] msft.vol = msft.dat[ , 5]/10^6 msft.ret = returns(msft) # Graph Frame: par(mfrow = c(1, 1)) # Return Value: return() } # ------------------------------------------------------------------------------ test.logpdfPlot = function() { # MSFT Data: msft.dat = MSFT msft = msft.dat[, 1] msft.vol = msft.dat[ , 5]/10^6 msft.ret = returns(msft) # Graph Frame: par(mfrow = c(1, 1)) # logpdfPlot - fBasics:::.logpdfPlot(x = msft.ret, labels = FALSE) fBasics:::.logpdfPlot(x = msft.ret, type = "log-log") # ... CHECK WARNINGS # ... CHECK COLORS # logpdfPlot - fBasics:::.logpdfPlot(x = msft.ret, labels = FALSE) fBasics:::.logpdfPlot(x = msft.ret, type = "log-log", labels = FALSE) # ... CHECK WARNINGS # ... CHECK COLORS # Return Value: return() } # ------------------------------------------------------------------------------ test.qqgausPlot = function() { # MSFT Data: msft.dat = MSFT msft = msft.dat[, 1] msft.vol = msft.dat[ , 5]/10^6 msft.ret = returns(msft) # Graph Frame: par(mfrow = c(1, 1)) # qqgaussPlot - fBasics:::.qqgaussPlot(x = msft.ret) # qqgaussPlot - fBasics:::.qqgaussPlot(x = msft.ret, labels = FALSE) # Return Value: return() } # ------------------------------------------------------------------------------ test.scalinglawPlot = function() { # MSFT Data: msft.dat = MSFT msft = msft.dat[, 1] msft.vol = msft.dat[ , 5]/10^6 msft.ret = returns(msft) # Graph Frame: par(mfrow = c(1, 1)) # scalinglawPlot - scalinglawPlot(x = msft.ret, span = 4) # ... CHECK COLORS # scalinglawPlot - scalinglawPlot(x = msft.ret, span = 4, labels = FALSE) # ... CHECK COLORS # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.ReturnSeriesBasics.R0000644000176000001440000001160111370220776021701 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2007, Diethelm Wuertz, GPL # Diethelm Wuertz # info@rmetrics.org # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: TAILORED PLOT FUNCTIONS: # seriesPlot Returns a tailored return series plot # histPlot Returns a tailored histogram plot # densityPlot Returns a tailored kernel density estimate plot # qqnormPlot Returns a tailored normal quantile-quantile plot # FUNCTION: BASIC STATISTICS: # basicStats Returns a basic statistics summary # FUNCTION: DESCRIPTION: # .distCheck Checks consistency of distributions # FUNCTION: SPLUS FUNCTIONALITY: # stdev S-PLUS: Returns the standard deviation of a vector ################################################################################ test.seriesPlot = function() { # Description: # Returns a tailored return series plot # Time Series: tS = MSFT[, "Close"] # Series Plot: par(mfrow = c(2, 1)) seriesPlot(tS) seriesPlot(tS) points(tS, col = "red", pch = 19, cex = 0.7) # Return Value: return() } # ------------------------------------------------------------------------------ test.histPlot = function() { # Description: # Returns a tailored histogram plot # Time Series: tS = MSFT[, "Close"] # Histogram Plot: par(mfrow = c(1, 1)) histPlot(tS) histPlot(tS, add.fit = FALSE) # Return Value: return() } # ------------------------------------------------------------------------------ test.densityPlot = function() { # Description: # Returns a tailored kernel density estimate plot # Time Series: tS = MSFT[, "Close"] # Density Plot: par(mfrow = c(1, 1)) densityPlot(tS) # Return Value: return() } # ------------------------------------------------------------------------------ test.qqnormPlot = function() { # Description: # Returns a tailored normal quantile-quantile plot # Time Series: tS = MSFT[, "Close"] # Quantile Plot: par(mfrow = c(1, 1)) qqnormPlot(tS) # Return Value: return() } # ------------------------------------------------------------------------------ test.basicStats = function() { # Description: # Returns a basic statistics summary # Time Series: tS = MSFT Close = tS[, "Close"] # Univariate timeSeries - basicStats(x, ci = 0.95) basicStats(Close) # basicStats(as.numeric(Close)) basicStats(as.matrix(Close)) basicStats(as.data.frame(Close)) basicStats(as.ts(Close)) # Multivariate - timeSeries - basicStats(x, ci = 0.95) basicStats(tS) basicStats(as.matrix(tS)) basicStats(as.data.frame(tS)) basicStats(as.ts(tS)) # Return Value: return() } # ------------------------------------------------------------------------------ test.distCheck = function() { # Description: # Checks consistency of distributions # Arguments: # .distCheck(fun = "norm", n = 1000, seed = 4711, ...) # Normal Distribution Check: fBasics:::.distCheck() # Return Value: return() } # ------------------------------------------------------------------------------ test.stdev = function() { # Description: # S-PLUS: Returns the standard deviation of a vector # Time Series: tS = MSFT # stdev - Univariate: tU = tS[, 1] # S-Plus Compatible: stdev(tU) ## stdev(as.numeric(tU)) ## CHECK !!! stdev(as.vector(tU)) stdev(as.ts(tU)) # Base R: sd(tU) ## sd(as.numeric(tU)) ## CHECK !!! sd(as.vector(tU)) sd(as.ts(tU)) # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.NormalityTests.R0000644000176000001440000001156011370220776021127 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2006, Diethelm Wuertz, GPL # Diethelm Wuertz # info@rmetrics.org # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: NORMALITY TESTS: # normalTest Test suite for normality tests # ksnormTest One sample Kolmogorov-Smirnov normality test # shapiroTest Shapiro-Wilk normality test # jarqueberaTest Jarque-Bera normality test # dagoTest D'Agostino normality test # FUNCTION: FROM NORTEST PACKAGE: # adTest Anderson-Darling normality test # cvmTest Cramer-von Mises normality test # lillieTest Lilliefors (Kolmogorov-Smirnov) normality test # pchiTest Pearson chi-square normality test # sfTest Shapiro-Francia normality test # FUNCTION: MORE TESTS ... # runsTest Runs test for detecting non-randomness [tseries] # gofnorm Reports on several tests of normality # FUNCTION ADDON: DESCRIPTION: # jbTable Table of finite sample p values for the JB test # pjb Computes probabilities for the Jarque Bera Test # qjb Computes quantiles for the Jarque Bera Test # jbTest Performs finite sample adjusted JB LM and ALM test ################################################################################ test.NormalityTests = function() { # Normal Data: RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") set.seed(4711, kind = "Marsaglia-Multicarry") X = rnorm(50) TEST = ksnormTest(X) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") TEST = shapiroTest(X) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") TEST = jarqueberaTest(X) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") if(try(require(akima))) { TEST = jbTest(X) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") } TEST = dagoTest(X) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") TEST = adTest(X) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") TEST = cvmTest(X) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") TEST = lillieTest(X) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") TEST = pchiTest(X) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") TEST = sfTest(X) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") # Return Value: return() } # ------------------------------------------------------------------------------ test.NormalityTests.MSFT = function() { X = returnSeries(MSFT)[, 1] TEST = ksnormTest(X) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") TEST = shapiroTest(X) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") TEST = jarqueberaTest(X) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") if(try(require(akima))) { TEST = jbTest(X) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST")} TEST = dagoTest(X) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") TEST = adTest(X) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") TEST = cvmTest(X) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") TEST = lillieTest(X) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") TEST = pchiTest(X) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") TEST = sfTest(X) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.JarqueBeraPValues.R0000644000176000001440000000501711370220776021447 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2007, Diethelm Wuertz, GPL # Diethelm Wuertz # info@rmetrics.org # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: JARQUE-BERA DATA TABLES: # .jbTable Finite sample p values for the Jarque Bera test # .jbPlot Plots probability # .pjb Returns probabilities for the JB Test given quantiles # .qjb Returns quantiles for the ADF Test given probabilities # DATA: Description: # .jbLM Jarque-Bera Lagrange Multiplier Test Data # .jbALM Jarque Bera Augmented Lagrange Multiplier Test Data ################################################################################ test.jbTable = function() { if (FALSE) { require(akima) # Jarque-Bera Table: # .jbTable(type = c("LM", "ALM"), size = c("mini", "small", "all")) table = .jbTable() table # Perspective Plot: # .jbPlot(type = c("LM", "ALM")) .jbPlot() } # Return Value: return() } # ------------------------------------------------------------------------------ test.jbData = function() { # Jarque-Bera LM Data: class(fBasics:::.jbLM) class(fBasics:::.jbLM()) head(fBasics:::.jbLM()) # Jarque-Bera ALM Data: class(fBasics:::.jbALM) class(fBasics:::.jbALM()) head(fBasics:::.jbALM()) # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.HyperbolicDistribution.R0000644000176000001440000001257111370220776022631 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2006, Diethelm Wuertz, GPL # Diethelm Wuertz # info@rmetrics.org # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DESCRIPTION: # dgh Returns density for generalized hyperbolic DF # pgh Returns probability for generalized hyperbolic DF # qgh Returns quantiles for generalized hyperbolic DF # rgh Returns random variates for generalized hyperbolic DF # FUNCTION: DESCRIPTION: # dhyp Returns density for hyperbolic DF # phyp Returns probability for hyperbolic DF # qhyp Returns quantiles for hyperbolic DF # rhyp Returns random variates for hyperbolic DF # hypMode Computes the hyperbolic mode # FUNCTION: DESCRIPTION: # dnig Returns density for inverse Gaussian DF # pnig Returns probability for for inverse Gaussian DF # qnig Returns quantiles for for inverse Gaussian DF # rnig Returns random variates for inverse Gaussian DF # FUNCTION: DESCRIPTION: # hypSlider Displays hyperbolic distribution function # nigSlider Displays normal inverse Gausssian distribution function ################################################################################ test.gh = function() { # gh() Distribution: RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") set.seed(4711, kind = "Marsaglia-Multicarry") test = fBasics:::.distCheck("gh", alpha = 1.3, beta = 0.3, delta = 1.7, mu = 0.2, lambda = 0.8, n = 2000, robust = FALSE) print(test) checkTrue(mean(test) == 1) # Return Value: return() } # ------------------------------------------------------------------------------ test.hyp = function() { # hyp() Distribution - Parameterization 1: RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") set.seed(4711, kind = "Marsaglia-Multicarry") test = fBasics:::.distCheck("hyp", alpha = 1.2, beta = 0.2, delta = 1.9, mu = 0.1, pm = "1", n = 1000, robust = FALSE) print(test) checkTrue(mean(test) == 1) # hyp() Distribution - Parameterization 2: RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") set.seed(4711, kind = "Marsaglia-Multicarry") test = fBasics:::.distCheck("hyp", alpha = 0.9, beta = -0.3, delta = 1.4, mu = -0.1, pm = "2", n = 1000, robust = FALSE) print(test) checkTrue(mean(test) == 1) # hyp() Distribution - Parameterization 3: RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") set.seed(4711, kind = "Marsaglia-Multicarry") fBasics:::.distCheck("hyp", alpha = 0.9, beta = -0.3, delta = 1.4, mu = -0.1, pm = "3", n = 1000, robust = FALSE) print(test) checkTrue(mean(test) == 1) # hyp() Distribution - Parameterization 4: if (FALSE) { RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") set.seed(4711, kind = "Marsaglia-Multicarry") fBasics:::.distCheck("hyp", alpha = 1.6, beta = -0.3, delta = 1.4, mu = 0.1, pm = "4", n = 1000, robust = FALSE) # CHECK print(test) checkTrue(mean(test) == 1) } # Return Value: return() } # ------------------------------------------------------------------------------ test.nig = function() { # nig() Distribution: RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") set.seed(4711, kind = "Marsaglia-Multicarry") test = fBasics:::.distCheck("nig", alpha = 2.1, beta = 0.1, delta = 1.5, mu = -0.1, n = 1000, robust = FALSE) print(test) checkTrue(mean(test) == 1) # Return Value: return() } # ------------------------------------------------------------------------------ test.hypSlider = function() { # Arguments ? # hypSlider() # Try: # hypSlider() NA # Return Value: return() } # ------------------------------------------------------------------------------ test.nigSlider = function() { # Arguments ? # nigSlider # Try: # nigSlider() NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.Heaviside.R0000644000176000001440000000335111370220776020026 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: HEAVISIDE AND RELATED FUNCTIONS: # Heaviside Computes Heaviside unit step function # Sign Another signum function # Delta Computes delta function # Boxcar Computes boxcar function # Ramp Computes ramp function ################################################################################ test.Heaviside <- function() { NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.DistributionFits.R0000644000176000001440000001721112027076511021426 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2006, Diethelm Wuertz, GPL # Diethelm Wuertz # info@rmetrics.org # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DESCRIPTION: # 'fDISTFIT' S4 Class representation # print.fDISTFIT Prints Results from a Fitted Distribution # FUNCTION: NORMAL AND T DISTRIBUTION: # nFit Fits parameters of a Normal density # tFit Fits parameters of a Student-t density # FUNCTION: STABLE DISTRIBUTION: # stableFit Fits parameters of a stable density # FUNCTION: GENERALIZED DISTRIBUTION: # ghFit Fits parameters of a generalized hyperbolic density # hypFit Fits parameters of a hyperbolic density # nigFit Fits parameters of a normal inverse Gaussian density ################################################################################ test.nFit <- function() { # Graph Frame: par(mfrow = c(1, 1)) # Simulate normal random variates: RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") set.seed(4711, kind = "Marsaglia-Multicarry") s = rnorm(n = 2000, mean = 1, sd = 0.5) # Fit: cat("nFit():\n") print(ans <- nFit(x = s)) # Precision within 10% ? relErrorTest = c( ( (ans@fit$estimate[1] - 1.0)/1.0 < 0.10 ), ( (ans@fit$estimate[2] - 0.5)/0.5 < 0.10 )) print(relErrorTest) checkTrue(as.logical(mean(relErrorTest))) # Return Value: return() } # ------------------------------------------------------------------------------ test.tFit = function() { # Graph Frame: par(mfrow = c(1, 1)) # Simulate random variates t(4): RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") set.seed(4711, kind = "Marsaglia-Multicarry") s = rt(n = 2000, df = 4) # Fit: ans = tFit(x = s, df = 2*var(s)/(var(s)-1), trace = FALSE) # Precision of df within 10% ? relErrorTest = c( ( (ans@fit$estimate[1] - 4.0)/4.0 < 0.10 )) print(ans) print(relErrorTest) checkTrue(as.logical(mean(relErrorTest))) # Return Value: return() } # ------------------------------------------------------------------------------ test.ghFit = function() { # Graph Frame: par(mfrow = c(1, 1)) # Simulate random variates: RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") set.seed(4711, kind = "Marsaglia-Multicarry") s = rgh(n = 2000, alpha = 0.8, beta = 0.2, delta = 2, mu = -0.4, lambda = 1) # Fit: ans = ghFit(x = s, alpha = 1, beta = 0, delta = 1, mu = 0, lambda = 1, trace = FALSE) # Precision of parameters within 10% ? relErrorTest = c( ( (ans@fit$estimate[1] - 0.8)/( 0.8) < 0.10 ), ( (ans@fit$estimate[2] - 0.2)/( 0.2) < 0.10 ), ( (ans@fit$estimate[3] - 2.0)/( 2.0) < 0.10 ), ( (ans@fit$estimate[4] + 0.4)/(-0.4) < 0.10 ), ( (ans@fit$estimate[5] - 2.0)/( 2.0) < 0.10 )) print(ans) print(relErrorTest) checkTrue(as.logical(mean(relErrorTest))) # Return Value: return() } # ------------------------------------------------------------------------------ test.hypFit = function() { # Graph Frame: par(mfrow = c(1, 1)) # Simulate normal random variates: RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") set.seed(4711, kind = "Marsaglia-Multicarry") s = rhyp(n = 2000, alpha = 1.5, beta = 0.8, delta = 0.5, mu = -1) # Fit: ans = hypFit(s, alpha = 1, beta = 0, delta = 1, mu = mean(s), trace = FALSE) # Precision of parameters within 10% ? relErrorTest = c( ( (ans@fit$estimate[1] - 1.5)/( 1.5) < 0.10 ), ( (ans@fit$estimate[2] - 0.8)/( 0.8) < 0.10 ), ( (ans@fit$estimate[3] - 0.5)/( 0.5) < 0.10 ), ( (ans@fit$estimate[4] + 1.0)/(-1.0) < 0.10 )) print(ans) print(relErrorTest) checkTrue(as.logical(mean(relErrorTest))) # Return Value: return() } # ------------------------------------------------------------------------------ test.nigFit <- function() { # Graph Frame: par(mfrow = c(1, 1)) # Simulate normal random variates: RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") set.seed(4711, kind = "Marsaglia-Multicarry") th.0 <- c(alpha = 1.5, beta = -0.7, delta = 0.5, mu = -1.0) s <- do.call(rnig, c(list(n = 2000), th.0)) # Fit {includes plot !} print(ans <- nigFit(s, alpha = 1, beta = 0, delta = 1, mu = mean(s), trace = FALSE)) # Precision of parameters within 10% / 35% ? est <- ans@fit$estimate relErr <- (est - th.0) / th.0 checkTrue(all(abs(relErr) < 0.35))# 35% ## 10% / 35$ is silly, rather use SE scale: print(SE <- ans@fit$se.coef) print(cbind(th.0, est, SE))# NaN SE[is.na(SE)] <- .1 # arbitrary cat("t-ratios:\n"); print(t. <- (est - th.0)/ SE) checkTrue(all(abs(t.) < 6))## 6 is too large, but have -5.476 for beta # Return Value: return() } # ------------------------------------------------------------------------------ test.stableFit <- function() { # Graph Frame: par(mfrow = c(1, 1)) # Simulate stable random variates: RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") set.seed(4711, kind = "Marsaglia-Multicarry") th.0 <- c(alpha=1.8, beta=0.3, gamma = 1, delta = 0.1) s <- do.call(stabledist::rstable, c(list(n = 500), th.0)) # Fit {includes plot !} cat("stableFit():\n") print(ans <- stableFit(x = s, alpha = 1.5)) # Precision of parameters within 5% / 10% / ...? est <- ans@fit$estimate arelErr <- abs(relErr <- (est - th.0) / th.0) print(cbind(th.0, est, relErr)) checkTrue(all(arelErr[c("alpha","gamma")] < 0.05) && arelErr[["beta"]] < 0.15 && arelErr[["delta"]] < 1.50) # 150% ! # MLE Fit: if (FALSE) { # as this takes rather too long time ... print(system.time( ans <- stableFit(x = s, alpha = 1.5, type = "mle", trace = TRUE) ## .mleStableFit(s, 1.75, 0, 1, 0) )) ## user {lynne, 2012-09} ## 159.7 # The result would be: print(ans) ## Precision of parameters within 5% / 10% / ...? est <- ans@fit$estimate arelErr <- abs(relErr <- (est - th.0) / th.0) print(cbind(th.0, est, relErr)) checkTrue(all(arelErr[1:3] < 0.02) && arelErr[["delta"]] < 0.42) } # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runTests.R0000644000176000001440000000452711370220776016622 0ustar ripleyuserspkg <- "fBasics" if(require("RUnit", quietly = TRUE)) { library(package=pkg, character.only = TRUE) if(!(exists("path") && file.exists(path))) path <- system.file("unitTests", package = pkg) ## --- Testing --- ## Define tests testSuite <- defineTestSuite(name = paste(pkg, "unit testing"), dirs = path) if(interactive()) { cat("Now have RUnit Test Suite 'testSuite' for package '", pkg, "' :\n", sep='') str(testSuite) cat('', "Consider doing", "\t tests <- runTestSuite(testSuite)", "\nand later", "\t printTextProtocol(tests)", '', sep = "\n") } else { ## run from shell / Rscript / R CMD Batch / ... ## Run tests <- runTestSuite(testSuite) if(file.access(path, 02) != 0) { ## cannot write to path -> use writable one tdir <- tempfile(paste(pkg, "unitTests", sep="_")) dir.create(tdir) pathReport <- file.path(tdir, "report") cat("RUnit reports are written into ", tdir, "/report.(txt|html)", sep = "") } else { pathReport <- file.path(path, "report") } ## Print Results: printTextProtocol(tests, showDetails = FALSE) printTextProtocol(tests, showDetails = FALSE, fileName = paste(pathReport, "Summary.txt", sep = "")) printTextProtocol(tests, showDetails = TRUE, fileName = paste(pathReport, ".txt", sep = "")) ## Print HTML Version to a File: ## printHTMLProtocol has problems on Mac OS X if (Sys.info()["sysname"] != "Darwin") printHTMLProtocol(tests, fileName = paste(pathReport, ".html", sep = "")) ## stop() if there are any failures i.e. FALSE to unit test. ## This will cause R CMD check to return error and stop tmp <- getErrors(tests) if(tmp$nFail > 0 | tmp$nErr > 0) { stop(paste("\n\nunit testing failed (#test failures: ", tmp$nFail, ", R errors: ", tmp$nErr, ")\n\n", sep="")) } } } else { cat("R package 'RUnit' cannot be loaded -- no unit tests run\n", "for package", pkg,"\n") } ################################################################################ fBasics/inst/unitTests/Makefile0000644000176000001440000000042011370220776016274 0ustar ripleyusersPKG=fBasics TOP=../.. SUITE=doRUnit.R R=R all: inst test inst: # Install package -- but where ?? -- will that be in R_LIBS ? cd ${TOP}/..;\ ${R} CMD INSTALL ${PKG} test: # Run unit tests export RCMDCHECK=FALSE;\ cd ${TOP}/tests;\ ${R} --vanilla --slave < ${SUITE} fBasics/inst/unitTests/HeavisideSlider.R0000644000176000001440000000312011370220776020023 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: HEAVISIDE AND RELATED FUNCTIONS: # .heavisideSlider Displays Heaviside and related functions ################################################################################ test.heavisideSlider = function() { # Slider: # .heavisideSlider() NA # Return Value: return() } ################################################################################ fBasics/inst/COPYRIGHT.html0000644000176000001440000002041111370220776015072 0ustar ripleyusers Rmetrics::COPYRIGHT

Rmetrics Copyrights

2005-12-18 Built 221.10065 

  
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Copyrights (C) for 

    R:  
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    Version R 2.0.0 claims:
    - The stub packages from 1.9.x have been removed.
    - All the datasets formerly in packages 'base' and 'stats' have
      been moved to a new package 'datasets'. 
    - Package 'graphics' has been split into 'grDevices' (the graphics
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    - Packages must have been re-installed for this version, and
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    - Package names must now be given exactly in library() and
      require(), regardless of whether the underlying file system is
      case-sensitive or not.    

________________________________________________________________________________
for 
    
    Rmetrics:
      (C) 1999-2005, Diethelm Wuertz, GPL
      Diethelm Wuertz 
      www.rmetrics.org
      info@rmetrics.org
 
________________________________________________________________________________
for non default loaded basic packages part of R's basic distribution

    MASS:    
      Main Package of Venables and Ripley's MASS.
      We assume that MASS is available. 
      Package 'lqs' has been returned to 'MASS'.  
      S original by Venables & Ripley.
      R port by Brian Ripley .
      Earlier work by Kurt Hornik and Albrecht Gebhardt.
    methods: 
      Formally defined methods and classes for R objects, plus other 
      programming tools, as described in the reference "Programming 
      with Data" (1998), John M. Chambers, Springer NY. 
      R Development Core Team.
    mgcv:   
      Routines for GAMs and other generalized ridge regression
      with multiple smoothing parameter selection by GCV or UBRE.
      Also GAMMs by REML or PQL. Includes a gam() function.
      Simon Wood 
    nnet: 
      Feed-forward Neural Networks and Multinomial Log-Linear Models
      Original by Venables & Ripley. 
      R port by Brian Ripley .
      Earlier work by Kurt Hornik and Albrecht Gebhardt.
      
________________________________________________________________________________
for the code partly included as builtin functions from other R ports:

    fBasics:CDHSC.F
      GRASS program for distributional testing.
      By James Darrell McCauley 
      Original Fortran Source by Paul Johnson EZ006244@ALCOR.UCDAVIS.EDU>
    fBasics:nortest
      Five omnibus tests for the composite hypothesis of normality
      R-port by Juergen Gross 
    fBasics:SYMSTB.F
      Fast numerical approximation to the Symmetric Stable distribution 
      and density functions.  
      By Hu McCulloch 
    fBasics:tseries
      Functions for time series analysis and computational finance.
      Compiled by Adrian Trapletti 
         
    fCalendar:date     
      The tiny C program from Terry Therneau  is used
      R port by Th. Lumley ,
      K. Halvorsen , and 
      Kurt Hornik 
    fCalendar:holidays
      The holiday information was collected from the internet and 
      governmental sources obtained from a few dozens of websites
    fCalendar:libical
      Libical is an Open Source implementation of the IETF's 
      iCalendar Calendaring and Scheduling protocols. (RFC 2445, 2446, 
      and 2447). It parses iCal components and provides a C API for 
      manipulating the component properties, parameters, and subcomponents.
    fCalendar:vtimezone
      Olsen's VTIMEZONE database consists of data files are released under 
      the GNU General Public License, in keeping with the license options of 
      libical. 
     
    fSeries:bdstest.c
      C Program to compute the BDS Test.
      Blake LeBaron
    fSeries:fracdiff  
      R functions, help pages and the Fortran Code for the 'fracdiff' 
      function are included. 
      S original by Chris Fraley 
      R-port by Fritz Leisch 
      since 2003-12: Martin Maechler
    fSeries:lmtest
      R functions and help pages for the linear modelling tests are included .
      Compiled by Torsten Hothorn ,
      Achim Zeileis , and
      David Mitchell
    fSeries:mda    
      R functions, help pages and the Fortran Code for the 'mars' function
      are implemeted.
      S original by Trevor Hastie & Robert Tibshirani,
      R port by Friedrich Leisch, Kurt Hornik and Brian D. Ripley 
    fSeries:modreg
      Brian Ripley and the R Core Team
    fSeries:polspline   
      R functions, help pages and the C/Fortran Code for the 'polymars' 
      function are implemented
      Charles Kooperberg 
    fSeries:systemfit
      Simultaneous Equation Estimation Package.
      R port by Jeff D. Hamann  and 
      Arne Henningsen 
    fSeries:tseries
      Functions for time series analysis and computational finance.
      Compiled by Adrian Trapletti 
    fSeries:UnitrootDistribution:
      The program uses the Fortran routine and the tables 
      from J.G. McKinnon. 
    fSeries:urca
      Unit root and cointegration tests for time series data.
      R port by Bernhard Pfaff .
     
    fExtremes:evd
      Functions for extreme value distributions.
      R port by Alec Stephenson 
      Function 'fbvpot' by Chris Ferro.
    fExtremes:evir
      Extreme Values in R
      Original S functions (EVIS) by Alexander McNeil 
      R port by Alec Stephenson   
    fExtremes:ismev
      An Introduction to Statistical Modeling of Extreme Values
      Original S functions by Stuart Coles 
      R port/documentation by Alec Stephenson 
      
    fOptions
      Option Pricing formulas are implemented along the book and 
      the Excel spreadsheets of E.G. Haug, "The Complete Guide to Option 
      Pricing"; documentation is partly taken from www.derivicom.com which 
      implements a C Library based on Haug. For non-academic and commercial 
      use we recommend the professional software from "www.derivicom.com".  
    fOptions:SOBOL.F
      ACM Algorithm 659 by P. Bratley and B.L. Fox
      Extension on Algorithm 659 by S. Joe and F.Y. Kuo
    fOptions:CGAMA.F
      Complex gamma and related functions.
      Fortran routines by Jianming Jin.
    fOptions:CONHYP.F
      Confluenet Hypergeometric and related functions.
      ACM Algorithm 707 by mark Nardin, W.F. Perger, A. Bhalla
             
    fPortfolio:mvtnorm
      Multivariate Normal and T Distribution.
      Alan Genz , 
      Frank Bretz 
      R port by Torsten Hothorn 
    fPortfolio:quadprog
      Functions to solve Quadratic Programming Problems.
      S original by Berwin A. Turlach  
      R port by Andreas Weingessel 
    fPortfolio:sn
      The skew-normal and skew-t distributions.
      R port by Adelchi Azzalini 
    fPortfolio:tseries
      Functions for time series analysis and computational finance.
      Compiled by Adrian Trapletti
fBasics/R/0000755000176000001440000000000012137277776012100 5ustar ripleyusersfBasics/R/zzz.R0000644000176000001440000000360212137277776013061 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # info@rmetrics.org # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ ## .First.lib = ## function(lib, pkg) ## { ## ### # Startup Mesage and Desription: ## ### MSG <- if(getRversion() >= "2.5") packageStartupMessage else message ## ### dsc <- packageDescription(pkg) ## ### if(interactive() || getOption("verbose")) { ## ### # not in test scripts ## ### MSG(sprintf("Rmetrics Package %s (%s) loaded.", pkg, dsc$Version)) ## ### } ## setRmetricsOptions(.x.save = NA) ## } .onLoad <- function(libname, pkgname) setRmetricsOptions(.x.save = NA) if(!exists("Sys.setenv", mode = "function")) # pre R-2.5.0, use "old form" Sys.setenv <- Sys.putenv ################################################################################ fBasics/R/utils-unitrootNA.R0000644000176000001440000000471011370220773015444 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # .unirootNA Computes zero of a function without error exit ################################################################################ .unirootNA <- function(f, interval, lower = min(interval), upper = max(interval), tol = .Machine$double.eps^0.25, ...) { # A function implemented by Diethelm Wuertz # Description: # Searches the interval from lower to upper for a # root (i.e., zero) of the function f with respect # to its first argument. # Arguments: # see 'uniroot' # Value: # Returns the x value of f where the root is located. If # now root exists NA will be returned. In the last case # the function doesn't terminate with an error like in the # case of the standard function uniroot. # Details: # R: # uniroot(f, interval, lower = min(interval), upper = max(interval), # tol = .Machine$double.eps^0.25, # maxiter = 1000, ...) # uniroot(f, interval, lower = min(interval), upper = max(interval), # tol = .Machine$double.eps^.25, # keep.xy = F, f.lower = NA, f.upper = NA, ...) # Example: # .unirootNA(sin, c(1, 2)); .unirootNA(sin, c(-1, 1)) # FUNCTION: # There is no Root: if (is.null(args(f))) { if (f(lower) * f(upper) >=0) return(NA) } else { if (f(lower, ...) * f(upper, ...) >= 0) return(NA) } # There is a Root: ans = uniroot(f = f, interval = interval, lower = lower, upper = upper, tol = tol, ...) # Return Value: ans$root } ################################################################################ fBasics/R/utils-unitTest.R0000644000176000001440000001104111415310241015142 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # .runitTest Perorms RUnit Tests # .rmetricsPackages Lists all Rmetrics packages # .rmetricsUnitTest Performs RUnit tests for all Rmetrics packages ################################################################################ .runitTest <- function(package = "Rmetrics") { # A function implemented by Diethelm Wuertz # Description: # Performs RUnit tests # Example: # .runitTest"fCalendar") # FUNCTION: if (!require(RUnit, quietly = TRUE)) stop("\n -- Package RUnit not available -- \n\n") pkg = package library(package = pkg, character.only = TRUE) # if(!(exists("path") && file.exists(path))) path <- system.file("unitTests", package = pkg) # --- Testing --- # Define tests testSuite <- defineTestSuite(name = paste(pkg, "unit testing"), dirs = path) cat("Now have RUnit Test Suite 'testSuite' for package '", pkg, "' :\n", sep='') str(testSuite) cat('', "Consider doing", "\t tests <- runTestSuite(testSuite)", "\nand later", "\t printTextProtocol(tests)", '', sep = "\n") tests <- runTestSuite(testSuite) if(file.access(path, 02) != 0) { # cannot write to path -> use writable one tdir <- tempfile(paste(pkg, "unitTests", sep="_")) dir.create(tdir) pathReport <- file.path(tdir, "report") cat("RUnit reports are written into ", tdir, "/report.(txt|html)", sep = "") } else { pathReport <- file.path(path, "report") } # Print TXT Report to File: printTextProtocol(tests) printTextProtocol(tests, fileName = paste(pathReport, ".txt", sep = "")) # Print HTML Report to File: fileName = paste(pathReport, ".html", sep = "") printHTMLProtocol(tests, fileName = fileName) # Repair href Links: protocol.html = scan(file = fileName, what = character(0)) protocol.html = gsub('href=\"', 'href=\"file://', protocol.html) write(protocol.html, fileName) # stop() if there are any failures i.e. FALSE to unit test. # This will cause R CMD check to return error and stop if(getErrors(tests)$nFail > 0) { stop("one of the unit tests failed") } # Check for RUnit: # ... do we need this ? cat("R package 'RUnit' cannot be loaded -- no unit tests run\n", "for package", pkg,"\n") # Where you can find me ... cat("\nHTML Report saved to", fileName, "\n\n") # Return Value: invisible() } # ------------------------------------------------------------------------------ .rmetricsPackages <- function() { # A function implemented by Diethelm Wuertz # Description: # Lists all Rmetrics packages # FUNCTION: # All Rmetrics Packages: ans = c( "fUtilities", "fEcofin", "fCalendar", "timeSeries", "fImport", "fBasics", "fArma", "fGarch", "fNonlinear", "fUnitRoots", "fTrading", "fMultivar", "fRegression", "fExtremes", "fCopulae", "fOptions", "fExoticOptions", "fAsianOptions", "fAssets", "fPortfolio") # Return Value: ans } # ------------------------------------------------------------------------------ .rmetricsUnitTest = function() { # A function implemented by Diethelm Wuertz # Description: # Performs RUnit tests for all Rmetrics packages # FUNCTION: # Do Unit Tests: Packages = .rmetricsPackages() for (package in Packages) { cat("\n\nPackage:", package, "\n") .runitTest(package) } # Return Value: invisible() } ################################################################################ fBasics/R/utils-symbolTable.R0000644000176000001440000000344411370220773015622 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # symbolTable Shows a table of plot symbols from a given font ################################################################################ symbolTable <- function(font = par('font'), cex = 0.7) { # A function implemented by Diethelm Wuertz # Description: # Shows a table of plot characters from a given font # Example: # symbolTable() # Author: # Unknown, piece of code found on the internet. # FUNCTION: # Table: plot(0, 0, xlim = c(-1, 11), ylim = c(0, 26), type = 'n', axes = FALSE, xlab = '', ylab = '', main = "Table of Plot Characters") j = -1 for(i in 0:255) { if(i %% 25 == 0) {j = j+1; k = 26} k = k-1 points(j, k, pch = i, font = font, cex = cex, col = 2) text(j + 0.50, k, i, cex = cex) } # Return Value: invisible(font) } ################################################################################ fBasics/R/utils-recessionLines.R0000644000176000001440000000371211370220773016330 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # .recessionLines Adds vertical recession lines to a plot ################################################################################ .recessionLines <- function() { # A function implemented by Diethelm Wuertz # Description: # Adds vertical recession lines to a plot # Arguments: # none # References: # http://www.nber.org/cycles/cyclesmain.html # FUNCTION: # Add Recession Liones: abline(v = as.POSIXct(c( "1920-01-01", "1921-08-01", "1923-05-01", "1924-07-01", "1926-10-01", "1927-11-01", "1929-08-01", "1933-03-01", "1945-01-01", "1945-10-01", "1948-11-01", "1949-10-01", "1953-07-01", "1954-05-01", "1957-08-01", "1958-04-01", "1960-04-01", "1961-02-01", "1969-12-01", "1970-11-01", "1973-11-01", "1975-03-01", "1990-07-01", "1991-03-01", "2001-03-01", "2001-11-01", "2007-12-07")), col = "grey") } ################################################################################ fBasics/R/utils-randLCG.R0000644000176000001440000001242111415310241014600 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: PORTABLE INNOVATIONS: # set.lcgseed Sets initial random seed # get.lcgseed Gets the current valus of the random seed # FUNCTION: PORTABLE RVS GENERATORS: # runif.lcg Generates uniform linear congruational rvs # rnorm.lcg Generates normal linear congruational rvs # rt.lcg Generates Student-t linear congruational rvs ################################################################################ ################################################################################ # set.lcgseed Sets initial random seed # get.lcgseed Gets the current valus of the random seed # .lcg.seed = 4711 # ------------------------------------------------------------------------------ set.lcgseed <- function(seed = 4711) { # A function implemented by Diethelm Wuertz # Description: # Sets the random seed for the linear congruential # random number generator # Notes: # A Simple Portable Random Number Generator for Use in R and Splus # Use this generator only for comparisons of Programs in R and Splus !!! # Method: A linear congruential generator with # LCG(a=13445, c=0, m=2^31-1, X0) # Note, this is a random number generator which passes the bitwise # randomness test. # Reference: # http://csep1.phy.ornl.gov/rn/node13.html # N. S. Altman. ``Bitwise Behavior of Random Number Generators,'' # SIAM J. Sci. Stat. Comput., 9(5), September, pps. 941-949, 1988 # Example: # set.lcgseed(4711) # cbind(runif.lcg(100), rnorm.lcg(100), rt.lcg(100, df=4)) # FUNCTION: # Return Value: # .lcg.seed <<- seed setRmetricsOptions(lcg.seed = seed) } # ------------------------------------------------------------------------------ get.lcgseed <- function() { # A function implemented by Diethelm Wuertz # Description: # Returns the random seed for the linear congruential # random number generator # FUNCTION # Return Value: getRmetricsOptions("lcg.seed") } ################################################################################ # runif.lcg Uniform linear congruational generator # rnorm.lcg Normal linear congruational generator # rt.lcg Student-t linear congruational generator runif.lcg <- function(n, min = 0, max = 1) { # A function implemented by Diethelm Wuertz # Description: # A linear congruential generator for uniform distributed # random numbers # Notes: # Important - Use this generator only for comparisons of # Programs in R and SPlus (and not for production) !!! # Portable Random Numbers: # A linear congruential generator # LCG(a=13445, c=0, m=2^31-1, X0) # This is a random number generator which # passes the bitwise randomness test # References: # http://csep1.phy.ornl.gov/rn/node13.html # N. S. Altman. ``Bitwise Behavior of Random Number Generators,'' # SIAM J. Sci. Stat. Comput., 9(5), September, pps. 941-949, 1988 # FUNCTION: # Initialize: # if(!exists(".lcg.seed")) .lcg.seed <<- 4711 # Generate: r.lcg = rep(0, times = n) a = 13445 c = 0 m = 2^31-1 for (i in 1:n) { lcg.seed <- getRmetricsOptions("lcg.seed") setRmetricsOptions(lcg.seed = (a * lcg.seed + c) %% m) r.lcg[i] = getRmetricsOptions("lcg.seed") / m } r.lcg = (max-min)*r.lcg + min # Return Value: r.lcg } # ------------------------------------------------------------------------------ rnorm.lcg <- function(n, mean = 0, sd = 1) { # A function implemented by Diethelm Wuertz # Description: # A linear congruential generator for normal distributed # random numbers # FUNCTION: # This is slow, but portable between R and SPlus (qnorm(runif.lcg(n = n, min = 0, max = 1)) - mean)/sd^2 } # ------------------------------------------------------------------------------ rt.lcg <- function(n, df) { # A function implemented by Diethelm Wuertz # Description: # A linear congruential generator for Sudent-t distributed # random numbers # FUNCTION: # This is slow, but portable between R and SPlus qt(runif.lcg(n = n, min = 0, max = 1), df = df) } ################################################################################ fBasics/R/utils-printControl.R0000644000176000001440000000345411415310241016031 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: CONTROL ATTRIBUTES: # print.control Prints unlisted control attributes # FUNCTION: DESCRIPTION: # .print Used in regression package ################################################################################ print.control <- function(x, ...) { # Return Value: print(unlist(x)) } ################################################################################ .print <- function(x, ...) { UseMethod(".print") } # ------------------------------------------------------------------------------ .plot <- function(x, ...) { UseMethod(".plot") } # ------------------------------------------------------------------------------ .summary <- function(object, ...) { UseMethod(".summary") } # ------------------------------------------------------------------------------ .predict <- function(object, ...) { UseMethod(".predict") } ################################################################################ fBasics/R/utils-listIndex.R0000644000176000001440000000320311415310241015267 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # listIndex Extracts R package index ################################################################################ listIndex <- function(package, character.only = FALSE) { # A function implemented by Diethelm Wuertz & Yohan Chalabi # Description: # Extracts R package index # Example: # listIndex("fSeries") # FUNCTION: # Extract Index: if (!character.only) package <- as.character(substitute(package)) cmd = paste("library(help =", package, ")", sep = "" ) ans = eval(parse(text = cmd)) name = ans$name parh = ans$path description = ans$info[[1]] index = ans$info[[2]] cat("\n", package, "Index:\n\n") cat(paste(" ", index), sep = "\n") } ################################################################################ fBasics/R/utils-listFunctions.R0000644000176000001440000000470211731575035016214 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # listFunctionss Lists all functions in a package # countFunctions Counts all functions in a package ################################################################################ listFunctions <- function(package, character.only = FALSE) { # A function implemented by Diethelm Wuertz & Yohan Chalabi # Description: # Lists all functions in a package # Arguments: # package - a character string, the name of the Package # Author: # Original code from B. Ripley # FUNCTION: # List: if (!character.only) package <- as.character(substitute(package)) if(require(package, character.only = TRUE, quietly = TRUE)) { env <- paste("package", package, sep = ":") nm <- ls(env, all.names = TRUE) ans = nm[unlist(lapply(nm, function(n) exists(n, where = env, mode = "function", inherits = FALSE)))] } else { ans = character(0) } # Return Value: ans } # ------------------------------------------------------------------------------ countFunctions <- function(package, character.only = FALSE) { # A function implemented by Diethelm Wuertz & Yohan Chalabi # Description: # Counts all functions in a package # Arguments: # package - a character string, the name of the Package # FUNCTION: # Count: if (!character.only) package <- as.character(substitute(package)) ans = length(listFunctions(package, character.only = TRUE)) names(ans) = package # Return Value: ans } ################################################################################ fBasics/R/utils-listDescription.R0000644000176000001440000000341711415310241016512 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # listDescription Extracts R package description ################################################################################ listDescription <- function(package, character.only = FALSE) { # A function implemented by Diethelm Wuertz & Yohan Chalabi # Description: # Extracts package description # Example: # listDescription("fSeries") # FUNCTION: # Extract Description: if (!character.only) package <- as.character(substitute(package)) cmd = paste("library(help =", package, ")", sep = "" ) ans = eval(parse(text = cmd)) name = ans$name parh = ans$path description = ans$info[[1]] # DW: next line for proper insertion added description = gsub("\n", "\n ", description) index = ans$info[[2]] cat("\n", package, "Description:\n\n") cat(paste(" ", description), sep = "\n") } ################################################################################ fBasics/R/utils-interactivePlot.R0000644000176000001440000001100011370220773016504 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: PLOT UTILITIES: # interactivePlot Plots several graphs interactively ################################################################################ interactivePlot <- function(x, choices = paste("Plot", 1:9), plotFUN = paste("plot.", 1:9, sep = ""), which = "all", ...) { # A function implemented by Diethelm Wuertz # Description: # Plot method for an object of class "template". # Arguments: # x - an object to be plotted # choices - the character string for the choice menu # plotFUN - the names of the plot functions # which - plot selection, which graph should be # displayed. If a character string named "ask" the # user is interactively asked which to plot, if # a logical vector of length N, those plots which # are set "TRUE" are displayed, if a character string # named "all" all plots are displayed. # Note: # At maximum 9 plots are supported. # FUNCTION: # Some cecks: if (length(choices) != length(plotFUN)) stop("Arguments choices and plotFUN must be of same length.") if (length(which) > length(choices)) stop("Arguments which has incorrect length.") if (length(which) > length(plotFUN)) stop("Arguments which has incorrect length.") if (length(choices) > 9) stop("Sorry, only 9 plots at max are supported.") # Internal "askPlot" Function: multPlot = function(x, choices, ...) { # Selective Plot: selectivePlot <- function(x, choices, FUN, which){ # Internal Function: askPlot <- function(x, choices, FUN) { # Pick and Plot: pick = 1 setRmetricsOptions(n.plots = length(choices)) while (pick > 0) { pick = menu ( choices = paste("plot:", choices), title = "\nMake a plot selection (or 0 to exit):") if (pick > 0) match.fun(FUN[pick])(x) } } if (as.character(which[1]) == "ask") { askPlot(x, choices = choices, FUN = FUN, ...) } else { for (i in 1:getRmetricsOptions("n.plots")) if (which[i]) match.fun(FUN[i])(x) } invisible() } # match Functions, up to nine ... if (length(plotFUN) < 9) plotFUN = c(plotFUN, rep(plotFUN[1], times = 9 - length(plotFUN))) plot.1 = match.fun(plotFUN[1]); plot.2 = match.fun(plotFUN[2]) plot.3 = match.fun(plotFUN[3]); plot.4 = match.fun(plotFUN[4]) plot.5 = match.fun(plotFUN[5]); plot.6 = match.fun(plotFUN[6]) plot.7 = match.fun(plotFUN[7]); plot.8 = match.fun(plotFUN[8]) plot.9 = match.fun(plotFUN[9]) pick = 1 while (pick > 0) { pick = menu ( ### choices = paste("plot:", choices), choices = paste(" ", choices), title = "\nMake a plot selection (or 0 to exit):") # up to 9 plot functions ... switch (pick, plot.1(x), plot.2(x), plot.3(x), plot.4(x), plot.5(x), plot.6(x), plot.7(x), plot.8(x), plot.9(x) ) } } # Plot: if (is.numeric(which)) { Which = rep(FALSE, times = length(choices)) Which[which] = TRUE which = Which } if (which[1] == "all") { which = rep(TRUE, times = length(choices)) } if (which[1] == "ask") { multPlot(x, choices, ...) } else { for ( i in 1:length(which) ) { FUN = match.fun(plotFUN[i]) if (which[i]) FUN(x) } } # Return Value: invisible(x) } ################################################################################ fBasics/R/utils-hessian.R0000644000176000001440000000516611370220773015002 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # tsHessian Computes Two Sided Hessian matrix ################################################################################ tsHessian <- function(x, fun, ...) { # Description: # Computes two sided (TS) approximated Hessian # Source: # A function borrowed from Kevin Sheppard's Matlab garch toolbox # as implemented by Alexios Ghalanos in his rgarch package # Notes: # optionally requires package Matrix (added as suggestion) # FUNCTION: # Settings: n = length(x) fx <- fun(x, ...) eps = .Machine$double.eps # Compute the stepsize: h = eps^(1/3) * apply( as.data.frame(x), 1, FUN = function(z) max(abs(z), 1.0e-2)) xh = x + h h = xh - x ee = diag(h) # Matrix(diag(h), sparse = TRUE) # Compute forward and backward steps: gp = vector(mode = "numeric", length = n) for(i in 1:n) gp[i] <- fun(x + ee[, i], ...) gm = vector(mode = "numeric", length = n) for(i in 1:n) gm[i] <- fun(x - ee[, i], ...) H = h %*% t(h) Hm = H Hp = H # Compute double forward and backward steps: for(i in 1:n){ for(j in i:n){ Hp[i, j] <- fun(x + ee[, i] + ee[, j], ...) Hp[j, i] <- Hp[i, j] Hm[i, j] <- fun(x - ee[, i] - ee[, j], ...) Hm[j, i] <- Hm[i, j] } } # Compute the Hessian: for(i in 1:n){ for(j in i:n){ H[i, j] = ((Hp[i, j] - gp[i] - gp[j] + fx + fx - gm[i] - gm[j] + Hm[i, j]) / H[i, j]) / 2 H[j, i] = H[i, j] } } # Return Value: return(H) } ################################################################################ fBasics/R/utils-getS4.R0000644000176000001440000000746411732460116014340 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Description. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # getCall Extracts the call slot from a S4 object # getModel Extracts the model slot from a S4 object # getTitle Extracts the title slot from a S4 object # getDescription Extracts the description slot from a S4 object # getSlot Extracts a specified slot from a S4 object # getArgs Shows the arguments of a S4 functiong ################################################################################ if(getRversion() < "2.14") { getCall <- function(x) slot(x, "call") } ## since 2.14.0, getCall() is an S3 generic in 'stats' ## *AND* its default method also works for S4 and a "call" slot # ------------------------------------------------------------------------------ getModel <- function(object) { # A function implemented by Rmetrics # Description: # gets the "model" slot from an object of class 4 # Arguments: # object - an object of class S4 # FUNCTION: # Return Value: slot(object, "model") } # ------------------------------------------------------------------------------ getTitle <- function(object) { # A function implemented by Rmetrics # Description: # gets the "title" slot from an object of class 4 # Arguments: # object - an object of class S4 # FUNCTION: # Return Value: slot(object, "title") } # ------------------------------------------------------------------------------ getDescription <- function(object) { # A function implemented by Rmetrics # Description: # Extracts the "description" slot from an object of class 4 # Arguments: # object - an object of class S4 # FUNCTION: # Return Value: slot(object, "description") } # ------------------------------------------------------------------------------ getSlot <- function(object, slotName) { # a simple wrapper around slot to avoid compatibilty troubles with # other packages. # FUNCTION: slot(object, slotName) } # ------------------------------------------------------------------------------ getArgs <- function(f, signature = character()) { # A function implemented by Diethelm Wuertz # Description: # Shows the arguments of a S4 functiong # Arguments: # f - a generic function or the character-string name of one # signature - the signature of classes to match to the arguments # of f # Note: # such a function is missing in the methods package, # see, e.g he function getMethod() # FUNCTION: # Get arguments: fun <- getMethod(f, signature)@.Data test <- class(try(body(fun)[[2]][[3]], silent = TRUE)) if (test == "function") { ans <- args(body(fun)[[2]][[3]]) } else { ans <- args(fun) } cat(substitute(f), ",", paste(signature, collapse = ","), ":\n", sep = "") # Return Value: ans } ################################################################################ fBasics/R/utils-decor.R0000644000176000001440000000777311370220773014452 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # decor Adds horizontal grid and L shaped box # hgrid Adds horizontal grid lines # vgrid Adds vertical grid lines # boxL Adds L-shaped box # box_ Adds unterlined box # .xrug Adds rugs on x axis # .yrug Adds rugs on y axis # copyright Adds copyright notice ################################################################################ decor <- function() { # A function implemented by Diethelm Wuertz # Description: # Adds a horizontal grid and L shaped box # FUNCTION: # Add: hgrid() boxL() # Return Value: invisible() } ################################################################################ hgrid <- function(ny = NULL, ...) { # A function implemented by Diethelm Wuertz # Description: # Adds horizontal grid lines # FUNCTION: # Add: grid(NA, ny, ...) # Return Value: invisible() } # ------------------------------------------------------------------------------ vgrid <- function(nx = NULL, ...) { # A function implemented by Diethelm Wuertz # Description: # Adds vertical grid lines # FUNCTION: # Add: grid(nx, NA, ...) # Return Value: invisible() } # ------------------------------------------------------------------------------ boxL <- function(col = "white") { # A function implemented by Diethelm Wuertz # Description: # Adds L-shaped box # Add: box() box(bty = "7", col = col) # Return Value: invisible() } # ------------------------------------------------------------------------------ box_ <- function(col = c("white", "black")) { # A function implemented by Diethelm Wuertz # Description: # Adds unterlined box # FUNCTION: # Add: box(bty = "c", col = col[1]) box(bty = "]", col = col[2]) box(bty = "7", col = col[1]) # Return Value: invisible() } # ------------------------------------------------------------------------------ .xrug <- function(x) { # A function implemented by Diethelm Wuertz # Description: # Adds rugs on x axis # FUNCTION: # Add: rug(as.vector(x), ticksize = 0.01, side = 1, quiet = TRUE) # Return Value: invisible() } # ------------------------------------------------------------------------------ .yrug <- function(x) { # A function implemented by Diethelm Wuertz # Description: # Adds rugs on y axis # FUNCTION: # Add: rug(as.vector(x), ticksize = 0.01, side = 2, quiet = TRUE) # Return Value: invisible() } # ------------------------------------------------------------------------------ copyright <- function() { # A function implemented by Diethelm Wuertz # Description: # Adds copyright notice # FUNCTION: # Add: Year = substr(Sys.Date(), 1, 4) mtext(paste("(c) Rmetrics", Year), side = 4, line = 0, adj = 0, font = 1, cex = 0.7*par("cex"), col = "grey") # Return Value: invisible() } ################################################################################ fBasics/R/utils-colorTable.R0000644000176000001440000000425211370220773015431 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DESCRIPTION: # colorTable Shows a table of plot color codes ################################################################################ colorTable <- function(cex = 0.7) { # A function implemented by Diethelm Wuertz # Description: # Displays a table of plot colors. # Author: # Unknown, piece of code found on the internet. # Example: # colorTable() # FUNCTION: # Plot: plot(0, 0, xlim = c(-1, 10), ylim = c(0, 10), type = 'n', axes = FALSE, xlab = '', ylab = '', cex = cex, main = "Table of Color Codes") ## DW ## j = -1 j = -2 ## here I have shifted the plot to the left ... DW for(i in 0:99) { if(i %% 10 == 0) {j = j+1; k = 10} k = k-1 points(j, k, pch = 15, col = i, cex = 2) text(j+0.45, k, i, cex = cex)} # Return Value: invisible() } ################################################################################ fBasics/R/utils-colorRGB.R0000644000176000001440000001057511613257742015027 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # .asRGB Converts any R color to RGB (red/green/blue) # .chcode Changes from one to another number system # .hex.to.dec Converts heximal numbers do decimal numbers # .dec.to.hex Converts decimal numbers do heximal numbers ################################################################################ .asRGB <- function(col = rainbowPalette(64), alpha = FALSE) { # A function implemented by Diethelm Wuertz # Description: # Converts any R color to RGB (red/green/blue) # Arguments: # col - vector of any of the three kind of R colors, i.e., either a # color name (an element of colors()), a hexadecimal string of # the form "#rrggbb", or an integer i meaning palette()[i]. # alpha - a logical value indicating whether alpha channel values # should be returned. # FUNCTION: # Color Conversion: result <- col2rgb(col = col, alpha = alpha) # Return Value: t(result) } # ------------------------------------------------------------------------------ .chcode <- function(b, base.in = 2, base.out = 10, digits = "0123456789ABCDEF") { # A function implemented by Diethelm Wuertz # Description: # Changes from one to another number system # Arguments: # b - number specified in the input base # b.in - input base # b.out - output base # digits - digits string # Value: # returns the input in the form represented by the output base # Author: # Peter Wolf Universitaet Bielefeld # from: http://tolstoy.newcastle.edu.au/R/help/05/04/2085.html # FUNCTION: # Change Number System: digits = substring(digits,1:nchar(digits),1:nchar(digits)) if (length(base.in) == 1) base.in = rep(base.in, max(nchar(b) - 1)) if (is.numeric(b)) b = as.character(as.integer(b)) b.num = lapply(strsplit(b, ""), function(x) {match(x, digits)-1} ) result = lapply(b.num, function(x) {cumprod(rev(c(base.in,1))[1:length(x)]) %*% rev(x)} ) number = unlist(result) # DW Print Output Suppressed # cat("decimal representation:",number,"\n") if (length(base.out) == 1) { base.out<-rep(base.out, 1+ceiling(log(max(number), base = base.out))) } n.base = length(base.out) result = NULL for(i in n.base:1){ result = rbind(number %% base.out[i], result) number = floor(number/base.out[i]) } result[]<-digits[result+1] ans = apply(result, 2, paste, collapse = "") # Return Value: ans } # ------------------------------------------------------------------------------ .hex.to.dec <- function(b) { # A function implemented by Diethelm Wuertz # Description: # Converts heximal numbers do decimal numbers # Arguments: # b - a heximal number # Value: # returns a heximal numbers as decimal numbers # FUNCTION: # Hex to Bin: ans = as.numeric(.chcode(b, base.in = 16, base.out = 10)) # Return Value: ans } # ------------------------------------------------------------------------------ .dec.to.hex <- function(b) { # A function implemented by Diethelm Wuertz # Description: # Converts decimal numbers do heximal numbers # Arguments: # x - a decimal number # Value: # returns a decimal numbers as heximal numbers # FUNCTION: # Decimal to Hex: ans = .chcode(b, base.in = 10, base.out = 16) # Return Value: ans } ################################################################################ fBasics/R/utils-colorPalettes.R0000644000176000001440000005754711370220773016202 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: COLOR PALETTES: # rainbowPalette Creates a rainbow color palette # heatPalette Creates a heat color palette # terrainPalette Creates a terrain color palette # topoPalette Creates a topo color palette # cmPalette Creates a cm color palette # greyPalette Creates a grey palette # timPalette Creates a cyan, yellow, to orange palette # FUNCTION: COLOR RAMPS: # rampPalette Creates a color ramp palette # seqPalette Creates a sequential color palette # divPalette Creates a diverging color palette # qualiPalette Creates a qualitative color palette # focusPalette Creates a focus color palette # monoPalette Creates a mono color palette ################################################################################ ################################################################################ # FUNCTION: DESCRIPTION: # rainbowPalette Creates a rainbow color palette # heatPalette Creates a heat color palette # terrainPalette Creates a terrain color palette # topoPalette Creates a topo color palette # cmPalette Creates a cm color palette rainbowPalette <- function(n = 64, ...) { # A function implemented by Diethelm Wuertz # FUNCTION: # Return Value: rainbow(n = n, ...) } # ------------------------------------------------------------------------------ heatPalette <- function(n = 64, ...) { # A function implemented by Diethelm Wuertz # FUNCTION: # Return Value: heat.colors(n, ...) } # ------------------------------------------------------------------------------ terrainPalette <- function(n = 64, ...) { # A function implemented by Diethelm Wuertz # FUNCTION: # Return Value: terrain.colors(n, ...) } # ------------------------------------------------------------------------------ topoPalette <- function(n = 64, ...) { # A function implemented by Diethelm Wuertz # FUNCTION: # Return Value: topo.colors(n, ...) } # ------------------------------------------------------------------------------ cmPalette <- function(n = 64, ...) { # A function implemented by Diethelm Wuertz # FUNCTION: # Return Value: cm.colors(n, ...) } ################################################################################ # FUNCTION: COLOR/GREY PALETTES: # greyPalette Creates a grey palette # timPalette Creates a cyan, yellow, to orange palette greyPalette <- function(n = 64, ...) { # A function implemented by Diethelm Wuertz # Description: # Create a vector of n gamma-corrected gray colors. # Arguments: # n - the number of greys to be constructed # start, end - the range of the color palette # gamma a gamma-correction # Value: # returns a grey palette like rainbow does # for color palettes # FUNCTION: # Compose: ans = gray.colors(n, ...) # Return Value: ans } # ------------------------------------------------------------------------------ timPalette <- function(n = 64) { # A function implemented by Diethelm Wuertz # Description: # Creates a cyan, yellow, to orange palette # Notes: # 'Tim.colors' in 'fields' package goes from blue to red, and passes # through the colors cyan, yellow, and orange. Also known as Jet # color-map in Matlab. You can also easily design your own color map # using 'rgb' function from 'gdDevices'. # From: # FUNCTION: orig = c( "#00008F", "#00009F", "#0000AF", "#0000BF", "#0000CF", "#0000DF", "#0000EF", "#0000FF", "#0010FF", "#0020FF", "#0030FF", "#0040FF", "#0050FF", "#0060FF", "#0070FF", "#0080FF", "#008FFF", "#009FFF", "#00AFFF", "#00BFFF", "#00CFFF", "#00DFFF", "#00EFFF", "#00FFFF", "#10FFEF", "#20FFDF", "#30FFCF", "#40FFBF", "#50FFAF", "#60FF9F", "#70FF8F", "#80FF80", "#8FFF70", "#9FFF60", "#AFFF50", "#BFFF40", "#CFFF30", "#DFFF20", "#EFFF10", "#FFFF00", "#FFEF00", "#FFDF00", "#FFCF00", "#FFBF00", "#FFAF00", "#FF9F00", "#FF8F00", "#FF8000", "#FF7000", "#FF6000", "#FF5000", "#FF4000", "#FF3000", "#FF2000", "#FF1000", "#FF0000", "#EF0000", "#DF0000", "#CF0000", "#BF0000", "#AF0000", "#9F0000", "#8F0000", "#800000") if (n == 64) return(orig) rgb.tim = t(col2rgb(orig)) temp = matrix(NA, ncol = 3, nrow = n) x = seq(0, 1, , 64) xg = seq(0, 1, , n) for (k in 1:3) { hold = spline(x, rgb.tim[, k], n = n)$y hold[hold < 0] = 0 hold[hold > 255] = 255 temp[, k] = round(hold) } ans = rgb(temp[, 1], temp[, 2], temp[, 3], maxColorValue = 255) # Return Value: ans } ################################################################################ # Package: colorRamps # Type: Package # Title: Builds pleasing color tables # Version: 1.0 # Date: 2007-04-05 # Author: Tim Keitt # Maintainer: Tim Keitt # Description: Builds single and double gradient color maps # License: GPL # Packaged: Thu Apr 5 16:34:42 2007; tkeitt .blue2redPalette <- function(n) { # A copy from contributed R-package colorRamps # FUNCTION: # Color Ramp: n2 = ceiling(n / 2) red = rep(c(0, 1), each = n2)[1:n] green = 1 - abs(seq(-1, 1, length.out = n)) blue = rev(red) # Return Value: rgb(red, green, blue) } # ------------------------------------------------------------------------------ .green2redPalette <- function(n) { # A copy from contributed R-package colorRamps # FUNCTION: # Color Ramp: n2 = ceiling(n / 2) red = rep(c(0, 1), each = n2)[1:n] blue = 1 - abs(seq(-1, 1, length.out = n)) green = rev(red) # Return Value: rgb(red, green, blue) } # ------------------------------------------------------------------------------ .blue2greenPalette <- function(n) { # A copy from contributed R-package colorRamps # FUNCTION: # Color Ramp: n2 = ceiling(n / 2) green = rep(c(0, 1), each = n2)[1:n] red = 1 - abs(seq(-1, 1, length.out = n)) blue = rev(green) # Return Value: rgb(red, green, blue) } # ------------------------------------------------------------------------------ .purple2greenPalette <- function(n) { # A copy from contributed R-package colorRamps # FUNCTION: # Color Ramp: red = rep(0.5, length.out = n) green = seq(0, 1, length.out = n) blue = rev(green) # Return Value: rgb(red, green, blue) } # ------------------------------------------------------------------------------ .blue2yellowPalette <- function(n) { # A copy from contributed R-package colorRamps # FUNCTION: # Color Ramp: red = seq(0, 1, length.out = n) green = red blue = rev(red) # Return Value: rgb(red, green, blue) } # ------------------------------------------------------------------------------ .cyan2magentaPalette <- function(n) { # A copy from contributed R-package colorRamps # FUNCTION: # Color Ramp: red = seq(0, 1, length.out = n) green = rev(red) blue = rep(1, n) # Return Value: rgb(red, green, blue) } # ------------------------------------------------------------------------------ rampPalette <- function(n, name = c("blue2red", "green2red", "blue2green", "purple2green", "blue2yellow", "cyan2magenta")) { # Description: # Creates a color ramp palette # FUNCTION: # Color Ramp: name = match.arg(name) funPalette = match.fun(paste(".", name, "Palette", sep = "")) ans = funPalette(n) # Return Value: ans } ################################################################################ # Package: RColorBrewer # Version: 1.0-2 # Date: 2007-10-21 # Title: ColorBrewer palettes # Author: Erich Neuwirth # Maintainer: Erich Neuwirth # Depends: R (>= 2.0.0) # Description: The packages provides palettes for drawing nice maps # shaded according to a variable. # License: Apache License 2.0 seqPalette <- function(n, name = c("Blues", "BuGn", "BuPu", "GnBu", "Greens", "Greys", "Oranges", "OrRd", "PuBu", "PuBuGn", "PuRd", "Purples", "RdPu", "Reds", "YlGn", "YlGnBu", "YlOrBr", "YlOrRd")) { # A function implemented by Diethelm Wuertz # Description: # Creates a sequential color palette # FUNCTION: # Color Sets: # Blues, BuGn, BuPu, GnBu, Greens, Greys, Oranges, OrRd, PuBu, # PuBuGn, PuRd, Purples, RdPu, Reds, YlGn, YlGnBu, YlOrBr, YlOrRd. Blues = rgb(c(247,222,198,158,107,66,33,8,8), c(251,235,219,202,174,146,113,81,48), c(255,247,239,225,214,198,181,156,107),maxColorValue=255) BuGn = rgb(c(247,229,204,153,102,65,35,0,0), c(252,245,236,216,194,174,139,109,68), c(253,249,230,201,164,118,69,44,27),maxColorValue=255) BuPu = rgb(c(247,224,191,158,140,140,136,129,77), c(252,236,211,188,150,107,65,15,0), c(253,244,230,218,198,177,157,124,75),maxColorValue=255) GnBu = rgb(c(247,224,204,168,123,78,43,8,8), c(252,243,235,221,204,179,140,104,64), c(240,219,197,181,196,211,190,172,129),maxColorValue=255) Greens = rgb(c(247,229,199,161,116,65,35,0,0), c(252,245,233,217,196,171,139,109,68), c(245,224,192,155,118,93,69,44,27),maxColorValue=255) Greys = rgb(c(255,240,217,189,150,115,82,37,0), c(255,240,217,189,150,115,82,37,0), c(255,240,217,189,150,115,82,37,0),maxColorValue=255) Oranges = rgb(c(255,254,253,253,253,241,217,166,127), c(245,230,208,174,141,105,72,54,39), c(235,206,162,107,60,19,1,3,4),maxColorValue=255) OrRd = rgb(c(255,254,253,253,252,239,215,179,127), c(247,232,212,187,141,101,48,0,0), c(236,200,158,132,89,72,31,0,0),maxColorValue=255) PuBu = rgb(c(255,236,208,166,116,54,5,4,2), c(247,231,209,189,169,144,112,90,56), c(251,242,230,219,207,192,176,141,88),maxColorValue=255) PuBuGn = rgb(c(255,236,208,166,103,54,2,1,1), c(247,226,209,189,169,144,129,108,70), c(251,240,230,219,207,192,138,89,54),maxColorValue=255) PuOr = rgb(c(127,179,224,253,254,247,216,178,128,84,45), c(59,88,130,184,224,247,218,171,115,39,0), c(8,6,20,99,182,247,235,210,172,136,75),maxColorValue=255) PuRd = rgb(c(247,231,212,201,223,231,206,152,103), c(244,225,185,148,101,41,18,0,0), c(249,239,218,199,176,138,86,67,31),maxColorValue=255) Purples = rgb(c(252,239,218,188,158,128,106,84,63), c(251,237,218,189,154,125,81,39,0), c(253,245,235,220,200,186,163,143,125),maxColorValue=255) RdPu = rgb(c(255,253,252,250,247,221,174,122,73), c(247,224,197,159,104,52,1,1,0), c(243,221,192,181,161,151,126,119,106),maxColorValue=255) Reds = rgb(c(255,254,252,252,251,239,203,165,103), c(245,224,187,146,106,59,24,15,0), c(240,210,161,114,74,44,29,21,13),maxColorValue=255) YlGn = rgb(c(255,247,217,173,120,65,35,0,0), c(255,252,240,221,198,171,132,104,69), c(229,185,163,142,121,93,67,55,41),maxColorValue=255) YlGnBu = rgb(c(255,237,199,127,65,29,34,37,8), c(255,248,233,205,182,145,94,52,29), c(217,177,180,187,196,192,168,148,88),maxColorValue=255) YlOrBr = rgb(c(255,255,254,254,254,236,204,153,102), c(255,247,227,196,153,112,76,52,37), c(229,188,145,79,41,20,2,4,6),maxColorValue=255) YlOrRd = rgb(c(255,255,254,254,253,252,227,189,128), c(255,237,217,178,141,78,26,0,0), c(204,160,118,76,60,42,28,38,38),maxColorValue=255) # Compose: name = match.arg(name) orig = eval(parse(text = name)) rgb = t(col2rgb(orig)) temp = matrix(NA, ncol = 3, nrow = n) x = seq(0, 1, , length(orig)) xg = seq(0, 1, , n) for (k in 1:3) { hold = spline(x, rgb[, k], n = n)$y hold[hold < 0] = 0 hold[hold > 255] = 255 temp[, k] = round(hold) } palette = rgb(temp[, 1], temp[, 2], temp[, 3], maxColorValue = 255) # Return Value: palette } # ------------------------------------------------------------------------------ divPalette <- function(n, name = c("BrBG", "PiYG", "PRGn", "PuOr", "RdBu", "RdGy", "RdYlBu", "RdYlGn", "Spectral")) { # A function implemented by Diethelm Wuertz # Description: # Creates a diverging color palette # FUNCTION: # Color Sets: # BrBG, PiYG, PRGn, PuOr, RdBu, RdGy, RdYlBu, RdYlGn, Spectral. BrBG = rgb(c(84,140,191,223,246,245,199,128,53,1,0), c(48,81,129,194,232,245,234,205,151,102,60), c(5,10,45,125,195,245,229,193,143,94,48),maxColorValue=255) PiYG = rgb(c(142,197,222,241,253,247,230,184,127,77,39), c(1,27,119,182,224,247,245,225,188,146,100), c(82,125,174,218,239,247,208,134,65,33,25),maxColorValue=255) PRGn = rgb(c(64,118,153,194,231,247,217,166,90,27,0), c(0,42,112,165,212,247,240,219,174,120,68), c(75,131,171,207,232,247,211,160,97,55,27),maxColorValue=255) PuOr = rgb(c(127,179,224,253,254,247,216,178,128,84,45), c(59,88,130,184,224,247,218,171,115,39,0), c(8,6,20,99,182,247,235,210,172,136,75),maxColorValue=255) RdBu = rgb(c(103,178,214,244,253,247,209,146,67,33,5), c(0,24,96,165,219,247,229,197,147,102,48), c(31,43,77,130,199,247,240,222,195,172,97),maxColorValue=255) RdGy = rgb(c(103,178,214,244,253,255,224,186,135,77,26), c(0,24,96,165,219,255,224,186,135,77,26), c(31,43,77,130,199,255,224,186,135,77,26),maxColorValue=255) RdYlBu = rgb(c(165,215,244,253,254,255,224,171,116,69,49), c(0,48,109,174,224,255,243,217,173,117,54), c(38,39,67,97,144,191,248,233,209,180,149),maxColorValue=255) RdYlGn = rgb(c(165,215,244,253,254,255,217,166,102,26,0), c(0,48,109,174,224,255,239,217,189,152,104), c(38,39,67,97,139,191,139,106,99,80,55),maxColorValue=255) Spectral = rgb(c(158,213,244,253,254,255,230,171,102,50,94), c(1,62,109,174,224,255,245,221,194,136,79), c(66,79,67,97,139,191,152,164,165,189,162),maxColorValue=255) # Compose: name = match.arg(name) orig = eval(parse(text = name)) rgb = t(col2rgb(orig)) temp = matrix(NA, ncol = 3, nrow = n) x = seq(0, 1, , length(orig)) xg = seq(0, 1, , n) for (k in 1:3) { hold = spline(x, rgb[, k], n = n)$y hold[hold < 0] = 0 hold[hold > 255] = 255 temp[, k] = round(hold) } palette = rgb(temp[, 1], temp[, 2], temp[, 3], maxColorValue = 255) # Return Value: palette } # ------------------------------------------------------------------------------ qualiPalette <- function(n, name = c("Accent", "Dark2", "Paired", "Pastel1", "Pastel2", "Set1", "Set2", "Set3")) { # A function implemented by Diethelm Wuertz # Description: # Creates a qualitative color palette # FUNCTION: # Color Sets: Accent = rgb(c(127,190,253,255,56,240,191,102), c(201,174,192,255,108,2,91,102), c(127,212,134,153,176,127,23,102),maxColorValue=255) Dark2 = rgb(c(27,217,117,231,102,230,166,102), c(158,95,112,41,166,171,118,102), c(119,2,179,138,30,2,29,102),maxColorValue=255) Paired = rgb(c(166,31,178,51,251,227,253,255,202,106,255,177), c(206,120,223,160,154,26,191,127,178,61,255,89), c(227,180,138,44,153,28,111,0,214,154,153,40),maxColorValue=255) Pastel1 = rgb(c(251,179,204,222,254,255,229,253,242), c(180,205,235,203,217,255,216,218,242), c(174,227,197,228,166,204,189,236,242),maxColorValue=255) Pastel2 = rgb(c(179,253,203,244,230,255,241,204), c(226,205,213,202,245,242,226,204), c(205,172,232,228,201,174,204,204),maxColorValue=255) Set1 = rgb(c(228,55,77,152,255,255,166,247,153), c(26,126,175,78,127,255,86,129,153), c(28,184,74,163,0,51,40,191,153),maxColorValue=255) Set2 = rgb(c(102,252,141,231,166,255,229,179), c(194,141,160,138,216,217,196,179), c(165,98,203,195,84,47,148,179),maxColorValue=255) Set3 = rgb(c(141,255,190,251,128,253,179,252,217,188,204,255), c(211,255,186,128,177,180,222,205,217,128,235,237), c(199,179,218,114,211,98,105,229,217,189,197,111),maxColorValue=255) # Compose: name = match.arg(name) orig = eval(parse(text = name)) rgb = t(col2rgb(orig)) temp = matrix(NA, ncol = 3, nrow = n) x = seq(0, 1, , length(orig)) xg = seq(0, 1, , n) for (k in 1:3) { hold = spline(x, rgb[, k], n = n)$y hold[hold < 0] = 0 hold[hold > 255] = 255 temp[, k] = round(hold) } palette = rgb(temp[, 1], temp[, 2], temp[, 3], maxColorValue = 255) # Return Value: palette } ################################################################################ # Package: PerformanceAnalytics # Type: Package # Title: Econometric tools for performance and risk analysis. # Version: 0.9.5 # Date: 2007-06-29 # Author: Peter Carl, Brian G. Peterson # Maintainer: Brian G. Peterson # Description: Library of econometric functions for performance and risk # analysis. This library aims to aid practitioners and researchers in # utilizing the latest research in analysis of non-normal return streams. # In general, this library is most tested on return (rather than price) # data on a monthly scale, but most functions will work with daily or # irregular return data as well. # Depends: R (>= 2.4.0), fExtremes, fPortfolio, quadprog, tseries, Hmisc # License: GPL # URL: http://braverock.com/R/ # Packaged: Tue Jul 10 04:30:47 2007 focusPalette <- function(n, name = c("redfocus", "greenfocus", "bluefocus")) { # A function implemented by Diethelm Wuertz # Description: # Creates a color palette for graphs # Source: # Contributed R package PerformanceAnalytics # Details: # This is not a function, per se, but a way to set up specific color # pallets for use in the charts we use. These pallets have been # designed to create readable, comparable line and bar graphs with # specific objectives outlined before each category below. # We use this approach rather than generating them on the fly for two # reasons: 1) fewer dependencies on libraries that don't need to be # called dynamically; and 2) to guarantee the color used for the n-th # column of data. # # FOCUS PALETTE # Colorsets designed to provide focus to the data graphed as the first # element. This palette is best used when there is clearly an important # data set for the viewer to focus on, with the remaining data being # secondary, tertiary, etc. Later elements graphed in diminishing # values of gray. These were generated with RColorBrewer, using the 8 # level "grays" palette and replacing the darkest with the focus color. # For best results, replace the highlight color with the first color # of the equal weighted palette from below. This will coordinate # charts with different purposes. # # MONOCHROME PALETTES: # Colorsets for monochrome color displays. # FUNCTION: # Match Arguments: name = match.arg(name) # Focus Palettes: redfocus = c( "#CB181D", "#252525", "#525252", "#737373", "#969696", "#BDBDBD", "#D9D9D9", "#F0F0F0") greenfocus = c( "#41AB5D", "#252525", "#525252", "#737373", "#969696", "#BDBDBD", "#D9D9D9", "#F0F0F0") bluefocus = c( "#0033FF", "#252525", "#525252", "#737373", "#969696", "#BDBDBD", "#D9D9D9", "#F0F0F0") # Compose: orig = eval(parse(text = name)) rgb = t(col2rgb(orig)) temp = matrix(NA, ncol = 3, nrow = n) x = seq(0, 1, , length(orig)) xg = seq(0, 1, , n) for (k in 1:3) { hold = spline(x, rgb[, k], n = n)$y hold[hold < 0] = 0 hold[hold > 255] = 255 temp[, k] = round(hold) } palette = rgb(temp[, 1], temp[, 2], temp[, 3], maxColorValue = 255) # Return Value: palette } # ------------------------------------------------------------------------------ monoPalette <- function(n, name = c("redmono", "greenmono", "bluemono")) { # A function implemented by Diethelm Wuertz # Description: # Creates a mono color palette # Source: # Contributed R package PerformanceAnalytics # FUNCTION: # Match Arguments: name = match.arg(name) # Monochrome Palettes: redmono = c( "#99000D", "#CB181D", "#EF3B2C", "#FB6A4A", "#FC9272", "#FCBBA1", "#FEE0D2", "#FFF5F0") greenmono = c( "#005A32", "#238B45", "#41AB5D", "#74C476", "#A1D99B", "#C7E9C0", "#E5F5E0", "#F7FCF5") bluemono = c( "#084594", "#2171B5", "#4292C6", "#6BAED6", "#9ECAE1", "#C6DBEF", "#DEEBF7", "#F7FBFF") # Compose: orig = eval(parse(text = name)) rgb = t(col2rgb(orig)) temp = matrix(NA, ncol = 3, nrow = n) x = seq(0, 1, , length(orig)) xg = seq(0, 1, , n) for (k in 1:3) { hold = spline(x, rgb[, k], n = n)$y hold[hold < 0] = 0 hold[hold > 255] = 255 temp[, k] = round(hold) } palette = rgb(temp[, 1], temp[, 2], temp[, 3], maxColorValue = 255) # Return Value: palette } ################################################################################ fBasics/R/utils-colorLocator.R0000644000176000001440000000712411720123756016010 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: COLOR NAMES: # colorLocator Plots R's 657 named colors for selection # colorMatrix Returns matrix of R's color names ################################################################################ ################################################################################ # Package: epitools # Version: 0.4-9 # Date: 2007-06-27 # Title: Epidemiology Tools # Author: Tomas Aragon # Maintainer: Tomas Aragon # Depends: R (>= 2.1.0) # Description: EpiTools: R Package for Epidemiologic Data and Graphics # License: GPL version 2 or newer # URL: http://www.epitools.net # Packaged: Wed Jun 27 20:27:33 2007; Tomas colorLocator <- function(locator = FALSE, cex.axis = 0.7) { # A function implemented by Diethelm Wuertz # Description: # Plots R's 657 named colors for selection # Source: # Code borrowed from contributed R-package epitools # FUNCTION: # Plot: xx <- rep(1:30, 22) yy <- rep(1:22, rep(30, 22)) yyy <- matrix(yy, ncol = 22) cm <- colorMatrix() matplot(xx[1:30], yyy, pch = 15, type = "n", axes = FALSE, xlab = "colorMatrix[row, ]", ylab = "colorMatrix[ , col]", main = "Identify Color Names.") ## title(sub = "Source: www.epitools.net", cex.sub = 0.7) points(xx, yy, type = "p", pch = 15, cex = 2, col = c(colors(), NA, NA, NA)) axis(1, at = c(0:30 + 0.5), labels = FALSE, tick = TRUE) axis(1, at = 1:30, labels = 1:30, cex.axis = cex.axis, tick = FALSE) axis(2, at = c(0:22 + 0.5), labels = FALSE, tick = TRUE) axis(2, at = 1:22, labels = 1:22, cex.axis = cex.axis, tick = FALSE, las = 1) # Locator: if (locator) { lxy <- locator() xy <- round(data.frame(lxy)) xym <- as.matrix(xy) ## return located := data.frame(xy, color.names = cm[xym]) } else { invisible(cm) } } # ------------------------------------------------------------------------------ colorMatrix <- function() { # A function implemented by Diethelm Wuertz # Description: # Returns matrix of R's color names # Source: # Code borrowed from contributed R-package epitools # FUNCTION: # Color Names: x <- matrix(c(colors(), NA, NA, NA), 30, 22) # Return Value: invisible(x) } ################################################################################ fBasics/R/utils-colIds.R0000644000176000001440000000711511370220773014561 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # GENERATION: DESCRIPTION: # colIds Retrieves column names of a matrix-like object # rowIds Retrieves row names of a matrix-like object # colIds<- Sets column names of a matrix-like object # rowIds<- Sets row names of a matrix-like object ################################################################################ colIds <- function(x, ...) { # A function implemented by Diethelm Wuertz # Description: # Retrieves row names of a matrix-like object # FUNCTION: # Convert to Matrix x = as.matrix(x) # Return Value: colnames(x, ...) } # ------------------------------------------------------------------------------ rowIds <- function(x, ...) { # A function implemented by Diethelm Wuertz # Description: # Retrieves row names of a matrix-like object # FUNCTION: # Convert to Matrix x = as.matrix(x) # Return Value: rownames(x, ...) } # ------------------------------------------------------------------------------ "colIds<-" <- function(x, value) { # A function implemented by Diethelm Wuertz # Description: # Sets column names of a matrix-like object # FUNCTION: # Column Names: dn = dimnames(x) if(is.null(dn)) { if(is.null(value)) return(x) if((nd = length(dim(x))) < 2) stop("Object has less than two dimensions") dn = vector("list", nd) } if(length(dn) < 2) stop("Object has less than two dimensions") if(is.null(value)) dn[2] = list(NULL) else dn[[2]] = value dimnames(x) = dn # Return Value: x } # ------------------------------------------------------------------------------ "rowIds<-" <- function(x, value) { # A function implemented by Diethelm Wuertz # Description: # Sets row names of a matrix-like object # FUNCTION: # Row names: dn = dimnames(x) if(is.null(dn)) { if(is.null(value)) return(x) if((nd = length(dim(x))) < 1) stop("attempt to set rownames on object with no dimensions") dn = vector("list", nd) } if(length(dn) < 1) stop("attempt to set rownames on object with no dimensions") if(is.null(value)) dn[1] = list(NULL) else dn[[1]] = value dimnames(x) = dn # Return Value: x } ################################################################################ fBasics/R/utils-characterTable.R0000644000176000001440000000566411370220773016257 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DESCRIPTION: # characterTable Shows a table of character codes ################################################################################ characterTable <- function(font = 1, cex = 0.7) { # A function implemented by Diethelm Wuertz # Description: # Prints numeric equivalents to all latin characters. # Notes: # The printed version doesn't allways corresponds to the # screen display. The character on line "xy" and column # "z" of the table has code "xyz". These codes can be # used as any other characters. # e.g. title("\347\340 et \340") # Note on S: # As the command line window of Splus can't print special # characters # cat("\347\340 et \340") # will not print the special characters, at least under # 4.5 and under 2000. # Author: # Source from Pierre Joyet, pierre.joyet@bluewin.ch # Example: # for (i in 1:20) characterTable(font = i) # FUNCTION: # Table: v = 40:377 v = v[v %% 100 < 80 & v %% 10 < 8] par(mar = c(5, 5, 4, 2) + 0.1) plot(-1:7, seq(4, 33, length = 9), type = "n", axes = FALSE, xlab = "", ylab = "", cex = cex, main = "Table of Characters") k = 1 for(i in 4:31) for(j in 0:7) { text(j, 35 - i, eval(parse(text = paste("\"\\", v[k], "\"", sep = ""))), font = font, cex = cex) k = k + 1 } text(0:7, rep(33, 7), as.character(0:7), font = 3, cex = cex) text(rep(-1, 28), 31:4, as.character(c(4:7, 10:17, 20:27, 30:37)), font = 3, cex = cex) # Return Value: invisible(font) } ################################################################################ fBasics/R/utils-baseMethods.R0000644000176000001440000003023311370220773015577 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: BASIC EXTENSIONS: # align aligns time series objects by approximation # align.default align default method # atoms Extracts atoms from 'timeSeries' object # atoms.default atoms default method # attach attach a database to the R path # attach.default attach default method # colnames<- colnames<- has become a generic function # colnames<-.default colnames<- default method # cor cor has become a generic function # cor.default cor default method # cov var has become a generic function # cov.default var default method # log log has become a generic function # log.default log default method # outlier outlier added generic function # outlier.default outlier default method # rownames<- rownames<- has become a generic function # rownames<-.default rownames<- default method # rank rank has become a generic function # rank.default rank default method # sample sample has become a generic function # sample.default sample default method # sort sort has become a generic function # sort.default sort default method # stdev stdev added generic function # stdev.default stdev default method # termPlot termPlot has become a generic function # termPlot.default termPlot default method # var var has become a generic function # var.default var default method # volatility volatility has become a generic function # volatility.default volatility default method ################################################################################ ## It is bad practice to create S3 generic from function especially ## when one wants to use S4 methods -> this can lead to tricky dispatch ## problems. ## .conflicts.OK = TRUE # ------------------------------------------------------------------------------ ## align <- ## function(x, y, xout, ...) ## { ## # A function implemented by Diethelm Wuertz ## # FUNCTION: ## # Return Value: ## UseMethod("align") ## } ## # ------------------------------------------------------------------------------ ## align.default <- ## function(x, y, xout, method = "linear", n = 50, rule = 1, f = 0, ## ties = mean, ...) ## { ## # A function implemented by Diethelm Wuertz ## # FUNCTION: ## # Align by Approximation: ## ans = approx(x = x, y = y, xout = xout, method = method, n = n, ## rule = rule, f = f, ties = ties, ...) ## # Return Value: ## ans ## } # ------------------------------------------------------------------------------ ## atoms <- ## function(x, ...) ## { ## # A function implemented by Diethelm WUertz ## # FUNCTION: ## # Return Value: ## UseMethod("atoms") ## } ## # ------------------------------------------------------------------------------ ## atoms.default <- ## function(x, ...) ## { ## # A function implemented by Diethelm WUertz ## # FUNCTION: ## # Return Value: ## invisible(x) ## } # ------------------------------------------------------------------------------ ## attach <- ## function(what, pos = 2, name = deparse(substitute(what)), ## warn.conflicts = TRUE) ## { ## # A function implemented by Diethelm Wuertz ## # FUNCTION: ## # Return Value: ## UseMethod("attach") ## } ## # ------------------------------------------------------------------------------ ## attach.default <- base::attach # ------------------------------------------------------------------------------ ## "colnames<-" = ## function(x, value) ## { ## # A function implemented by Diethelm Wuertz ## # FUNCTION: ## # Return Value: ## UseMethod("colnames<-") ## } ## # ------------------------------------------------------------------------------ ## `colnames<-.default` <- base::`colnames<-` # ------------------------------------------------------------------------------ ## cor <- ## function(x, y = NULL, use = "all.obs", ## method = c("pearson", "kendall", "spearman")) ## { ## # A function implemented by Diethelm Wuertz ## # FUNCTION: ## # Return Value: ## UseMethod("cor") ## } # ------------------------------------------------------------------------------ ## cor.default <- stats::cor ## # ------------------------------------------------------------------------------ ## cov <- ## function(x, y = NULL, use = "all.obs", ## method = c("pearson", "kendall", "spearman")) ## { ## # A function implemented by Diethelm Wuertz ## # FUNCTION: ## # Return Value: ## UseMethod("cov") ## } ## # ------------------------------------------------------------------------------ ## cov.default <- stats::cov ## # ------------------------------------------------------------------------------ ## log <- ## function(x, base = exp(1)) ## { # A function implemented by Diethelm Wuertz ## # FUNCTION: ## # Return Value: ## UseMethod("log") ## } ## # ------------------------------------------------------------------------------ ## log.default <- ## function(x, base = exp(1)) ## { ## # A function implemented by Diethelm Wuertz ## # FUNCTION: ## # Return Value: ## base::log(x, base) ## } # ------------------------------------------------------------------------------ ## rank <- ## function(x, na.last = TRUE, ## ties.method = c("average", "first", "random", "max", "min")) ## { ## # A function implemented by Diethelm Wuertz ## # FUNCTION: ## # Return Value: ## UseMethod("rank") ## } # ------------------------------------------------------------------------------ ## rank.default <- ## function(x, na.last = TRUE, ## ties.method = c("average", "first", "random", "max", "min")) ## { ## # A function implemented by Diethelm Wuertz ## # FUNCTION: ## # Return Value: ## base::rank(x, na.last = na.last, ties.method = ties.method) ## } # ------------------------------------------------------------------------------ ## sample <- ## function(x, ...) ## { # A function implemented by Diethelm Wuertz ## # FUNCTION: ## # Return Value: ## UseMethod("sample") ## } ## # ------------------------------------------------------------------------------ ## sample.default <- ## function(x, size, replace = FALSE, prob = NULL, ...) ## { ## # FUNCTION: ## base::sample(x, size, replace = replace, prob = prob) ## } # ------------------------------------------------------------------------------ ## if(getRversion() < "2.4.0") { ## # Note: ## # sort() has been S3 generic in 'base' since 2.4.0 ## # Otherwise use something that works here ## sort <- function(x, decreasing = FALSE, ...) ## { ## if (!is.logical(decreasing) || length(decreasing) != 1) ## stop("'decreasing' must be a length-1 logical vector.\nDid you intend to set 'partial'?") ## UseMethod("sort") ## } ## sort.default <- function(x, decreasing = FALSE, ...) { ## if (is.object(x)) ## x[order(x, decreasing = decreasing)] ## else base::sort(x, decreasing = decreasing, ...) ## } ## }# endif {only for outdated R} # ------------------------------------------------------------------------------ ## outlier <- ## function(x, sd = 5, complement = TRUE, ...) ## { ## # A function implemented by Diethelm Wuertz ## # FUNCTION: ## UseMethod("outlier") ## } # ------------------------------------------------------------------------------ ## outlier.default <- ## function(x, sd = 5, complement = TRUE, ...) ## { ## # A function implemented by Diethelm Wuertz ## # Description: ## # Returns outliers ## # Arguments: ## # x - a numeric vector ## # sd - a numeric value of standard deviations, e.g. 5 ## # means that values larger or smaller tahn five ## # times the standard deviation of the series will ## # be detected. ## # complement - a logical flag, should the outlier series ## # or its complements be returned. ## # Note: ## # This function is thought to find splits in financial ## # price or index data. If a price or index is splitted we ## # observe in the returns a big jump of several standard ## # deviations. ## # FUNCTION: ## # Find Outliers: ## SD = sd * sd(x) ## if (complement) { ## ans = x[x <= SD] ## } else { ## ans = x[x > SD] ## names(ans) = as.character(which(x > SD)) ## } ## # Return Value: ## ans ## } # ------------------------------------------------------------------------------ ## "rownames<-" = ## function(x, value) ## { # A function implemented by Diethelm Wuertz ## # FUNCTION: ## # Return Value: ## UseMethod("rownames<-") ## } ## # ------------------------------------------------------------------------------ ## `rownames<-.default` <- base::`rownames<-` # ------------------------------------------------------------------------------ stdev.default <- function(x, na.rm = FALSE) { # A function implemented by Diethelm Wuertz # FUNCTION: # Return Value: stats::sd(x = x, na.rm = na.rm) } # ------------------------------------------------------------------------------ stdev <- function(x, na.rm = FALSE) UseMethod("stdev") # ------------------------------------------------------------------------------ termPlot <- function(model, ...) { # A function implemented by Diethelm Wuertz # FUNCTION: # Return Value: UseMethod("termPlot") } # ------------------------------------------------------------------------------ termPlot.default <- function(model, ...) { # A function implemented by Diethelm Wuertz # FUNCTION: # Return Value: stats::termplot(model, ...) } # ------------------------------------------------------------------------------ ## var <- ## function(x, y = NULL, na.rm = FALSE, use) ## { ## # A function implemented by Diethelm Wuertz ## # FUNCTION: ## # Return Value: ## UseMethod("var") ## } ## # ------------------------------------------------------------------------------ ## var.default <- stats::var # ------------------------------------------------------------------------------ volatility <- function(object, ...) { # A function implemented by Diethelm Wuertz # FUNCTION: # Return Value: UseMethod("volatility") } # ------------------------------------------------------------------------------ volatility.default <- function(object, ...) { # A function implemented by Diethelm Wuertz # FUNCTION: # Compute volatility: x = object ans = (x - mean(x))^2 # Return Value: ans } ################################################################################ fBasics/R/utils-as.matrix.ts.R0000644000176000001440000000451711370220773015702 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DESCRIPTION: # as.matrix.ts Converts univariate ts to 1-column matrix # as.matrix.mts Converts multivariate ts to matrix ################################################################################ ## as.matrix.ts <- ## function(x, ...) ## { ## # A function implemented by Diethelm Wuertz ## # Description: ## # Coerces a "ts" object into a matrix ## # FUNCTION: ## # Transform: ## ans = as.matrix.default(unclass(x)) ## attr(ans, "tsp")<-NULL ## rownames(ans)<-NULL ## colnames(ans)<-NULL ## # Return Value: ## ans ## } ## # ------------------------------------------------------------------------------ ## as.matrix.mts <- ## function(x, ...) ## { ## # A function implemented by Diethelm Wuertz ## # Description: ## # Coerces a multivariate "ts" object into a matrix ## # FUNCTION: ## # Transform: ## ans = as.matrix.default(unclass(x)) ## attr(ans, "tsp")<-NULL ## rownames(ans)<-NULL ## colnames(ans)<-NULL ## # Return Value: ## ans ## } ################################################################################ fBasics/R/utils-Heaviside.R0000644000176000001440000001002011370220773015232 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: HEAVISIDE AND RELATED FUNCTIONS: # Heaviside Computes Heaviside unit step function # Sign Another signum function # Delta Computes delta function # Boxcar Computes boxcar function # Ramp Computes ramp function ################################################################################ Heaviside <- function(x, a = 0) { # A function implemented by Diethelm Wuertz # Description: # Computes the Heaviside or unit step function. # Arguments: # x - a numeric vector. # a - the location of the break. # Details: # The Heaviside step function is 1 for x>a, 1/2 for x=a, # and 0 for xa, 0 for x=a, # and -1 for x # License: GPL version 2 or newer # Thanks: # to Spencer Grave for contributions. # FUNCTION: # Data Set Name: DNAME = deparse(substitute(x)) # Convert Type: if (class(x) == "fREG") x = residuals(x) x = as.vector(x) # Call: call = match.call() # Test: x = sort(x) n = length(x) if (n < 8) stop("sample size must be greater than 7") var.x <- var(x) if(var.x > 0){ p = pnorm((x - mean(x))/sqrt(var.x)) h = (2 * seq(1:n) - 1) * (log(p) + log(1 - rev(p))) ### DW modified: h = h[is.finite(h)] n = length(h) ### Continue: A = -n - mean(h) AA = (1 + 0.75/n + 2.25/n^2) * A if (AA < 0.2) { PVAL = 1 - exp(-13.436 + 101.14 * AA - 223.73 * AA^2) } else if (AA < 0.34) { PVAL = 1 - exp(-8.318 + 42.796 * AA - 59.938 * AA^2) } else if (AA < 0.6) { PVAL = exp(0.9177 - 4.279 * AA - 1.38 * AA^2) } else { PVAL = exp(1.2937 - 5.709 * AA + 0.0186 * AA^2) } # Result: if (PVAL > 1) { PVAL = 1 # was NA, suggested by Spencer Graves to modify W = NA } } else { A <- Inf PVAL <- 0 } names(PVAL) = "" test = list( statistic = c(A = A), p.value = PVAL, method = "Anderson - Darling Normality Test", data.name = DNAME) # Add: if (is.null(title)) title = "Anderson - Darling Normality Test" if (is.null(description)) description = description() # Return Value: ans = new("fHTEST", call = call, data = list(x = x), test = test, title = as.character(title), description = as.character(description)) } # ------------------------------------------------------------------------------ cvmTest <- function(x, title = NULL, description = NULL) { # A function implemented by Diethelm Wuertz # Description: # Performs the Cramer-von Mises normality test # Arguments: # x - a numeric vector of data values. # description - a brief description of the porject of type # character. # title - a character string which allows for a project title. # Notes: # A copy from contributed R-package 'nortest' # Source: # Package: nortest # Title: Tests for Normality # Version: 1.0 # Author: Juergen Gross # Description: 5 omnibus tests for the composite hypothesis of normality # Maintainer: Juergen Gross # License: GPL version 2 or newer # FUNCTION: # Data Set Name: DNAME = deparse(substitute(x)) # Convert Type: if (class(x) == "fREG") x = residuals(x) x = as.vector(x) # Call: call = match.call() # Test: x = sort(x) n = length(x) if (n < 8) stop("sample size must be greater than 7") p = pnorm((x - mean(x))/sqrt(var(x))) W = (1/(12 * n) + sum((p - (2 * seq(1:n) - 1)/(2 * n))^2)) WW = (1 + 0.5/n) * W # P Value: if (WW < 0.0275) { PVAL = 1 - exp(-13.953 + 775.5 * WW - 12542.61 * WW^2) } else if (WW < 0.051) { PVAL = 1 - exp(-5.903 + 179.546 * WW - 1515.29 * WW^2) } else if (WW < 0.092) { PVAL = exp(0.886 - 31.62 * WW + 10.897 * WW^2) } else { PVAL = exp(1.111 - 34.242 * WW + 12.832 * WW^2) } # Result: if (PVAL > 1) { PVAL = 1 W = NA } names(PVAL) = "" test = list( statistic = c(W = W), p.value = PVAL, method = "Cramer - von Mises Test", data.name = DNAME) # Add: if (is.null(title)) title = "Cramer - von Mises Normality Test" if (is.null(description)) description = description() # Return Value: new("fHTEST", call = call, data = list(x = x), test = test, title = as.character(title), description = as.character(description) ) } # ------------------------------------------------------------------------------ lillieTest <- function(x, title = NULL, description = NULL) { # A function implemented by Diethelm Wuertz # Description: # Performs the Lilliefors (Kolmogorov-Smirnov) normality test # Arguments: # x - a numeric vector of data values. # description - a brief description of the porject of type # character. # title - a character string which allows for a project title. # Notes: # A copy from contributed R-package 'nortest' # Source: # Package: nortest # Title: Tests for Normality # Version: 1.0 # Author: Juergen Gross # Description: 5 omnibus tests for the composite hypothesis of normality # Maintainer: Juergen Gross # License: GPL version 2 or newer # FUNCTION: # Data Set Name: DNAME = deparse(substitute(x)) # Convert Type: if (class(x) == "fREG") x = residuals(x) x = as.vector(x) # Call: call = match.call() # Test: x = sort(x) n = length(x) if (n < 5) stop("sample size must be greater than 4") p = pnorm((x - mean(x))/sqrt(var(x))) Dplus = max(seq(1:n)/n - p) Dminus = max(p - (seq(1:n) - 1)/n) K = max(Dplus, Dminus) if (n <= 100) { Kd = K nd = n } else { Kd = K * ((n/100)^0.49) nd = 100 } # P Value: PVAL = exp(-7.01256 * Kd^2 * (nd + 2.78019) + 2.99587 * Kd * sqrt(nd + 2.78019) - 0.122119 + 0.974598/sqrt(nd) + 1.67997/nd) if (PVAL > 0.1) { KK = (sqrt(n) - 0.01 + 0.85/sqrt(n)) * K if (KK <= 0.302) { PVAL = 1 } else if (KK <= 0.5) { PVAL = 2.76773 - 19.828315 * KK + 80.709644 * KK^2 - 138.55152 * KK^3 + 81.218052 * KK^4 } else if (KK <= 0.9) { PVAL = -4.901232 + 40.662806 * KK - 97.490286 * KK^2 + 94.029866 * KK^3 - 32.355711 * KK^4 } else if (KK <= 1.31) { PVAL = 6.198765 - 19.558097 * KK + 23.186922 * KK^2 - 12.234627 * KK^3 + 2.423045 * KK^4 } else { PVAL = 0 } } names(PVAL) = "" # Result: test = list( statistic = c(D = K), p.value = PVAL, method = "Lilliefors Test", data.name = DNAME) # Add: if (is.null(title)) title = "Lilliefors (KS) Normality Test" if (is.null(description)) description = description() # Return Value: new("fHTEST", call = call, data = list(x = x), test = test, title = as.character(title), description = as.character(description) ) } # ------------------------------------------------------------------------------ pchiTest <- function(x, title = NULL, description = NULL) { # A function implemented by Diethelm Wuertz # Description: # Performs the Pearson chi-square normality test # Arguments: # x - a numeric vector of data values. # description - a brief description of the porject of type # character. # title - a character string which allows for a project title. # Details: # n.classes - an integer value, the classes are build is such # a way that they are equiprobable under the hypothesis # of normality. We use Moore's default value. # adjust - a logical flag, if TRUE (default), the p-value # is computed from a chi-square distribution with # n.classes-3 degrees of freedom, otherwise from a # chi-square distribution with n.classes-1 degrees of # freedom. We print the results for both # Notes: # A copy from contributed R-package 'nortest' # Source: # Package: nortest # Title: Tests for Normality # Version: 1.0 # Author: Juergen Gross # Description: 5 omnibus tests for the composite hypothesis of normality # Maintainer: Juergen Gross # License: GPL version 2 or newer # FUNCTION: # Data Set Name: DNAME = deparse(substitute(x)) # Convert Type: if (class(x) == "fREG") x = residuals(x) x = as.vector(x) # Call: call = match.call() # Moore's Default Value: n.classes = ceiling(2 * (length(x)^(2/5))) names(n.classes) = "Number of Classes" # Test: n = length(x) dfd = c(2, 0) names(dfd) = c("TRUE", "FALSE") # adjust num = floor(1 + n.classes * pnorm(x, mean(x), sqrt(var(x)))) count = tabulate(num, n.classes) prob = rep(1/n.classes, n.classes) xpec = n * prob h = ((count - xpec)^2)/xpec P = sum(h) # For Splus compatibility: # pvalue = pchisq(P, n.classes - dfd - 1, lower.tail = FALSE) PVAL = c(1 - pchisq(P, n.classes - dfd[1] - 1), 1 - pchisq(P, n.classes - dfd[2] - 1)) names(PVAL) = c("Adhusted", "Not adjusted") # Return Value: test = list( statistic = c(P = P), p.value = PVAL, method = "Pearson Chi-Square Normality Test", data.name = DNAME, parameter = n.classes) # Add: if (is.null(title)) title = "Pearson Chi-Square Normality Test" if (is.null(description)) description = description() # Return Value: new("fHTEST", call = call, data = list(x = x), test = test, title = as.character(title), description = as.character(description) ) } # ------------------------------------------------------------------------------ sfTest <- function(x, title = NULL, description = NULL) { # A function implemented by Diethelm Wuertz # Description: # Performs the Shapiro-Francia normality test # Arguments: # x - a numeric vector of data values. # description - a brief description of the porject of type # character. # title - a character string which allows for a project title. # Notes: # A copy from contributed R-package 'nortest' # Source: # Package: nortest # Title: Tests for Normality # Version: 1.0 # Author: Juergen Gross # Description: 5 omnibus tests for the composite hypothesis of normality # Maintainer: Juergen Gross # License: GPL version 2 or newer # FUNCTION: # Data Set Name: DNAME = deparse(substitute(x)) # Convert Type: if (class(x) == "fREG") x = residuals(x) x = as.vector(x) # Call: call = match.call() # Test: x = sort(x) n = length(x) if ((n < 5 || n > 5000)) stop("sample size must be between 5 and 5000") y = qnorm(ppoints(n, a = 3/8)) W = cor(x, y)^2 u = log(n) v = log(u) mu = -1.2725 + 1.0521 * (v - u) sig = 1.0308 - 0.26758 * (v + 2/u) z = (log(1 - W) - mu)/sig # For Splus compatibility: # pval = pnorm(z, lower.tail = FALSE) PVAL = 1 - pnorm(z) names(PVAL) = "" # Test: test = list( statistic = c(W = W), p.value = PVAL, method = "Shapiro-Francia Normality Test", data.name = DNAME) # Add: if (is.null(title)) title = "Shapiro - Francia Normality Test" if (is.null(description)) description = description() # Return Value: new("fHTEST", call = call, data = list(x = x), test = test, title = as.character(title), description = as.character(description)) } ################################################################################ # AUGMENTED FINITE SAMPLE JB TEST: .jb.test <- function(x) { # A function implemented by Diethelm Wuertz # Description: # S3 version type finite sample adjusted JB test # Arguments: # Notes: # S3 Version type of test. # See also the Jarque-Bera test in Adrian Trapletti's # contributed "tseries" R package. # FUNCTION: # Data Set Name: DNAME = deparse(substitute(x)) # Check: if (NCOL(x) > 1) stop("x is not a vector or univariate time series") if (any(is.na(x))) stop("NAs in x") # LM Coefficients: n = length(x) c1lm = 6/n c2lm = 3 c3lm = 24/n # ALM Coefficients: c1alm = 6*(n-2) / ((n+1)*(n+3)) c2alm = 3*(n-1) / (n+1) c3alm = 24*n*(n-2)*(n-3) / ((n+1)^2*(n+3)*(n+5)) # Statistic: m1 = sum(x)/n m2 = sum((x - m1)^2)/n m3 = sum((x - m1)^3)/n m4 = sum((x - m1)^4)/n b1 = (m3/m2^(3/2)) b2 = (m4/(m2*m2)) STATISTIC = b1*b1/c1lm + ((b2-c2lm)^2)/c3lm ALM = b1*b1/c1alm + ((b2-c2alm)^2)/c3alm names(STATISTIC) = "LM" # The rest goes as parameters ... pvalLM = 1 - .pjb(STATISTIC, N = n, type = "LM") pvalALM = 1 - .pjb(ALM, N = n, type = "ALM") PARAMETER = c(ALM, n, pvalLM, pvalALM) names(PARAMETER) = c( "ALM", "Sample Size", "LM p-value", "ALM p-value" ) # P Values: PVAL = 1 - pchisq(STATISTIC[1], df = 2) names(PVAL) = "" # Method String: METHOD = "Jarque Bera Test" # Result: Digits = 3 RVAL = list( statistic = round(STATISTIC, digits = Digits), parameter = round(PARAMETER, digits = Digits), p.value = round(PVAL, digits = Digits), method = METHOD, data.name = DNAME) # Return Value: class(RVAL) = "htest" RVAL } # ------------------------------------------------------------------------------ jbTest <- function(x, title = NULL, description = NULL) { # A function implemented by Diethelm Wuertz # Description: # Performs finite sample adjusted Jarque-Bera LM and ALM test # Arguments: # x - a univariate timeSeries object or numeric vector # Notes: # S3 Version type of test. # FUNCTION: # Data Set Name: DNAME = deparse(substitute(x)) # Call: call = match.call() # Convert Type: if (class(x) == "fREG") x = residuals(x) x = as.vector(x) # Test: test = .jb.test(x) class(test) = "list" parameter = test$parameter[2] statistic = c(test$statistic, test$parameter[1]) p.value = c(test$parameter[3], test$parameter[4], Asymptotic = test$p.value[[1]]) test$parameter = parameter test$statistic = statistic test$p.value = p.value # Add Title: if (is.null(title)) title = "Jarque - Bera Normality Test" if (is.null(description)) description = description() # Return Value: new("fHTEST", call = call, data = list(x = x), test = test, title = as.character(title), description = as.character(description) ) } ################################################################################ fBasics/R/test-locationTest.R0000644000176000001440000001670111370220773015634 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: LOCATION TESTS: # locationTest Performs locations tests on two samples # .tTest Unpaired t test for differences in mean # .kw2Test Kruskal-Wallis test for differences in locations ################################################################################ locationTest <- function(x, y, method = c("t", "kw2"), title = NULL, description = NULL) { # A function implemented by Diethelm Wuertz # Description: # Correlation Tests # FUNCTION: # Test: method = match.arg(method) if (method == "t") { ans = .tTest(x, y, title = title, description = description) } if (method == "kw2") { ans = .kw2Test(x, y, title = title, description = description) } # Return Value: ans } # ------------------------------------------------------------------------------ .tTest <- function(x, y, title = NULL, description = NULL) { # A function implemented by Diethelm Wuertz # Description: # Tests if two population means are equal. # Arguments: # x, y - two numeric vector of data values or time series objects # description - a brief description of the porject of type character. # title - a character string which allows for a project title. # FUNCTION: # Call: call = match.call() # Test: test = list() # Data Set Name: DNAME = paste(deparse(substitute(x)), "and", deparse(substitute(y))) test$data.name = DNAME # Convert Type: x = as.vector(x) y = as.vector(y) # Asymptotic Test: two.sided = t.test(x = x, y = y, alternative = "two.sided", mu = 0, paired = FALSE, var.equal = FALSE, conf.level = 0.95) less = t.test(x = x, y = y, alternative = "less", mu = 0, paired = FALSE, var.equal = FALSE, conf.level = 0.95) greater = t.test(x = x, y = y, alternative = "greater", mu = 0, paired = FALSE, var.equal = FALSE, conf.level = 0.95) # Assume Equal Variances: two.sided.equal = t.test(x = x, y = y, alternative = "two.sided", mu = 0, paired = FALSE, var.equal = TRUE, conf.level = 0.95) less.equal = t.test(x = x, y = y, alternative = "less", mu = 0, paired = FALSE, var.equal = TRUE, conf.level = 0.95) greater.equal = t.test(x = x, y = y, alternative = "greater", mu = 0, paired = FALSE, var.equal = TRUE, conf.level = 0.95) # Sample Estimates: PARAMETER = c(length(x), length(y), 0) names(PARAMETER) = c( "x Observations", "y Observations", "mu") test$parameter = PARAMETER # Sample Estimates: ESTIMATE = c(two.sided$estimate, var(x), var(y)) names(ESTIMATE) = c("Mean of x", "Mean of y", "Var of x", "Var of y") test$estimate = ESTIMATE # P Values: PVAL = c( two.sided$p.value, less$p.value, greater$p.value, two.sided.equal$p.value, less.equal$p.value, greater.equal$p.value) names(PVAL) = c( "Alternative Two-Sided", "Alternative Less", "Alternative Greater", "Alternative Two-Sided | Equal Var", "Alternative Less | Equal Var", "Alternative Greater | Equal Var") test$p.value = PVAL # Statistic: STATISTIC = c( two.sided$statistic, two.sided.equal$statistic) names(STATISTIC) = c( " T", "T | Equal Var") test$statistic = STATISTIC # Confidence Intervals: CONF.INT = cbind( a = two.sided$conf.int, b = less$conf.int, c = greater$conf.int, d = two.sided.equal$conf.int, e = less.equal$conf.int, f = greater.equal$conf.int) # For Splus compatibility use named a CONF.INT # and dimnames instead of colnames! dimnames(CONF.INT)[[2]] = c( "Two-Sided", " Less", " Greater", "Two-Sided | Equal Var", " Less | Equal Var", " Greater | Equal Var") test$conf.int = CONF.INT # Add: if (is.null(title)) title = "t Test" if (is.null(description)) description = date() # Return Value: new("fHTEST", call = call, data = list(x = x, y = y), test = test, title = as.character(title), description = as.character(description) ) } # ------------------------------------------------------------------------------ .kw2Test <- function(x, y, title = NULL, description = NULL) { # A function implemented by Diethelm Wuertz # Description: # Performs a Kruskal-Wallis rank sum test of the null that # the location parameters of the distribution of x are the # same in each group (sample). The alternative is that they # differ in at least one. # Arguments: # x, y - two numeric vector of data values or time series objects # description - a brief description of the porject of type character. # title - a character string which allows for a project title. # Note: # A function linked to "stats" # FUNCTION: # Call: call = match.call() # Test: test = list() # Data Set Name: DNAME = paste(deparse(substitute(x)), "and", deparse(substitute(y))) test$data.name = DNAME # Convert Type: x = as.vector(x) y = as.vector(y) # Sample Estimates: ESTIMATE = c(mean(x), mean(y), var(x), var(y)) names(ESTIMATE) = c("Mean of x", "Mean of y", "Var of x", "Var of y") test$estimate = ESTIMATE # Parameter: PARAMETER = c(length(x), length(y)) names(PARAMETER) = c( "x Observations", "y Observations") test$parameter = PARAMETER # Operate on Lists: x = list(x = x, y = y) if (length(x) < 2) stop("x must be a list with at least 2 elements") k = length(x) l = sapply(x, "length") g = factor(rep(1 : k, l)) x = unlist(x) # Test: n = length(x) if (n < 2) stop("not enough observations") r = rank(x) TIES = table(x) # Statistic: STATISTIC = sum(tapply(r, g, "sum")^2 / tapply(r, g, "length")) STATISTIC = ((12 * STATISTIC / (n * (n + 1)) - 3 * (n + 1)) / (1 - sum(TIES^3 - TIES) / (n^3 - n))) names(STATISTIC) = "KW chi-squared" test$statistic = STATISTIC # P Value: PVAL = 1 - pchisq(STATISTIC, 1) names(PVAL) = "" test$p.value = PVAL # Add: if(is.null(title)) title = "Kruskal-Wallis Two Sample Test" if(is.null(description)) description = date() # Return Value: new("fHTEST", call = call, data = list(x = x, y = y), test = test, title = as.character(title), description = as.character(description) ) } ################################################################################ fBasics/R/test-ks2Test.R0000644000176000001440000000577711370220773014536 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # ks2Test Performs a two sample Kolmogorov-Smirnov test ################################################################################ ks2Test <- function(x, y, title = NULL, description = NULL) { # A function implemented by Diethelm Wuertz # Description: # Two-sample Kolmogorov-Smirnov test. # Arguments: # x - a numeric vector of data values. # description - a brief description of the project of type # character. # title - a character string which allows for a project title. # Note: # A function partly copied from "stats" # FUNCTION: # Call: call = match.call() # Test: test = list() # Data Set Name: DNAME = paste(deparse(substitute(x)), "and", deparse(substitute(y))) test$data.name = DNAME # Convert Type: x = as.vector(x) y = as.vector(y) # Compute Test: two.sided = ks.test(x = x, y = y, alternative = "two.sided") exact = ks.test(x = x, y = y, exact = TRUE, alternative = "two.sided") less = ks.test(x = x, y = y, alternative = "less") greater = ks.test(x = x, y = y, alternative = "greater") # P Value: PVAL = c( two.sided$p.value, exact$p.value, less$p.value, greater$p.value) names(PVAL) = c( "Alternative Two-Sided", "Alternative Exact Two-Sided", "Alternative Less", "Alternative Greater") test$p.value = PVAL # Statistic: STATISTIC = c( two.sided$statistic, less$statistic, greater$statistic) names(STATISTIC) = c( "D | Two Sided", " D^- | Less", "D^+ | Greater") test$statistic = STATISTIC # Add: if (is.null(title)) title = "Kolmogorov-Smirnov Two Sample Test" if (is.null(description)) description = date() # Return Value: new("fHTEST", call = call, data = list(x = x, y = y), test = test, title = as.character(title), description = as.character(description) ) } ################################################################################ fBasics/R/test-jbTable.R0000644000176000001440000001457311370220773014534 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # .jbTable Finite sample p values for the Jarque Bera test # .jbPlot Plots probabilities # .pjb Returns probabilities for JB given quantiles # .qjb Returns quantiles for JB given probabilities ################################################################################ .jbTable <- function(type = c("LM", "ALM"), size = c("mini", "small", "all")) { # A function implemented by Diethelm Wuertz # Description: # Finite sample p values for the Jarque Bera test # Details: # The function jbTable() returns a data.frame with rows denoting # size and columns denoting probabilities 0 < p < 1. # Note: # x=-3:0; y=0:3; z=outer(x,y,"*"); rownames(z)=x; colnames(z)=y; z # Example: # .jbTable() # FUNCTION: # Check Arguments: type = match.arg(type) size = match.arg(size) # Create Table: if (type == "LM") { Table = t(.jbLM()) } else if (type == "ALM") { Table = t(.jbALM()) } # Select Size: if (size == "small") { Table = t(Table) n = dim(Table)[1] Table = Table[c(matrix(1:(n-2), byrow = TRUE, ncol = 22)[, 1], n), ] Table = Table[-(1:17),] Table = t(Table) } else if (size == "mini") { p = c( "0.01", "0.0251189", "0.05", "0.1", "0.9", "0.95", "0.9751687", "0.99") Table = Table[, p] Table = rbind(Table, qchisq(as.numeric(p), df = 2)) Table = round(Table, 3) colnames(Table) = substr(colnames(Table), 1, 5) nRows = dim(Table)[1] rownames(Table)[nRows] = "Inf" } ans = list( x = as.numeric(rownames(Table)), y = as.numeric(colnames(Table)), z = Table) # Add Control: attr(ans, "control") <- c(table = "jb", type = type, size = size) # Return Value: class(ans) = "gridData" ans } # ------------------------------------------------------------------------------ .jbPlot <- function(type = c("LM", "ALM")) { # A function implemented by Diethelm Wuertz # Match Arguments: type = match.arg(type) # Load Table: Y = .jbTable(size = "small") X = cbind(expand.grid(x = Y$x, y = Y$y), z = as.vector(Y$z)) x = X[, 1] # N y = X[, 3] # q-Stat z = X[, 2] # p-Value # Interpolate: ans = akimaInterp(log(x), log(y), z, gridPoints = 101, extrap = FALSE) persp(ans, theta = 40, xlab = "log(N)", ylab = "log(q)", zlab = "p", main = paste(type, "Jarque Bera")) # Return Value: invisible(NULL) } # ------------------------------------------------------------------------------ .pjb <- function(q, N = Inf, type = c("LM", "ALM")) { # A function implemented by Diethelm Wuertz # Description: # Returns probabilities for the JB Test given quantiles # Arguments: # q = quantiles (0.020 < q < 12) # N = sample sizes # Details: # Uses "small" table for interpolation. # Example: # q=c(0.03,1,5,9); for (N in c(4,1000,50000,Inf)) print(.pjb(q, N)) # FUNCTION: # Match Arguments: type = match.arg(type) # Check N: stopifnot(length(N) == 1) # Grid Points: Y = .jbTable(type = type, size = "small") if (N < 10000) { # Interpolate on log-Scale: X = cbind(expand.grid(x = Y$x, y = Y$y), z = as.vector(Y$z)) x = log(X[, 1]) # N y = log(X[, 3]) # q-Stat z = X[, 2] # p-Value # Out Points: xo = rep(log(N), times = length(q)) yo = log(q) # Interpolate: p = linearInterpp(x, y, z, xo = xo, yo = yo)[, 3] } else if (N > 10000) { p = pchisq(as.numeric(q), df = 2) } # Result: if (N < 5) controlN = "< 5" if (N >= 5 & N <= 10000) controlN = as.character(N) if (N > 10000) controlN = "> 10000" if (!is.finite(N)) controlN = "Inf" names(p) = as.character(q) attr(p, "control") <- c(N = controlN) # Return Value: p } # ------------------------------------------------------------------------------ .qjb <- function(p, N = Inf, type = c("LM", "ALM")) { # A function implemented by Diethelm Wuertz # Description: # Returns quantiles for the ADF Test given probabilities # Arguments: # p = probabilities (0.01 < p < 0.99) # N = sample sizes # Details: # Uses "small" table for interpolation. # Example: # p=(1:9)/10; for (N in c(4,1000,50000,Inf)) print(.qjb(p, N)) # FUNCTION: # Match Arguments: type = match.arg(type) statistic = match.arg(statistic) # Check N: stopifnot(length(N) == 1) # Grid Points: Y = .jbTable(type = type, size = "small") if (N < 10000) { # Interpolate on log-Scale: X = cbind(expand.grid(x = Y$x, y = Y$y), z = as.vector(Y$z)) x = log(X[, 1]) # N y = X[, 2] # p-Value z = X[, 3] # q-Stat # Out Points: xo = rep(log(N), times = length(p)) yo = p # Interpolate - Scaling Required: xStd = sqrt(var(x)) q = linearInterpp(x/xStd, y, z, xo = xo/xStd, yo = yo)[, 3] } else { # Asymptotic Limit: q = qchisq(as.numeric(p), df = 2) } # Result: if (N < 5) controlN = "< 5" if (N >= 5 & N <= 10000) controlN = as.character(N) if (N > 10000) controlN = "> 10000" if (!is.finite(N)) controlN = "Inf" names(q) = as.character(p) attr(q, "control") <- c(N = controlN) # Return Value: q } ################################################################################ fBasics/R/test-jbLM.R0000644000176000001440000174264111370220773014022 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # DATA: DESCRIPTION: # .jbLM Jarque-Bera Lagrange Multiplier Data ################################################################################ .jbLM <- function() { structure(list("5" = c(0.052470165, 0.052479988, 0.052481227, 0.052482714, 0.052484752, 0.0524849, 0.052487004, 0.052487547, 0.052492897, 0.052493878, 0.052493879, 0.052494842, 0.052496092, 0.052497834, 0.052499018, 0.052505397, 0.052509442, 0.052511664, 0.052515321, 0.052517838, 0.052529162, 0.05253247, 0.052537375, 0.05254833, 0.052553431, 0.052558648, 0.052559815, 0.052561665, 0.052570098, 0.052574164, 0.05258048, 0.05258408, 0.052586498, 0.052588564, 0.052593144, 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WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # DATA: DESCRIPTION: # .jbALM Jarque Bera Augmented Lagrange Multiplier Data ################################################################################ .jbALM <- function() { structure(list("5" = c(3.6559e-05, 3.6676e-05, 3.8408e-05, 3.9805e-05, 4.0283e-05, 4.0549e-05, 4.062e-05, 4.1218e-05, 4.2351e-05, 4.2355e-05, 4.2797e-05, 4.3593e-05, 4.3764e-05, 4.3945e-05, 4.4328e-05, 4.4677e-05, 4.4694e-05, 4.5125e-05, 4.5133e-05, 4.5314e-05, 4.5404e-05, 4.6125e-05, 4.7878e-05, 4.791e-05, 4.8059e-05, 4.8158e-05, 4.8537e-05, 4.8671e-05, 4.9346e-05, 5.2005e-05, 5.2045e-05, 5.2874e-05, 5.4154e-05, 5.4401e-05, 5.6475e-05, 5.7065e-05, 5.7268e-05, 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FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # 'fHTEST' S4 Class Representation # show.fHTEST S4 Print Method ################################################################################ setClass("fHTEST", representation( call = "call", data = "list", test = "list", title = "character", description = "character") ) # ------------------------------------------------------------------------------ setMethod("show", "fHTEST", function(object) { # A function implemented by Diethelm Wuertz # Source: # This function copies code from base:print.htest # FUNCTION: # Unlike print the argument for show is 'object'. x = object # Title: cat("\nTitle:\n ", x@title, "\n", sep = "") # Call: # cat("\nCall:\n", deparse(x@call), "\n", sep = "") # Data Name: # cat("\nData Name:\n", ans@data.name, "\n", sep = "") # Test Results: test = x@test cat("\nTest Results:\n", sep = "") # Tests from tseries package: # Parameter: if (!is.null(test$parameter)) { parameter = test$parameter Names = names(parameter) cat(" PARAMETER:\n") for ( i in 1: length(Names) ) cat(paste(" ", names(parameter[i]), ": ", format(round(parameter[i], 3)), "\n", sep = "") ) } # Sample Estimates: if (!is.null(test$estimate)) { estimate = test$estimate Names = names(estimate) cat(" SAMPLE ESTIMATES:\n") for (i in 1:length(Names)) { cat(paste(" ", Names[i], ": ", round(estimate[i], digits = 4), "\n", sep = "" ) ) } } # Statistic: if (!is.null(test$statistic)) { statistic = test$statistic Names = names(statistic) cat(" STATISTIC:\n") for (i in 1:length(Names)) { if (!is.na(statistic[i])) { cat(paste(" ", Names[i], ": ", round(statistic[i], digits = 4), "\n", sep = "" ) ) } } } # P-Value: if (!is.null(test$p.value)) { pval = test$p.value Names = names(pval) if (Names[1] == "") space = "" else space = ": " cat(" P VALUE:\n") for (i in 1:length(Names)) { if (!is.na(pval[i])) { if (class(version) != "Sversion") { cat(paste(" ", Names[i], space, format.pval(pval[i], digits = 4), " \n", sep = "" ) ) } else { cat(paste(" ", Names[i], space, round(pval[i], digits = 4), " \n", sep = "" ) ) } } } } # Confidence Interval: if (!is.null(test$conf.int)) { conf = test$conf.int # For SPlus compatibility use dimnames istead of colnames! colNames = dimnames(conf)[[2]] cat(" CONFIDENCE INTERVAL:\n") for (i in 1:length(colNames)) { cat(paste(" ", colNames[i], ": ", round(conf[1, i], digits = 4), ", ", round(conf[2, i], digits = 4), "\n", sep = "" ) ) } } # More Specific Output Results: if (!is.null(test$output)) { cat(test$output, fill = FALSE, sep = "\n") } # Description: cat("\nDescription:\n ", x@description, sep = "") cat("\n\n") # Return Value: # invisible() # made visible by DW }) ################################################################################ fBasics/R/test-correlationTest.R0000644000176000001440000002404511370220773016345 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # correlationTest Performs correlation tests on two samples # pearsonTest Pearson product moment correlation coefficient # kendallTest Kendall's tau correlation test # spearmanTest Spearman's rho correlation test ################################################################################ correlationTest <- function(x, y, method = c("pearson", "kendall", "spearman"), title = NULL, description = NULL) { # A function implemented by Diethelm Wuertz # Description: # Performs Correlation Tests # FUNCTION: # Test: method = match.arg(method) if (method[1] == "pearson") { ans = pearsonTest(x, y, title = title, description = description) } if (method[1] == "kendall") { ans = kendallTest(x, y, title = title, description = description) } if (method[1] == "spearman") { ans = spearmanTest(x, y, title = title, description = description) } # Return Value: ans } # ------------------------------------------------------------------------------ pearsonTest <- function(x, y, title = NULL, description = NULL) { # A function implemented by Diethelm Wuertz # Description: # A test for association between paired samples # Arguments: # x - a numeric vector of data values. # description - a brief description of the porject of type # character. # title - a character string which allows for a project title. # Note: # A function linked to "stats" # FUNCTION: # Call: call = match.call() # Test: test = list() # Data Set Name: DNAME = paste(deparse(substitute(x)), "and", deparse(substitute(y))) test$data.name = DNAME # Convert Type: x = as.vector(x) y = as.vector(y) stopifnot(length(x) == length(y)) # Test: two.sided = cor.test(x = x, y = y, alternative = "two.sided", method = "pearson") less = cor.test(x = x, y = y, alternative = "less", method = "pearson") greater = cor.test(x = x, y = y, alternative = "greater", method = "pearson") # Sample Estimates: ESTIMATE = two.sided$estimate names(ESTIMATE) = "Correlation" test$estimate = ESTIMATE # Parameter DF = two.sided$parameter names(DF) = "Degrees of Freedom" test$parameter = DF # P Values: PVAL = c( two.sided$p.value, less$p.value, greater$p.value) names(PVAL) = c( "Alternative Two-Sided", "Alternative Less", "Alternative Greater") test$p.value = PVAL # Confidences Levels: if (!is.null(two.sided$conf.int)) { CONF.INT = cbind( a = two.sided$conf.int, b = less$conf.int, c = greater$conf.int) dimnames(CONF.INT)[[2]] = c( "Two-Sided", " Less", " Greater") test$conf.int = CONF.INT } # Statistic: STATISTIC = two.sided$statistic names(STATISTIC) = "t" test$statistic = STATISTIC # Add: if (is.null(title)) title = "Pearson's Correlation Test" if (is.null(description)) description = date() # Return Value: new("fHTEST", call = call, data = list(x = x, y = y), test = test, title = as.character(title), description = as.character(description) ) } # ------------------------------------------------------------------------------ kendallTest <- function(x, y, title = NULL, description = NULL) { # A function implemented by Diethelm Wuertz # Description: # A test for association between paired samples # Arguments: # x - a numeric vector of data values. # description - a brief description of the porject of type # character. # title - a character string which allows for a project title. # Note: # A function linked to "stats" # FUNCTION: # Call: call = match.call() # Test: test = list() # Data Set Name: DNAME = paste(deparse(substitute(x)), "and", deparse(substitute(y))) test$data.name = DNAME # Convert Type: x = as.vector(x) y = as.vector(y) stopifnot(length(x) == length(y)) # Test: two.sided = cor.test(x = x, y = y, alternative = "two.sided", method = "kendall") less = cor.test(x = x, y = y, alternative = "less", method = "kendall") greater = cor.test(x = x, y = y, alternative = "greater", method = "kendall") # Exact Test: if (class(version) != "Sversion") { two.sided.exact = cor.test(x = x, y = y, exact = TRUE, alternative = "two.sided", method = "kendall") less.exact = cor.test(x = x, y = y, exact = TRUE, alternative = "less", method = "kendall") greater.exact = cor.test(x = x, y = y, exact = TRUE, alternative = "greater", method = "kendall") } else { two.sided.exact = list() two.sided.exact$p.value = two.sided.exact$statistic = NA less.exact = list() less.exact$p.value = less.exact$statistic = NA greater.exact = list() greater.exact$p.value = greater.exact$statistic = NA } # Sample Estimates: ESTIMATE = two.sided$estimate names(ESTIMATE) = "tau" test$estimate = ESTIMATE # P Values: PVAL = c( two.sided$p.value, two.sided.exact$p.value, less$p.value, less.exact$p.value, greater$p.value, greater.exact$p.value) if (is.na(two.sided.exact$p.value)) { names(PVAL) = c( "Alternative Two-Sided", "Alternative Two-Sided | Exact", "Alternative Less", "Alternative Less | Exact", "Alternative Greater", "Alternative Greater | Exact") } else { names(PVAL) = c( "Alternative Two-Sided", "Alternative Two-Sided | Exact", "Alternative Less", "Alternative Less | Exact", "Alternative Greater", "Alternative Greater | Exact") } test$p.value = PVAL # Statistic: # STATISTIC = c( # two.sided$statistic, two.sided.exact$statistic, # less$statistic, less.exact$statistic, # greater$statistic, greater.exact$statistic) STATISTIC = c( two.sided$statistic, two.sided.exact$statistic) # names(STATISTIC) = c( # "z | Two-Sided", "T | Two-Sided | Exact", # "z | Less", "T | Less | Exact", # "z | Greater", "T | Greater | Exact") names(STATISTIC) = c( "z", "T | Exact") test$statistic = STATISTIC # Add: if (is.null(title)) title = "Kendall's tau Correlation Test" if (is.null(description)) description = date() # Return Value: new("fHTEST", call = call, data = list(x = x, y = y), test = test, title = as.character(title), description = as.character(description) ) } # ------------------------------------------------------------------------------ spearmanTest <- function(x, y, title = NULL, description = NULL) { # A function implemented by Diethelm Wuertz # Description: # A test for association between paired samples # Arguments: # x - a numeric vector of data values. # description - a brief description of the porject of type # character. # title - a character string which allows for a project title. # Note: # # A function linked to "stats" # FUNCTION: # Call: call = match.call() # Test: test = list() # Data Set Name: DNAME = paste(deparse(substitute(x)), "and", deparse(substitute(y))) test$data.name = DNAME # Convert Type: x = as.vector(x) y = as.vector(y) stopifnot(length(x) == length(y)) # Test: two.sided = cor.test(x = x, y = y, alternative = "two.sided", method = "spearman") less = cor.test(x = x, y = y, alternative = "less", method = "spearman") greater = cor.test(x = x, y = y, alternative = "greater", method = "spearman") # Sample Estimates: ESTIMATE = two.sided$estimate names(ESTIMATE) = "rho" test$estimate = ESTIMATE # P Values: PVAL = c( two.sided$p.value, less$p.value, greater$p.value) names(PVAL) = c( "Alternative Two-Sided", "Alternative Less", "Alternative Greater") test$p.value = PVAL # Statistic: STATISTIC = two.sided$statistic names(STATISTIC) = "S" test$statistic = STATISTIC # Add: if (is.null(title)) title = "Spearman's rho Correlation Test" if (is.null(description)) description = date() # Return Value: new("fHTEST", call = call, data = list(x = x, y = y), test = test, title = as.character(title), description = as.character(description) ) } ################################################################################ fBasics/R/stats-sampleRobMoments.R0000644000176000001440000000552511370220773016634 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # sampleMED Returns sample median # sampleIQR Returns sample inter quartal range # sampleSKEW Returns robust sample skewness # sampleKURT Returns robust sample kurtosis ################################################################################ sampleMED <- function(x) { # A function implemented by Diethelm Wuertz # Description: # Returns sample median # FUNCTION: # Sample Median: x = as.vector(x) med = c(MED = quantile(x, 0.50)[[1]]) # Return Value: med } # ------------------------------------------------------------------------------ sampleIQR = function(x) { # A function implemented by Diethelm Wuertz # Description: # Returns sample inter quartal range # FUNCTION: # Sample Inter Quartile Range x = as.vector(x) iqr = c(IQR = diff(quantile(x, c(0.25, 0.75)))[[1]]) # Return Value: iqr } # ------------------------------------------------------------------------------ sampleSKEW = function(x) { # A function implemented by Diethelm Wuertz # Description: # Returns robust sample skewness # FUNCTION: # Robust Sample Skew x = as.vector(x) q = quantile(x, c(0.25, 0.5, 0.75)) skew = c(SKEW = q[[3]] + q[[1]] - 2*q[[2]]) / (q[[3]]-q[[1]]) # Return Value: skew } # ------------------------------------------------------------------------------ sampleKURT = function(x) { # A function implemented by Diethelm Wuertz # Description: # Returns robust sample kurtosis # FUNCTION: # Robust Sample Kurtosis x = as.vector(x) q = quantile(x, (1:7)/8) kurt = c(KURT = (q[[7]] - q[[5]] + q[[3]] - q[[1]]) / (q[[6]] - q[[2]])) # Return Value: kurt } ################################################################################ fBasics/R/stats-sampleLmoments.R0000644000176000001440000000472411370220773016345 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # sampleLmoments Computes sample L-moments ################################################################################ sampleLmoments <- function(x, rmax=4) { # A function implemented by Diethelm Wuertz # Description # Computes sample L-moments # Note: # This function is borrowed from package ... # Author: # FUNCTION: # L Moments: data <- as.matrix(x) n <- dim(data)[1] p <- dim(data)[2] x <- array(, c(p, n)) L <- array(, c(p, rmax)) for (i in 1:p) x[i, ] <- sort(data[, i]) if (rmax == 1) return(rowMeans(x)) bcoef <- array(, c(rmax, n)) bcoefm <- array(, c(rmax, p, n)) b <- array(, c(p, rmax)) bcoef[1, ] <- seq(0, 1, by = (1/(n-1))) bcoefm[1, , ] <- t(array(rep(bcoef[1, ], p), c(n, p))) b[, 1] <- rowMeans(x) b[, 2] <- rowMeans(bcoefm[1, , ] * x) L[, 1] = b[, 1] if (rmax > 2) { for (r in 2:(rmax-1)) { rr <- r+1 bcoef[r, ]<-bcoef[r-1,]*seq((-(r-1)/(n-r)),1, by = (1/(n-r))) bcoefm[r, , ]<-t(array(rep(bcoef[r,],p),c(n, p))) b[, rr] <- rowMeans(bcoefm[r, , ]*x) } } for (r in 1:(rmax-1)) { L[, r+1] <- 0 for (k in 0:r) { kk <- k+1 L[, r+1] <- L[, r+1]+(-1)^(r-k)*gamma(r+k+1) / (gamma(k+1)^2) / gamma(r-k+1)*b[, kk] } } L = as.vector(L) names(L) = paste("L", 1:rmax, sep = "") # Return Value: L } ################################################################################ fBasics/R/stats-rowStats.R0000644000176000001440000000641311370220773015170 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # rowStats Computes sample statistics by row # rowSums Computes sums of values in each row # rowMeans Computes means of values in each row # rowSds Computes standard deviation of each row # rowVars Computes sample variance by row # rowSkewness Computes sample skewness by row # rowKurtosis Computes sample kurtosis by row # rowMaxs Computes maximum values in each row # rowMins Computes minimum values in each row # rowProds Computes product of values in each row # rowQuantiles Computes product of values in each row # FUNCTION: NO LONGER USED: # rowAvgs Computes sample mean by row # rowStdevs Computes sample variance by row ################################################################################ rowStats <- function(x, FUN, ...) { # A function implemented by Diethelm Wuertz # Description: # Computes sample statistics by row # FUNCTION: # Statistics: apply(na.omit(as.matrix(x), ...), 1, FUN, ...) } # ------------------------------------------------------------------------------ # rowSums() # is part of R's base package # ------------------------------------------------------------------------------ # rowMeans <- # is part of R's base package # ------------------------------------------------------------------------------ rowSds <- function(x, ...) { rowStats(x, "sd", ...) } rowVars <- function(x, ...) { rowStats(x, "var", ...) } rowSkewness <- function(x, ...) { rowStats(x, "skewness", ...) } rowKurtosis <- function(x, ...) { rowStats(x, "kurtosis", ...) } rowMaxs <- function(x, ...) { rowStats(x, "max", ...) } rowMins <- function(x, ...) { rowStats(x, "min", ...) } rowProds <- function(x, ...) { rowStats(x, "prod", ...) } # ------------------------------------------------------------------------------ rowQuantiles <- function(x, prob = 0.05, ...) { # A function implemented by Diethelm Wuertz # FUNCTION: # Row Quantiles: stopifnot(length(prob) == 1) rowStats(x, "quantile", probs = prob, ...) } ################################################################################ rowAvgs <- function(x, ...) rowMeans(x, ...) rowStdevs <- rowSds ################################################################################ fBasics/R/stats-maxdd.R0000644000176000001440000002240511370220773014436 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Description. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # maxddStats Expectation of Drawdowns for BM with drift # .maxddStats Utility function called by "maxddStats" # FUNCTION: DISTRIBUTION AND RANDOM VARIATES: # dmaxdd Density function of mean Max-Drawdowns # pmaxdd Probability function of mean Max-Drawdowns # rmaxdd Random Variates of mean Max-Drawdowns ################################################################################ maxddStats <- function(mean = 0, sd = 1, horizon = 1000) { # A function implemented by Diethelm Wuertz # Description: # Calculates Expectation Value E[D] of maximum Drawdowns of # Brownian Motion for a given drift "mu", variance "sigma", # and runtime "horizon" of the Brownian Motion process. # Arguments: # mu - Drift of Brownian Motion, a numeric vector. # sigma - Standard Deviation of Brownian Motion, a numeric value. # horizon - Runtime of the process, the time horizon of the # investor, a numeric value. # Details: # Interpolates the data given in the table of Appendix B # in Magdon-Ismail et al. [2003], "On the Maximum Drawdown # of Brownian Motion". The interpolation is done with R's # function spline(). # Notes. # This computes for a vector of horizon values. # FUNCTION: # Statistics: ans = NULL for (i in 1:length(horizon)) { ans = c(ans, .maxddStats(mu = mean, sigma = sd, horizon = horizon[i])) } # Return Value: ans } # ------------------------------------------------------------------------------ .maxddStats <- function(mu = 0, sigma = 1, horizon = 1000) { # A function implemented by Diethelm Wuertz # Description: # Utility function called by "maxddStats" # Arguments: # see function "maxddStats # FUNCTION: # Internal Function - POSITIVE CASE: mu > 0 # Left Table from Appendix B: QP = function(x) { gamma = sqrt(pi/8) vqn = t(matrix(c( 0.0005,0.019690, 0.0010,0.027694, 0.0015,0.033789, 0.0020,0.038896, 0.0025,0.043372, 0.0050,0.060721, 0.0075,0.073808, 0.0100,0.084693, 0.0125,0.094171, 0.0150,0.102651, 0.0175,0.110375, 0.0200,0.117503, 0.0225,0.124142, 0.0250,0.130374, 0.0275,0.136259, 0.0300,0.141842, 0.0325,0.147162, 0.0350,0.152249, 0.0375,0.157127, 0.0400,0.161817, 0.0425,0.166337, 0.0450,0.170702, 0.0500,0.179015, 0.0600,0.194248, 0.0700,0.207999, 0.0800,0.220581, 0.0900,0.232212, 0.1000,0.243050, 0.2000,0.325071, 0.3000,0.382016, 0.4000,0.426452, 0.5000,0.463159, 1.5000,0.668992, 2.5000,0.775976, 3.5000,0.849298, 4.5000,0.905305, 10.000,1.088998, 20.000,1.253794, 30.000,1.351794, 40.000,1.421860, 50.000,1.476457, 150.00,1.747485, 250.00,1.874323, 350.00,1.958037, 450.00,2.020630, 1000.0,2.219765, 2000.0,2.392826, 3000.0,2.494109, 4000.0,2.565985, 5000.0,2.621743), 2)) # Interpolation: if (x < 0.0005) { y = gamma*sqrt(2*x) } if (x >= 0.0005 & x <= 5000) { y = spline(log(vqn[,1]), vqn[,2], n = 1, xmin = log(x), xmax = log(x))$y } if (x > 5000) { y = 0.25*log(x) + 0.49088} # Return Value: y } # Internal Function - NEGATIVE CASE: mu < 0 # Right Table from Appendix B: QN = function(x) { gamma = sqrt(pi/8) vqn = t(matrix(c( 0.0005,0.019965, 0.0010,0.028394, 0.0015,0.034874, 0.0020,0.040369, 0.0025,0.045256, 0.0050,0.064633, 0.0075,0.079746, 0.0100,0.092708, 0.0125,0.104259, 0.0150,0.114814, 0.0175,0.124608, 0.0200,0.133772, 0.0225,0.142429, 0.0250,0.150739, 0.0275,0.158565, 0.0300,0.166229, 0.0325,0.173756, 0.0350,0.180793, 0.0375,0.187739, 0.0400,0.194489, 0.0425,0.201094, 0.0450,0.207572, 0.0475,0.213877, 0.0500,0.220056, 0.0550,0.231797, 0.0600,0.243374, 0.0650,0.254585, 0.0700,0.265472, 0.0750,0.276070, 0.0800,0.286406, 0.0850,0.296507, 0.0900,0.306393, 0.0950,0.316066, 0.1000,0.325586, 0.1500,0.413136, 0.2000,0.491599, 0.2500,0.564333, 0.3000,0.633007, 0.3500,0.698849, 0.4000,0.762455, 0.5000,0.884593, 1.0000,1.445520, 1.5000,1.970740, 2.0000,2.483960, 2.5000,2.990940, 3.0000,3.492520, 3.5000,3.995190, 4.0000,4.492380, 4.5000,4.990430, 5.0000,5.498820), 2)) # Interpolation: if (x < 0.0005) { y = gamma*sqrt(2*x) } if (x >= 0.0005 & x <= 5) { y = spline(vqn[,1], vqn[,2], n = 1, xmin = x, xmax = x)$y } if (x > 5) { y = x + 1/2 } # Return Value: y } # Result: ED = NULL for (i in 1:length(mu)) { if (mu[i] == 0) { gamma = sqrt(pi/8) ED[i] = 2 * gamma * sigma * sqrt(horizon) } else { x = mu[i]^2 * horizon / ( 2 * sigma^2 ) if (mu[i] > 0) { ED[i] = ( 2* sigma^2 / mu[i] ) * QP(x) } if (mu[i] < 0) { ED[i] = - ( 2* sigma^2 / mu[i] ) * QN(x) } } } # Return Value: ED } ################################################################################ dmaxdd <- function(x, sd = 1, horizon = 100, N = 1000) { # Description: # Calculates for a trendless Brownian process (mean=0) and # standard deviation "sd" the density from the probability # that the maximum drawdown "D" is larger or equal to "h" # in the interval [0,T], where "T" denotes the time horizon # of the investor. # Arguments: # x - Vector of Drawdowns # sd - Standard Deviation # horizon - Time horizon of the investor # N - number of summands # Notes: # The drift dependent case is not yet implemented! # FUNCTION: # Use "h", "sigma" to be conform with Magdon-Ismael et al. [2003] h = x sigma = sd # Settings: n = 1:N pn = 2 * sin((n-0.5)*pi) * sigma^2 * (n-0.5) * pi * horizon en = sigma^2 * (n-0.5)^2 * pi^2 * horizon / 2 # Loop over all Drawdowns: result = rep(NA, times = length(h)) for (i in 1:length(h)) { if (h[i] == 0) { result[i] = 0 } else { g = pn * exp(-en/(h[i]^2)) / h[i]^3 result[i] = sum(g) } } # Return Value: result } # ------------------------------------------------------------------------------ pmaxdd <- function(q, sd = 1, horizon = 100, N = 1000) { # Description: # Calculates for a trendless Brownian process (mean=0) # with standard deviation "sd" the probability that the # maximum drawdown "D" is larger or equal to "h" in the # interval [0,T], where "T" denotes the time horizon of # the investor. # Arguments: # q - Vector of Drawdowns # sd - Standard Deviation # horizon - Time horizon of the investor # N - number of summands # Value: # GD(h) - eqn(14) In Magdon-Ismail et al. # Notes: # The drift dependent case is not yet implemented! # FUNCTION: # Use "h", "sigma" to be conform with Magdon-Ismael et al. [2003] h = q sigma = sd # Settings: n = 1:N pn = 2 * ( sin((n-0.5)*pi) / ((n-0.5)*pi) ) en = sigma^2 * (n-0.5)^2 * pi^2 * horizon / 2 # Loop over all Drawdowns: result = rep(NA, times = length(h)) for (i in 1:length(h)) { g = pn * ( 1 - exp(-en/(h[i]^2)) ) result[i] = sum(g) } # Return Value: result } # ------------------------------------------------------------------------------ rmaxdd <- function(n, mean = 0, sd = 1, horizon = 100) { # Description: # Generates for a Brownian process with mean "mean" and # standard deviation "sd" random variates of maximum # Drawdowns. # Arguments: # n - Number of Drawdown rvs # mean - Drift # sd - Standard Deviation # horizon - Time horizon of the investor # FUNCTION: # Simulation of "n" max Drawdowns "h": result = NULL for (i in 1:n) { D = cumsum(rnorm(horizon, mean = mean, sd = sd)) result[i] = max(cummax(D)-D) } # Return Value: result } ################################################################################ fBasics/R/stats-interpLinear.R0000644000176000001440000001144512015176605016000 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: BIVARIATE GRIDDED INTERPOLATION: # linearInterp Interpolates linearly irregularly spaced data points # linearInterpp Interpolates linearly pointwise ################################################################################ linearInterp <- function(x, y = NULL, z = NULL, gridPoints = 21, xo = seq(min(x), max(x), length = gridPoints), yo = seq(min(y), max(y), length = gridPoints)) { # A function implemented by Diethelm Wuertz # Description: # Interpolates Linearly Irregularly Distributed Data Points # Arguments: # x, y, z - either three numeric vectors of equal length or if # y and z are NULL, a list with entries x, y, a, or named # data.frame with x in the first, y in the second, and z in # the third column. # gridPoints - number of grid points in x and y direction. # xo, yo, a sequence of data points spanning the grid # Note: # Extrapolation is not possible in the case of linear interpolation. # Value: # A list with three elements, $x and $y which are vectors of length # 'gridPoints' and $z which is a matrix of size 'gridPoints^2'. # Requirements: # akima Builtin Fortran Code. # Example: # set.seed(1953) # x = runif(999)-0.5; y = runif(999)-0.5; z = cos(2*pi*(x^2+y^2)) # ans = linearInterp(x, y, z) # persp(ans, theta = -50, phi = 30, col = "steelblue") # Note: # Uses Fortran akima Builtin # FUNCTION: if (!require(akima, quietly = TRUE)) stop("\n -- Package akima not available -- \n\n") # Arguments: if (is.list(x)) x = matrix(unlist(x), ncol = 3) if (is.data.frame(x)) x = as.matrix.data.frame(x) if (is.matrix(x)) { z = x[, 3] y = x[, 2] x = x[, 1] } # Interpolation: ans = interp.old(x, y, z, xo, yo, ncp = 0, extrap = FALSE, duplicate = "median", dupfun = NULL) colnames(ans$z) = as.character(signif(ans$x, round(log(gridPoints), 0))) rownames(ans$z) = as.character(signif(ans$y, round(log(gridPoints), 0))) class(ans) = "gridData" # Return Value: ans } # ------------------------------------------------------------------------------ linearInterpp <- function(x, y = NULL, z = NULL, xo, yo) { # A function implemented by Diethelm Wuertz # Description: # Interpolates Linearly Irregularly Distributed Data Points # Arguments: # x, y, z - either three numeric vectors of equal length or if # y and z are NULL, a list with entries x, y, a, or named # data.frame with x in the first, y in the second, and z in # the third column. # gridPoints - number of grid points in x and y direction. # xo, yo, a sequence of data points for pointwise interpolation # Note: # Extrapolation is not possible in the case of linear interpolation. # Value: # A list with three elements, $x and $y which are vectors of length # 'gridPoints' and $z which is a matrix of size 'gridPoints^2'. # Requirements: # akima Builtin Fortran Code. # Example: # set.seed(1953) # x = runif(999)-0.5; y = runif(999)-0.5; z = cos(2*pi*(x^2+y^2)) # ans = linearInterpp(x, y, z, c(mean(x), 0, 100), c(mean(y), 0, 100)) # persp(ans, theta = -50, phi = 30, col = "steelblue") # Note: # Uses Fortran akima Builtin # FUNCTION: if (!require(akima, quietly = TRUE)) stop("\n -- Package akima not available -- \n\n") # Arguments: if (is.list(x)) x = matrix(unlist(x), ncol = 3) if (is.data.frame(x)) x = as.matrix.data.frame(x) if (is.matrix(x)) { z = x[, 3] y = x[, 2] x = x[, 1] } # Interpolation: ans = akima:::interpp.old(x, y, z, xo, yo, ncp = 0, extrap = FALSE, duplicate = "median", dupfun = NULL) ans = data.frame(x = ans$x, y = ans$y, z = ans$z) # Return Value: ans } ################################################################################ fBasics/R/stats-interpKrige.R0000644000176000001440000000622711370220773015630 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # krigeInterp Kriges irregularly distributed data points ################################################################################ krigeInterp <- function(x, y = NULL, z = NULL, gridPoints = 21, xo = seq(min(x), max(x), length = gridPoints), yo = seq(min(y), max(y), length = gridPoints), extrap = FALSE, polDegree = 6) { # A function implemented by Diethelm Wuertz # Description: # Kriges Irregularly Distributed Data Points # Arguments: # x, y, z - either three numeric vectors of equal length or if # y and z are NULL, a list with entries x, y, a, or named # data.frame with x in the first, y in the second, and z in # the third column. # gridPoints - number of grid points in x and y direction. # xo, yo, a sequence of data points spanning the grid # extrap - a logical, if TRUE then the data points are extrapolated. # polDegree - polynomial degree, an integer ranging between 1 and 6. # Value: # A list with three elements, $x and $y which are vectors of length # 'gridPoints' and $z which is a matrix of size 'gridPoints^2'. # Example: # x = runif(999)-0.5; y = runif(999)-0.5; z = cos(2*pi*(x^2+y^2)) # require(spatial) # ans = krigeInterp(x, y, z, extrap = FALSE) # persp(ans, theta = -50, phi = 30, col = "steelblue") # Note: # Requires Recommended R Package "spatial" # FUNCTION: if (!require(spatial, quietly = TRUE)) stop("\n -- Package spatial not available -- \n\n") # Arguments: if (is.list(x)) x = matrix(unlist(x), ncol = 3) if (is.data.frame(x)) x = as.matrix.data.frame(x) if (is.matrix(x)) { z = x[, 3] y = x[, 2] x = x[, 1] } # Interpolate: krige = surf.gls(np = polDegree, covmod = expcov, x = x, y = y, z = z, d = 0.5, alpha = 1) ans = prmat(krige, xl = min(xo), xu = max(xo), yl = min(yo), yu = max(yo), n = gridPoints-1) # Extrapolate ? # - this should be done more efficiently if (!extrap) { E = akimaInterp(x = x, y = y, z = z, gridPoints = gridPoints, extrap = extrap) ans$z[is.na(E$z)] = NA } class(ans) = "gridData" # Return Value: ans } ################################################################################ fBasics/R/stats-interpAkima.R0000644000176000001440000001244112015176574015612 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # Note, do linear Interpolation with the "old" algorithm, Spline Interpolation # with the "new" algorithm! ################################################################################ ################################################################################ # FUNCTION: DESCRIPTION: # akimaInterp Interpolates irregularly spaced points # akimaInterpp Interpolates and smoothes pointwise ################################################################################ akimaInterp <- function(x, y = NULL, z = NULL, gridPoints = 21, xo = seq(min(x), max(x), length = gridPoints), yo = seq(min(y), max(y), length = gridPoints), extrap = FALSE) { # A function implemented by Diethelm Wuertz # Description: # Interpolates and Smoothes Irregularly Distributed Data Points # Arguments: # x, y, z - either three numeric vectors of equal length or if # y and z are NULL, a list with entries x, y, a, or named # data.frame with x in the first, y in the second, and z in # the third column. # gridPoints - number of grid points in x and y direction. # xo, yo, a sequence of data points spanning the grid # extrap - a logical, if TRUE then the data points are extrapolated.\ # Value: # A list with three elements, $x and $y which are vectors of length # 'gridPoints' and $z which is a matrix of size 'gridPoints^2'. # Requirements: # akima Builtin Fortran Code. # Example: # set.seed(1953) # x = runif(999)-0.5; y = runif(999)-0.5; z = cos(2*pi*(x^2+y^2)) # ans = akimaInterp(x, y, z, extrap = FALSE) # persp(ans, theta = -50, phi = 30, col = "steelblue") # ans = akimaInterp(x, y, z, extrap = TRUE) # persp(ans, theta = -50, phi = 30, col = "steelblue") # Note: # Uses Fortran akima Builtin # FUNCTION: if (!require(akima, quietly = TRUE)) stop("\n -- Package akima not available -- \n\n") # Arguments: if (is.list(x)) x = matrix(unlist(x), ncol = 3) if (is.data.frame(x)) x = as.matrix.data.frame(x) if (is.matrix(x)) { z = x[, 3] y = x[, 2] x = x[, 1] } # Interpolation: ans = interp.new(x, y, z, xo, yo, linear = FALSE, ncp = NULL, extrap = extrap, duplicate = "median", dupfun = NULL) colnames(ans$z) = as.character(signif(ans$x, round(log(gridPoints), 0))) rownames(ans$z) = as.character(signif(ans$y, round(log(gridPoints), 0))) class(ans) = "gridData" # Return Value: ans } # ------------------------------------------------------------------------------ akimaInterpp <- function(x, y = NULL, z = NULL, xo, yo, extrap = FALSE) { # A function implemented by Diethelm Wuertz # Description: # Interpolates and Smoothes Irregularly Distributed Data Points # Arguments: # x, y, z - either three numeric vectors of equal length or if # y and z are NULL, a list with entries x, y, a, or named # data.frame with x in the first, y in the second, and z in # the third column. # gridPoints - number of grid points in x and y direction. # xo, yo, a sequence of data points for pointwise interpolation # Note: # Extrapolation is not possible in the case of linear interpolation. # Value: # A list with three elements, $x and $y which are vectors of length # 'gridPoints' and $z which is a matrix of size 'gridPoints^2'. # Requirements: # akima Builtin Fortran Code. # Example: # set.seed(1953) # x = runif(999)-0.5; y = runif(999)-0.5; z = cos(2*pi*(x^2+y^2)) # ans = akimaInterpp(x, y, z, c(mean(x), 0, 100), c(mean(y), 0, 100)) # persp(ans, theta = -50, phi = 30, col = "steelblue") # Note: # Uses Fortran akima Builtin # FUNCTION: if (!require(akima, quietly = TRUE)) stop("\n -- Package akima not available -- \n\n") # Arguments: if (is.list(x)) x = matrix(unlist(x), ncol = 3) if (is.data.frame(x)) x = as.matrix.data.frame(x) if (is.matrix(x)) { z = x[, 3] y = x[, 2] x = x[, 1] } # Settings: duplicate = "median" dupfun = NULL linear = TRUE # Interpolation: ans = akima:::interpp.new(x, y, z, xo, yo, extrap = extrap, duplicate = "median", dupfun = NULL) ans = data.frame(matrix(unlist(t(ans)), 3)) colnames(ans) = c("x", "y", "z") # Return Value: ans } ################################################################################ fBasics/R/stats-basicStats.R0000644000176000001440000000721111370220773015437 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: BASIC STATISTICS: # basicStats Returns a basic statistics summary ################################################################################ basicStats = function(x, ci = 0.95) { # A function implemented by Diethelm Wuertz # Description: # Calculates Basic Statistics # Arguments: # x - an object which can be transformed by the function # as.matrix() into an object of class matrix. # ci - a numeric value setting the confidence interval. # Value: # a two-column data frame, where the first column takes the # value of the statistics, and the second its name, e.g. # "nobs", "NAs", "Minimum", "Maximum", "1. Quartile", # "3. Quartile", "Mean", "Median", "Sum", "SE Mean", # "LCL Mean", "UCL Mean", "Variance", "Stdev", "Skewness", # "Kurtosis") # FUNCTION: # Univariate/Multivariate: y = as.matrix(x) # Handle Column Names: if (is.null(colnames(y))) { Dim = dim(y)[2] if (Dim == 1) { colnames(y) = paste(substitute(x), collapse = ".") } else if (Dim > 1) { colnames(y) = paste(paste(substitute(x), collapse = ""), 1:Dim, sep = "") } } # Internal Function - CL Levels: cl.vals = function(x, ci) { x = x[!is.na(x)] n = length(x) if(n <= 1) return(c(NA, NA)) se.mean = sqrt(var(x)/n) t.val = qt((1 - ci)/2, n - 1) mn = mean(x) lcl = mn + se.mean * t.val ucl = mn - se.mean * t.val c(lcl, ucl) } # Basic Statistics: nColumns = dim(y)[2] ans = NULL for (i in 1:nColumns) { X = y[, i] # Observations: X.length = length(X) X = X[!is.na(X)] X.na = X.length - length(X) # Basic Statistics: z = c( X.length, X.na, min(X), max(X), as.numeric(quantile(X, prob = 0.25, na.rm = TRUE)), as.numeric(quantile(X, prob = 0.75, na.rm = TRUE)), mean(X), median(X), sum(X), sqrt(var(X)/length(X)), cl.vals(X, ci)[1], cl.vals(X, ci)[2], var(X), sqrt(var(X)), skewness(X), kurtosis(X) ) # Row Names: znames = c( "nobs", "NAs", "Minimum", "Maximum", "1. Quartile", "3. Quartile", "Mean", "Median", "Sum", "SE Mean", "LCL Mean", "UCL Mean", "Variance", "Stdev", "Skewness", "Kurtosis") # Output as data.frame result = matrix(z, ncol = 1) row.names(result) = znames ans = cbind(ans, result) } # Column Names: colnames(ans) = colnames(y) # Return Value: data.frame(round(ans, digits = 6)) } ################################################################################ fBasics/R/plot.R0000644000176000001440000002661211370220773013167 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: INTERNAL USED PLOT FUNCTIONS: # .residualsPlot Returns a residual series plot # .acfPlot Returns a autocorrelation function plot # .pacfPlot Returns a partial ACF plot # .mrlPlot Returns a mean residual life plot # FUNCTION: INTERNAL USED BIVARIATE PLOT FUNCTIONS: # .responsesPlot Returns a response series plot # .firePlot Returns a fitted values vs.residuals plot # FUNCTION: INTERNAL THREE-DIMENSIONAL PLOT UTILITIES: # .circlesPlot Returns a circles plot indexing 3rd variable # .perspPlot Returns a perspective plot in 2 dimensions # .contourPlot Returns a contour plot in 2 dimensions # .histStack Returns a stacked histogram plot ################################################################################ .residualsPlot <- function(x, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns time series graph of residuals # Arguments: # x - an univariate time series of residuals # FUNCTION: # Get Data: x = as.vector(x) # Plot: plot(x, type = "l", ylab = "Residuals", main = "Residual Series", col = "steelblue", ...) rug(x, ticksize = 0.01, side = 4) grid() abline(h = 0, col = "grey") # Return Value: invisible() } # ------------------------------------------------------------------------------ .acfPlot <- function(x, ...) { # A function implemented by Diethelm Wuertz # FUNCTION: # Convert Type: x = as.vector(x) # ACF: acf(x, ...) # Return Value: invisible() } # ------------------------------------------------------------------------------ .pacfPlot <- function(x, ...) { # A function implemented by Diethelm Wuertz # FUNCTION: # Convert Type: x = as.vector(x) # ACF: pacf(x, ...) # Return Value: invisible() } # ------------------------------------------------------------------------------ .mrlPlot <- function(x, ci = 0.95, umin = mean(x), umax = max(x), nint = 100, doplot = TRUE, plottype = c("autoscale", ""), labels = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Create a mean residual life plot with # confidence intervals. # References: # A function originally written by S. Coles # FUNCTION: # Convert Type: x = as.vector(x) # Settings: plottype = plottype[1] # Convert x to a vector, if the input is a data.frame. if (is.data.frame(x)) x = x[,1] sx = xu = xl = rep(NA, nint) u = seq(umin, umax, length = nint) for (i in 1:nint) { x = x[x >= u[i]] sx[i] = mean(x - u[i]) sdev = sqrt(var(x)) n = length(x) xu[i] = sx[i] + (qnorm((1 + ci)/2) * sdev) / sqrt(n) xl[i] = sx[i] - (qnorm((1 + ci)/2) * sdev) / sqrt(n) } # Plot: if (doplot) { if (labels) { xlab = "Threshold: u" ylab = "Mean Excess: e" main = "Mean Residual Live Plot" } else { main = xlab = ylab = "" } if (plottype == "autoscale") { ylim = c(min(xl[!is.na(xl)]), max(xu[!is.na(xu)])) plot(u, sx, type = "o", pch = 19, col = "steelblue", xlab = xlab, ylab = ylab, ylim = ylim, main = main, ...) } else { plot(u[!is.na(xl)], sx[!is.na(xl)], type = "o", pch = 19, col = "steelblue", xlab = xlab, ylab = ylab, main = main, ...) } lines(u[!is.na(xl)], xl[!is.na(xl)], col = "brown") lines(u[!is.na(xu)], xu[!is.na(xu)], col = "brown") if (labels) { grid() text = paste("ci =", as.character(round(ci, 3))) mtext(text, side = 4, adj = 0, cex = 0.7) } } # Result result = data.frame(threshold = u, mrl = sx) # Return Value: if (doplot) return(invisible(result)) else return(result) } ################################################################################ .responsesPlot <- function(x, y = NULL, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns time series graph of responses and fitted values # Arguments: # x - an univariate time series of responses # y - an univariate time series of fitted values # FUNCTION: # Get Data: x = as.vector(x) y = as.vector(y) # Responses Plot: plot(x, type = "l", ylab = "Responses", main = "Responses & Fitted Values", col = "steelblue", ...) rug(x, ticksize = 0.01, side = 4) grid() abline(h = 0, col = "grey") # Add fitted values: if (!is.null(y)) points(y, pch = 19, col = "red") # Return Value: invisible() } # ------------------------------------------------------------------------------ .firePlot <- function(x, y, method = c("scatter", "hist"), ...) { # A function implemented by Diethelm Wuertz # Description: # Returns fitted values vs. residuals plots # Arguments: # x - univariate time series (residuals) # y - univariate time series (fitted) # FUNCTION: # Check Arguments: method = match.arg(method) # Get Data: x = as.vector(x) y = as.vector(y) if (method == "scatter") { # Scatter Plot: plot(x, y, xlab = "Fitted Values", ylab = "Residuals", main = "Residuals vs Fitted", pch = 19, col = "steelblue") panel.smooth(x, y) abline(h = 0, lty = 3, col = "grey") rug(x, ticksize = 0.01, side = 3) rug(y, ticksize = 0.01, side = 4) } else if (method == "hist") { # Histogram Plot: # Save default, for resetting ... def.par = par(no.readonly = TRUE) # Layout: nf = layout(matrix(c(2, 0, 1, 3), 2, 2, byrow = TRUE), c(3, 1), c(1, 3), TRUE) # Scatterplot: par(mar = c(3 ,3, 1, 1)) plot(x, y, xlim = range(x), ylim = range(y), xlab = "", ylab = "", pch = 19, col = "steelblue") panel.smooth(x, y) abline(h = 0, lty = 3, col = "grey") rug(x, side = 3) rug(y, side = 4) # Histogram: xhist = hist(x, nclass = 15, plot = FALSE) yhist = hist(y, nclass = 15, plot = FALSE) top = max(c(xhist$counts, yhist$counts)) # X-Side: par(mar = c(0, 3, 1, 1)) Main = "\n Fitted" barplot(xhist$counts, axes = FALSE, ylim = c(0, top), space = 0, col = "steelblue", border = "white", main = Main) abline(h = 0, lwd = 2, col = "grey") # Y-Side: par(mar = c(3, 0, 1, 1)) barplot(yhist$counts, axes = FALSE, xlim = c(0, top), space = 0, col = "steelblue", , border = "white", horiz = TRUE, main = "Residuals") abline(v = 0, lwd = 2, col = "grey") # Reset: par(def.par) } # Return Value: invisible() } ################################################################################ .circlesPlot <- function(x, y = NULL, z = NULL, scale = 1, points = TRUE, labels = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Creates a scatterplot with circle size as third variable # Example: # circlesPlot(x=rnorm(50), y=rnorm(50), z=rnorm(50)) # circlesPlot(x=rnorm(50), y=rnorm(50), z=rnorm(50), labels= FALSE) # FUNCTION: # Transfor Input: if (is.list(x)) x = matrix(unlist(x), ncol = 3) if (is.data.frame(x)) x = as.matrix.data.frame(x) if (is.matrix(x)) { z = x[, 3] y = x[, 2] x = x[, 1] } nX = length(x) nY = length(y) # nZ = length(z) stopifnot(nX == nY) # stopifnot(nX == nZ || nX*nY == nZ) # Create Circle Plot: if (labels) { plot(x, y, type = "n") } else { plot(x, y, xlab = "", ylab = "", type = "n") } symbols(x, y, add = TRUE, circles = abs(z)^scale, inches = 0.25, fg = "black", bg = "steelblue", ...) X = x[z < 0] Y = y[z < 0] Z = z[z < 0] symbols(X, Y, add = TRUE, circles = abs(Z)^scale, inches = 0.25, fg = "black", bg = "orange", ...) if (points) points(x, y, pch = 19) grid() # Return Value: invisible(NULL) } # ------------------------------------------------------------------------------ .perspPlot <- function(x, y, z, theta = -40, phi = 30, col = "steelblue", ps = 9, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns a perspecvtive plot # Notes: # A synonyme call for function 'persp' # FUNCTION: # Perspective Plot: if (class(version) == "Sversion") { # we assume SPlus: ans = persp(x = x, y = y, z = z, ...) } else { # R: par(ps = ps) if (!exists("ticktype")) ticktype = "detailed" if (!exists("expand")) expand = 0.6 if (!exists("r")) r = 500 ans = persp(x = x, y = y, z = z, theta = theta, phi = phi, col = col, ticktype = ticktype, expand = expand, ...) } # Return Value: invisible(ans) } # ------------------------------------------------------------------------------ .contourPlot <- function(x, y, z, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns a contour plot # Notes: # A synonyme call for function 'contour' # FUNCTION: # Contour Plot: if (class(version) == "Sversion") { # we assume SPlus: ans = contour(x = x, y = y, z = z, ...) } else { # R: ans = contour(x = x, y = y, z = z, ...) } # Return Value: invisible(ans) } # ------------------------------------------------------------------------------ .histStack <- function(x, y = NULL, space = 0, ylab = "frequency", ...) { # A function implemented by Diethelm Wuertz # Description: # Returns a stacked histogram Plot # Example: # .histStack(rnorm(1000, -1), rnorm(1000, 1)) # FUNCTION: # Compute Histograms: breaks = hist(c(x, y))$breaks bars = rbind( hist(x, breaks = breaks, plot = FALSE)$counts, hist(y, breaks = breaks, plot = FALSE)$counts) # Plot: barplot(bars, space = space, ylab = ylab, ...) at = seq(along = breaks) - 1 modulo = ceiling(length(at)/10) sel = (at%%modulo == 0) axis(side = 1, at = at[sel], labels = paste(breaks)[sel]) # Return Value: invisible() } ################################################################################ fBasics/R/plot-varpie.R0000644000176000001440000001310011370220773014437 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # Function: DESCRIPTION: # .varpiePlot Plots a pie with variable radii # .varpieDemo Creates some demo plots ################################################################################ .varpiePlot <- function(x, radius = 0.8, labels = names(x), edges = 200, clockwise = FALSE, init.angle = if (clockwise) 90 else 0, density = NULL, angle = 45, col = NULL, border = NULL, lty = NULL, main = NULL, ...) { # A function implemented by Diethelm Wuertz # Description: # Plots a pie with variable radii. # Note: # The idea is given in: # http://addictedtor.free.fr/packages/A2R/lastVersion/R/spieChart.R # Source: # Package: A2R # Type: Package # Title: Romain Francois misc functions # Version: 0.0-4 # Date: 2005-08-05 # Author: Romain Francois # Maintainer: Romain Francois # Description: Some functions of Romain Francois collection # License: GPL version 2 or newer # URL: http://addictedtor.free.fr/packages # FUNCTION: # Pie: if (length(radius) == 1) { # Normal Pie Plot: pie(x =x, labels = labels, edges = edges, radius = radius, clockwise = clockwise, init.angle = init.angle, density = density, angle = angle, col = col, border = border, lty = lty, main = main, ...) } else { # Variable Pie Plot: radius = radius/max(radius) X = x if (!is.numeric(x) || any(is.na(x) | x < 0)) stop("'x' values must be positive.") if (is.null(labels)) { labels = as.character(1:length(x)) } else { labels = as.graphicsAnnot(labels) } x = c(0, cumsum(x)/sum(x)) dx = diff(x) nx = length(dx) plot.new() pin = par("pin") xlim = ylim = c(-1, 1) if (pin[1] > pin[2]) { xlim = (pin[1]/pin[2]) * xlim } else { ylim = (pin[2]/pin[1]) * ylim } plot.window(xlim, ylim, "", asp = 1) if (is.null(col)) col = rainbow(n) if (is.null(border)) border = "darkgrey" col = if (is.null(density)) col else par("fg") pie(x = X, col = "white", lty = NULL, radius = max(radius), border = border, labels = labels, ...) phi = seq(0, 2*pi, length = 361) for (i in 1:length(radius)) lines(radius[i]*cos(phi), radius[i]*sin(phi), col = border) col = rep(col, length.out = nx) border = rep(border, length.out = nx) lty = rep(lty, length.out = nx) angle = rep(angle, length.out = nx) density = rep(density, length.out = nx) twopi = if (clockwise) -2 * pi else 2 * pi t2xy = function(t, i) { t2p = twopi * t + init.angle * pi/180 list(x = radius[i] * cos(t2p), y = radius[i] * sin(t2p)) } for (i in 1:nx) { n = max(2, floor(edges * dx[i])) P = t2xy(seq(x[i], x[i + 1], length = n), i) polygon(c(P$x, 0), c(P$y, 0), density = density[i], angle = angle[i], border = border[i], col = col[i], lty = lty[i]) P = t2xy(mean(x[i + 0:1]), i) # lab = as.character(labels[i]) # if (!is.na(lab) && nchar(lab)) { # lines(c(1, 1.05) * P$x, c(1, 1.05) * P$y, col = "red") # text(1.1 * P$x, 1.1 * P$y, labels[i], xpd = TRUE, # col = "red", adj = ifelse(P$x < 0, 1, 0), ...) } } title(main = main, ...) } # Return Values: invisible(NULL) } # ------------------------------------------------------------------------------ .varpieDemo <- function() { # Description: # Creates some demo plots # FUNCTION: # Demos: par (ask = TRUE) n = 64; phi = seq(0, pi/4, length = n) X = Y = runif(32) .varpiePlot(X, radius = Y, labels = "", border = "white") points(0, 0, pch = 19, cex = 4) n = 32; phi = seq(0, pi/4, length = n) X = abs(cos(phi)); Y = abs(sin(phi)) .varpiePlot(X, radius = Y, labels = "", border = "white") points(0, 0, pch = 19, cex = 4) n = 64; phi = seq(0, pi, length = n) X = abs(cos(phi)); Y = abs(sin(phi)) .varpiePlot(X, radius = Y, labels = "", border = "white", init.angle = -90) points(0, 0, pch = 19, cex = 4) n = 64; phi = seq(0, pi, length = n) X = abs(cos(phi)); Y = abs(sin(phi)) .varpiePlot(X, radius = Y, labels = "", col = heat.colors(n), border = "white", init.angle = -90) points(0, 0, pch = 19, cex = 4) } ################################################################################ fBasics/R/plot-subStars.R0000644000176000001440000001054411370220773014770 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: SLIDER MENU: # .subStars Creates a sub stars plot ################################################################################ .subStars <- function(x, keyOffset = c(0, 0), loc = NULL, palette = NULL) { # Description: # Creates a sub stars plot # Details: # this is the stars::graphics() function modified in such a # way, that it works together with the subplot builtin. # FUNCTION: # Palette: if (is.null(palette)) palette = palette(rainbow(12, s = 0.6, v = 0.75)) # Graph Frame: xCol = ncol(x) yCol = nrow(x) NY = NX = ceiling(sqrt(xCol)) if (NX * NY == xCol) NY = NY + 1 if (is.null(loc)) { for (nx in 1:NY) for (ny in 1:NX) loc = rbind(loc, c(nx, ny)) loc = loc[1:xCol, ] loc[, 2] = NY + 1 - loc[, 2] } # Stars - Settings: x = t(x) locations = loc len = 0.4 key.loc = c(NX + 1, 1) + keyOffset xlim = c(1, NX + 0.5) ylim = c(0, NY + 1) op <- par("xpd") on.exit(par(op)) par(xpd = TRUE) labels = dimnames(x)[[1]] key.labels = dimnames(x)[[2]] n.loc <- nrow(x) n.seg <- ncol(x) xloc <- locations[, 1] yloc <- locations[, 2] angles <- seq.int(0, 2 * pi, length.out = n.seg + 1)[-(n.seg + 1)] x <- apply(x, 2, function(x) (x - min(x, na.rm = TRUE))/diff(range(x, na.rm = TRUE))) mx <- max(x <- x * len) if (is.null(xlim)) xlim <- range(xloc) + c(-mx, mx) if (is.null(ylim)) ylim <- range(yloc) + c(-mx, mx) deg <- pi/180 plot(0, type = "n", xlim = xlim, ylim = ylim, main = "", sub = "", xlab = "", ylab = "", asp = 1, axes = FALSE) s.x <- xloc + x * rep.int(cos(angles), rep.int(n.loc, n.seg)) s.y <- yloc + x * rep.int(sin(angles), rep.int(n.loc, n.seg)) aangl <- c(angles, if (TRUE) 2 * pi else pi) for (i in 1:n.loc) { px <- py <- numeric() for (j in 1:n.seg) { k <- seq.int(from = aangl[j], to = aangl[j + 1], by = 1 * deg) px <- c(px, xloc[i], s.x[i, j], x[i, j] * cos(k) + xloc[i], NA) py <- c(py, yloc[i], s.y[i, j], x[i, j] * sin(k) + yloc[i], NA) } polygon(px, py, col = 1:n.seg, lwd = 0.25, lty = 1) } text(xloc, yloc - mx + 0.04, labels, cex = 0.7, font = 2, adj = c(0.5, 1)) key.x <- len * cos(angles) + key.loc[1] key.y <- len * sin(angles) + key.loc[2] px <- py <- numeric() for (j in 1:n.seg) { k <- seq.int(from = aangl[j], to = aangl[j + 1], by = 1 * deg) px <- c(px, key.loc[1], key.x[j], len * cos(k) + key.loc[1], NA) py <- c(py, key.loc[2], key.y[j], len * sin(k) + key.loc[2], NA) } polygon(px, py, col = 1:n.seg, lwd = 0.25, lty = 1) lab.angl <- angles + (angles[2] - angles[1])/2 label.x <- 1.15 * len * cos(lab.angl) + key.loc[1] label.y <- 1.15 * len * sin(lab.angl) + key.loc[2] for (k in 1:n.seg) { text.adj <- c(if (lab.angl[k] < 90 * deg || lab.angl[k] > 270 * deg) 0 else if (lab.angl[k] > 90 * deg && lab.angl[k] < 270 * deg) 1 else 0.5, if (lab.angl[k] <= 90 * deg) (1 - lab.angl[k]/(90 * deg))/2 else if (lab.angl[k] <= 270 * deg) (lab.angl[k] - 90 * deg)/(180 * deg) else 1 - (lab.angl[k] - 270 * deg)/(180 * deg)) text(label.x[k], label.y[k], labels = key.labels[k], cex = 0.7, adj = text.adj) } # Return Value: invisible(locations) } ################################################################################ fBasics/R/plot-subPlot.R0000644000176000001440000001566311370220773014621 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # .emptyPlot Creates an empty plot page # .subPlot Creates a sub plot # .cnvrt.coords Converts coordinates ################################################################################ .emptyPlot <- function() { # Description: # Creates an empty plot page # FUNCTION: # Plot: plot(c(0, 1), c(0, 1), type = "n", xlab = "", ylab = "", ann = TRUE, axes = FALSE) # Return Value: invisible() } # ------------------------------------------------------------------------------ # Package: TeachingDemos # Title: Demonstrations for teaching and learning # Version: 1.6 # Author: Greg Snow # Description: This package is a set of demonstration functions that can # be used in a classroom to demonstrate statistical concepts, or on your # own to better understand the concepts or the programming. # Maintainer: Greg Snow # License: Artistic-2.0 # Date: 2007-09-07 # Suggests: tcltk, lattice, tkrplot, MASS, rgl, sgeostat, mapproj # Enhances: maptools, qcc # LazyData: true # Packaged: Wed Nov 28 12:13:53 2007; hornik # ------------------------------------------------------------------------------ # Example: # plot(c(0, 1), c(0, 1)); .subPlot(hist(rnorm(100)), x = 0.5, y = 0.5) # ------------------------------------------------------------------------------ .subPlot <- function(fun, x, y = NULL, size = c(1, 1), vadj = 0.5, hadj = 0.5, pars = NULL) { # Description: # Creates a sub plot # FUNCTION: # Par: old.par <- par(no.readonly=TRUE) on.exit(par(old.par)) if(missing(x)) x <- locator(2) xy <- xy.coords(x,y) if(length(xy$x) != 2){ pin <- par('pin') tmp <- .cnvrt.coords(xy$x[1],xy$y[1],'usr')$plt x <- c( tmp$x - hadj*size[1]/pin[1], tmp$x + (1-hadj)*size[1]/pin[1] ) y <- c( tmp$y - vadj*size[2]/pin[2], tmp$y + (1-vadj)*size[2]/pin[2] ) xy <- .cnvrt.coords(x,y,'plt')$fig } else { xy <- .cnvrt.coords(xy,,'usr')$fig } par(pars) par(plt=c(xy$x,xy$y), new = TRUE) fun tmp.par <- par(no.readonly = TRUE) return(invisible(tmp.par)) } # ------------------------------------------------------------------------------ .cnvrt.coords <- function(x,y=NULL,input=c('usr','plt','fig','dev','tdev')) { # Description: # Converts Coordinates # FUNCTION: input <- match.arg(input) xy <- xy.coords(x,y) cusr <- par('usr') cplt <- par('plt') cfig <- par('fig') cdin <- par('din') comi <- par('omi') cdev <- c(comi[2]/cdin[1],(cdin[1]-comi[4])/cdin[1], comi[1]/cdin[2],(cdin[2]-comi[3])/cdin[2]) if(input=='usr'){ usr <- xy plt <- list() plt$x <- (xy$x-cusr[1])/(cusr[2]-cusr[1]) plt$y <- (xy$y-cusr[3])/(cusr[4]-cusr[3]) fig <- list() fig$x <- plt$x*(cplt[2]-cplt[1])+cplt[1] fig$y <- plt$y*(cplt[4]-cplt[3])+cplt[3] dev <- list() dev$x <- fig$x*(cfig[2]-cfig[1])+cfig[1] dev$y <- fig$y*(cfig[4]-cfig[3])+cfig[3] tdev <- list() tdev$x <- dev$x*(cdev[2]-cdev[1])+cdev[1] tdev$y <- dev$y*(cdev[4]-cdev[3])+cdev[3] return( list( usr=usr, plt=plt, fig=fig, dev=dev, tdev=tdev ) ) } if(input=='plt') { plt <- xy usr <- list() usr$x <- plt$x*(cusr[2]-cusr[1])+cusr[1] usr$y <- plt$y*(cusr[4]-cusr[3])+cusr[3] fig <- list() fig$x <- plt$x*(cplt[2]-cplt[1])+cplt[1] fig$y <- plt$y*(cplt[4]-cplt[3])+cplt[3] dev <- list() dev$x <- fig$x*(cfig[2]-cfig[1])+cfig[1] dev$y <- fig$y*(cfig[4]-cfig[3])+cfig[3] tdev <- list() tdev$x <- dev$x*(cdev[2]-cdev[1])+cdev[1] tdev$y <- dev$y*(cdev[4]-cdev[3])+cdev[3] return( list( usr=usr, plt=plt, fig=fig, dev=dev, tdev=tdev ) ) } if(input=='fig') { fig <- xy plt <- list() plt$x <- (fig$x-cplt[1])/(cplt[2]-cplt[1]) plt$y <- (fig$y-cplt[3])/(cplt[4]-cplt[3]) usr <- list() usr$x <- plt$x*(cusr[2]-cusr[1])+cusr[1] usr$y <- plt$y*(cusr[4]-cusr[3])+cusr[3] dev <- list() dev$x <- fig$x*(cfig[2]-cfig[1])+cfig[1] dev$y <- fig$y*(cfig[4]-cfig[3])+cfig[3] tdev <- list() tdev$x <- dev$x*(cdev[2]-cdev[1])+cdev[1] tdev$y <- dev$y*(cdev[4]-cdev[3])+cdev[3] return( list( usr=usr, plt=plt, fig=fig, dev=dev, tdev=tdev ) ) } if(input=='dev'){ dev <- xy fig <- list() fig$x <- (dev$x-cfig[1])/(cfig[2]-cfig[1]) fig$y <- (dev$y-cfig[3])/(cfig[4]-cfig[3]) plt <- list() plt$x <- (fig$x-cplt[1])/(cplt[2]-cplt[1]) plt$y <- (fig$y-cplt[3])/(cplt[4]-cplt[3]) usr <- list() usr$x <- plt$x*(cusr[2]-cusr[1])+cusr[1] usr$y <- plt$y*(cusr[4]-cusr[3])+cusr[3] tdev <- list() tdev$x <- dev$x*(cdev[2]-cdev[1])+cdev[1] tdev$y <- dev$y*(cdev[4]-cdev[3])+cdev[3] return( list( usr=usr, plt=plt, fig=fig, dev=dev, tdev=tdev ) ) } if(input=='tdev'){ tdev <- xy dev <- list() dev$x <- (tdev$x-cdev[1])/(cdev[2]-cdev[1]) dev$y <- (tdev$y-cdev[3])/(cdev[4]-cdev[3]) fig <- list() fig$x <- (dev$x-cfig[1])/(cfig[2]-cfig[1]) fig$y <- (dev$y-cfig[3])/(cfig[4]-cfig[3]) plt <- list() plt$x <- (fig$x-cplt[1])/(cplt[2]-cplt[1]) plt$y <- (fig$y-cplt[3])/(cplt[4]-cplt[3]) usr <- list() usr$x <- plt$x*(cusr[2]-cusr[1])+cusr[1] usr$y <- plt$y*(cusr[4]-cusr[3])+cusr[3] tdev <- list() tdev$x <- dev$x*(cdev[2]-cdev[1])+cdev[1] tdev$y <- dev$y*(cdev[4]-cdev[3])+cdev[3] return( list( usr=usr, plt=plt, fig=fig, dev=dev, tdev=tdev ) ) } } ################################################################################ fBasics/R/plot-seriesPlot.R0000644000176000001440000001044611370220773015314 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # seriesPlot Dispalys a time Series Plot # cumulatedPlot Displays a cumulated series given the returns # returnPlot Displays returns given the cumulated series # drawdownPlot Displays drawdown series from returns ################################################################################ seriesPlot <- function(x, labels = TRUE, type = "l", col = "steelblue", title = TRUE, grid = TRUE, box = TRUE, rug = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Dispalys a time Series Plot # Arguments: # x - an uni- or multivariate return series of class 'timeSeries' # or any other object which can be transformed by the function # 'as.timeSeries()' into an object of class 'timeSeries'. # FUNCTION: # timeSeries: stopifnot(is.timeSeries(x)) N = NCOL(x) Units = colnames(x) if (length(col) == 1) col = rep(col, times = N) # Series Plots: for (i in 1:N) { X = x[, i] plot(x = X, type = type, col = col[i], ann = FALSE, ...) # Add Title: if (title) { title(main = Units[i], xlab = "Time", ylab = "Value") } else { title(...) } # Add Grid: if(grid) grid() # Add Box: if(box) box() # Add Rugs: if(rug) rug(as.vector(X), ticksize = 0.01, side = 2, quiet = TRUE) } # Return Value: invisible() } # ------------------------------------------------------------------------------ cumulatedPlot <- function(x, index = 100, labels = TRUE, type = "l", col = "steelblue", title = TRUE, grid = TRUE, box = TRUE, rug = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Displays a cumulated series given the returns # FUNCTION: # timeSeries: stopifnot(is.timeSeries(x)) x = index * exp(colCumsums(x)) seriesPlot(x, labels = labels, type = type, col = col, title = title, grid = grid, box = box, rug = rug, ...) # Return Value: invisible() } # ------------------------------------------------------------------------------ returnPlot <- function(x, labels = TRUE, type = "l", col = "steelblue", title = TRUE, grid = TRUE, box = TRUE, rug = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Displays returns given the cumulated series # FUNCTION: # timeSeries: stopifnot(is.timeSeries(x)) x = returns(x, ...) seriesPlot(x, labels = labels, type = type, col = col, title = title, grid = grid, box = box, rug = rug, ...) # Return Value: invisible() } # ------------------------------------------------------------------------------ drawdownPlot <- function(x, labels = TRUE, type = "l", col = "steelblue", title = TRUE, grid = TRUE, box = TRUE, rug = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Displays drawdowns given the return series # FUNCTION: # timeSeries: stopifnot(is.timeSeries(x)) x = drawdowns(x, ...) seriesPlot(x, labels = labels, type = type, col = col, title = title, grid = grid, box = box, rug = rug, ...) # Return Value: invisible() } ################################################################################ fBasics/R/plot-scalinglawPlot.R0000644000176000001440000000734411370220773016151 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # scalinglawPlot Evaluates and displays scaling law behavior ################################################################################ scalinglawPlot <- function(x, span = ceiling(log(length(x)/252)/log(2)), doplot = TRUE, labels = TRUE, trace = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Investigates the scaling law. # The input "x" requires log-returns. # Arguments: # x - an uni- or multivariate return series of class 'timeSeries' # or any other object which can be transformed by the function # 'as.timeSeries()' into an object of class 'timeSeries'. # labels - a logical flag, by default true. Should a default # main title and labels addet to the plot? # FUNCTION: # Settings: if (!is.timeSeries(x)) x = as.timeSeries(x) Units = colnames(x) # Labels: if (labels) { main = "Scaling Law Plot" xlab = "log-time" ylab = "log-volatility" } else { main = xlab = ylab = "" } X = x DIM = dim(X)[2] Intercept = Exponent = InverseExponent = NULL for (i in 1:DIM) { # Get Data: x = as.vector(as.matrix(X)[, i]) if (labels) main = Units[i] # Settings: logtimesteps = span xmean = mean(x) # x have to be logarithmic returns y = (x-xmean) logprices = cumsum(y) # Scaling Power Low: scale = function(nx, logprices) { sum(abs(diff(logprices, lag = (2^nx))))} nx = 0:logtimesteps; x = nx*log(2) y = log(apply(matrix(nx), 1, scale, logprices)) # fit = lsfit(x, y)$coefficients # Runs in both environments, R and SPlus: fit = lsfit(x, y) # Robust Fit: # fit = l1fit(x, y) # Fit Result: Fit = unlist(fit)[1:2] alpha = 1.0/Fit[2] if (doplot) { plot(x, y, main = main, xlab = xlab, ylab = ylab, ...) abline(Fit[1], Fit[2]) abline(Fit[1], 0.5, col = "steelblue") } if (labels) grid() # Trace: if (trace) { cat ("\nScaling Law: ", Units[i]) cat ("\n Plot Intercept ", fit$coefficients[1]) cat ("\n Plot Slope ", fit$coefficients[2]) cat ("\n Plot Inverse Slope ", 1/fit$coefficients[2]) cat ("\n\n") } Intercept = c(Intercept, fit$coefficients[1]) Exponent = c(Exponent, fit$coefficients[2]) InverseExponent = c(InverseExponent, 1/fit$coefficients[2]) } names(Intercept) = Units names(Exponent) = Units names(InverseExponent) = Units result = list( Intercept = Intercept, Exponent = Exponent, InverseExponent = InverseExponent) # Return Value: invisible(result) } ################################################################################ fBasics/R/plot-qqPlot.R0000644000176000001440000002375711370220773014454 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # qqnormPlot Returns a tailored normal quantile-quantile plot # qqnigPlot Returns a tailored NIG quantile-quantile plot # qqghtPlot Returns a tailored GHT quantile-quantile plot # qqgldPlot Returns a tailored LD quantile-quantile plot ################################################################################ qqnormPlot <- function(x, labels = TRUE, col = "steelblue", pch = 19, title = TRUE, mtext = TRUE, grid = FALSE, rug = TRUE, scale = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Example of a Normal quantile plot of data x to provide a visual # assessment of its conformity with a normal (data is standardised # first). # Arguments: # x - an univariate return series of class 'timeSeries' # or any other object which can be transformed by the function # 'as.timeSeries()' into an object of class 'timeSeries'. # Details: # The ordered data values are posterior point estimates of the # underlying quantile function. So, if you plot the ordered data # values (y-axis) against the exact theoretical quantiles (x-axis), # you get a scatter that should be close to a straight line if the # data look like a random sample from the theoretical distribution. # This function chooses the normal as the theory, to provide a # graphical/visual assessment of how normal the data appear. # To help with assessing the relevance of sampling variability on # just "how close" to the normal the data appears, we add (very) # approximate posterior 95% intervals for the uncertain quantile # function at each point (Based on approximate theory) . # Author: # Based on code written by Mike West, mw@stat.duke.edu # Note: # Source from # http://www.stat.duke.edu/courses/Fall99/sta290/Notes/ # Example: # x = rnorm(100); qqnormPlot(x); qqnormPlot(x, labels = FALSE) # FUNCTION: # Settings: if (!is.timeSeries(x)) x = as.timeSeries(x) Units = x@units x = as.vector(x) n = length(x) # Fit: p = (1:n)/(n+1) if (scale) x = (x-mean(x))/sqrt(var(x)) par = c(mean = mean(x), var = var(x)) # Quantiles: x = sort(x) p = ppoints(x) if (scale) z = qnorm(p) else z = qnorm(p, mean(x), sd(x)) # Plot: if (labels) { xlab = "Normal Quantiles" ylab = paste(Units, "Ordered Data") plot(z, x, xlab = xlab, ylab = ylab, col = col, pch = 19, ...) } else { plot(z, x, ...) } # Title: if(title) { title(main = "NORM QQ PLOT") } # Margin Text: if (mtext) { Text = "Confidence Intervals: 95%" mtext(Text, side = 4, adj = 0, col = "darkgrey", cex = 0.7) } # Grid: if (grid) { grid() } # Add Diagonal Line: abline(0, 1, col = "grey") # Add Rugs: if(rug) { rug(z, ticksize = 0.01, side = 1, quiet = TRUE) rug(x, ticksize = 0.01, side = 2, quiet = TRUE) } # 95% Confidence Intervals: s = 1.96*sqrt(p*(1-p)/n) pl = p-s i = pl<1 & pl>0 lower = quantile(x, probs = pl[i]) lines(z[i], lower, col = "brown") pl = p+s i = pl < 1 & pl > 0 upper = quantile(x, probs = pl[i]) lines(z[i], upper, col = "brown") abline(h = mean(x), col = "grey") abline(v = mean(x), col = "grey") # Result: ans = list(x = z, y = x) attr(ans, "control")<-par # Return Value: invisible(ans) } # ------------------------------------------------------------------------------ qqnigPlot <- function(x, labels = TRUE, col = "steelblue", pch = 19, title = TRUE, mtext = TRUE, grid = FALSE, rug = TRUE, scale = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Displays a NIG quantile-quantile Plot # Arguments: # x - an univariate return series of class 'timeSeries' # or any other object which can be transformed by the function # 'as.timeSeries()' into an object of class 'timeSeries'. # Example: # qqnigPlot(rnig(100)) # FUNCTION: # Settings: if (!is.timeSeries(x)) x = as.timeSeries(x) stopifnot(isUnivariate(x)) Units = x@units x = as.vector(x) n = length(x) ## YC: no scaling ## FIXME: should take care of too small time series # Fit: fit = nigFit(x, doplot = FALSE, trace = FALSE) par = fit@fit$estimate names(par) = c("alpha", "beta", "delta", "mu") # Quantiles: x = sort(x) p = ppoints(x) z = qnig(p, par[1], par[2], par[3], par[4]) # Plot: if (labels) { xlab = "Theoretical Quantiles" ylab = "Sample Quantiles" plot(z, x, xlab = xlab, ylab = ylab, col = col, pch = pch, ...) } else { plot(z, x, ...) } # Title: if (title) { title(main = "NIG QQ Plot") } # Margin Text: rpar = signif(par, 3) text = paste( "alpha =", rpar[1], "| beta =", rpar[2], "| delta =", rpar[3], "| mu =", rpar[4]) mtext(text, side = 4, adj = 0, col = "grey", cex = 0.7) # Grid: if (grid) { grid() } # Add Fit: abline(lsfit(z, x)) # Add Rugs: if(rug) { rug(z, ticksize = 0.01, side = 3, quiet = TRUE) rug(x, ticksize = 0.01, side = 4, quiet = TRUE) } # Result: ans = list(x = z, y = x) attr(ans, "control") <- par # Return Value: invisible(ans) } # ------------------------------------------------------------------------------ qqghtPlot <- function(x, labels = TRUE, col = "steelblue", pch = 19, title = TRUE, mtext = TRUE, grid = FALSE, rug = TRUE, scale = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Displays a GHT quantile-quantile Plot # Arguments: # x - an univariate return series of class 'timeSeries' # or any other object which can be transformed by the function # 'as.timeSeries()' into an object of class 'timeSeries'. # Example: # qqnigPlot(rgh(100)) # FUNCTION: # Settings: if (!is.timeSeries(x)) x = as.timeSeries(x) stopifnot(isUnivariate(x)) Units = x@units x = as.vector(x) n = length(x) # Fit: fit = ghtFit(x, doplot = FALSE, trace = FALSE) par = fit@fit$estimate names(par) = c("beta", "delta", "mu", "nu") # Plot: x <- sort(x) p <- ppoints(x) z <- qght(p, par[1], par[2], par[3], par[4]) if (labels) { plot(z, x, col = col, ann = FALSE, ...) } else { plot(z, x, ...) } # Add Grid: if (grid) { grid() } # Add title: if (title) { title( main = "GHT QQ Plot", xlab = "Theoretical Quantiles", ylab = "Sample Quantiles") } # Add Fit: abline(lsfit(z, x)) # Add Rugs: if(rug) { rug(z, ticksize = 0.01, side = 3, quiet = TRUE) rug(x, ticksize = 0.01, side = 4, quiet = TRUE) } # Result: ans = list(x = z, y = x) attr(ans, "control")<-par # Return Value: invisible(ans) } ################################################################################ qqgldPlot <- function(x, labels = TRUE, col = "steelblue", pch = 19, title = TRUE, mtext = TRUE, grid = FALSE, rug = TRUE, scale = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Displays a Generalized lambda Distribution quantile-quantile Plot # Arguments: # x - an univariate return series of class 'timeSeries' # or any other object which can be transformed by the function # 'as.timeSeries()' into an object of class 'timeSeries'. # Example: # qqgldPlot(rgld(100)) # FUNCTION: # Settings: if (!is.timeSeries(x)) x = as.timeSeries(x) stopifnot(isUnivariate(x)) Units = x@units x = as.vector(x) n = length(x) ## YC: no scaling ## FIXME: should take care of too small time series # Fit: fit = gldFit(x, doplot = FALSE, trace = FALSE) par = fit@fit$estimate names(par) = c("lambda1", "lambda2", "lambda3", "lambda4") # Quantiles: x = sort(x) p = ppoints(x) z = qgld(p, par[1], par[2], par[3], par[4]) # Plot: if (labels) { xlab = "Theoretical Quantiles" ylab = "Sample Quantiles" plot(z, x, xlab = xlab, ylab = ylab, col = col, pch = pch, ...) } else { plot(z, x, ...) } # Title: if (title) { title(main = "NIG QQ Plot") } # Margin Text: rpar = signif(par, 3) text = paste( "lambda1 =", rpar[1], "| lambda2 =", rpar[2], "| lambda3 =", rpar[3], "| lambda4 =", rpar[4]) mtext(text, side = 4, adj = 0, col = "grey", cex = 0.7) # Grid: if (grid) { grid() } # Add Fit: abline(lsfit(z, x)) # Add Rugs: if(rug) { rug(z, ticksize = 0.01, side = 3, quiet = TRUE) rug(x, ticksize = 0.01, side = 4, quiet = TRUE) } # Result: ans = list(x = z, y = x) attr(ans, "control") <- par # Return Value: invisible(ans) } ################################################################################fBasics/R/plot-histPlot.R0000644000176000001440000002263611370220773014775 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: TAILORED DENSITY FUNCTIONS: # histPlot Returns a tailored histogram plot # densityPlot Returns a tailored kernel density estimate plot # logDensityPlot Returns a tailored log kernel density estimate plot # .plot.histogram Replaces here the function plot.histogram ################################################################################ histPlot <- function(x, labels = TRUE, col = "steelblue", fit = TRUE, title = TRUE, grid = TRUE, rug = TRUE, skip = FALSE, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns a probability histogram plot for each column of a # timeSeries object # Arguments: # x - an uni- or multivariate return series of class 'timeSeries' # or any other object which can be transformed by the function # 'as.timeSeries()' into an object of class 'timeSeries'. # FUNCTION: # timeSeries: stopifnot(is.timeSeries(x)) N = NCOL(x) Units = colnames(x) if (length(col) == 1) col = rep(col, times = N) # Histogram Plots: for (i in 1:N) { # Histogram: X = as.vector(x[, i]) if (skip) X = X[X != 0] # Plot: if (labels) { H = hist(x = X, , breaks = "FD", plot = FALSE) .plot.histogram(H, col = col[i], freq = FALSE, ...) box() } else { H = hist(x = X, plot = FALSE, ...) .plot.histogram(H, col = col[i], freq = FALSE, ...) } # Add Title: if(title) { title(main = paste(Units[i], "Histogram"), xlab = "Value", ylab = "Probability") } # Add Fit: if (fit) { mean = mean(X) sd = sd(X) xlim = range(H$breaks) s = seq(xlim[1], xlim[2], length = 201) lines(s, dnorm(s, mean, sd), lwd = 2, col = "brown") } # Add Mean: if (labels) { abline(v = mean, lwd = 2, col = "orange") Text = paste("Mean:", signif(mean, 3)) mtext(Text, side = 4, adj = 0, col = "darkgrey", cex = 0.7) } # Add Grid: if (grid) grid() # Add Zero Line: if(labels) { abline(h = 0, col = "grey") } # Add Rug Plot: if(rug) { rug(X, ticksize = 0.01, quiet = TRUE) } } # Return Value: invisible() } # ------------------------------------------------------------------------------ densityPlot <- function(x, labels = TRUE, col = "steelblue", fit = TRUE, hist = TRUE, title = TRUE, grid = TRUE, rug = TRUE, skip = FALSE, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns density plots for each column of a timeSeries object # FUNCTION: # timeSeries: stopifnot(is.timeSeries(x)) N = NCOL(x) Units = colnames(x) if (length(col) == 1) col = rep(col, times = N) # Density Plots: for (i in 1:N) { # Density: X = as.vector(x[, i]) if (skip) X = X[X != 0] # Underlay Histogram: if (hist) { H = hist(x = X, , breaks = "FD", plot = FALSE) plot(x = H$mids, y = H$density, type = "h", lwd = 2, main = "", xlab = "", ylab = "", col = "grey") } # Plot Density: D = density(X, ...) if (hist) { lines(D$x, D$y, lwd = 2, col = "brown") } else { plot(D, col = col[i], ann = FALSE, ...) } # Add Title: if (title) { title(main = Units[i], xlab = "Value", ylab = "Density") } # Add Fit: if (fit) { mean = mean(X) sd = sd(X) xlim = range(H$breaks) s = seq(xlim[1], xlim[2], length = 201) lines(s, dnorm(s, mean, sd), lwd = 2, col = "darkgrey") } # Add Mean: if (labels) { abline(v = mean, lwd = 2, col = "orange") Text = paste("Mean:", signif(mean, 3)) mtext(Text, side = 4, adj = 0, col = "darkgrey", cex = 0.7) } # Add Grid: if (grid) { grid() } # Add Zero Line: if(labels) { abline(h = 0, col = "grey") } # Add Rug Plot: if(rug) { rug(X, ticksize = 0.01, quiet = TRUE) } } # Return Value: invisible() } # ------------------------------------------------------------------------------ logDensityPlot <- function(x, labels = TRUE, col = "steelblue", robust = TRUE, title = TRUE, grid = TRUE, rug = TRUE, skip = FALSE, ...) { # A function implemented by Diethelm Wuertz # Description: # Displays a pdf plot on logarithmic scale # Details: # Returns a pdf plot on a lin-log scale in comparison to a Gaussian # density plot Two type of fits are available: a normal density with # fitted sample mean and sample standard deviation, or a normal # density with Hubers robust mean and standard deviation corfrected # by the bandwidth of the Kernel estimator. # FUNCTION: # timeSeries: stopifnot(is.timeSeries(x)) N = NCOL(x) Units = colnames(x) if (length(col) == 1) col = rep(col, times = N) # Log Density: for (i in 1:N) { # Transform Data: X = as.vector(x[, i]) if (skip) X = X[X != 0] # Kernel and Histogram Estimators: Density = density(X, ...) Histogram = hist(X, breaks = "FD", plot = FALSE) # Plot Frame: plot(Histogram$mids, log(Histogram$density), type = "n", ann = FALSE, xlim = range(Density$x), ylim = log(range(Density$y)), ...) # Add Title: if(title) { title(main = paste(Units[i], "Log Density"), xlab = "Value", ylab = "Log Density") } # Add Kernel Density Estimated Points: points(Density$x, log(Density$y), pch = 19, cex = 0.7, col = "grey") # Sample Line Fit: s = seq(min(Density$x), max(Density$x), length = 1001) # Robust Fit: if (robust) { h = MASS::hubers(X) logDensity = log(dnorm(s, mean = h[[1]], sd = sqrt(h[[2]]^2+Density$bw^2))) minLogDensity = log(min(Density$y)) lines( x = s[logDensity > minLogDensity], y = logDensity[logDensity > minLogDensity], col = "red", lwd = 2) # Standard Fit: lines(s, log(dnorm(s, mean(X), sd(X))), col = "orange", lwd = 2) } # Plot Histogram: points(Histogram$mids, log(Histogram$density), pch = 151, col = "steelblue") # Grid: if (grid) { grid() } # Add Rug Plot: if(rug) { rug(x, ticksize = 0.01, quiet = TRUE) } } # Return Value: invisible() } ################################################################################ .plot.histogram <- function(x, freq = equidist, density = NULL, angle = 45, col = NULL, border = "white", lty = NULL, main = NULL, sub = NULL, xlab = NULL, ylab = NULL, xlim = range(x$breaks), ylim = NULL, axes = TRUE, labels = FALSE, add = FALSE, ...) { # This replacement of plot.histogram() suppresses title # printing which would be otherwise not possible! equidist <- if(is.logical(x$equidist)) x$equidist else { h <- diff(x$breaks) ; diff(range(h)) < 1e-7 * mean(h) } if(freq && !equidist) warning("the AREAS in the plot are wrong -- rather use freq=FALSE") y <- if (freq) x$counts else { ## x$density -- would be enough, but ## for back compatibility y <- x$density; if(is.null(y)) x$intensities else y} nB <- length(x$breaks) if(is.null(y) || 0 == nB) stop("'x' is wrongly structured") if(!add) { if(is.null(ylim)) ylim <- range(y, 0) if (missing(ylab)) ylab <- if (!freq) "Density" else "Frequency" plot.new() plot.window(xlim, ylim, "") #-> ylim's default from 'y' # title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...) if(axes) { axis(1, ...) axis(2, ...) } } rect(x$breaks[-nB], 0, x$breaks[-1], y, col = col, border = border, angle = angle, density = density, lty = lty) if((logl <- is.logical(labels) && labels) || is.character(labels)) text(x$mids, y, labels = if(logl) { if(freq) x$counts else round(x$density,3) } else labels, adj = c(0.5, -0.5)) invisible() } ################################################################################ fBasics/R/plot-counts.R0000644000176000001440000000604011370220773014471 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # .counts1Plot Creates a plot of counts # .counts2Plot Creates an alternative plot of counts ################################################################################ .counts1Plot= function(x, ...) { # A function implemented by Diethelm Wuertz # Description: # Creates a plot of counts # Arguments: # x - a vector of counts # Note: # From R-help, ref. Gabor Grothendiek # Example: # x <- c(26,26,27,27,27,27,28,28,28,28,28,28,28,28,28,28,28,28, # 28,29,29,29,29,29,29,29,29,29,29,29,30,30,30,30,30,30,30, # 30,31,31,31,31,32,33,33,33,34,34,35,43); .counts1Plot(x) # FUNCTION: # Plot Range: xmin = min(x) xmax = max(x) xlim = c(xmin - 0.1*(xmax-xmin), xmax + 0.1*(xmax-xmin)) # Plot plot(seq(y) - match(y, y) ~ y, list(y = sort(x)), xlim = xlim, ylab = "", yaxt = "n", pch = 19, frame.plot = FALSE, ...) # Return Value: invisible() } # ------------------------------------------------------------------------------ .counts2Plot <- function(x, ...) { # A function implemented by Diethelm Wuertz # Description: # Creates a plot of counts # Arguments: # x - a vector of counts # Note: # From R-help, ref. Jinsong Zhao # Example: # x <- c(26,26,27,27,27,27,28,28,28,28,28,28,28,28,28,28,28,28, # 28,29,29,29,29,29,29,29,29,29,29,29,30,30,30,30,30,30,30, # 30,31,31,31,31,32,33,33,33,34,34,35,43); .counts2Plot(x) # FUNCTION: # Tabulate: x.table <- table(x) x.x <- as.numeric(names(x.table)) x.y <- as.numeric(x.table) n <- length(x.x) x.final <- NULL for (i in 1:n) { tmp <- data.frame(x = rep(x.x[i], x.y[i]), y = 1:x.y[i]) x.final <- rbind(x.final, tmp) } # Plot Range: xmin = min(x) xmax = max(x) xlim = c(xmin - 0.1*(xmax-xmin), xmax + 0.1*(xmax-xmin)) # Plot: plot(x.final, yaxt = "n", ylab = "", xlim = xlim, pch = 19, frame.plot = FALSE, ...) # Return Value: invisible(x) } ################################################################################ fBasics/R/plot-boxPlot.R0000644000176000001440000001320111370220773014602 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # boxPlot Produces a side-by-side standard box plot # boxPercentilePlot Produces a side-by-side box-percentile plot ################################################################################ boxPlot <- function(x, col = "steelblue", title = TRUE, ...) { # A function Implemented by Diethelm Wuertz # Description: # Produces a standard box plot # Arguments: # x - an uni- or multivariate return series of class 'timeSeries' # or any other object which can be transformed by the function # 'as.matrix()' into an object of class 'matrix'. # Optional Arguments: # las, oma - allows to change style of X labels and creates # required space below plot. # Try: e.g. las = 3, and oma = c(9, 0, 0, 0) # FUNCTION: # Settings: x = as.matrix(x) assetNames = colnames(x) # Plot: ans = boxplot(as.data.frame(x), col = col, ...) abline(h = 0 , lty = 3) # Add Title: if (title) { title(main = "Box Plot", ylab = "Value") } # Result: colnames(ans$stats) = ans$names rownames(ans$stats) = c("lower whisker", "lower hinge", "median", "upper hinge", "upper whisker") # Return Value: invisible(ans) } # ------------------------------------------------------------------------------ boxPercentilePlot <- function(x, col = "steelblue", title = TRUE, ...) { # A modified copy from Hmisc # Description: # Produces a side-by-side box-percentile plot # Details: # Box-percentile plots are similiar to boxplots, except box-percentile # plots supply more information about the univariate distributions. At # any height the width of the irregular "box" is proportional to the # percentile of that height, up to the 50th percentile, and above the # 50th percentile the width is proportional to 100 minus the percentile. # Thus, the width at any given height is proportional to the percent of # observations that are more extreme in that direction. As in boxplots, # the median, 25th and 75th percentiles are marked with line segments # across the box. [Source: Hmisc] # Arguments: # x - an uni- or multivariate return series of class 'timeSeries' # or any other object which can be transformed by the function # 'as.matrix()' into an object of class 'matrix'. # FUNCTION: # Settings: x = as.matrix(x) assetNames = colnames(x) n = ncol(x) all.x = list() for (i in 1:n) all.x[[i]] = as.vector(x[, i]) centers = seq(from = 0, by = 1.2, length = n) ymax = max(sapply(all.x, max, na.rm = TRUE)) ymin = min(sapply(all.x, min, na.rm = TRUE)) xmax = max(centers) + 0.5 xmin = -0.5 # Plot: if (length(col) == 1) col = rep(col, times = n) plot(c(xmin, xmax), c(ymin, ymax), type = "n", xlab = "", ylab = "", xaxt = "n", ...) xpos = NULL for (i in 1:n) { # plot.values = .bpxAssetsPlot(all.x[[i]], centers[i]) y = all.x[[i]] offset = centers[i] y = y[!is.na(y)] n = length(y) delta = 1/(n + 1) prob = seq(delta, 1 - delta, delta) quan = sort(y) med = median(y) q1 = median(y[y < med]) q3 = median(y[y > med]) first.half.p = prob[quan <= med] second.half.p = 1 - prob[quan > med] plotx = c(first.half.p, second.half.p) options(warn = -1) qx = approx(quan, plotx, xout = q1)$y q1.x = c(-qx, qx) + offset qx = approx(quan, plotx, xout = q3)$y options(warn = 0) q3.x = c(-qx, qx) + offset q1.y = c(q1, q1) q3.y = c(q3, q3) med.x = c(-max(first.half.p), max(first.half.p)) + offset med.y = c(med, med) plot.values = list(x1 = (-plotx) + offset, y1 = quan, x2 = plotx + offset, y2 = quan, q1.y = q1.y, q1.x = q1.x, q3.y = q3.y, q3.x = q3.x, med.y = med.y, med.x = med.x) # Continue: xpos = c(xpos, mean(plot.values$med.x)) x.p = c(plot.values$x1, plot.values$x2) y.p = c(plot.values$y1, plot.values$y2) polygon(x.p, y.p, col = col[i], border = "grey", ...) lines(plot.values$x1, plot.values$y1) lines(plot.values$x2, plot.values$y2) lines(plot.values$q1.x, plot.values$q1.y) lines(plot.values$q3.x, plot.values$q3.y) lines(plot.values$med.x, plot.values$med.y) } axis(side = 1, at = xpos, labels = assetNames, ...) abline(h = 0, lty = 3, col = "black") # Add Title: if (title) { title(main = "Box Percentiles", ylab = "Value") } # Return Value: invisible() } ################################################################################ fBasics/R/plot-acfPlot.R0000644000176000001440000002302511370220773014550 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # acfPlot Displays tailored autocorrelations function plot # pacfPlot Displays tailored partial autocorrelation function plot # teffectPlot Estimates and displays the Taylor effect # lacfPlot Displays lagged autocorrelations ################################################################################ acfPlot <- function(x, labels = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Autocorrelations function plot # Arguments: # x - an uni- or multivariate return series of class 'timeSeries' # or any other object which can be transformed by the function # 'as.timeSeries()' into an object of class 'timeSeries'. # labels - a logical flag, by default true. Should a default # main title and labels addet to the plot? # FUNCTION: # Settings: if (!is.timeSeries(x)) x = as.timeSeries(x) Units = colnames(x) # Labels: if (labels) { main = "" xlab = "lag" ylab = "ACF" } else { main = xlab = ylab = "" } # ACF: for (i in 1:dim(x)[2]) { if (labels) main = Units[i] ans = acf(x = as.vector(x[, i]), main = main, xlab = xlab, ylab = ylab, ...) } # Return Value: invisible(ans) } # ------------------------------------------------------------------------------ pacfPlot <- function(x, labels = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Partial autocorrelation function plot # Arguments: # x - an uni- or multivariate return series of class 'timeSeries' # or any other object which can be transformed by the function # 'as.timeSeries()' into an object of class 'timeSeries'. # labels - a logical flag, by default true. Should a default # main title and labels addet to the plot? # FUNCTION: # Settings: if (!is.timeSeries(x)) x = as.timeSeries(x) Units = colnames(x) # Labels: if (labels) { main = "" xlab = "lag" ylab = "PACF" } else { main = xlab = ylab = "" } # Partial ACF: for (i in 1:dim(x)[2]) { if (labels) main = Units[i] ans = pacf(x = as.vector(x[, i]), main = main, xlab = xlab, ylab = ylab, ...) } # Return Value: invisible(ans) } # ------------------------------------------------------------------------------ teffectPlot <- function(x, deltas = seq(from = 0.2, to = 3.0, by = 0.2), lag.max = 10, ymax = NA, standardize = TRUE, labels = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Evaluate and Display Taylor Effect # Arguments: # x - an uni- or multivariate return series of class 'timeSeries' # or any other object which can be transformed by the function # 'as.timeSeries()' into an object of class 'timeSeries'. # labels - a logical flag, by default true. Should a default # main title and labels addet to the plot? # FUNCTION: # Settings: if (!is.timeSeries(x)) x = as.timeSeries(x) Units = colnames(x) # Labels: if (labels) { main = "" xlab = "Exponent Delta" ylab = "Autocorrelation" } else { main = xlab = ylab = "" } # Taylor Effect: ans = list() maxDelta = NULL for (i in 1:dim(x)[2]) { if (labels) main = Units[i] X = as.vector(x[, i]) # Standardize: if(standardize) X = (X-mean(X))/sqrt(var(X)) data = matrix(data = rep(0, times = lag.max*length(deltas)), nrow = lag.max, byrow = TRUE) for (id in 1:length(deltas)) data[,id] = as.double(acf(abs(X)^deltas[id], lag.max = lag.max, type = "corr", plot = FALSE)$acf)[2:(lag.max+1)] if (is.na(ymax)) ymax = max(data) # Plot: if (labels) { plot(deltas, data[1,], ylim = c(0, ymax), type = "n", main = main, xlab = xlab, ylab = ylab, ...) } else { plot(deltas, data[1,], type = "n", main = main, xlab = xlab, ylab = ylab, ...) } xl = 1:length(deltas) for (il in 1:(lag.max)) { yp = max(data[il, ]) yl = xl[data[il, ] == yp] lines(deltas, data[il, ], col = il) points(deltas[yl], yp, pch = 19) maxDelta = c(maxDelta, deltas[yl]) lines (c(1, 1), c(0, ymax)) } # Grid: if (labels) { mtext("Taylor Effect", side = 4, adj = 0, col = "darkgrey", cex = 0.7) grid() } ans[[i]] = data } names(ans) = Units # Deltas for max Peak: ans$maxDelta = maxDelta # Return Value: invisible(ans) } # ------------------------------------------------------------------------------ lacfPlot <- function(x, n = 12, lag.max = 20, type = c("returns", "values"), labels = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Computes the lagged autocorrelation function # Arguments: # x - an uni- or multivariate return series of class 'timeSeries' # or any other object which can be transformed by the function # 'as.timeSeries()' into an object of class 'timeSeries'. # type - a character string which specifies the type of the input # series, either "returns" or series "values". In the case of # a return series as input, the required value series is # computed by cumulating the financial returns: 'exp(colCumsums(x))' # labels - a logical flag, by default true. Should a default # main title and labels addet to the plot? # FUNCTION: # Settings: if (!is.timeSeries(x)) x = as.timeSeries(x) Units = colnames(x) # Cumulated Returns: type = match.arg(type) if (type == "values") { cumX = x } else if (type == "returns") { cumX = exp(colCumsums(x)) } # Labels: if (labels) { main = "" xlab = "tau" ylab = "Lagged Correlation" } else { main = xlab = ylab = "" } cumRho = cumLagged = NULL DIM = dim(cumX)[2] for (i in 1:DIM) { # Get Data: x = as.vector(as.matrix(cumX)[, i]) if (labels) main = Units[i] # Truncate to multiple of n: N = trunc(length(x)/n) M = length(x) - n*N if (M > 0) x = x[-c(1:M)] # One Step Volatilities: x.ret = c(0, diff(log(x))) x.mat = matrix(x.ret, byrow = TRUE, ncol = n) u = apply(abs(x.mat), 1, mean) # n-step Volatilities: index = n*(1:N) v = abs(c(0, diff(log(x[index])))) # Zero Tau: L = length(u) RhoZero = cor(u, v) # print(RhoZero) # Positive Tau: RhoPos = NULL for (tau in 1:lag.max) { X = u[-((L-tau+1):L)] X2 = X Y = v[-((L-tau+1):L)] Y2 = v[-(1:tau)] X.mean = mean(X) Y.mean = mean(Y) X1 = sum((X - X.mean)^2) Y1 = sum((Y - Y.mean)^2) XY1 = sum( (X2-X.mean)*(Y2-Y.mean) ) rho = XY1/sqrt(X1*Y1) RhoPos = c(RhoPos, rho) } # Negative Tau: RhoNeg = NULL for (tau in 1:lag.max) { X = v[-((L-tau+1):L)] X2 = X Y = u[-((L-tau+1):L)] Y2 = u[-(1:tau)] X.mean = mean(X) Y.mean = mean(Y) X1 = sum((X - X.mean)^2) Y1 = sum((Y - Y.mean)^2) XY1 = sum( (X2-X.mean)*(Y2-Y.mean) ) rho = XY1/sqrt(X1*Y1) RhoNeg = c(RhoNeg, rho) } # Correlations: Lagged = RhoPos - RhoNeg Rho = c(rev(RhoNeg), RhoZero, RhoPos) # Plot: plot(x = (-lag.max):(lag.max), y = Rho, type = "l", xlab = xlab, ylab = ylab, ylim = c(min(Lagged), max(Rho)), main = main, ...) Text = paste("n =", n, " | lag =", lag.max) mtext(Text, side = 4, adj = 0, col ="darkgrey", cex = 0.7) points(-lag.max:lag.max, Rho, pch = 19, cex = 0.7) lines(0:lag.max, c(0, Lagged), col = "red") points(0:lag.max, c(0, Lagged), pch = 19, cex = 0.7, col = "red") abline(h = 0, col = "grey", lty = 3) ci = 1/sqrt(length(u)) abline(h = +ci, col = "blue") abline(h = -ci, col = "blue") if (labels) grid() # Grid: if (labels) grid() cumRho = rbind(cumRho, Rho) cumLagged = c(cumLagged, Lagged) } # Return Value: invisible(list(Rho = cumRho, Lagged = cumLagged)) } ################################################################################ fBasics/R/matrix-vech.R0000644000176000001440000000373411370220773014440 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # Function: DESCRIPTION: # vec Stacks a matrix as column vector # vech Stacks a lower triangle matrix ################################################################################ vec <- function(x) { # A function implemented by Diethelm Wuertz # Description: # Stacks a matrix as column vector # Details: # vec(X) = (X11, X21, ..., XN1, X12, X22, ..., XNN)' # Note: # Example for a 3x3 Matrix: # X11, X21, X22, X31, X32, X33 # FUNCTION: # Return Value: t(t(as.vector(x))) } # ------------------------------------------------------------------------------ vech <- function(x) { # A function implemented by Diethelm Wuertz # Description: # Stacks a lower triangle matrix # Details: # vech is the operator that stacks the lower triangle # of a NxN matrix as an N(N+1)/2x1 vector: # vech(X) =(X11, X21, X22, X31, ..., XNN)' # Note: # Example for a 3x3 Matrix: # X11, X21, X22, X31, X32, X33 # FUNCTION: # Return Value: t(x[!upper.tri(x)]) } ################################################################################ fBasics/R/matrix-tslag.R0000644000176000001440000000520111370220773014614 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # TIME SERIES DESCRIPTION: # tslag Lagged/leading vector/matrix of selected orders # .tslag1 Internal Function used by tslag ################################################################################ tslag <- function(x, k = 1, trim = FALSE) { # A function implemented by Diethelm Wuertz # Description: # Creates a lagged or leading vector/matrix of selected order(s). # Arguments: # x - a vector of data, missing values (NA) are allowed. # k - the number of positions the new series is to lag # or to lead the input series. # trim - a logical flag, if TRUE, the missing values at the # beginning or end of the returned series will be trimmed. # The default value is FALSE. # Details: # With a positive value of "k" we get a lagged series and with # a negative value we get a leading series. # Examples: # tslag(rnorm(10), 2) # tslag(rnorm(10), -2:2) # tslag(rnorm(10), -2:2, trim = TRUE) # FUNCTION: # Bind: ans = NULL for ( i in k) { ans = cbind(ans, .tslag1(x, i)) } # Trim: if (trim) { indexes = (1:length(ans[,1]))[!is.na(apply(ans, 1, sum))] ans = ans[indexes, ] } # As Vector: if (length(k) == 1) ans = as.vector(ans) # Return Value: ans } # ------------------------------------------------------------------------------ .tslag1 <- function(x, k) { # A function implemented by Diethelm Wuertz # Description: # Internal Function used by function tslag. # FUNCTION: y = x if (k > 0) y = c(rep(NA, times = k), x[1:(length(x)-k)]) if (k < 0) y = c(x[(-k+1):length(x)], rep(NA, times = -k)) # Return Value: y } ################################################################################ fBasics/R/matrix-triang.R0000644000176000001440000000323611370220773014774 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # GENERATION: DESCRIPTION: # triang Extracs the lower tridiagonal part from a matrix # Triang Extracs the upper tridiagonal part from a matrix ################################################################################ triang <- function(x) { # A function implemented by Diethelm Wuertz # Description: # Returns lower triangle matrix # FUNCTION: # Triangulate: x[row(x) < col(x)] = 0 # Return Value: x } # ------------------------------------------------------------------------------ Triang <- function(x) { # A function implemented by Diethelm Wuertz # Description: # Returns upper triangle matrix # FUNCTION: # Triangulate x[row(x) > col(x)] = 0 # Return Value: x } ################################################################################ fBasics/R/matrix-tr.R0000644000176000001440000000252511370220773014135 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # tr Returns the trace of a matrix ################################################################################ tr <- function(x) { # A function implemented by Diethelm Wuertz # Description: # Returns the trace of a matrix # FUNCTION: # Trace: if (dim(x)[1] != dim(x)[2] ) { return(NA) } else { return(sum(diag(x))) } # Return Value: invisible() } ################################################################################ fBasics/R/matrix-rk.R0000644000176000001440000000261711370220773014126 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # rk Returns the rank of a matrix ################################################################################ rk <- function(x, method = c("qr", "chol")) { # A function implemented by Diethelm Wuertz # Description: # Returns the rank of a matrix # FUNCTION: # Rank: method = method[1] if (method == "chol") { ans = attr(chol(x, pivot = TRUE), "rank") } else { ans = qr(x)$rank } # Return Value: ans } ################################################################################ fBasics/R/matrix-posDefinite.R0000644000176000001440000000776011370220773015767 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # isPositiveDefinite Checks if the matrix X is positive definite # makePositiveDefinite Forces the matrix x to be positive definite ################################################################################ isPositiveDefinite <- function(x) { # A function implemented by Diethelm Wuertz # Description: # Checks if the matrix x is positive definite # Arguments: # x - a symmetric matrix or any other rectangular object # describing a covariance matrix which can be converted into # a matrix by the function 'as.matrix'. # FUNCTION: # Transform: x = as.matrix(x) # Check if matrix is positive definite: ans = .is.positive.definite(m = x) # Return Value: ans } # ------------------------------------------------------------------------------ .is.positive.definite <- function(m, tol, method = c("eigen", "chol")) { # Author: # Copyright 2003-05 Korbinian Strimmer # Rank, condition, and positive definiteness of a matrix # GNU General Public License, Version 2 method = match.arg(method) if (!is.matrix(m)) { m = as.matrix(m) } if (method == "eigen") { eval = eigen(m, only.values = TRUE)$values if( missing(tol) ) { tol = max(dim(m))*max(abs(eval))*.Machine$double.eps } if (sum(eval > tol) == length(eval)) { return(TRUE) } else { return(FALSE) } } else if (method == "chol") { val = try(chol(m), silent = TRUE) if (class(val) == "try-error") { return(FALSE) } else { return(TRUE) } } } # ------------------------------------------------------------------------------ makePositiveDefinite <- function(x) { # A function implemented by Diethelm Wuertz # Description: # Forces the matrix x to be positive definite # Arguments: # x - a symmetric matrix or any other rectangular object # describing a covariance matrix which can be converted into # a matrix by the function 'as.matrix'. # FUNCTION: # Make Positive Definite: ans = .make.positive.definite(m = x) # Return Value: ans } # ------------------------------------------------------------------------------ .make.positive.definite <- function(m, tol) { # Author: # Copyright 2003-05 Korbinian Strimmer # Rank, condition, and positive definiteness of a matrix # GNU General Public License, Version 2 # Method by Higham 1988 if (!is.matrix(m)) { m = as.matrix(m) } d = dim(m)[1] if ( dim(m)[2] != d ) { stop("Input matrix is not square!") } es = eigen(m) esv = es$values if (missing(tol)) { tol = d*max(abs(esv))*.Machine$double.eps } delta = 2*tol # factor two is just to make sure the resulting # matrix passes all numerical tests of positive definiteness tau = pmax(0, delta - esv) dm = es$vectors %*% diag(tau, d) %*% t(es$vectors) # print(max(DA)) # print(esv[1]/delta) return( m + dm ) } ################################################################################ fBasics/R/matrix-pdl.R0000644000176000001440000000442711370220773014272 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # pdl Regressor matrix for polynomial distributed lags ################################################################################ pdl <- function(x, d = 2, q = 3, trim = FALSE) { # A function implemented by Diethelm Wuertz # Description: # Regressor matrix for polynomial distributed lags # Aruments: # x - a numeric vector. # d - an integer specifying the order of the polynomial. # q - an integer specifying the number of lags to use in # creating polynomial distributed lags. This must be # greater than d. # trim - a logical flag; if TRUE, the missing values at # the beginning of the returned matrix will be trimmed. # Value: # Returns a matrix representing the regressor matrix. # Example: # stack.loss = c( # 42, 37, 37, 28, 18, 18, 19, 20, 15, 14, 14, # 13, 11, 12, 8, 7, 8, 8, 9, 15, 15) # pdl(stack.loss) # FUNCTION: # Check: stopifnot(q > d) # Polynomial distributed lags: M = tslag(x, 1:q, FALSE) C = NULL for (i in 0:d) { C = rbind(C, (1:q)^i) } Z = NULL for (i in 1:(d+1)) { Z = cbind(Z, apply(t(C[i,]*t(M)), 1, sum)) } Z[, 1] = Z[, 1] + x # Trim: if (trim) { indexes = (1:length(Z[,1]))[!is.na(apply(Z, 1, sum))] Z = Z[indexes, ] } # Return Value: Z } ################################################################################ fBasics/R/matrix-pascal.R0000644000176000001440000000376311370220773014760 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # pascal Creates a Pascal matrix ################################################################################ pascal <- function(n) { # A function implemented by Diethelm Wuertz # Description: # Creates a Pascal matrix # Arguments: # n - the dimension of the square matrix # Details: # http://mathworld.wolfram.com/PascalMatrix.html # Pascal matrices are symmetric and positive definite. # The determinant of a Pascal matrix is 1. # The inverse of a Pascal matrix has integer entries. # If lambda is an eigenvalue of a Pascal matrix, # then 1/lambda is also an eigenvalue of the matrix. # The Cholesky factor of a Pascal matrix consists of # the elements of Pascal's triangle # FUNCTION: # Pascal: N = n-1 n.over.r = function(n, r) { prod(1:n) / (prod(1:(n-r)) * prod(1:r) ) } X = rep(1, N) for ( i in 1:N ) for ( j in 1:N ) X = c(X, n.over.r(i+j, j)) X = cbind(rep(1, N+1), matrix(X, byrow = TRUE, ncol = N)) # Return Value: X } ################################################################################ fBasics/R/matrix-norm.R0000644000176000001440000000430211370220773014456 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # norm Returns the norm of a matrix ################################################################################ norm <- function(x, p = 2) { # A function implemented by Diethelm Wuertz # Description: # Returns the spectral norm of a matrix # Details: # http://mathworld.wolfram.com/MatrixNorm.html: # For p = 1 # The maximum absolute column sum norm |A|_1 is defined # as the maximum of the sum of the absolute valued elements # of columns of the matrix. # For p = 2: # The spectral |A|_2 norm is "the" of a matrix. This value # is computed as the square root of the maximum eigenvalue # of A^H A where A^H is the conjugate transpose. # For p = Inf: # The maximum absolute row sum norm |A|_inf is defined # as the maximum of the sum of the absolute valued elements # of rows of the matrix. # FUNCTION: # Compute Norm: ans = NA if (p == 1) { x = abs(x) ans = max(apply(x, 2, sum)) } if (p == 2) { ans = sqrt(max(eigen(t(x) %*% x)$values)) } if (p == Inf) { x = abs(x) ans = max(apply(x, 1, sum)) } if (is.na(ans)) stop("Invalid value for p") # Return value: ans } ################################################################################ fBasics/R/matrix-kron.R0000644000176000001440000000241211370220773014454 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # LINEAR ALGEBRA: DESCRIPTION: # kron Returns the Kronecker product ################################################################################ kron <- function(x, y) { # A function implemented by Diethelm Wuertz # Description: # Returns the Kronecker product # FUNCTION: # Kronecker Product: ans = x %x% y # Return Value: ans } ################################################################################ fBasics/R/matrix-inv.R0000644000176000001440000000247011370220773014303 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # LINEAR ALGEBRA: DESCRIPTION: # inv Returns the inverse of a matrix ################################################################################ inv <- function(x) { # A function implemented by Diethelm Wuertz # Description: # Returns the inverse of a matrix # FUNCTION: # Inverse: # ans = chol2inv(chol(x)) # or ... ans = base::solve(x) # Return Value: ans } ################################################################################ fBasics/R/matrix-hilbert.R0000644000176000001440000000333011370220773015134 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # GENERATION: DESCRIPTION: # hilbert Creates a Hilbert matrix ################################################################################ hilbert <- function(n) { # A function implemented by Diethelm Wuertz # Description: # Creates a Hilbert matrix # Arguments: # n - the dimension of the square matrix # FUNCTION: # Hilbert: i = 1:n X = 1 / outer(i - 1, i, "+") # Return Value: X } ################################################################################ fBasics/R/matrix-gridVector.R0000644000176000001440000000427711370220773015626 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # gridVector Creates from two vectors rectangular grid coordinates ################################################################################ gridVector <- function(x, y = NULL) { # A function implemented by Diethelm Wuertz # Description: # Creates from two vectors rectangular grid coordinates # Arguments: # x, y - two numeric vectors of length m and n which span the # rectangular grid of size m times n. # Details: # The two vectors x and y span a rectangular grid with nx=length(x) # times ny=length(y) points which are returned as a matrix of size # (nx*ny) times 2. # Value: # returns a list with two elements X and Y each of length m # times n # Example: # > gridVector(1:3, 1:2) # [,1] [,2] # [1,] 1 1 # [2,] 2 1 # [3,] 3 1 # [4,] 1 2 # [5,] 2 2 # [6,] 3 2 # FUNCTION: # DW: if (is.null(y)) y = x # Prepare for Input: nx = length(x) ny = length(y) xoy = cbind(rep(x, ny), as.vector(matrix(y, nx, ny, byrow = TRUE))) X = matrix(xoy, nx * ny, 2, byrow = FALSE) # Return Value: list(X = X[,1], Y = X[,2]) } ################################################################################ fBasics/R/matrix-colVec.R0000644000176000001440000000344611370220773014726 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # colVec Creates a column vector from a data vector # rowVec Creates a row vector from a data vector ################################################################################ colVec <- function(x) { # A function implemented by Diethelm Wuertz # Description: # Converts a vector to a column vector # Details: # A column vector is a matrix with one column. # Return Value: # FUNCTION: # Double Transpose: ans = t(t(x)) # Return Value: ans } # ------------------------------------------------------------------------------ rowVec <- function(x) { # A function implemented by Diethelm Wuertz # Description: # Converts a vector to a row vector # Details: # A row vector is a matrix with one row. # FUNCTION: # Transpose: ans = t(x) # Return Value: ans } ################################################################################ fBasics/R/gui-stylizedFacts.R0000644000176000001440000004476411370220773015633 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: STYLIZED FACTS GUI: # .stylizedFactsGUI Opens a GUI for stylized facts # .lmacfPlot Estimates and displays the long memory ACF # .logpdfPlot Displays a pdf plot on logarithmic scale(s) # .ccfPlot Displays tailored cross correlation function plot # .qqgaussPlot Displays a tailored Gaussian quantile-quantile plot ################################################################################ .lmacfPlot <- function(x, lag.max = max(2, floor(10*log10(length(x)))), ci = 0.95, type = c("both", "acf", "hurst"), labels = TRUE, trace = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Evaluate and display long memory autocorrelation Function. # Arguments: # x - an uni- or multivariate return series of class 'timeSeries' # or any other object which can be transformed by the function # 'as.timeSeries()' into an object of class 'timeSeries'. # labels - a logical flag, by default true. Should a default # main title and labels addet to the plot? # FUNCTION: # Settings: if (!is.timeSeries(x)) x = as.timeSeries(x) Units = colnames(x) X = x # Match Arguments: type = match.arg(type) # Labels: if (labels) { main1 = "" xlab1 = "lag" ylab1 = "ACF" main2 = "" xlab2 = "log lag" ylab2 = "log ACF" } else { main1 = xlab1 = ylab1 = "" main2 = xlab2 = ylab2 = "" } Fitted = list() Hurst = NULL DIM = dim(X)[2] for (i in 1:DIM) { # Get Data: x.ret = as.matrix(X)[, i] x = abs(x.ret) if (labels) main1 = main2 = Units[i] # ACF: z = acf(x, lag.max = lag.max, type = "correlation", plot = FALSE) z$acf[1] = 0 cl = qnorm(0.5 + ci/2)/sqrt(z$n.used) z.min = min(z$acf, -cl) # lin-lin plot excluding one: x = seq(0, lag.max, by = 1) y = z$acf if (type == "both" | type == "acf") { plot(x = x[-1], y = y[-1], type = "l", main = main1, col = "steelblue", xlab = xlab1, ylab = ylab1, xlim = c(0, lag.max), ylim = c(-2*cl, max(y[-1])), ...) # abline(h = 0, lty = 3) if (trace) { cat ("\nLong Memory Autocorrelation Function:") paste (cat ("\n Maximum Lag "), cat(lag.max)) paste (cat ("\n Cut-Off ConfLevel "), cat(cl)) } ACF = acf(x.ret, lag.max = lag.max, plot = FALSE)$acf[,,1] lines(x = 1:lag.max, y = ACF[-1], type = "l", col = "steelblue") lines(x = c(-0.1, 1.1)*lag.max, y = c(+cl, +cl), lty = 3, col = "darkgrey") lines(x = c(-0.1, 1.1)*lag.max, y = c(-cl, -cl), lty = 3, col = "darkgrey") } # log-log Plot of ACF: x = x[y > cl] y = y[y > cl] # log-log: if (length(x) < 10) { Fit = c(NA, NA) hurst = NA cat("\n The time series exhibits no long memory! \n") } else { Fit = lsfit(log(x), log(y)) fit = unlist(Fit)[1:2] hurst = 1 + fit[2]/2 if (type == "both" | type == "hurst") { plot(x = log(x), y = log(y), type = "l", xlab = xlab2, ylab = ylab2, main = main2, col = "steelblue", ...) Text = paste("Hurst Exponent:", signif(hurst, 3)) mtext(Text, side = 4, adj = 0, col = "darkgrey", cex = 0.7) # Grid: if (labels) grid() } ### fit = l1fit(log(x), log(y))$coefficients abline(fit[1], fit[2], col = 1) if (trace) { paste (cat ('\n Plot-Intercept '), cat(fit[1])) paste (cat ('\n Plot-Slope '), cat(fit[2])) paste (cat ('\n Hurst Exponent '), cat(hurst), cat("\n")) } } # Save Results: if (DIM == 1) Fitted = Fit else Fitted[[i]] = Fit Hurst = c(Hurst, hurst) } # Return Value: invisible(list(fit = Fitted, hurst = Hurst)) } # ------------------------------------------------------------------------------ .logpdfPlot = function(x, breaks = "FD", type = c("lin-log", "log-log"), doplot = TRUE, labels = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Displays a pdf plot on logarithmic scale(s) # Details: # Returns a pdf plot on a lin-log scale in # comparison to a Gaussian density plot # and return the break-midpoints and the # counts obtained from the histogram of # the empirical data. # Arguments: # x - an uni- or multivariate return series of class 'timeSeries' # or any other object which can be transformed by the function # 'as.timeSeries()' into an object of class 'timeSeries'. # labels - a logical flag, by default true. Should a default # main title and labels addet to the plot? # FUNCTION: # Settings: if (!is.timeSeries(x)) x = as.timeSeries(x) Units = colnames(x) # Select Type: type = match.arg(type) # Labels: if (labels) { if (type == "lin-log") { main = "log PDF" xlab = "x" ylab = "log PDF" } else if (type == "log-log") { main = "log PDF" xlab = "log x" ylab = "log PDF" } } else { main = xlab = ylab = "" } X = x DIM = ncol(X) for (i in 1:DIM) { # Get Data: x = as.vector(X[, i]) if (labels) main = Units[i] # Lin-Log Plot: if (type == "lin-log") { # Histogram PDF: result = hist(x, breaks = breaks, plot = FALSE) prob.counts = result$counts/sum(result$counts) / diff(result$breaks)[1] histogram = list(breaks = result$breaks, counts = prob.counts) # Histogram Count & Break-Midpoints: yh = histogram$counts xh = histogram$breaks xh = xh[1:(length(xh)-1)] + diff(xh)/2 xh = xh[yh > 0] yh = log(yh[yh > 0]) if (doplot) { par(err = -1) plot(xh, yh, type = "p", pch = 19, col = "steelblue", main = main, xlab = xlab, ylab = ylab, ...) Text = "Scales: log-log" mtext(Text, side = 4, adj =0, col = "darkgrey", cex = 0.7) } # Compare with a Gaussian Plot: xg = seq(from = xh[1], to = xh[length(xh)], length = 301) yg = log(dnorm(xg, mean(x), sqrt(var(x)))) if (doplot) { par(err = -1) lines(xg, yg, col = "brown") } # Return Value: result = list(breaks = xh, counts = yh, fbreaks = xg, fcounts = yg) } # Log-Log Plot: if (type == "log-log") { # Histogram PDF: result = hist(x, breaks = breaks, plot = FALSE) prob.counts = result$counts/sum(result$counts) / diff(result$breaks)[1] histogram = list(breaks = result$breaks, counts = prob.counts) # Histogram Count & Breaks: yh = histogram$counts xh = histogram$breaks xh = xh[1:(length(xh)-1)] + diff(xh)/2 xh = xh[yh > 0] yh = yh[yh > 0] yh1 = yh[xh < 0] xh1 = abs(xh[xh < 0]) yh2 = yh[xh > 0] xh2 = xh[xh > 0] if (doplot) { plot(log(xh1), log(yh1), type = "p", pch = 19, col = "darkgreen", main = main, xlab = xlab, ylab = ylab, ...) Text = "Scales: log-log" mtext(Text, side = 4, adj =0, col = "darkgrey", cex = 0.7) par(err = -1) points(log(xh2), log(yh2), col = 2, ...) } # Compare with a Gaussian plot: xg = seq(from = min(xh1[1], xh[2]), to = max(xh1[length(xh1)], xh2[length(xh2)]), length = 301) xg = xg[xg > 0] yg = log(dnorm(xg, mean(x), sqrt(var(x)))) if (doplot) { par(err = -1) lines(log(xg), yg, col = "brown") } # Result: result = list(breaks = c(xh1, xh2), counts = c(yh1, yh2), fbreaks = c(-rev(xg), xg), fcounts = c(-rev(yg), yg)) } # Grid: if (labels) grid() } # Return Value: invisible(result) } # ------------------------------------------------------------------------------ .qqgaussPlot <- function(x, span = 5, col = "steelblue", labels = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns a simple Quantile-Quantile plot. # Arguments: # x - an uni- or multivariate return series of class 'timeSeries' # or any other object which can be transformed by the function # 'as.timeSeries()' into an object of class 'timeSeries'. # labels - a logical flag, by default true. Should a default # main title and labels addet to the plot? # FUNCTION: # Settings: # if (SPLUSLIKE) splusLikePlot(TRUE) # Settings: if (!is.timeSeries(x)) x = as.timeSeries(x) Units = colnames(x) # Labels: if (labels) { main = "Normal QQ Plot" xlab = "Theoretical Quantiles" ylab = "Sample Quantiles" } else { main = xlab = ylab = "" } X = x DIM = dim(X)[2] for (i in 1:DIM) { # Get Data: x = as.vector(as.matrix(X)[, i]) if (labels) main = Units[i] # Standardized qqnorm(): y = (x-mean(x)) / sqrt(var(x)) # Further Settings: y[abs(y) < span] lim = c(-span, span) # Plot qqnorm: qqnorm(y, main = main, xlab = xlab, ylab = ylab, xlim = lim, ylim = lim, col = col, ...) # Add Line: qqline(y, ...) # Grid: if (labels) grid() } # Return Value: invisible(x) } # ------------------------------------------------------------------------------ .ccfPlot <- function(x, y, lag.max = max(2, floor(10*log10(length(x)))), type = c("correlation", "covariance", "partial"), labels = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Cross correlation function plot # Arguments: # x - an univariate 'timeSeries' object # labels - a logical flag, by default true. Should a default # main title and labels addet to the plot? # FUNCTION: # Convert Type: if (class(x) == "timeSeries") stopifnot(isUnivariate(x)) if (class(y) == "timeSeries") stopifnot(isUnivariate(y)) x = as.vector(x) y = as.vector(y) # Labels: if (labels) { main = "Crosscorrelation Function" xlab = "lag" ylab = "CCF" } else { main = xlab = ylab = "" } # Result: # A copy from R's ccf - So you can use it also under SPlus: X = cbind(x = x, y = y) acf.out = acf(X, lag.max = lag.max, plot = FALSE, type = type[1]) lag = c(rev(acf.out$lag[-1, 2, 1]), acf.out$lag[, 1, 2]) y = c(rev(acf.out$acf[-1, 2, 1]), acf.out$acf[, 1, 2]) acf.out$acf = array(y, dim = c(length(y), 1, 1)) acf.out$lag = array(lag, dim = c(length(y), 1, 1)) acf.out$snames = paste(acf.out$snames, collapse = " & ") plot(acf.out, main = main, xlab = xlab, ylab = ylab, ...) # Return Value: invisible(acf.out) } ################################################################################ .stylizedFactsGUI <- function(x, mfrow = c(3, 3)) { # A function implemented by Diethelm Wuertz # Description: # Opens a GUI for stylized facts # FUNCTION: # Refresh Code: stylizedFactsRefreshCode <- function(...) { # Settings: selectedAsset = .tdSliderMenu(no = 1) type = as.integer(.tdSliderMenu(obj.name = "stylizedFactsType")) Unit = colnames(x) # ACF Plot: if (type == 1) { if (selectedAsset == 0) { par(mfrow = mfrow) acfPlot(x) } else { par(mfrow = c(1, 1)) acfPlot(x[, selectedAsset]) } } # PACF Plot: if (type == 2) { if (selectedAsset == 0) { par(mfrow = mfrow) pacfPlot(x) } else { par(mfrow = c(1, 1)) pacfPlot(x[, selectedAsset]) } } # Volatility ACF Plot: if (type == 3) { if (selectedAsset == 0) { par(mfrow = mfrow) acfPlot(abs(x)) } else { par(mfrow = c(1, 1)) acfPlot(abs(x[, selectedAsset])) } } # Taylor Effect Plot: if (type == 4) { if (selectedAsset == 0) { par(mfrow = mfrow) teffectPlot(x) } else { par(mfrow = c(1, 1)) teffectPlot(x[, selectedAsset]) } } # Long Memory ACF: if (type == 5) { if (selectedAsset == 0) { par(mfrow = mfrow) .lmacfPlot(abs(x)) } else { par(mfrow = c(1, 1)) .lmacfPlot(abs(x[, selectedAsset])) } } # Lagged Autocorrelation Plot: if (type == 6) { if (selectedAsset == 0) { par(mfrow = mfrow) lacfPlot(x) } else { par(mfrow = c(1, 1)) lacfPlot(x[, selectedAsset]) } } # PDF plot on lin-log Scale: if (type == 7) { if (selectedAsset == 0) { par(mfrow = mfrow) .logpdfPlot(x) } else { par(mfrow = c(1, 1)) .logpdfPlot(x[, selectedAsset]) } } # PDF plot on log-log Scale: if (type == 8) { if (selectedAsset == 0) { par(mfrow = mfrow) .logpdfPlot(x, type = "log-log") } else { par(mfrow = c(1, 1)) .logpdfPlot(x[, selectedAsset], type = "log-log") } } # Simple Normal Quantile-Quantile Plot if (type == 9) { if (selectedAsset == 0) { par(mfrow = mfrow) .qqgaussPlot(x, pch = 19) } else { par(mfrow = c(1, 1)) .qqgaussPlot(x[, selectedAsset], pch = 19) } } # Scaling Law Plot: if (type == 10) { if (selectedAsset == 0) { par(mfrow = mfrow) scalinglawPlot(x, pch = 19) } else { par(mfrow = c(1, 1)) scalinglawPlot(x[, selectedAsset], pch = 19) } } } nAssets = dim(x)[2] .tdSliderMenu( stylizedFactsRefreshCode, names = c("Selected Asset"), minima = c( 0), maxima = c( nAssets), resolutions = c( 1), starts = c( 0), but.functions = list( function(...){ .tdSliderMenu(obj.name = "stylizedFactsType", obj.value = "1") stylizedFactsRefreshCode()}, function(...){ .tdSliderMenu(obj.name = "stylizedFactsType", obj.value = "2") stylizedFactsRefreshCode()}, function(...){ .tdSliderMenu(obj.name = "stylizedFactsType", obj.value = "3") stylizedFactsRefreshCode()}, function(...){ .tdSliderMenu(obj.name = "stylizedFactsType", obj.value = "4") stylizedFactsRefreshCode()}, function(...){ .tdSliderMenu(obj.name = "stylizedFactsType", obj.value = "5") stylizedFactsRefreshCode()}, function(...){ .tdSliderMenu(obj.name = "stylizedFactsType", obj.value = "6") stylizedFactsRefreshCode()}, function(...){ .tdSliderMenu(obj.name = "stylizedFactsType", obj.value = "7") stylizedFactsRefreshCode()}, function(...){ .tdSliderMenu(obj.name = "stylizedFactsType", obj.value = "8") stylizedFactsRefreshCode()}, function(...){ .tdSliderMenu(obj.name = "stylizedFactsType", obj.value = "9") stylizedFactsRefreshCode()}, function(...){ .tdSliderMenu(obj.name = "stylizedFactsType", obj.value = "10") stylizedFactsRefreshCode()} ), but.names = c( "1 ACF Function of Returns", "2 Partial ACF of Returns", "3 ACF of absolute Returns", "4 Taylor Effect", "5 Long Memory ACF of abs Returns", "6 Lagged Autocorrelations", "7 Lin-Log Tail Density Plot", "8 Log-Log Lower Tail Density", "9 Simple Normal QQ Plot", "10 Scaling Law Plot"), title = "Stylized Facts GUI" ) .tdSliderMenu(obj.name = "type", obj.value = "1", no = 1) # return Value: invisible() } ################################################################################ fBasics/R/gui-stableSlider.R0000644000176000001440000000742411731575056015420 0ustar ripleyusers # This R package is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This R package 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: STABLE SLIDERS: # stableSlider Displays stable distribution function ################################################################################ stableSlider <- function(col= "steelblue", col.med = "gray30") { # A function implemented by Diethelm Wuertz # Description: # Displays the stable distribution # FUNCTION: # Internal Function: refresh.code <- function(...) { # Sliders: N = .sliderMenu(no = 1) alpha = .sliderMenu(no = 2) beta = .sliderMenu(no = 3) gamma = .sliderMenu(no = 4) delta = .sliderMenu(no = 5) pm = .sliderMenu(no = 6) # Compute Data: x.rng <- round(qstable(c(1,99)/100, alpha, beta, gamma, delta, pm), digits = 2) xmin <- x.rng[1]; xmax <- x.rng[2] s = seq(xmin, xmax, length = N) f = dstable(s, alpha, beta, gamma, delta, pm) F = pstable(s, alpha, beta, gamma, delta, pm) med <- qstable(0.5, alpha, beta, gamma, delta, pm) main1 = paste("Stable Density\n", "alpha = ", as.character(alpha), " | ", "beta = ", as.character(beta), " | ", "gamma = ", as.character(gamma), " | ", "delta = ", as.character(delta)) main2 = paste("Stable Probability\n", "xmin [ 1%] = ", as.character(xmin), " | ", "xmax [99%] = ", as.character(xmax), " | ", "pm = ", as.character(pm)) # Frame: op <- par(mfrow = c(2, 1), cex = 0.7) ; on.exit(par(op)) # Density: plot(s, f, type = "l", main = main1, xlim = x.rng, col = col) abline (h = 0, lty = 3) # Probability: plot(s, F, type = "l", xlim = x.rng, ylim = c(0, 1), col = col, main = main2) abline(h = 0:1, lty = 3) lines(c(par("usr")[1],med,med), c(0.5 ,0.5, 0), lty = 2, col=col.med) text(med, 0.1, "median", adj=0, col=col.med) axis(1, labels=expression(delta), at = delta, col = "red", col.axis="red", lwd=1.5, line = .5, tck = 1/8, hadj = -1, padj = -4) } # Open Slider Menu: .sliderMenu(refresh.code, names = c("no. points", "alpha","beta","gamma","delta", "pm"), minima = c( 50, 0. , -1. , 0. , -5. , 0), maxima = c( 1000, 2. , +1. , 5. , +5. , 2), resolutions = c( 50, 0.1, 0.1, 0.5, 0.5, 1), starts = c( 50, 1.8, 0. , 1. , 0. , 0)) } ################################################################################ fBasics/R/gui-sliderMenu.R0000644000176000001440000002074611731574762015117 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: SLIDER MENU: # .sliderMenu Opens a teching demo slider menu # .tdSliderMenu Opens a teching demo slider and button menu ################################################################################ .slider.env = new.env() # ------------------------------------------------------------------------------ .sliderMenu <- function(refresh.code, names, minima, maxima, resolutions, starts, title = "Slider", no = 0, set.no.value = 0) { # A function implemented by Diethelm Wuertz # Description: # Starts a slider menu # Source: # Built on code written by Peter Wolf # FUNCTION: # Requirement: if (!require(tcltk, quietly = TRUE)) stop("\n -- Package tcltk not available -- \n\n") # Environment: if (!exists(".slider.env")) { .slider.env <<- new.env() } if (no != 0) { options(show.error.messages = FALSE) ans <- as.numeric(tclvalue(get(paste("slider", no, sep = ""), envir = .slider.env))) options(show.error.messages = TRUE) return(ans) } if (set.no.value[1] != 0) { try(eval(parse(text = paste("tclvalue(slider", set.no.value[1], ")<-", set.no.value[2], sep = "")), envir = .slider.env), silent = TRUE) return(set.no.value[2]) } # Toplevel: nt = tktoplevel() tkwm.title(nt, title) # Slider: for (i in seq(names)) { eval(parse(text = paste("assign(\"slider", i, "\", tclVar(starts[i]), envir = .slider.env)", sep = ""))) tkpack(fr<-tkframe(nt), anchor = "sw") lab = tklabel(fr, text = names[i], anchor = "sw") sc = tkscale(fr, command = refresh.code, from = minima[i], to = maxima[i], showvalue = TRUE, resolution = resolutions[i], orient = "horiz") assign("sc", sc, envir = .slider.env) tkgrid(sc, lab) eval(parse(text = paste("tkconfigure(sc, variable = slider", i, ")", sep = "")), envir = .slider.env) } tkpack(fr<-tkframe(nt), anchor = "sw") # Quit: quitButton = tkbutton(fr, text = " Quit ", command = function() { tkdestroy(nt) } ) # Reset: resetButton = tkbutton(fr, text = " Start | Reset ", command = function() { for (i in seq(starts)) eval(parse(text = paste("tclvalue(slider", i, ")<-", starts[i], sep="")), envir = .slider.env) refresh.code() } ) # Compose: tkgrid(resetButton, quitButton, sticky = "sew") } # ------------------------------------------------------------------------------ .tdSliderMenu <- function(sl.functions, names, minima, maxima, resolutions, starts, but.functions, but.names, no, set.no.value, obj.name, obj.value, reset.function, title) { # A function implemented by Diethelm Wuertz # Description # Opens a teching demo slider menu # Notes: # Build on ideas and code from: # R Package: TeachingDemos # Title: Demonstrations for teaching and learning # Version: 1.5 # Author: Greg Snow # Description: This package is a set of demonstration functions # that can be used in a classroom to demonstrate statistical # concepts, or on your own to better understand the concepts # or the programming. # Maintainer: Greg Snow # License: Artistic # FUNCTION: # Requirement: if (!require(tcltk, quietly = TRUE)) stop("\n -- Package tcltk not available -- \n\n") # Setup: if(!missing(no)) { return(as.numeric(tclvalue(get(paste(".tdSlider", no, sep=""), envir = .slider.env)))) } if(!missing(set.no.value)){ try(eval(parse(text=paste("tclvalue(.tdSlider", set.no.value[1],")<-", set.no.value[2], sep = "")), envir = .slider.env)) return(set.no.value[2]) } if(!exists(".slider.env")) { .slider.env <<- new.env() } if(!missing(obj.name)){ if(!missing(obj.value)) { assign(obj.name, obj.value, envir = .slider.env) } else { obj.value <- get(obj.name, envir = .slider.env) } return(obj.value) } if(missing(title)) { title = "Control Widget" } # GUI Settings: nt <- tktoplevel() tkwm.title(nt, title) tkwm.geometry(nt, "+0+0") # Buttons: tkpack( f.but <- tkframe(nt), fill = "x") # Quit Button: quitCMD = function() { tkdestroy(nt) } tkpack( tkbutton(f.but, text = "Quit", command = quitCMD, anchor = "sw"), side = "right", fill = "y") # Reset Button: if(missing(reset.function)) { reset.function <- function(...) print("relax") } if(!is.function(reset.function)) { reset.function<-eval(parse(text = paste("function(...){",reset.function,"}"))) } resetCMD = function() { for(i in seq(names)) eval(parse(text = paste("tclvalue(.tdSlider",i,")<-", starts[i], sep = "")), envir = .slider.env) reset.function() } tkpack( tkbutton(f.but, text = "Reset", command = resetCMD, anchor = "sw"), side = "right", fill = "y") if (missing(but.names)) { but.names <- NULL } for (i in seq(but.names)) { but.fun <- if (length(but.functions) > 1) but.functions[[i]] else but.functions if (!is.function(but.fun)) { but.fun <- eval(parse(text = paste("function(...){", but.fun, "}"))) } tkpack( tkbutton(f.but, text = but.names[i], command = but.fun, anchor = "nw"), # side = "right", fill = "x" ) } # Sliders: if(missing(names)) { names <- NULL } if(missing(sl.functions)) { sl.functions <- function(...){} } for(i in seq(names)){ eval(parse(text = paste("assign('.tdSlider",i,"', tclVar(starts[i]), env = .slider.env)", sep = ""))) tkpack(fr <- tkframe(nt)) lab <- tklabel(fr, text = names[i], anchor = "sw", width = "35") sc <- tkscale(fr, from = minima[i], to = maxima[i], showvalue = TRUE, resolution = resolutions[i], orient = "horiz") tkpack(lab, sc, anchor = "sw", side = "right"); assign("sc", sc, envir = .slider.env) eval(parse(text=paste("tkconfigure(sc,variable=.tdSlider",i,")", sep="")), envir = .slider.env) sl.fun <- if(length(sl.functions)>1) sl.functions[[i]] else sl.functions if(!is.function(sl.fun)) sl.fun<-eval(parse(text=paste("function(...){", sl.fun,"}"))) tkconfigure(sc, command = sl.fun) } assign("slider.values.old", starts, envir = .slider.env) # Return Value: invisible(nt) } ################################################################################ fBasics/R/gui-returnSeries.R0000644000176000001440000001166711415310241015457 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: GRAPHICAL USER INTERFACE: # returnSeriesGUI pens a GUI for return series plots ################################################################################ returnSeriesGUI <- function(x) { # A function implemented by Diethelm Wuertz # Descriptions: # Opens a GUI for return series plots # Arguments: # x - an uni- or multivariate timeSeries object # FUNCTION: # Check: stopifnot(class(x) == "timeSeries") # Settings: N = ceiling(sqrt(ncol(x))) mfrow = c(N, N) returnSeriesRefreshCode <- function(...) { # Settings: selectedAsset = .tdSliderMenu(no = 1) type = as.integer(.tdSliderMenu(obj.name = "returnSeriesType")) Unit = colnames(x) # Print Basic Return Statistics: if (type == 1) { if (selectedAsset == 0) { print(basicStats(x)) } else { print(basicStats(x[, selectedAsset])) } } # Return Series Plot: if (type == 2) { if (selectedAsset == 0) { par(mfrow = mfrow) seriesPlot(x) } else { par(mfrow = c(1, 1)) seriesPlot(x[, selectedAsset]) } } # Cumulate Return Series Plot if (type == 3) { if (selectedAsset == 0) { par(mfrow = mfrow) seriesPlot(100*exp(colCumsums(x))) } else { par(mfrow = c(1, 1)) seriesPlot(100*exp(colCumsums(x[, selectedAsset]))) abline(h = 100, col = "grey") } } # Histogram Plot: if (type == 4) { if (selectedAsset == 0) { par(mfrow = mfrow) histPlot(x, skipZeros = TRUE) } else { par(mfrow = c(1, 1)) histPlot(x[, selectedAsset], skipZeros = TRUE) } } # Density Plot: if (type == 5) { if (selectedAsset == 0) { par(mfrow = mfrow) densityPlot(x) } else { par(mfrow = c(1, 1)) densityPlot(x[, selectedAsset]) } } # Normal QQ Plot: if (type == 6) { if (selectedAsset == 0) { par(mfrow = mfrow) qqnormPlot(x) } else { par(mfrow = c(1, 1)) qqnormPlot(x[, selectedAsset]) } } } nAssets = dim(x)[2] .tdSliderMenu( returnSeriesRefreshCode, names = c("Selected Asset"), minima = c( 0), maxima = c( nAssets), resolutions = c( 1), starts = c( 0), but.functions = list( function(...){ .tdSliderMenu(obj.name = "returnSeriesType", obj.value = "1") returnSeriesRefreshCode()}, function(...){ .tdSliderMenu(obj.name = "returnSeriesType", obj.value = "2") returnSeriesRefreshCode()}, function(...){ .tdSliderMenu(obj.name = "returnSeriesType", obj.value = "3") returnSeriesRefreshCode()}, function(...){ .tdSliderMenu(obj.name = "returnSeriesType", obj.value = "4") returnSeriesRefreshCode()}, function(...){ .tdSliderMenu(obj.name = "returnSeriesType", obj.value = "5") returnSeriesRefreshCode()}, function(...){ .tdSliderMenu(obj.name = "returnSeriesType", obj.value = "6") returnSeriesRefreshCode()} ), but.names = c( "1 Basic Return Statistics Table", "2 Return Series Plot", "3 Cumulated Return Series Plot", "4 Histogram Plot", "5 Density Plot", "6 Normal Quantile-Quantile Plot"), title = "Return Series GUI" ) .tdSliderMenu(obj.name = "type", obj.value = "1", no = 1) # Return Value() invisible() } ################################################################################ fBasics/R/gui-nigSlider.R0000644000176000001440000000612611415310241014677 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # nigSlider Displays normal inverse Gausssian distribution function ################################################################################ nigSlider <- function() { # A function implemented by Diethelm Wuertz # Normal Inverse Gaussian Distribution: # dnig(x, alpha = 1, beta = 0, delta = 1, mu = 0) # FUNCTION: # Internal Function: refresh.code = function(...) { # Sliders: N = .sliderMenu(no = 1) alpha = .sliderMenu(no = 2) beta = .sliderMenu(no = 3) delta = .sliderMenu(no = 4) mu = .sliderMenu(no = 5) # Plot Data: xmin = round(qnig(0.01, alpha, beta, delta, mu), digits = 2) xmax = round(qnig(0.99, alpha, beta, delta, mu), digits = 2) s = seq(xmin, xmax, length = N) y1 = dnig(s, alpha, beta, delta, mu) y2 = pnig(s, alpha, beta, delta, mu) main1 = paste("NIG Density\n", "alpha = ", as.character(alpha), " | ", "beta = ", as.character(beta), " | ", "delta = ", as.character(delta), " | ", "mu = ", as.character(mu)) main2 = paste("NIG Probability\n", "xmin 0.01% = ", as.character(xmin), " | ", "xmax 0.99% = ", as.character(xmax), " | ") # Frame: par(mfrow = c(2, 1), cex = 0.7) # Density: plot(s, y1, type = "l", xlim = c(xmin, xmax), col = "steelblue") abline (h = 0, lty = 3) title(main = main1) # Probability: plot(s, y2, type = "l", xlim = c(xmin, xmax), ylim = c(0, 1), col = "steelblue" ) abline(h = 0.0, lty = 3) abline(h = 1.0, lty = 3) abline(h = 0.5, lty = 3) abline(v = mu, lty = 3, col = "red") title(main = main2) # Frame: par(mfrow = c(1, 1), cex = 0.7) } # Open Slider Menu: .sliderMenu(refresh.code, names = c( "N", "alpha", "beta", "delta", "mu"), minima = c( 50, 0.00, -2.00, 0.00, -5.0), maxima = c(1000, 2.00, +2.00, 10.00, +5.0), resolutions = c( 50, 0.20, 0.20, 1.00, 1.0), starts = c( 50, 1.00, 0.00, 1.00, 0.0)) } ################################################################################ fBasics/R/gui-hypSlider.R0000644000176000001440000000722711370220773014737 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DESCRIPTION: # hypSlider Displays hyperbolic distribution function ################################################################################ hypSlider <- function() { # A function implemented by Diethelm Wuertz # Description: # Displays interactively Hyperbolic Function # Details: # Hyperbolic Distribution: # dhyp(x, alpha = 1, beta = 0, delta = 1, mu = 0, pm = c(1, 2, 3, 4)) # FUNCTION: # Internal Function: refresh.code = function(...) { # Sliders: N = .sliderMenu(no = 1) alpha = .sliderMenu(no = 2) beta = .sliderMenu(no = 3) delta = .sliderMenu(no = 4) mu = .sliderMenu(no = 5) pm = .sliderMenu(no = 6) # Plot Data: xmin = round(qhyp(0.01, alpha, beta, delta, mu, pm), digits = 2) xmax = round(qhyp(0.99, alpha, beta, delta, mu, pm), digits = 2) s = seq(xmin, xmax, length = N) y1 = dhyp(s, alpha, beta, delta, mu, pm) y2 = phyp(s, alpha, beta, delta, mu, pm) main1 = paste("HYP Density\n", "alpha = ", as.character(alpha), " | ", "beta = ", as.character(beta), " | ", "delta = ", as.character(delta), " | ", "mu = ", as.character(mu) ) main2 = paste("HYP Probability\n", "xmin 0.01% = ", as.character(xmin), " | ", "xmax 0.99% = ", as.character(xmax), " | ", "pm = ", as.character(pm) ) # Frame par(mfrow = c(2, 1), cex = 0.7) # Density: plot(s, y1, type = "l", xlim = c(xmin, xmax), col = "steelblue") abline (h = 0, lty = 3) title(main = main1) # Probability: plot(s, y2, type = "l", xlim = c(xmin, xmax), ylim = c(0, 1), col = "steelblue" ) abline(h = 0.0, lty = 3) abline(h = 1.0, lty = 3) abline(h = 0.5, lty = 3) abline(v = mu, lty = 3, col = "red") title(main = main2) # Reset Frame: par(mfrow = c(1, 1), cex = 0.7) } # Open Slider Menu: .sliderMenu(refresh.code, names = c( "N","alpha","beta","delta", "mu","pm"), minima = c( 50, 0.00, -2.00, 0.00, -5.0, 1), maxima = c(1000, 2.00, +2.00, 5.00, +5.0, 4), resolutions = c( 50, 0.20, 0.20, 1.00, 1.0, 1), starts = c( 50, 1.00, 0.00, 1.00, 0.0, 1)) } ################################################################################ fBasics/R/gui-ghSlider.R0000644000176000001440000000632711415310241014523 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # ghSlider Displays generalized hyperbolic distribution function ################################################################################ ghSlider <- function() { # A function implemented by Diethelm Wuertz # Generalized Hyperbolic Distribution: # dhyp(x, alpha = 1, beta = 0, delta = 1, lamba = 1) # FUNCTION: # Internal Function: refresh.code = function(...) { # Sliders: N = .sliderMenu(no = 1) alpha = .sliderMenu(no = 2) beta = .sliderMenu(no = 3) delta = .sliderMenu(no = 4) mu = .sliderMenu(no = 5) lambda = .sliderMenu(no = 6) # Plot Data: xmin = round(qgh(0.01, alpha, beta, delta, mu, lambda), digits = 2) xmax = round(qgh(0.99, alpha, beta, delta, mu, lambda), digits = 2) s = seq(xmin, xmax, length = N) y1 = dgh(s, alpha, beta, delta, mu, lambda) y2 = pgh(s, alpha, beta, delta, mu, lambda) main1 = paste("GH Density\n", "alpha = ", as.character(alpha), " | ", "beta = ", as.character(beta), " | ", "delta = ", as.character(delta), " | ", "mu = ", as.character(mu), "|", "lambda = ", as.character(lambda) ) main2 = paste("GH Probability\n", "xmin 0.01% = ", as.character(xmin), " | ", "xmax 0.99% = ", as.character(xmax)) # Frame par(mfrow = c(2, 1), cex = 0.7) # Density: plot(s, y1, type = "l", xlim = c(xmin, xmax), col = "steelblue") abline (h = 0, lty = 3) title(main = main1) # Probability: plot(s, y2, type = "l", xlim = c(xmin, xmax), ylim = c(0, 1), col = "steelblue" ) abline(h = 0.0, lty = 3) abline(h = 1.0, lty = 3) abline(h = 0.5, lty = 3) abline(v = mu, lty = 3, col = "red") title(main = main2) # Reset Frame: par(mfrow = c(1, 1), cex = 0.7) } # Open Slider Menu: .sliderMenu(refresh.code, names = c( "N","alpha","beta","delta", "mu","lambda"), minima = c( 50, 0.00, -2.00, 0.00, -5.0, -4), maxima = c(1000, 2.00, +2.00, 5.00, +5.0, 4), resolutions = c( 50, 0.20, 0.20, 1.00, 1.0, 0.5), starts = c( 50, 1.00, 0.00, 1.00, 0.0, 1)) } ################################################################################ fBasics/R/gui-HeavisideSlider.R0000644000176000001440000000506311415310241016022 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: HEAVISIDE SLIDER: # .heavisideSlider Displays Heaviside and related functions ################################################################################ .heavisideSlider <- function() { # A function implemented by Diethelm Wuertz # Description # Displays Heaviside and related functions # FUNCTION: # Internal Function: refresh.code = function(...) { # Sliders: a = .sliderMenu(no = 1) by = .sliderMenu(no = 2) # Frame: par(mfrow = c(2, 2), cex = 0.7) # FGN TimeSeries: x = seq(-10, 10, by = by) H = Heaviside(x, a) Sign = Sign(x, a) Delta = Delta(x, a) Boxcar = Boxcar(x, a) Ramp = Ramp(x, a) plot(x, H, type = "b", ylim = c(-1, 1), col = "steelblue", pch = 19) title(main = paste("Heaviside | a =", a)) grid() plot(x, Sign, type = "b", ylim = c(-1, 1), col = "steelblue", pch = 19) title(main = paste("Sign | a =", a)) grid() plot(x, Boxcar, type = "b", ylim = c(-1, 1), col = "steelblue", pch = 19) title(main = paste("Boxcar | a =", a)) grid() plot(x, Ramp, type = "b", col = "steelblue", pch = 19) title(main = paste("Ramp | a =", a)) grid() # Reset Frame: par(mfrow = c(1, 1), cex = 0.7) } # Open Slider Menu: .sliderMenu(refresh.code, names = c( "a", "by"), minima = c( -10, 0.1), maxima = c( 10, 1.0), resolutions = c( 0.1, 0.1), starts = c( 0, 0.2)) } ################################################################################ fBasics/R/dist-tFit.R0000644000176000001440000000634211370220773014056 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # tFit Fits parameters of a Student-t density ################################################################################ tFit <- function(x, df = 4, doplot = TRUE, span = "auto", trace = FALSE, title = NULL, description = NULL, ...) { # A function implemented by Diethelm Wuertz # Description: # Return Maximum log-likelihood estimated # Note: # Function Calls: nlminb(), density() # Example: # tFit(rt(1000, df=4)) # FUNCTION: # Transform: x.orig = x x = as.vector(x) # Settings: CALL = match.call() # Log-likelihood Function: obj = function(x, y = x, trace) { # Prevent from negative df's if (x[1] <= 0) x[1] = getRmetricsOptions(".x.save") f = -sum(log(dt(y, x[1]))) # Print Iteration Path: if (trace) { cat("\n Objective Function Value: ", -f) cat("\n Students df Estimate: ", x[1], "\n") } setRmetricsOptions(.x.save = x[1]) f } # Minimization: r = nlm(f = obj, p = c(df), y = x, trace = trace) # Optional Plot: if (doplot) { if (span == "auto") { df = r$estimate[1] span.min = qt(0.001, df) span.max = qt(0.999, df) span = seq(span.min, span.max, length = 100) } par(err = -1) z = density(x, n = 100, ...) x = z$x[z$y > 0] y = z$y[z$y > 0] y.points = dt(span, df = r$estimate[1]) ylim = log(c(min(y.points), max(y.points))) plot(x, log(y), xlim = c(span[1], span[length(span)]), ylim = ylim, type = "p", xlab = "x", ylab = "log f(x)", ...) title("STUDENT-T: Parameter Estimation") lines(x = span, y = log(y.points), col = "steelblue") if (exists("grid")) grid() } # Add Title and Description: if (is.null(title)) title = "Student-t Parameter Estimation" if (is.null(description)) description = description() # Fit: fit = list(estimate = c(df = r$estimate), minimum = -r$minimum, code = r$code, gradient = r$gradient) # Return Value: new("fDISTFIT", call = as.call(CALL), model = "Student-t Distribution", data = as.data.frame(x.orig), fit = fit, title = as.character(title), description = description() ) } ################################################################################ fBasics/R/dist-stableFit.R0000644000176000001440000006032311370220773015064 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: STABLE DISTRIBUTION: # stableFit Fits parameters of a stable density # .phiStable Creates contour table for McCulloch estimators # .PhiStable Contour table created by function .phiStable() # .qStableFit Estimates parameters by McCulloch's approach # .mleStableFit Estimates stable parameters by MLE approach # .stablePlot Plots results of stable parameter estimates ################################################################################ stableFit <- function(x, alpha = 1.75, beta = 0, gamma = 1, delta = 0, type = c("q", "mle"), doplot = TRUE, control = list(), trace = FALSE, title = NULL, description = NULL) { # A function implemented by Diethelm Wuertz # Description # Stable Parameter Estimation # FUNCTION: # Start Values: Use Quantile Method: ans = .qStableFit(x, doplot, title, description) # Continue with MLE Approach: if (type[1] == "mle") { Alpha = ans@fit$estimate[1] Beta = ans@fit$estimate[2] Gamma = ans@fit$estimate[3] Delta = ans@fit$estimate[4] if (is.na(Alpha)) Alpha = alpha if (is.na(Beta)) Beta = beta if (is.na(Gamma)) Gamma = gamma if (is.na(Delta)) Delta = delta ans = .mleStableFit(x, Alpha, Beta, Gamma, Delta, doplot, control, trace, title, description) } # Return Value: ans } # ------------------------------------------------------------------------------ .phiStable = function() { # A function implemented by Diethelm Wuertz # Description: # Creates contour table for McCulloch estimators # Note: # Stable Distribution - delta=1 and gamma=0 fixed! # FUNCTION: # Settings: alpha = c(seq(0.50, 1.95, by = 0.1), 1.95, 1.99) beta = c(-0.95, seq(-0.90, 0.90, by = 0.10), 0.95) m = length(alpha) n = length(beta) # phi1: phi1 = function(alpha, beta) { ( qstable(0.95, alpha, beta) - qstable(0.05, alpha, beta) ) / ( qstable(0.75, alpha, beta) - qstable(0.25, alpha, beta) ) } # phi2: phi2 = function(alpha, beta) { ( ( qstable(0.95, alpha, beta) - qstable(0.50, alpha, beta) ) - ( qstable(0.50, alpha, beta) - qstable(0.05, alpha, beta) ) ) / ( qstable(0.95, alpha, beta) - qstable(0.05, alpha, beta) ) } # Phi: Phi1 = Phi2 = matrix(rep(0, n*m), ncol = n) for ( i in 1:m ) { for ( j in 1:n ) { Phi1[i,j] = phi1(alpha[i], beta[j]) Phi2[i,j] = phi2(alpha[i], beta[j]) print( c(alpha[i], beta[j], Phi1[i,j], Phi2[i,j]) ) } } #Plot: contour(alpha, beta, Phi1, levels = c(2.5, 3, 5, 10, 20, 40), xlab = "alpha", ylab = "beta", labcex = 1.5, xlim = c(0.5, 2.0)) contour(alpha, beta, Phi2, levels = c(-0.8, -0.6, -0.4, -0.2, 0, 0.2, 0.4, 0.6, 0.8), col = "red", labcex = 1.5, add = TRUE) # Result: .PhiStable = list(Phi1 = Phi1, Phi2 = Phi2, alpha = alpha, beta = beta) # Dump: if (FALSE) dump(".PhiStable", "PhiStable.R") # Return Value: .PhiStable } # ------------------------------------------------------------------------------ .PhiStable <- structure( # Contour table created by .phiStable() list( Phi1 = structure(c(28.1355600962322, 15.7196640771722, 10.4340722145276, 7.72099712337154, 6.14340919629241, 5.14081963876442, 4.45927409495436, 3.97057677836415, 3.60450193720703, 3.31957820409758, 3.09091185492284, 2.9029090263133, 2.74659551412658, 2.61782756430233, 2.51560631141019, 2.47408801877228, 2.44525363752631, 28.3483300097440, 15.8035063527296, 10.4727953966709, 7.74141823634036, 6.15576314177463, 5.14915506532249, 4.46531988542058, 3.97505189771183, 3.60736868847651, 3.32104702700423, 3.09111342044363, 2.90219825204304, 2.74552084086904, 2.61707392900167, 2.51531961206501, 2.47401179006119, 2.44525268403340, 28.9048147202382, 16.0601313263480, 10.6117049719320, 7.8237753182648, 6.20955682299195, 5.18679961805621, 4.49216000186678, 3.99373973242068, 3.61933010719953, 3.3275594162867, 3.09331226057454, 2.90155994459038, 2.74368539137288, 2.61555847928520, 2.51479444686423, 2.47387109904253, 2.44525093764076, 29.8026200973137, 16.5596243974224, 10.9360864131028, 8.04563520952583, 6.35889612112486, 5.28397230078563, 4.55431822343012, 4.03266384946799, 3.64246979110989, 3.33973615437707, 3.09810405845187, 2.90191073816964, 2.7422687239301, 2.61421528380001, 2.51433181630644, 2.47374639426837, 2.44524854839106, 31.1711919144382, 17.3256645589998, 11.4257682914949, 8.37916374365985, 6.59111369941092, 5.4435297531592, 4.65827423282770, 4.09593909348039, 3.67827370972420, 3.35788028768518, 3.10538249512478, 2.90322430476313, 2.7411383952518, 2.61307358265729, 2.51393064512464, 2.47363793686619, 2.44524473760244, 33.1948934984822, 18.4071500759968, 12.0794579487667, 8.80181848446003, 6.8744516193679, 5.6351929385469, 4.78568154439060, 4.17585254205654, 3.72404890551037, 3.38077811930023, 3.11457663716798, 2.90522469731234, 2.74043557937936, 2.61209710262770, 2.51359601849030, 2.47354590407541, 2.44523688699559, 35.9578028398622, 19.7936882741385, 12.8664734285429, 9.28349135864713, 7.18278427917665, 5.83729171895546, 4.91779143967590, 4.25960094973301, 3.77307337255403, 3.4057709031586, 3.12469898537975, 2.90767817230573, 2.7399388028832, 2.61130578115398, 2.51330169831474, 2.47347041290067, 2.4452243360266, 38.9647018181828, 21.2269841758554, 13.6587928142908, 9.75737930074977, 7.47910500668215, 6.02679068365276, 5.03975173429821, 4.33641171958119, 3.81844774991854, 3.42948489228359, 3.13453596844362, 2.91006724722144, 2.73965159366498, 2.61068126597691, 2.51309087992836, 2.47342375837911, 2.44524639012562, 41.7818369588849, 22.4602363370435, 14.3128567516975, 10.1435099877839, 7.72005687380391, 6.18048140792379, 5.13779081982581, 4.39769218775219, 3.85488105109873, 3.44880791704519, 3.14262443869137, 2.91215029990006, 2.73947335432536, 2.61026149741526, 2.51293721037603, 2.47333463795113, 2.44523871005695, 43.8597008728084, 23.308181530155, 14.7432127411421, 10.3930748979735, 7.87518470289541, 6.2796283727218, 5.20095216574708, 4.43724315622136, 3.87838777226609, 3.46128790559551, 3.14792806986726, 2.91355836113947, 2.7394048824397, 2.60999397178590, 2.51287974154683, 2.4733261698261, 2.44523291863013, 44.6351177921226, 23.612190997173, 14.8937659530932, 10.4791077126402, 7.9285095195091, 6.31375867971341, 5.2228691158165, 4.45085115209764, 3.88645952709398, 3.46562279514434, 3.14978921717977, 2.91402779433739, 2.73939000980242, 2.60993524495366, 2.51289337121820, 2.47330017392183, 2.44524525291192, 43.8597008728085, 23.3081815301550, 14.7432127411420, 10.3930748979735, 7.87518470289542, 6.2796283727218, 5.20095216574709, 4.43724315622136, 3.87838777226609, 3.46128790559551, 3.14792806986726, 2.91355836113947, 2.7394048824397, 2.60999397178590, 2.51287974154683, 2.4733261698261, 2.44523291863013, 41.7818369588849, 22.4602363370436, 14.3128567516975, 10.1435099877840, 7.72005687380392, 6.18048140792379, 5.13779081982581, 4.39769218775219, 3.85488105109874, 3.44880791704519, 3.14262443869137, 2.91215029990006, 2.73947335432536, 2.61026149741526, 2.51293721037603, 2.47333463795113, 2.44523871005695, 38.9647018181828, 21.2269841758554, 13.6587928142909, 9.75737930074976, 7.47910500668216, 6.02679068365277, 5.0397517342982, 4.33641171958119, 3.81844774991854, 3.42948489228359, 3.13453596844362, 2.91006724722144, 2.73965159366498, 2.61068126597691, 2.51309087992836, 2.47342375837911, 2.44524639012562, 35.9578028398623, 19.7936882741384, 12.8664734285429, 9.28349135864712, 7.18278427917667, 5.83729171895546, 4.91779143967590, 4.25960094973301, 3.77307337255403, 3.4057709031586, 3.12469898537975, 2.90767817230572, 2.7399388028832, 2.61130578115398, 2.51330169831474, 2.47347041290067, 2.4452243360266, 33.1948934984822, 18.4071500759967, 12.0794579487667, 8.80181848446, 6.8744516193679, 5.6351929385469, 4.78568154439059, 4.17585254205653, 3.72404890551037, 3.38077811930023, 3.11457663716797, 2.90522469731234, 2.74043557937936, 2.61209710262770, 2.51359601849030, 2.47354590407541, 2.44523688699559, 31.1711919144381, 17.3256645589999, 11.4257682914948, 8.37916374365983, 6.59111369941092, 5.44352975315921, 4.65827423282769, 4.09593909348039, 3.67827370972420, 3.35788028768518, 3.10538249512478, 2.90322430476312, 2.7411383952518, 2.61307358265729, 2.51393064512464, 2.47363793686619, 2.44524473760245, 29.8026200973136, 16.5596243974222, 10.9360864131028, 8.04563520952581, 6.35889612112487, 5.28397230078562, 4.55431822343012, 4.03266384946799, 3.64246979110989, 3.33973615437707, 3.09810405845187, 2.90191073816964, 2.7422687239301, 2.61421528380001, 2.51433181630644, 2.47374639426837, 2.44524854839106, 28.904814720238, 16.0601313263481, 10.6117049719320, 7.82377531826481, 6.20955682299195, 5.18679961805620, 4.49216000186678, 3.99373973242068, 3.61933010719952, 3.3275594162867, 3.09331226057455, 2.90155994459037, 2.74368539137288, 2.61555847928520, 2.51479444686423, 2.47387109904253, 2.44525093764076, 28.3483300097438, 15.8035063527296, 10.4727953966709, 7.74141823634038, 6.15576314177465, 5.1491550653225, 4.46531988542057, 3.97505189771183, 3.60736868847651, 3.32104702700424, 3.09111342044363, 2.90219825204304, 2.74552084086904, 2.61707392900167, 2.51531961206501, 2.47401179006119, 2.44525268403340, 28.1355600962322, 15.7196640771723, 10.4340722145275, 7.72099712337151, 6.1434091962924, 5.14081963876442, 4.45927409495435, 3.97057677836415, 3.60450193720703, 3.31957820409757, 3.09091185492284, 2.9029090263133, 2.74659551412658, 2.61782756430233, 2.51560631141019, 2.47408801877228, 2.44525363752631), .Dim = as.integer(c(17, 21))), Phi2 = structure(c(-0.984831521754346, -0.96178750287388, -0.925815277018118, -0.877909786944587, -0.820184465666658, -0.755031918545081, -0.684592921700777, -0.610570901457792, -0.534175805219629, -0.456236240234657, -0.377306837254648, -0.297867095171864, -0.218666685919607, -0.141143940005887, -0.0674987199592516, -0.0328701597273596, -0.00642990194045526, -0.984833720280198, -0.96124832221758, -0.92402252050786, -0.87419335313835, -0.81415246030398, -0.74662653505109, -0.674063099101783, -0.598388680797427, -0.520979416823626, -0.442739494396008, -0.364272193075272, -0.286088382456793, -0.208957354222478, -0.1342789733294, -0.0640144100029875, -0.0311490271069921, -0.006091726394748, -0.979153629182453, -0.95106168789293, -0.909673941239617, -0.85602448791855, -0.791828629762056, -0.720369142925194, -0.644748722301942, -0.567214164881932, -0.489279988980024, -0.411880943403832, -0.335605401995838, -0.260987626602553, -0.188786833912990, -0.120269390643364, -0.0570145517746281, -0.0277025095388298, -0.0054153433806392, -0.956099620177616, -0.91510100889139, -0.863494586547947, -0.804340589970157, -0.739811913292119, -0.670992067490536, -0.598508014750715, -0.523938483324115, -0.449119649196787, -0.375320171482799, -0.303334211640077, -0.233819259537015, -0.167632931803862, -0.105970645511563, -0.0499759998221647, -0.0242509183116938, -0.00473892214395457, -0.91091521430624, -0.854269872890404, -0.792380659464175, -0.728324327412516, -0.663641470500818, -0.598766118303959, -0.533390833007158, -0.466958108675312, -0.399718402638334, -0.332810169846175, -0.267433026630897, -0.204649764654678, -0.145582752525226, -0.0913961065988934, -0.042902827489912, -0.0207949382390745, -0.004062467796571, -0.838142578217344, -0.767730556629547, -0.699387193072487, -0.634346095207953, -0.572568000770375, -0.513428073110925, -0.456016815864829, -0.399237447140644, -0.342164056355244, -0.284798223846443, -0.228151639956042, -0.173628819918976, -0.122674652213850, -0.0765638152109311, -0.0358016086121756, -0.0173352283908653, -0.00338423605633909, -0.732960648615699, -0.655211639338905, -0.586717060962992, -0.525770503418058, -0.470570307907465, -0.419573011635011, -0.371434023729476, -0.324886123523844, -0.278750785874156, -0.232309518692667, -0.185933156371725, -0.140978784025337, -0.0990680854456507, -0.0615175707971035, -0.0286670029088599, -0.0138724303903631, -0.00270395845154647, -0.592691443906747, -0.517917221384721, -0.456902886405761, -0.405327421204478, -0.360251537992231, -0.319685291504111, -0.282167838420900, -0.246496132463733, -0.211596523966543, -0.176602222829403, -0.141397972727901, -0.106937786396409, -0.0748572933895385, -0.0462908166605395, -0.0215205174484398, -0.0104071747947459, -0.00203767706388774, -0.418561278354954, -0.359043286074154, -0.313110693536944, -0.275656631235694, -0.243720459128403, -0.215493360906447, -0.18976206222188, -0.165585707487220, -0.142157842427747, -0.118764989711438, -0.0951531051692878, -0.0718822809574559, -0.0501702371114745, -0.0309413187181704, -0.0143569748225324, -0.00693928545290556, -0.00135914851834483, -0.217058958479026, -0.183957984146554, -0.159253973812649, -0.139529858864502, -0.122956050946611, -0.108468261383110, -0.095363933833862, -0.0831489044941912, -0.0713807547092049, -0.0596749091239935, -0.0478406346829651, -0.0361189030171852, -0.0251638752545685, -0.0154951657479659, -0.00718251696063777, -0.00347157285038577, -0.000682830247122471, -1.30195010613456e-15, -7.12101008020212e-16, -9.8472751183435e-16, -1.13492047149954e-15, -1.21096413626332e-15, -1.05505102779748e-15, -1.11782289130157e-15, -8.13224377427617e-16, -5.85149540688739e-16, 4.61239028385592e-16, 1.45510576814449e-16, -4.733934001018e-16, -3.36773719381392e-16, 0, 9.23542032962673e-17, -1.87944839722067e-16, -2.8550539469612e-16, 0.217058958479025, 0.183957984146551, 0.159253973812647, 0.139529858864502, 0.12295605094661, 0.108468261383109, 0.0953639338338615, 0.0831489044941909, 0.0713807547092042, 0.0596749091239931, 0.0478406346829641, 0.036118903017185, 0.0251638752545679, 0.0154951657479665, 0.00718251696063749, 0.00347157285038558, 0.00068283024712209, 0.418561278354956, 0.359043286074156, 0.313110693536942, 0.275656631235694, 0.243720459128404, 0.215493360906444, 0.189762062221879, 0.165585707487219, 0.142157842427746, 0.118764989711438, 0.0951531051692875, 0.0718822809574556, 0.0501702371114741, 0.0309413187181703, 0.0143569748225322, 0.00693928545290556, 0.00135914851834492, 0.59269144390675, 0.517917221384721, 0.456902886405764, 0.405327421204478, 0.360251537992232, 0.319685291504111, 0.2821678384209, 0.246496132463732, 0.211596523966543, 0.176602222829402, 0.141397972727901, 0.106937786396409, 0.074857293389538, 0.0462908166605391, 0.0215205174484394, 0.0104071747947454, 0.00203767706388774, 0.7329606486157, 0.655211639338903, 0.58671706096299, 0.525770503418055, 0.470570307907467, 0.419573011635009, 0.371434023729475, 0.324886123523844, 0.278750785874155, 0.232309518692667, 0.185933156371724, 0.140978784025336, 0.0990680854456504, 0.0615175707971032, 0.0286670029088598, 0.0138724303903627, 0.00270395845154656, 0.838142578217343, 0.767730556629544, 0.699387193072486, 0.634346095207951, 0.572568000770374, 0.513428073110924, 0.456016815864827, 0.399237447140644, 0.342164056355244, 0.284798223846443, 0.228151639956042, 0.173628819918976, 0.122674652213850, 0.0765638152109311, 0.0358016086121755, 0.0173352283908648, 0.00338423605633909, 0.910915214306239, 0.854269872890404, 0.792380659464175, 0.728324327412515, 0.663641470500817, 0.598766118303959, 0.533390833007157, 0.466958108675312, 0.399718402638334, 0.332810169846175, 0.267433026630897, 0.204649764654678, 0.145582752525226, 0.0913961065988931, 0.0429028274899118, 0.020794938239074, 0.00406246779657053, 0.956099620177616, 0.915101008891389, 0.863494586547946, 0.804340589970156, 0.739811913292119, 0.670992067490536, 0.598508014750714, 0.523938483324114, 0.449119649196786, 0.375320171482798, 0.303334211640076, 0.233819259537015, 0.167632931803862, 0.105970645511562, 0.0499759998221642, 0.0242509183116936, 0.00473892214395428, 0.979153629182453, 0.95106168789293, 0.909673941239617, 0.85602448791855, 0.791828629762055, 0.720369142925193, 0.644748722301942, 0.567214164881931, 0.489279988980024, 0.411880943403833, 0.335605401995838, 0.260987626602552, 0.188786833912991, 0.120269390643364, 0.0570145517746277, 0.0277025095388299, 0.00541534338063891, 0.984833720280198, 0.96124832221758, 0.92402252050786, 0.87419335313835, 0.81415246030398, 0.746626535051091, 0.674063099101783, 0.598388680797427, 0.520979416823626, 0.442739494396008, 0.364272193075271, 0.286088382456793, 0.208957354222478, 0.1342789733294, 0.0640144100029874, 0.0311490271069919, 0.00609172639474771, 0.984831521754346, 0.96178750287388, 0.925815277018118, 0.877909786944586, 0.820184465666658, 0.75503191854508, 0.684592921700776, 0.610570901457792, 0.534175805219629, 0.456236240234657, 0.377306837254648, 0.297867095171864, 0.218666685919607, 0.141143940005886, 0.0674987199592513, 0.0328701597273591, 0.00642990194045459), .Dim = as.integer(c(17, 21))), alpha = c(0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 1.95, 1.99), beta = c(-0.95, -0.9, -0.8, -0.7, -0.6, -0.5, -0.4, -0.3, -0.2, -0.1, 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95)), .Names = c("Phi1", "Phi2", "alpha", "beta") ) # ------------------------------------------------------------------------------ .qStableFit <- function(x, doplot = TRUE, title = NULL, description = NULL) { # A function implemented by Diethelm Wuertz # Description: # Estimates stable parameters by McCulloch's approach # Note: # This implementation assumes delta=1 and gamma=0 # FUNCTION: # Settings: CALL = match.call() # Load Contour Table: # data(PhiStable) Phi1 = .PhiStable$Phi1 Phi2 = .PhiStable$Phi2 alpha = .PhiStable$alpha beta = .PhiStable$beta # Estimate phi: r = sort(x) N = length(r) q95 = r[round(0.95*N)] q75 = r[round(0.75*N)] q50 = r[round(0.50*N)] q25 = r[round(0.25*N)] q05 = r[round(0.05*N)] phi1 = max( (q95-q05) / (q75-q25), 2.4389 ) phi2 = ((q95-q50)-(q50-q05)) / (q95-q05) # print(c(min(Phi1), phi1, max(Phi1))) # print(c(min(Phi2), phi2, max(Phi2))) # Plot: if (doplot) { contour(alpha, beta, Phi1, levels = c(2.5, 3, 5, 10, 20, 40), xlab = "alpha", ylab = "beta", xlim = c(0.5, 2.0)) contour(alpha, beta, Phi2, levels = c(-0.8, -0.6, -0.4, -0.2, 0.2, 0.4, 0.6, 0.8), col = "red", add = TRUE) lines(c(0.5, 2), c(0, 0), col = "red") contour(alpha, beta, Phi1, levels = phi1, add = TRUE, lty = 3, col = "blue") contour(alpha, beta, Phi2, levels = phi2, add = TRUE, lty = 3, col = "blue") title(main = "Stable Quantiles") } # Extract Estimate from Contours, if possible: u = contourLines(alpha, beta, Phi1, levels = phi1) Len = length(u) if( Len > 0) { u = u[[1]][-1] v = contourLines(alpha, beta, Phi2, levels = phi2) # print("v") # print(v) v = v[[1]][-1] xout = seq(min(v$y), max(v$y), length = 200) z = approx(v$y, v$x, xout = xout)$y - approx(u$y, u$x, xout = xout)$y index = which.min(abs(z)) V = round(xout[index], 3) U = round(approx(v$y, v$x, xout = xout[index])$y, 3) if (doplot) points(U, V, pch = 19, cex = 1) } else { U = V = NA } # Add Title and Description: if (is.null(title)) title = "Stable Parameter Estimation" if (is.null(description)) description = description() if (is.na(U) | is.na(V)) { GAM = NA } else { phi3 = qstable(0.75, U, V) - qstable(0.25, U, V) GAM = (q75-q25) / phi3 } if (is.na(U) | is.na(V)) { DELTA = NA } else { phi4 = -qstable(0.50, U, V) + V*tan(pi*U/2) DELTA = phi4*GAM - V*GAM*tan(pi*U/2) + q50 } # Fit: fit = list(estimate = c(alpha = U, beta = V, gamma = GAM, delta = DELTA)) # Return Value: new("fDISTFIT", call = as.call(CALL), model = "Stable Distribution", data = as.data.frame(x), fit = fit, title = as.character(title), description = description() ) } # ------------------------------------------------------------------------------ .mleStableFit <- function(x, alpha = 1.75, beta = 0, gamma = 1, delta = 0, doplot = TRUE, control = list(), trace = FALSE, title = NULL, description = NULL) { # A function implemented by Diethelm Wuertz # Description: # Estimates stable parameters by MLE approach # Note: # This implementation assumes delta=1 and gamma=0 # FUNCTION: # Transform: x.orig = x x = as.vector(x) # Settings: CALL = match.call() # Log-likelihood Function: obj = function(x, y = x, trace = FALSE) { f = -mean(log(dstable(y, alpha = x[1], beta = x[2], gamma = x[3], delta = x[4]))) # Print Iteration Path: if (trace) { cat("\n Objective Function Value: ", -f) cat("\n Stable Estimate: ", x) cat("\n") } f } # Minimization: eps = 1e-4 r <- nlminb( objective = obj, start = c(alpha, beta, gamma, delta), lower = c( eps, -1+eps, 0+eps, -Inf), upper = c(2-eps, 1-eps, Inf, Inf), y = x, control = control, trace = trace) alpha = r$par[1] beta = r$par[2] gamma = r$par[3] delta = r$par[4] # Optional Plot: if (doplot) .stablePlot(x, alpha, beta, gamma, delta) # Add Title and Description: if (is.null(title)) title = "Stable Parameter Estimation" if (is.null(description)) description = description() # Fit: fit = list(estimate = c(alpha, beta, gamma, delta), minimum = -r$objective, code = r$convergence, gradient = r$gradient) # Return Value: new("fDISTFIT", call = as.call(CALL), model = "Stable Distribution", data = as.data.frame(x.orig), fit = fit, title = as.character(title), description = description() ) } # ------------------------------------------------------------------------------ .stablePlot <- function(x, alpha, beta, gamma, delta) { # A function implemented by Diethelm Wuertz # Description: # Estimates stable parameters by MLE approach # FUNCTION: # Plot: span.min = qstable(0.01, alpha, beta, gamma, delta) span.max = qstable(0.99, alpha, beta, gamma, delta) span = seq(span.min, span.max, length = 100) par(err = -1) z = density(x, n = 100) x = z$x[z$y > 0] y = z$y[z$y > 0] y.points = dstable(span, alpha, beta, gamma, delta) ylim = log(c(min(y.points), max(y.points))) plot(x, log(y), xlim = c(span[1], span[length(span)]), ylim = ylim, type = "p", xlab = "x", ylab = "log f(x)") title("Stable Distribution: Parameter Estimation") lines(x = span, y = log(y.points), col = "steelblue") grid() # Return Value: invisible() } ################################################################################ fBasics/R/dist-ssdFit.R0000644000176000001440000000430411370220773014400 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTIONS: DESCRIPTION # ssdFit Estimate probability densities using smoothing spline ANOVA # .print.ssd S3 Print Method ################################################################################ ssdFit <- function(x) { # A function implemented by Diethelm Wuertz # Description: # Estimate probability densities using smoothing spline ANOVA # models with cubic spline, linear spline, or thin-plate spline # marginals for numerical variables. # FUNCTION: # Parameter Fit: ans = .ssden(~x) # Return Value: ans } # ------------------------------------------------------------------------------ .print.ssd <- function(x, ...) { # A function implemented by Diethelm Wuertz # Description: # FUNCTION: # call cat("\nCall:\n", deparse(x$call), "\n\n", sep = "") # Terms: cat("Terms:\n") print.default(x$terms$labels) cat("\n") # terms overview cat("Number of unpenalized and penalized terms:\n\n") print.default(x$desc) cat("\n") cat("Smoothing parameters are selected by CV with alpha=", x$alpha, ".", sep = "") cat("\n") # the rest are suppressed invisible() } ################################################################################ fBasics/R/dist-ssd.R0000644000176000001440000000525111370220773013737 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTIONS: DESCRIPTION: # dssd Returns spline smoothed density estimate # pssd Returns spline smoothed probability estimate # qssd Returns spline smoothed quantiles estimate # rssd Returns spline smoothed random variates ################################################################################ dssd <- function(x, param, log = FALSE) { # A function implemented by Diethelm Wuertz # Description: # Evaluate density using smoothing spline ANOVA model # FUNCTION: # Density: ans = .dssden(object = param, x = x) # Log: if(log) ans = log(ans) # Return Value: ans } # ------------------------------------------------------------------------------ pssd <- function(q, param) { # A function implemented by Diethelm Wuertz # Description: # Evaluate probability using smoothing spline ANOVA model # FUNCTION: # Return Value: .pssden(object = param, q = q) } # ------------------------------------------------------------------------------ qssd <- function(p, param) { # A function implemented by Diethelm Wuertz # Description: # Evaluate quantiles using smoothing spline ANOVA model # FUNCTION: # Return Value: .qssden(object = param, p = p) } # ------------------------------------------------------------------------------ rssd <- function(n, param) { # A function implemented by Diethelm Wuertz # Description: # Generate random deviates using smoothing spline ANOVA model # FUNCTION: # Return Value: .qssden(object = param, p = runif(n)) } ################################################################################ fBasics/R/dist-snigFit.R0000644000176000001440000000322211370220773014545 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # snigFit Fits parameters of a standardized NIG density ################################################################################ snigFit = function(x, zeta = 1, rho = 0, scale = TRUE, doplot = TRUE, span = "auto", trace = TRUE, title = NULL, description = NULL, ...) { # Update Slots: if (is.null(title)) title = "SNIG Parameter Estimation" # Quick and dirty ... ans = sghFit(x, zeta = zeta, rho = rho, lambda = -0.5, include.lambda = FALSE, scale = scale, doplot = doplot, span = span, trace = trace, title = title, description = description, ...) # Update Slots: ans@call = match.call() ans@model = "Standarized NIG Distribution" # Return Value: ans } ################################################################################ fBasics/R/dist-snig.R0000644000176000001440000000750311370220773014110 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # dsnig Returns density of the SNIG distribution # psnig Returns probabilities of the SNIG distribution # qsnig Returns quantiles of the SNIG distribution # rsnig Generates SNIG distributed random variates # FUNCTION: DESCRIPTION: # .psnigC Fast psnig from C code # .qsnigC Fast qsnig from C code ################################################################################ dsnig <- function(x, zeta = 1, rho = 0, log = FALSE) { # Description: # Returns density of the snig distribution # FUNCTION: # Parameters: if (length(zeta) == 2) { rho = zeta[2] zeta = zeta[1] } # Compute Density - Quick and Dirty: ans = dsgh(x, zeta, rho, lambda = -0.5, log = log) # Log: if(log) ans = log(ans) # Return Value: ans } # ------------------------------------------------------------------------------ psnig <- function(q, zeta = 1, rho = 0) { # Description: # Returns probabilities of the snig distribution # FUNCTION: # Compute Probabilities - Quick and Dirty: ans = psgh(q, zeta, rho, lambda = -0.5) # Return Value: ans } # ------------------------------------------------------------------------------ qsnig <- function(p, zeta = 1, rho = 0) { # Description: # Returns quantiles of the snig distribution # FUNCTION: # Compute Quantiles: ans = qsgh(p, zeta, rho, lambda = -0.5) # Return Value: ans } # ------------------------------------------------------------------------------ rsnig <- function(n, zeta = 1, rho = 0) { # Description: # Generates snig distributed random variates # FUNCTION: # Generate Random Numbers: ans = rsgh(n, zeta, rho, lambda = -0.5) # Return Value: ans } ################################################################################ .psnigC <- function(q, zeta = 1, rho = 0) { # A function implemented by Diethelm Wuertz # Description: # Returns quantiles of the snig distribution # FUNCTION: # Compute Quantiles: param = .paramGH(zeta, rho, lambda = -0.5) ans = .pnigC(q, param[1], param[2], param[3], param[4]) # Return Value: ans } # ------------------------------------------------------------------------------ .qsnigC <- function(p, zeta = 1, rho = 0) { # A function implemented by Diethelm Wuertz # Description: # Returns quantiles of the snig distribution # FUNCTION: # Compute Quantiles: param = .paramGH(zeta, rho, lambda = -0.5) ans = .qnigC(p, param[1], param[2], param[3], param[4]) # Return Value: ans } ################################################################################ fBasics/R/dist-sght.R0000644000176000001440000001010311370220773014103 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # dsght Standardized generalized hyperbolic Student-t # psght Probability of the Standardized GHT # qsght Quantiles of the Standardized GHT # rsght Random variates of the Standardized GHT ################################################################################ dsght <- function(x, beta = 0.1, delta = 1, mu = 0, nu = 10, log = FALSE) { # A function implemented by Diethelm Wuertz # Description: # Returns density of the generalized hyperbolic Student-t # Arguments: # Example: # x = (-5):5; dght(x) # FUNCTION: # Parameters: if (length(beta) == 4) { nu = beta[4] mu = beta[3] delta = beta[2] beta = beta[1] } # Density: ans = dgh(x, alpha = abs(beta) + 1e-6, beta, delta, mu, lambda = -nu/2, log = log) # Return Value: ans } # ------------------------------------------------------------------------------ psght <- function(q, beta = 0.1, delta = 1, mu = 0, nu = 10) { # A function implemented by Diethelm Wuertz # Description: # Returns probabilities of the generalized hyperbolic Student-t # Arguments: # Example: # q = (-5):5; pght(q) # FUNCTION: # Cumulative Probability: ans = NULL for (Q in q) { Integral = integrate(dght, -Inf, Q, stop.on.error = FALSE, beta = beta, delta = delta, mu = mu, nu = nu) ans = c(ans, as.numeric(unlist(Integral)[1]) ) } # Return Value: ans } # ------------------------------------------------------------------------------ qsght <- function(p, beta = 0.1, delta = 1, mu = 0, nu = 10) { # A function implemented by Diethelm Wuertz # Description: # Returns quantiles of the generalized hyperbolic Student-t # Arguments: # Example: # p = (1:9)/10; qght(p); round(pght(qght(p)), digits = 4) # FUNCTION: # Internal Functions: .froot <- function(x, beta = beta, delta = delta, mu = mu, nu = nu, p) { pght(q = x, beta = beta, delta = delta, mu = mu, nu = nu) - p } # Loop over all p's: result = NULL for (pp in p) { lower = -1 upper = +1 counter = 0 iteration = NA while (is.na(iteration)) { iteration = fBasics:::.unirootNA(f = .froot, interval = c(lower, upper), beta = beta, delta = delta, mu = mu, nu = nu, p = pp) counter = counter + 1 lower = lower - 2^counter upper = upper + 2^counter } result = c(result, iteration) } # Return Value: ans = result + mu ans } # ------------------------------------------------------------------------------ rsght <- function(n, beta = 0.1, delta = 1, mu = 0, nu = 10) { # A function implemented by Diethelm Wuertz # Description: # Returns random Variates of generalized hyperbolic Student-t # Arguments: # Example: # r = rght(10) # FUNCTION: # Random Variates: x = rgh(n, alpha = abs(beta) + 1e-6, beta = beta, delta = delta, mu = mu, lambda = -nu/2) # Return Value: x } ################################################################################ fBasics/R/dist-sghFit.R0000644000176000001440000001031611370220773014370 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: GENERALIZED DISTRIBUTION: # sghFit Fits parameters of a standardized GH density ################################################################################ sghFit <- function(x, zeta = 1, rho = 0, lambda = 1, include.lambda = TRUE, scale = TRUE, doplot = TRUE, span = "auto", trace = TRUE, title = NULL, description = NULL, ...) { x.orig = x x = as.vector(x) if (scale) x = (x-mean(x)) / sd(x) eps = .Machine$double.eps^0.5 BIG = 1000 if (include.lambda) { # LLH Function: obj.include = function(x, y = x, trace) { f = -sum(log(dsgh(y, x[1], x[2], x[3], log = FALSE))) if (trace) { cat("\n Objective Function Value: ", -f) cat("\n Parameter Estimates: ", x[1], x[2], x[3], "\n") } f } # LLH Optimization: r = nlminb( start = c(zeta, rho, lambda), objective = obj.include, lower = c(eps, -0.9999, -2), upper = c(BIG, +0.9999, +5), y = x, trace = trace) names(r$par) <- c("zeta", "rho", "lambda") } else { # LLH Function: obj = function(x, y = x, lambda, trace) { f = -sum(log(dsgh(y, x[1], x[2], lambda, log = FALSE))) if (trace) { cat("\n Objective Function Value: ", -f) cat("\n Parameter Estimates: ", x[1], x[2], "\n") } f } # LLH Optimization: r = nlminb( start = c(zeta, rho), objective = obj, lower = c(eps, -0.9999), upper = c(BIG, +0.9999), y = x, lambda = lambda, trace = trace) r$par = c(r$par, lambda) names(r$par) <- c("zeta", "rho", "fix.lambda") } param = .paramGH(r$par[1], r$par[2], r$par[3]) if (trace) { cat("\n Standardized Parameters:", "\n ") print(r$par) names(param) = c("alpha", "beta", "delta", "mu") cat("\n 1st Parameterization:", "\n ") print(param) } # Default Title and Description: if (is.null(title)) title = "SGH Parameter Estimation" if (is.null(description)) description = description() # Fit: fit = list( estimate = r$par, minimum = -r$objective, code = r$convergence, param = param, mean = mean(x.orig), var = var(x.orig)) # Optional Plot: if (doplot) { if (span == "auto") span = seq(min(x), max(x), length = 101) z = density(x, n = 100, ...) x = z$x[z$y > 0] y = z$y[z$y > 0] y.points = dsgh(span, zeta = r$par[1], rho = r$par[2], lambda) ylim = log(c(min(y.points), max(y.points))) plot(x, log(y), xlim = c(span[1], span[length(span)]), ylim = ylim, type = "p", xlab = "x", ylab = "log f(x)", ...) title(main = title) lines(x = span, y = log(y.points), col = "steelblue") } # Return Value: new("fDISTFIT", call = match.call(), model = "Standarized GH Distribution", data = as.data.frame(x.orig), fit = fit, title = as.character(title), description = description()) } ################################################################################ fBasics/R/dist-sgh.R0000644000176000001440000001270611370220773013732 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # dsgh Returns density of the standardized gh distribution # psgh Returns probabilities of the sgh distribution # qsgh Returns quantiles of the sgh distribution # rsgh Generates sgh distributed random variates # FUNCTION: DESCRIPTION: # .kappaGH Returns modified Bessel function ratio # .deltaKappaGH Returns difference of Bessel function ratios # .paramGH Change parameterization to alpha, beta, delta mu ################################################################################ dsgh <- function(x, zeta = 1, rho = 0, lambda = 1, log = FALSE) { # A function implemented by Diethelm Wuertz # Description: # Returns density of the sgh distribution # FUNCTION: # Parameters: if (length(zeta) == 3) { lambda = zeta[3] rho = zeta[2] zeta = zeta[1] } # Compute Density: param = .paramGH(zeta, rho, lambda) ans = dgh(x, param[1], param[2], param[3], param[4], lambda, log) # Return Value: ans } # ------------------------------------------------------------------------------ psgh <- function(q, zeta = 1, rho = 0, lambda = 1) { # A function implemented by Diethelm Wuertz # Description: # Returns probabilities of the sgh distribution # FUNCTION: # Compute Probabilities: param = .paramGH(zeta, rho, lambda) ans = pgh(q, param[1], param[2], param[3], param[4], lambda) # Return Value: ans } # ------------------------------------------------------------------------------ qsgh <- function(p, zeta = 1, rho = 0, lambda = 1) { # A function implemented by Diethelm Wuertz # Description: # Returns quantiles of the sgh distribution # FUNCTION: # Compute Quantiles: param = .paramGH(zeta, rho, lambda) ans = qgh(p, param[1], param[2], param[3], param[4], lambda) # Return Value: ans } # ------------------------------------------------------------------------------ rsgh <- function(n, zeta = 1, rho = 0, lambda = 1) { # A function implemented by Diethelm Wuertz # Description: # Generates sgh distributed random variates # FUNCTION: # Generate Random Numbers: param = .paramGH(zeta, rho, lambda) ans = rgh(n, param[1], param[2], param[3], param[4], lambda) # Return Value: ans } ################################################################################ .kappaGH <- function(x, lambda = 1) { # A function implemented by Diethelm Wuertz # Description: # Returns modified Bessel function ratio # FUNCTION: # Check: stopifnot(x >= 0) stopifnot(length(lambda) == 1) # Ratio: if (lambda == -0.5) { # NIG: kappa = 1/x } else { # GH: kappa = ( besselK(x, lambda+1, expon.scaled = TRUE) / besselK(x, lambda, expon.scaled = TRUE) ) / x } # Return Value: kappa } # ------------------------------------------------------------------------------ .deltaKappaGH <- function(x, lambda = 1) { # A function implemented by Diethelm Wuertz # Description: # Returns difference of Bessel functions ratios # FUNCTION: # Difference in Ratios: if (lambda == -0.5) { # NIG: # Replace this with the recursion relation ... deltaKappa = .kappaGH(x, lambda+1) - .kappaGH(x, lambda) } else { # GH: deltaKappa = .kappaGH(x, lambda+1) - .kappaGH(x, lambda) } # Return Value: deltaKappa } ################################################################################ .paramGH <- function(zeta = 1, rho = 0 , lambda = 1) { # A function implemented by Diethelm Wuertz # Description: # Change parameterizations to alpha(zeta, rho, lambda) # FUNCTION: # Transformation: Rho2 = 1 - rho^2 alpha = zeta^2 * .kappaGH(zeta, lambda) / Rho2 alpha = alpha * ( 1 + rho^2 * zeta^2 * .deltaKappaGH(zeta, lambda) / Rho2) alpha = sqrt(alpha) beta = alpha * rho delta = zeta / ( alpha * sqrt(Rho2) ) mu = -beta * delta^2 * .kappaGH(zeta, lambda) # Return Value: c(alpha = alpha, beta = beta, delta = delta, mu = mu) } ################################################################################ fBasics/R/dist-normRobMoments.R0000644000176000001440000000723411370220773016132 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # normMED Returns true normal median # normIQR Returns true normal inter quartal range # normSKEW Returns true normal robust skewness # normKURT Returns true normal robust kurtosis ################################################################################ normMED <- function(mean =0, sd = 1) { # A function implemented by Diethelm Wuertz # Description: # Returns true gh median # Arguments: # mean - a numeric value, the location parameter # sd - a numeric value, the scale parameter # FUNCTION: # Median: Q = qnorm(p=0.5, mean, sd) med = c(MED = Q) # Return Value: med } # ------------------------------------------------------------------------------ normIQR <- function(mean=0, sd=1) { # A function implemented by Diethelm Wuertz # Description: # Returns true gh inter quartal range # Arguments: # mean - a numeric value, the location parameter # sd - a numeric value, the scale parameter # FUNCTION: # Inter Quartile Range: Q = numeric() Q[1] = qnorm(p=0.25, mean, sd) Q[2] = qnorm(p=0.75, mean, sd) iqr = c(IQR = Q[[2]] - Q[[1]]) # Return Value: iqr } # ------------------------------------------------------------------------------ normSKEW <- function(mean=0, sd=1) { # A function implemented by Diethelm Wuertz # Description: # Returns true gh robust gh skewness # Arguments: # mean - a numeric value, the location parameter # sd - a numeric value, the scale parameter # FUNCTION: # Robust Skewness: Q = numeric() Q[1] = qnorm(p=0.25, mean, sd) Q[2] = qnorm(p=0.50, mean, sd) Q[3] = qnorm(p=0.75, mean, sd) skew = c(SKEW = ( Q[[3]] + Q[[1]] - 2* Q[[2]] ) / (Q[[3]] - Q[[1]] ) ) # Return Value: skew } # ------------------------------------------------------------------------------ normKURT <- function(mean=0, sd=1) { # A function implemented by Diethelm Wuertz # Description: # Returns true gh robust gh kurtosis # Arguments: # mean - a numeric value, the location parameter # sd - a numeric value, the scale parameter # mu - a numeric value, the first shape parameter # nu - a numeric value, the second parameter # FUNCTION: # Robust Kurtosis: Q = numeric() for (p in (1:7)/8) Q = c(Q, qnorm(p, mean, sd)) kurt = c(KURT = (Q[[7]] - Q[[5]] + Q[[3]] - Q[[1]]) / (Q[[6]] - Q[[2]])) # Return Value: kurt } ################################################################################ fBasics/R/dist-normFit.R0000644000176000001440000000610511370220773014563 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # .normFit Fits parameters of a Normal density ################################################################################ # normFit is now in fBasics # ------------------------------------------------------------------------------ .normFit <- function(x, doplot = TRUE, span = "auto", title = NULL, description = NULL, ...) { # A function implemented by Diethelm Wuertz # Description: # Return Maximum log-likelihood estimated # Paramters for Normal Distribution # Notes: # Function Calls: nlminb(), density() # The function normFit() can be found in the Rmetrics # chapter GarchDistributions. # FUNCTION: # Transform: x.orig = x x = as.vector(x) # Settings: CALL = match.call() # MLE: N = length(x) mean = sum(x)/N sd = sqrt(sum((x-mean)^2)/N) # Optional Plot: if (doplot) { if (span == "auto") { span.min = qnorm(0.001, mean, sd) span.max = qnorm(0.999, mean, sd) span = seq(span.min, span.max, length = 100) } par(err = -1) z = density(x, n = 100, ...) x = z$x[z$y > 0] y = z$y[z$y > 0] y.points = dnorm(span, mean, sd) ylim = log(c(min(y.points), max(y.points))) plot(x, log(y), xlim = c(span[1], span[length(span)]), ylim = ylim, type = "p", xlab = "x", ylab = "log f(x)", ...) title("NORMAL: Parameter Estimation") lines(x = span, y = log(y.points), col = "steelblue") if (exists("grid")) grid() } # Add Title and Description: if (is.null(title)) title = "Normal Parameter Estimation" if (is.null(description)) description = description() # Fit: fit = list( estimate = c(mean = mean, sd = sd), minimum = sum(log(dnorm(x, mean, sd))), code = NA) # Return Value: new("fDISTFIT", call = as.call(CALL), model = "Normal Distribution", data = as.data.frame(x.orig), fit = fit, title = as.character(title), description = description() ) } # ------------------------------------------------------------------------------ nFit <- .normFit ################################################################################ fBasics/R/dist-norm.R0000644000176000001440000000251211370220773014116 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # dnorm Returns density of the normal distribution # pnorm Returns probabilities of mormal distribution # qnorm Returns quantiles of the normal distribution # rnorm Generates normal distributed random variates ################################################################################ ## The functions are part of R's base package ################################################################################ fBasics/R/dist-nigRobMoments.R0000644000176000001440000000631311370220773015731 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # nigMED Returns true NIG median # nigIQR Returns true NIG inter quartal range # nigSKEW Returns true NIG robust skewness # nigKURT Returns true NIG robust kurtosis ################################################################################ nigMED <- function(alpha=1, beta=0, delta=1, mu=0) { # A function implemented by Diethelm Wuertz # Description: # Returns true NIG median # FUNCTION: # NIG Median: Q = .qnigC(p=0.5, alpha, beta, delta, mu) med = c(MED = Q) # Return Value: med } # ------------------------------------------------------------------------------ nigIQR <- function(alpha=1, beta=0, delta=1, mu=0) { # A function implemented by Diethelm Wuertz # Description: # Returns true NIG inter quartal range # FUNCTION: # NIG Inter Quartile Range: Q = numeric() Q[1] = .qnigC(p=0.25, alpha, beta, delta, mu) Q[2] = .qnigC(p=0.75, alpha, beta, delta, mu) iqr = c(IQR = Q[[2]] - Q[[1]]) # Return Value: iqr } # ------------------------------------------------------------------------------ nigSKEW <- function(alpha=1, beta=0, delta=1, mu=0) { # A function implemented by Diethelm Wuertz # Description: # Returns true NIG robust skewness # FUNCTION: # NIG Robust Skewness: Q = numeric() Q[1] = .qnigC(p=0.25, alpha, beta, delta, mu) Q[2] = .qnigC(p=0.50, alpha, beta, delta, mu) Q[3] = .qnigC(p=0.75, alpha, beta, delta, mu) skew = c(SKEW = ( Q[[3]] + Q[[1]] - 2* Q[[2]] ) / (Q[[3]] - Q[[1]] ) ) # Return Value: skew } # ------------------------------------------------------------------------------ nigKURT <- function(alpha=1, beta=0, delta=1, mu=0) { # A function implemented by Diethelm Wuertz # Description: # Returns true NIG robust kurtosis # FUNCTION: # NIG Robust Kurtosis: Q = numeric() for (p in (1:7)/8) Q = c(Q, .qnigC(p, alpha, beta, delta, mu)) kurt = c(KURT = (Q[[7]] - Q[[5]] + Q[[3]] - Q[[1]]) / (Q[[6]] - Q[[2]])) # Return Value: kurt } ################################################################################ fBasics/R/dist-nigMoments.R0000644000176000001440000001246711370220773015275 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # nigMean Returns true NIG mean # nigVar Returns true NIG variance # nigSkew Returns true NIG skewness # nigKurt Returns true NIG kurtosis # FUNCTION: DESCRIPTION: # nigMoments Returns true NIG moments # FUNCTION: DESCRIPTION: # nigShapeTriangle Plots NIG Shape Triangle ################################################################################ nigMean <- function(alpha = 1, beta = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Returns true NIG mean # FUNCTION: # Mean: gamma = sqrt(alpha^2 - beta^2) ans = c(mean = (mu + delta * beta / gamma)[[1]]) # Return Value: ans } # ------------------------------------------------------------------------------ nigVar <- function(alpha = 1, beta = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Returns true NIG variance # FUNCTION: # Variance: gamma = sqrt(alpha^2 - beta^2) ans = c(var = (delta * alpha^2 / gamma^3)[[1]]) # Return Value: ans } # ------------------------------------------------------------------------------ nigSkew <- function(alpha = 1, beta = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Returns true NIG skewness # FUNCTION: # Skewness: gamma = sqrt(alpha^2 - beta^2) ans = c(skew = (3*beta / ( alpha * sqrt(delta*gamma) ))[[1]] ) # Return Value: ans } # ------------------------------------------------------------------------------ nigKurt <- function(alpha = 1, beta = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Returns true NIG kurtosis # FUNCTION: # Skewness: gamma = sqrt(alpha^2 - beta^2) ans = c(kurt = (3 * ( 1 + 4 * beta^2 / alpha^2) / (delta * gamma))[[1]]) # Return Value: ans } ############################################################################### nigMoments <- function(order, type = c("raw", "central", "mu"), alpha=1, beta=0, delta=1, mu=0) { # A function implemented by Diethelm Wuertz # Descriptions: # Returns true moments of the NIG distribution # FUNCTION: # Settings: type = match.arg(type) # Moments: lambda = -1/2 if (type == "raw") { ans = .ghRawMoments(order, alpha, beta, delta, mu, lambda) } else if (type == "central") { ans = .ghCentralMoments(order, alpha, beta, delta, mu, lambda) } else if (type == "central") { ans = .ghMuMoments(order, alpha, beta, delta, mu, lambda) } # Return Value: ans } ################################################################################ nigShapeTriangle <- function(object, add = FALSE, labels = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Plots NIG Shape Triangle # Arguments: # object - an object of class 'fDISTFIT' as returned by the # function nigFit() # Example: # nigShapeTriangle(nigFit(rnig(100), doplot = FALSE)) # FUNCTION: # Settings: stopifnot(class(object) == "fDISTFIT") # Plot Frame: if (labels) { xlab = "Asymmetry: chi" ylab = "Steepness: zeta" main = "NIG Shape Traingle" } else { xlab = ylab = main = "" } if (!add) { x = c(-1, 0, 1, -1) y = c( 1, 0, 1, 1) plot(x, y, type = "l", xlab = xlab, ylab = ylab, main = main, ...) for (s in c(0.8, 0.6, 0.4, 0.2)) lines(c(-s, s), c(s, s), lty = 3, col = "grey") lines(c(0, 0), c(0, 1), lty = 3, col = "grey") } # Transform: par = object@fit$estimate names(par) = c("alpha", "beta", "delta", "mu") alpha = par[1] beta = par[2] delta = par[3] mu = par[4] # Add Points: zeta = 1/sqrt(1+delta*sqrt(alpha^2-beta^2)) chi = zeta*(beta/alpha) if (labels) { points(chi, zeta, pch = 19, ...) } else { points(chi, zeta, ...) } # Result: ans = list(chi = chi[[1]], zeta = zeta[[1]]) attr(ans, "control")<-par # Return Value: ans } ################################################################################ fBasics/R/dist-nigMode.R0000644000176000001440000000312511370220773014526 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # nigMode Computes the normal inverse Gaussian mode ################################################################################ nigMode <- function(alpha = 1, beta = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Computes the mode of the Normal Inverse Gaussian PDF # FUNCTION: # Find Maximum: min = qnig(0.01, alpha, beta, delta, mu) max = qnig(0.99, alpha, beta, delta, mu) ans = optimize(f = dnig, interval = c(min, max), alpha = alpha, beta = beta, delta = delta, mu = mu, maximum = TRUE, tol = .Machine$double.eps)$maximum # Return Value: ans } ################################################################################ fBasics/R/dist-nigFit.R0000644000176000001440000003336311370220773014373 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: GENERALIZED DISTRIBUTION: # nigFit Fits parameters of a normal inverse Gaussian density # .nigFit.mle max Log-likelihood Estimation # .nigFit.gmm gemeralized method of moments estimation # .nigFit.mps maximum product spacings estimation # .nigFit.vmps minimum variance product spacings estimation ################################################################################ nigFit <- function(x, alpha = 1, beta = 0, delta = 1, mu = 0, method = c("mle", "gmm", "mps", "vmps"), scale = TRUE, doplot = TRUE, span = "auto", trace = TRUE, title = NULL, description = NULL, ...) { # A function implemented by Diethelm Wuertz # FUNCTION: # Settings: method = match.arg(method) # Select: if (method == "mle") { # MLE: fit = .nigFit.mle(x = x, alpha = alpha, beta = beta, delta = delta, mu = mu , scale = scale, doplot = doplot, span = span, trace = trace, title = title, description = description, ...) } else if (method == "gmm") { # GMM: fit = .nigFit.gmm(x = x, alpha = alpha, beta = beta, delta = delta, mu = mu , scale = scale, doplot = doplot, span = span, trace = trace, title = title, description = description, ...) } else if (method == "mps") { # MPS: fit = .nigFit.mps(x = x, alpha = alpha, beta = beta, delta = delta, mu = mu , scale = scale, doplot = doplot, span = span, trace = trace, title = title, description = description, ...) } else if (method == "vmps") { # MPS: fit = .nigFit.vmps(x = x, alpha = alpha, beta = beta, delta = delta, mu = mu , scale = scale, doplot = doplot, span = span, trace = trace, title = title, description = description, ...) } # Return Value: fit } # ------------------------------------------------------------------------------ .nigFit.mle <- function(x, alpha = 1, beta = 0, delta = 1, mu = 0, scale = TRUE, doplot = TRUE, add = FALSE, span = "auto", trace = TRUE, title = NULL, description = NULL, ...) { # A function implemented by Diethelm Wuertz # Description: # Fits parameters of a NIG using maximum log-likelihood # Example: # set.seed(4711); x = rnig(500); mle = .nigFit.mle(x); mle@fit$estimate # FUNCTION: # Transform: x.orig = x x = as.vector(x) if (scale) { SD = sd(x) x = x / SD } # Objective Function: obj = function(x, y = x, trace) { if (abs(x[2]) >= x[1]) return(1e9) f = -sum(dnig(y, x[1], x[2], x[3], x[4], log = TRUE)) # Print Iteration Path: if (trace) { cat("\n Objective Function Value: ", -f) cat("\n Parameter Estimates: ", x, "\n") } f } # Parameter Estimation: eps = 1e-10 BIG = 1000 fit = nlminb( start = c(alpha, beta, delta, mu), objective = obj, lower = c(eps, -BIG, eps, -BIG), upper = BIG, y = x, trace = trace) names(fit$par) <- c("alpha", "beta", "delta", "mu") # Rescale Result: if (scale) { fit$scaleParams = c(SD, SD, 1/SD, 1/SD) fit$par = fit$par / fit$scaleParams fit$objective = obj(fit$par, y = as.vector(x.orig), trace = trace) } else { fit$scaleParams = rep(1, time = length(fit$par)) } fit$scale = scale fit$estimate = fit$par fit$minimum = -fit$objective fit$code = fit$convergence # Standard Errors and t-Values: fit = .distStandardErrors(fit, obj, x) # Add Title and Description: if (is.null(title)) title = "Normal Inverse Gaussian Parameter Estimation" if (is.null(description)) description = description() # Optional Plot: if (doplot) .distFitPlot( fit, x = x.orig, FUN = "dnig", main = "NIG Parameter Estimation", span = span, add = add, ...) # Return Value: new("fDISTFIT", call = match.call(), model = "Normal Inverse Gaussian Distribution", data = as.data.frame(x.orig), fit = fit, title = as.character(title), description = description() ) } # ------------------------------------------------------------------------------ .nigFit.gmm <- function(x, scale = TRUE, doplot = TRUE, add = FALSE, span = "auto", trace = TRUE, title = NULL, description = NULL, ...) { # A function implemented by Diethelm Wuertz # Description: # Fits parameters of a NIG using GMM estimator # Example: # set.seed(4711); x = rnig(500); gmm = .nigFit.gmm(x)@fit$estimate; gmm # FUNCTION: # Transform: x.orig = x x = as.vector(x) if (scale) { SD = sd(x) x = x / SD } # Settings: CALL = match.call() # Parameter Estimation: obj <- function(Theta, x) { # Parameters: alpha = Theta[1] beta = Theta[2] delta = Theta[3] mu = Theta[4] names(Theta) <- c("alpha", "beta", "delta", "mu") # Trace: if (TRUE) print(Theta) # Moments: m1 <- x - .ghMuMoments(1, alpha, beta, delta, mu, lambda = -0.5) m2 <- x^2 - .ghMuMoments(2, alpha, beta, delta, mu, lambda = -0.5) m3 <- x^3 - .ghMuMoments(3, alpha, beta, delta, mu, lambda = -0.5) m4 <- x^4 - .ghMuMoments(4, alpha, beta, delta, mu, lambda = -0.5) # Result: f <- cbind(m1, m2, m3, m4) return(f) } r <- .gmm(g = obj, x = x, t0 = c(1, 0, 1, 0)) names(r$par) <- c("alpha", "beta", "delta", "mu") # Add Title and Description: if (is.null(title)) title = "Normal Inverse Gaussian Parameter Estimation" if (is.null(description)) description = description() # Rescale Result: if (scale) { r$par = r$par / c(SD, SD, 1/SD, 1/SD) r$objective = NA } fit = list(estimate = r$par) # Optional Plot: if (doplot) { x = as.vector(x.orig) if (span == "auto") span = seq(min(x), max(x), length = 51) z = density(x, n = 100, ...) x = z$x[z$y > 0] y = z$y[z$y > 0] y.points = dnig(span, r$par[1], r$par[2], r$par[3], r$par[4]) ylim = log(c(min(y.points), max(y.points))) if (add) { lines(x = span, y = log(y.points), col = "steelblue") } else { plot(x, log(y), xlim = c(span[1], span[length(span)]), ylim = ylim, type = "p", xlab = "x", ylab = "log f(x)", ...) title("NIG GMM Parameter Estimation") lines(x = span, y = log(y.points), col = "steelblue") } } # Return Value: new("fDISTFIT", call = as.call(CALL), model = "Normal Inverse Gaussian Distribution", data = as.data.frame(x.orig), fit = fit, title = as.character(title), description = description() ) } # ------------------------------------------------------------------------------ .nigFit.mps <- function(x, alpha = 1, beta = 0, delta = 1, mu = 0, scale = TRUE, doplot = TRUE, add = FALSE, span = "auto", trace = TRUE, title = NULL, description = NULL, ...) { # A function implemented by Diethelm Wuertz # Description: # Fits parameters of a NIG using maximum product spacings # Example: # set.seed(4711); x = rnig(500); mps = .nigFit.mps(x)@fit$estimate; mps # FUNCTION: # Transform: x.orig = x x = as.vector(x) if (scale) { SD = sd(x) x = x / SD } # Settings: CALL = match.call() # Parameter Estimation: obj <- function(x, y = x, trace) { if (abs(x[2]) >= x[1]) return(1e9) DH = diff(c(0, na.omit(.pnigC(sort(y), x[1], x[2], x[3], x[4])), 1)) f = -mean(log(DH[DH > 0]))*length(y) # Print Iteration Path: if (trace) { cat("\n Objective Function Value: ", -f) cat("\n Parameter Estimates: ", x[1], x[2], x[3], x[4], "\n") } f } eps = 1e-10 BIG = 1000 r = nlminb(start = c(alpha, beta, delta, mu), objective = obj, lower = c(eps, -BIG, eps, -BIG), upper = BIG, y = x, trace = trace) names(r$par) <- c("alpha", "beta", "delta", "mu") # Standard Errors: hessian = tsHessian(x = r$par, fun = obj, y = x, trace = FALSE) colnames(hessian) = rownames(hessian) = names(r$par) varcov = solve(hessian) par.ses = sqrt(diag(varcov)) if (scale) par.ses = par.ses / c(SD, SD, 1/SD, 1/SD) # Add Title and Description: if (is.null(title)) title = "NIG mps Parameter Estimation" if (is.null(description)) description = description() # Result: if (scale) { r$par = r$par / c(SD, SD, 1/SD, 1/SD) r$objective = obj(r$par, y = as.vector(x.orig), trace = trace) } fit = list( estimate = r$par, minimum = -r$objective, error = par.ses, code = r$convergence) # Optional Plot: if (doplot) { x = as.vector(x.orig) if (span == "auto") span = seq(min(x), max(x), length = 501) z = density(x, n = 100, ...) x = z$x[z$y > 0] y = z$y[z$y > 0] y.points = dnig(span, r$par[1], r$par[2], r$par[3], r$par[4]) ylim = log(c(min(y.points), max(y.points))) if (add) { lines(x = span, y = log(y.points), col = "steelblue") } else { plot(x, log(y), xlim = c(span[1], span[length(span)]), ylim = ylim, type = "p", xlab = "x", ylab = "log f(x)", ...) title("NIG MPS Parameter Estimation") lines(x = span, y = log(y.points), col = "steelblue") } } # Return Value: new("fDISTFIT", call = as.call(CALL), model = "Normal Inverse Gaussian Distribution", data = as.data.frame(x.orig), fit = fit, title = as.character(title), description = description() ) } # ------------------------------------------------------------------------------ .nigFit.vmps <- function (x, alpha = 1, beta = 0, delta = 1, mu = 0, scale = TRUE, doplot = TRUE, add = FALSE, span = "auto", trace = TRUE, title = NULL,description = NULL, ...) { # A function implemented by Yohan Chalabi # Description: # Fits parameters of a NIG using maximum product spacings # Example: # set.seed(4711); x = rnig(500); vmps = .nigFit.vmps(x)@fit$estimate; vmps # FUNCTION: # Transform: x.orig = x x = as.vector(x) if (scale) { SD = sd(x) x = x/SD } # Settings: CALL = match.call() # Parameter Estimation: obj <- function(x, y = x, trace) { if (abs(x[2]) >= x[1]) return(1e+9) DH = diff(c(0, na.omit(.pnigC(sort(y), x[1], x[2], x[3], x[4])), 1)) f = log(var(DH[DH > 0])) if (trace) { cat("\n Objective Function Value: ", -f) cat("\n Parameter Estimates: ", x[1], x[2], x[3], x[4], "\n") } f } eps = 1e-10 BIG = 1000 r = nlminb( start = c(alpha, beta, delta, mu), objective = obj, lower = c(eps, -BIG, eps, -BIG), upper = BIG, y = x, trace = trace) names(r$par) <- c("alpha", "beta", "delta", "mu") # Standard Errors: hessian = tsHessian(x = r$par, fun = obj, y = x, trace = FALSE) colnames(hessian) = rownames(hessian) = names(r$par) varcov = solve(hessian) par.ses = sqrt(diag(varcov)) if (scale) par.ses = par.ses / c(SD, SD, 1/SD, 1/SD) # Add Title and Description: if (is.null(title)) title = "NIG varMPS Parameter Estimation" if (is.null(description)) description = description() # Result: if (scale) { r$par = r$par/c(SD, SD, 1/SD, 1/SD) r$objective = obj(r$par, y = as.vector(x.orig), trace = trace) } fit = list( estimate = r$par, minimum = -r$objective, error = par.ses, code = r$convergence) # Optional Plot: if (doplot) { x = as.vector(x.orig) if (span == "auto") span = seq(min(x), max(x), length = 501) z = density(x, n = 100, ...) x = z$x[z$y > 0] y = z$y[z$y > 0] y.points = dnig(span, r$par[1], r$par[2], r$par[3], r$par[4]) ylim = log(c(min(y.points), max(y.points))) if (add) { lines(x = span, y = log(y.points), col = "steelblue") } else { plot(x, log(y), xlim = c(span[1], span[length(span)]), ylim = ylim, type = "p", xlab = "x", ylab = "log f(x)", ...) title("NIG varMPS Parameter Estimation") lines(x = span, y = log(y.points), col = "steelblue") } } # Return Value: new("fDISTFIT", call = as.call(CALL), model = "Normal Inverse Gaussian Distribution", data = as.data.frame(x.orig), fit = fit, title = as.character(title), description = description() ) } ################################################################################ fBasics/R/dist-nig.R0000644000176000001440000002037511720123756013731 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # dnig Returns density for inverse Gaussian DF # pnig Returns probability for for inverse Gaussian DF # qnig Returns quantiles for for inverse Gaussian DF # rnig Returns random variates for inverse Gaussian DF # FUNCTION: DESCRIPTION: # .pnigC Fast C implementation # .qnigC Fast C implementation ################################################################################ .dnig <- function(x, alpha, beta, delta, mu, log = FALSE) { x. <- x - mu log.a <- delta*sqrt(alpha^2-beta^2) + log(delta*alpha/pi) Sqrt <- sqrt(delta^2 + x.^2) log.K1 <- log(besselK(alpha * Sqrt, 1, expon.scaled = TRUE)) - alpha*Sqrt dnig <- log.a -log(Sqrt) + log.K1 + beta*x. if(log) dnig else exp(dnig) } dnig <- function(x, alpha = 1, beta = 0, delta = 1, mu = 0, log = FALSE) { # A function implemented by Diethelm Wuertz # Description: # Returns density for inverse Gaussian DF # Example: # x = rnorm(1000); u = dgh(x, 1.1, 0.2, 0.8, 0.4, -0.5) # v = dnig(rnorm(x), 1.1, 0.2, 0.8, 0.4); u -v # FUNCTION: # Parameters: if (length(alpha) == 4) { mu = alpha[4] delta = alpha[3] beta = alpha[2] alpha = alpha[1] } # Checks: if (alpha <= 0) stop("alpha must be greater than zero") if (delta <= 0) stop("delta must be greater than zero") if (abs(beta) >= alpha) stop("abs value of beta must be less than alpha") .dnig(x, alpha=alpha, beta=beta, delta=delta, mu=mu, log=log) } # ------------------------------------------------------------------------------ pnig <- function(q, alpha = 1, beta = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Returns probability for for inverse Gaussian DF # Function: # Probability: # pgh(q = q, alpha = alpha, beta = beta, delta = delta, mu = mu, # lambda = -0.5) if (alpha <= 0) stop("alpha must be greater than zero") if (delta <= 0) stop("delta must be greater than zero") if (abs(beta) >= alpha) stop("abs value of beta must be less than alpha") ans <- q for(i in seq_along(q)) { ans[i] <- integrate(.dnig, -Inf, q[i], stop.on.error = FALSE, alpha = alpha, beta = beta, delta = delta, mu = mu)$value } ans } # ------------------------------------------------------------------------------ qnig <- function(p, alpha = 1, beta = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Returns quantiles for for inverse Gaussian DF # FUNCTION: # Quantiles: # qgh(p = p, alpha = alpha, beta = beta, delta = delta, mu = mu, # lambda = -0.5) # Checks: if (alpha <= 0) stop("alpha must be greater than zero") if (delta <= 0) stop("delta must be greater than zero") if (abs(beta) >= alpha) stop("abs value of beta must be less than alpha") # Internal Function: .froot <- function(x, alpha, beta, delta, mu, p) pnig(q = x, alpha = alpha, beta = beta, delta = delta, mu = mu) - p # Quantiles: ans <- p for(i in seq_along(p)) { lower = -1 upper = +1 counter = 0 v <- NA while(is.na(v) && counter < 1000) { v <- .unirootNA(f = .froot, interval = c(lower, upper), alpha = alpha, beta = beta, delta = delta, mu = mu, p = p[i]) counter = counter + 1 lower = lower - 2^counter upper = upper + 2^counter } ans[i] <- v } # Return Value: ans } # ------------------------------------------------------------------------------ rnig <- function(n, alpha = 1, beta = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Return normal inverse Gaussian distributed random variates # Arguments: # n - number of deviates to be generated # alpha, beta - Shape Parameter, |beta| <= alpha # delta - Scale Parameter, 0 <= delta # mu - Location Parameter # FUNCTION: # Settings: gamma = sqrt(alpha*alpha - beta*beta) # GAMMA: if (gamma == 0) { # GAMMA = 0: V = rnorm(n)^2 Z = delta*delta / V X = sqrt(Z)*rnorm(n) } else { # GAMMA > 0: U = runif(n) V = rnorm(n)^2 # FIXED ... z1 <- function(v, delta, gamma) { delta/gamma + v/(2*gamma^2) - sqrt(v*delta/(gamma^3) + (v/(2*gamma^2))^2 ) } z2 <- function(v, delta, gamma) { (delta/gamma)^2 / z1(v = v, delta = delta, gamma = gamma) } pz1 <- function(v, delta, gamma) { delta / (delta + gamma * z1(v = v, delta = delta, gamma = gamma) ) } s = (1-sign(U-pz1(v = V, delta = delta, gamma = gamma)))/2 Z = z1(v = V, delta = delta, gamma = gamma)*s + z2(v = V, delta = delta, gamma = gamma)*(1-s) X = mu + beta*Z + sqrt(Z)*rnorm(n) } # Attributes: attr(X, "control") = c(dist = "nig", alpha = alpha, beta = beta, delta = delta, mu = mu) # Return Value: X } ################################################################################ .qnigC <- function(p, alpha = 1, beta = 0, delta = 1, mu = 0) { # Description: # Returns quantiles for for inverse Gaussian DF # Example: # p = runif(10); .qnigC(p) # FUNCTION: # Checks: if(alpha <= 0) stop("Invalid parameters: alpha <= 0.0\n") if(alpha^2 <= beta^2) stop("Invalid parameters: alpha^2 <= beta^2.0\n") if(delta <= 0) stop("Invalid parameters: delta <= 0.0\n") if((sum(is.na(p)) > 0)) stop("Invalid probabilities:\n",p,"\n") else if(sum(p < 0)+sum(p > 1) > 0) stop("Invalid probabilities:\n",p,"\n") # Evaluate NIG cdf by calling C function n <- length(p) q <- rep(0, n) retValues <- .C("qNIG", as.double(p), as.double(mu), as.double(delta), as.double(alpha), as.double(beta), as.integer(n), as.double(q), PACKAGE = "fBasics") quantiles <- retValues[[7]] quantiles[quantiles <= -1.78e+308] <- -Inf quantiles[quantiles >= 1.78e+308] <- Inf # Return Value: quantiles } # ------------------------------------------------------------------------------ .pnigC <- function(q, alpha = 1, beta = 0, delta = 1, mu = 0) { # Description: # Returns probabilities for for inverse Gaussian DF # IMPORTANT NOTE: # DW: C program fails, remains to check # Example: # q = sort(rnorm(10)); .pnigC(q) # FAILS # FUNCTION: # Checks: if(alpha <= 0) stop("Invalid parameters: alpha <= 0.0\n") if(alpha^2 <= beta^2) stop("Invalid parameters: alpha^2 <= beta^2.0\n") if(delta <= 0) stop("Invalid parameters: delta <= 0.0\n") if(sum(is.na(q)) > 0) stop("Invalid quantiles:\n", q) # Evaluate NIG cdf by calling C function n <- length(q) p <- rep(0, n) retValues <- .C("pNIG", as.double(q), as.double(mu), as.double(delta), as.double(alpha), as.double(beta), as.integer(n), as.double(p), PACKAGE = "fBasics") probs <- retValues[[7]] # Return Value: probs } ################################################################################ fBasics/R/dist-hypRobMoments.R0000644000176000001440000001015411613257742015760 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # hypMED Returns true hyp median # hypIQR Returns true hyp inter quartal range # hypSKEW Returns true hyp robust skewness # hypKURT Returns true hyp robust kurtosis ################################################################################ hypMED <- function(alpha = 1, beta = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Returns true gh median # Arguments: # beta - a numeric value, the location parameter # delta - a numeric value, the scale parameter # mu - a numeric value, the first shape parameter # nu - a numeric value, the second parameter # FUNCTION: # gh Median Q = qgh(p=0.5, alpha, beta, delta, mu, lambda=1) med = c(MED = Q) # Return Value: med } # ------------------------------------------------------------------------------ hypIQR <- function(alpha = 1, beta = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Returns true gh inter quartal range # Arguments: # beta - a numeric value, the location parameter # delta - a numeric value, the scale parameter # mu - a numeric value, the first shape parameter # nu - a numeric value, the second parameter # FUNCTION: # gh Inter Quartile Range Q = numeric() Q[1] = qgh(p=0.25, alpha, beta, delta, mu, lambda=1) Q[2] = qgh(p=0.75, alpha, beta, delta, mu, lambda=1) iqr = c(IQR = Q[[2]] - Q[[1]]) # Return Value: iqr } # ------------------------------------------------------------------------------ hypSKEW <- function(alpha = 1, beta = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Returns true gh robust gh skewness # Arguments: # beta - a numeric value, the location parameter # delta - a numeric value, the scale parameter # mu - a numeric value, the first shape parameter # nu - a numeric value, the second parameter # FUNCTION: # gh Robust Skewness: Q = numeric() Q[1] = qgh(p=0.25, alpha, beta, delta, mu, lambda=1) Q[2] = qgh(p=0.50, alpha, beta, delta, mu, lambda=1) Q[3] = qgh(p=0.75, alpha, beta, delta, mu, lambda=1) skew = c(SKEW = ( Q[[3]] + Q[[1]] - 2* Q[[2]] ) / (Q[[3]] - Q[[1]] ) ) # Return Value: skew } # ------------------------------------------------------------------------------ hypKURT <- function(alpha = 1, beta = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Returns true gh robust gh kurtosis # Arguments: # beta - a numeric value, the location parameter # delta - a numeric value, the scale parameter # mu - a numeric value, the first shape parameter # nu - a numeric value, the second parameter # FUNCTION: # gh Robust Kurtosis: Q = numeric() for (p in (1:7)/8) Q = c(Q, qgh(p, alpha, beta, delta, mu, lambda=1)) kurt = c(KURT = (Q[[7]] - Q[[5]] + Q[[3]] - Q[[1]]) / (Q[[6]] - Q[[2]])) # Return Value: kurt } ################################################################################ fBasics/R/dist-hypMoments.R0000644000176000001440000000677611370220773015326 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # hypMean Returns true hyperbolic mean # hypVar Returns true hyperbolic variance # hypSkew Returns true hyperbolic skewness # hypKurt Returns true hyperbolic kurtosis # FUNCTION: DESCRIPTION: # hypMoments Returns true hyperbolic moments ################################################################################ hypMean <- function(alpha=1, beta=0, delta=1, mu=0) { # A function implemented by Diethelm Wuertz # Description: # Returns true hyperbolic mean # FUNCTION: # Return Value: ghMean(alpha, beta, delta, mu, lambda=1) } # ------------------------------------------------------------------------------ hypVar <- function(alpha=1, beta=0, delta=1, mu=0) { # A function implemented by Diethelm Wuertz # Description: # Returns true hyperbolic variance # FUNCTION: # Return Value: ghVar(alpha, beta, delta, mu, lambda=1) } # ------------------------------------------------------------------------------ hypSkew <- function(alpha=1, beta=0, delta=1, mu=0) { # A function implemented by Diethelm Wuertz # Description: # Returns true hyperbolic skewness # FUNCTION: # Return Value: ghSkew(alpha, beta, delta, mu, lambda=1) } # ------------------------------------------------------------------------------ hypKurt <- function(alpha=1, beta=0, delta=1, mu=0) { # A function implemented by Diethelm Wuertz # Description: # Returns true hyperbolic kurtosis # FUNCTION: # Return Value: ghKurt(alpha, beta, delta, mu, lambda=1) } # ------------------------------------------------------------------------------ hypMoments <- function(order, type = c("raw", "central", "mu"), alpha=1, beta=0, delta=1, mu=0) { # A function implemented by Diethelm Wuertz # Descriptions: # Returns true moments of the hyperbolic distribution # FUNCTION: # Settings: type = match.arg(type) # Moments: lambda = 1 if (type == "raw") { ans = .ghRawMoments(order, alpha, beta, delta, mu, lambda) } else if (type == "central") { ans = .ghCentralMoments(order, alpha, beta, delta, mu, lambda) } else if (type == "central") { ans = .ghMuMoments(order, alpha, beta, delta, mu, lambda) } # Return Value: ans } ################################################################################ fBasics/R/dist-hypMode.R0000644000176000001440000000700111370220773014546 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # hypMode Computes the hyperbolic mode # FUNCTION: DESCRIPTION: # .hyp[1234]Mode Internal functions called by 'hypMode' ################################################################################ hypMode <- function(alpha = 1, beta = 0, delta = 1, mu = 0, pm = c(1, 2, 3, 4)) { # A function implemented by Diethelm Wuertz # Description: # Computes the mode of the Hyperbolic PDF # FUNCTION: # Settings: pm = pm[1] # Return Value: ans = NA if (pm == 1) return(.hyp1Mode(alpha, beta, delta, mu)) if (pm == 2) return(.hyp2Mode(alpha, beta, delta, mu)) if (pm == 3) return(.hyp3Mode(alpha, beta, delta, mu)) if (pm == 4) return(.hyp4Mode(alpha, beta, delta, mu)) } # ------------------------------------------------------------------------------ .hyp1Mode <- function(alpha = 1, beta = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Computes the mode of the Hyperbolic PDF # FUNCTION: # Mode: ans = mu + delta * beta / sqrt(alpha^2 - beta^2) # Return Value: ans } # ------------------------------------------------------------------------------ .hyp2Mode <- function(zeta = 1, rho = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Computes the hyperbolic mode in the 2nd parameterization # FUNCTION: # Parameter Change: alpha = zeta / ( delta * sqrt(1 - rho*rho) ) beta = alpha * rho # Return Value: ans = hypMode(alpha, beta, delta, mu) ans } # ------------------------------------------------------------------------------ .hyp3Mode <- function(xi = 1/sqrt(2), chi = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Computes the hyperbolic mode in the 3rd parameterization # FUNCTION: # Parameter Change: rho = chi / xi zeta = 1/xi^2 - 1 alpha = zeta / ( delta * sqrt(1 - rho*rho) ) beta = alpha * rho ans = hypMode(alpha, beta, delta, mu) # Return Value: ans } # ------------------------------------------------------------------------------ .hyp4Mode <- function(a.bar = 1, b.bar = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Computes the hyperbolic mode in the 4th parameterization # FUNCTION: # Parameter Change: alpha = a.bar / delta beta = b.bar / delta ans = hypMode(alpha, beta, delta, mu) # Return Value: ans } ################################################################################ fBasics/R/dist-hypFit.R0000644000176000001440000000650711370220773014416 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: GENERALIZED DISTRIBUTION: # hypFit Fits parameters of a hyperbolic density ################################################################################ hypFit <- function(x, alpha = 1, beta = 0, delta = 1, mu = 0, scale = TRUE, doplot = TRUE, span = "auto", trace = TRUE, title = NULL, description = NULL, ...) { # A function implemented by Diethelm Wuertz # Description: # Fits parameters of a hyperbolic density # FUNCTION: # Transform: x.orig = x x = as.vector(x) if (scale) { SD = sd(x) x = x / SD } # Settings: CALL = match.call() # Log-likelihood Function: ehypmle = function(x, y = x, trace) { if (abs(x[2]) >= x[1]) return(1e99) f = -sum(log(dhyp(y, x[1], x[2], x[3], x[4]))) # Print Iteration Path: if (trace) { cat("\n Objective Function Value: ", -f) cat("\n Parameter Estimates: ", x[1], x[2], x[3], x[4], "\n") } f } # Minimization: eps = 1e-10 BIG = 1000 r = nlminb(start = c(alpha, beta, delta, mu), objective = ehypmle, lower = c(eps, -BIG, eps, -BIG), upper = BIG, y = x, trace = trace) names(r$par) <- c("alpha", "beta", "delta", "mu") # Add Title and Description: if (is.null(title)) title = "Hyperbolic Parameter Estimation" if (is.null(description)) description = description() # Result: if (scale) { r$par = r$par / c(SD, SD, 1/SD, 1/SD) r$objective = ehypmle(r$par, y = as.vector(x.orig), trace = trace) } fit = list(estimate = r$par, minimum = -r$objective, code = r$convergence) # Optional Plot: if (doplot) { x = as.vector(x.orig) if (span == "auto") span = seq(min(x), max(x), length = 51) z = density(x, n = 100, ...) x = z$x[z$y > 0] y = z$y[z$y > 0] y.points = dhyp(span, r$par[1], r$par[2], r$par[3], r$par[4]) ylim = log(c(min(y.points), max(y.points))) plot(x, log(y), xlim = c(span[1], span[length(span)]), ylim = ylim, type = "p", xlab = "x", ylab = "log f(x)", ...) title("HYP Parameter Estimation") lines(x = span, y = log(y.points), col = "steelblue") } # Return Value: new("fDISTFIT", call = as.call(CALL), model = "Hyperbolic Distribution", data = as.data.frame(x.orig), fit = fit, title = title, description = description ) } ################################################################################ fBasics/R/dist-hyp.R0000644000176000001440000001342711370220773013752 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # dhyp Returns density for hyperbolic DF # phyp Returns probability for hyperbolic DF # qhyp Returns quantiles for hyperbolic DF # rhyp Returns random variates for hyperbolic DF ################################################################################ dhyp <- function(x, alpha = 1, beta = 0, delta = 1, mu = 0, pm = c("1", "2", "3", "4"), log = FALSE) { # A function implemented by Diethelm Wuertz # Description: # Returns Hyperbolic Density Function PDF # Arguments: # alpha, beta - Shape Parameter, |beta| <= alpha # delta - Scale Parameter, 0 <= delta # mu - Location Parameter # FUNCTION: # Parameters: if (length(alpha) == 4) { mu = alpha[4] delta = alpha[3] beta = alpha[2] alpha = alpha[1] } # Checks: if (alpha <= 0) stop("alpha must be greater than zero") if (delta <= 0) stop("delta must be greater than zero") if (abs(beta) >= alpha) stop("abs value of beta must be less than alpha") # Settings: pm = match.arg(pm) # Density: ans = NA if (pm == 1) ans = .dhyp1(x, alpha, beta, delta, mu) if (pm == 2) ans = .dhyp2(x, alpha, beta, delta, mu) if (pm == 3) ans = .dhyp3(x, alpha, beta, delta, mu) if (pm == 4) ans = .dhyp4(x, alpha, beta, delta, mu) # Log: if (log) ans = log(ans) # Return value: ans } # ------------------------------------------------------------------------------ phyp <- function(q, alpha = 1, beta = 0, delta = 1, mu = 0, pm = c("1", "2", "3", "4"), ...) { # A function implemented by Diethelm Wuertz # Description: # Return cumulative probability of Hyperbolic PDF # Arguments: # alpha, beta - Shape Parameter, |beta| <= alpha # delta - Scale Parameter, 0 <= delta # mu - Location Parameter # FUNCTION: # Checks: if (alpha <= 0) stop("alpha must be greater than zero") if (delta <= 0) stop("delta must be greater than zero") if (abs(beta) >= alpha) stop("abs value of beta must be less than alpha") # Settings: pm = match.arg(pm) # Return Value: ans = NA if (pm == 1) return(.phyp1(q, alpha, beta, delta, mu, ...)) if (pm == 2) return(.phyp2(q, alpha, beta, delta, mu, ...)) if (pm == 3) return(.phyp3(q, alpha, beta, delta, mu, ...)) if (pm == 4) return(.phyp4(q, alpha, beta, delta, mu, ...)) } # ------------------------------------------------------------------------------ qhyp <- function(p, alpha = 1, beta = 0, delta = 1, mu = 0, pm = c("1", "2", "3", "4"), ...) { # A function implemented by Diethelm Wuertz # Description: # Returns quantiles of Hyperbolic PDF # Arguments: # alpha, beta - Shape Parameter, |beta| <= alpha # delta - Scale Parameter, 0 <= delta # mu - Location Parameter # Note: # This procedure will not run under Splus. # FUNCTION: # Checks: if (alpha <= 0) stop("alpha must be greater than zero") if (delta <= 0) stop("delta must be greater than zero") if (abs(beta) >= alpha) stop("abs value of beta must be less than alpha") # Settings: pm = match.arg(pm) # Return Value: ans = NA if (pm == 1) return(.qhyp1(p, alpha, beta, delta, mu, ...)) if (pm == 2) return(.qhyp2(p, alpha, beta, delta, mu, ...)) if (pm == 3) return(.qhyp3(p, alpha, beta, delta, mu, ...)) if (pm == 4) return(.qhyp4(p, alpha, beta, delta, mu, ...)) } # ------------------------------------------------------------------------------ rhyp <- function(n, alpha = 1, beta = 0, delta = 1, mu = 0, pm = c("1", "2", "3", "4")) { # A function implemented by Diethelm Wuertz # Description: # Returns random deviates of Hyperbolic PDF # Arguments: # n - number of random deviates to be generated # alpha, beta - Shape Parameter, |beta| <= alpha # delta - Scale Parameter, 0 <= delta # mu - Location Parameter # Notes: # I have removed my original Fortran program and replaced it by # the dhyperb() function from the HyperbolicDist Package, written # by David Scott, Ai-Wei Lee, Jennifer Tso, Richard Trendall. # License: GPL # FUNCTION: # Checks: if (alpha <= 0) stop("alpha must be greater than zero") if (delta <= 0) stop("delta must be greater than zero") if (abs(beta) >= alpha) stop("abs value of beta must be less than alpha") # Settings: pm = match.arg(pm) # Result: ans = NA if (pm == 1) ans = .rhyp1(n, alpha, beta, delta, mu) if (pm == 2) ans = .rhyp2(n, alpha, beta, delta, mu) if (pm == 3) ans = .rhyp3(n, alpha, beta, delta, mu) if (pm == 4) ans = .rhyp4(n, alpha, beta, delta, mu) # Attributes: attr(ans, "control") = c(dist = "hyp", alpha = alpha, beta = beta, delta = delta, mu = mu) # Return Value: ans } ################################################################################ fBasics/R/dist-gldRobMoments.R0000644000176000001440000001055711370220773015727 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # gldMED Returns true GLD median # gldIQR Returns true GLD inter quartal range # gldSKEW Returns true GLD robust skewness # gldKURT Returns true GLD robust kurtosis ################################################################################ gldMED <- function(lambda1=0, lambda2=-1, lambda3=-1/8, lambda4=-1/8) { # A function implemented by Diethelm Wuertz # Description: # Returns true gld median # Arguments: # lambda1 - a numeric value, the location parameter # lambda2 - a numeric value, the scale parameter # lambda3 - a numeric value, the first shape parameter # lambda4 - a numeric value, the second parameter # FUNCTION: # gld Median Q = qgld(p=0.5, lambda1, lambda2, lambda3, lambda4) med = c(MED = Q) # Return Value: med } # ------------------------------------------------------------------------------ gldIQR <- function(lambda1=0, lambda2=-1, lambda3=-1/8, lambda4=-1/8) { # A function implemented by Diethelm Wuertz # Description: # Returns true gld inter quartal range # Arguments: # lambda1 - a numeric value, the location parameter # lambda2 - a numeric value, the scale parameter # lambda3 - a numeric value, the first shape parameter # lambda4 - a numeric value, the second parameter # FUNCTION: # gld Inter Quartile Range Q = numeric() Q[1] = qgld(p=0.25, lambda1, lambda2, lambda3, lambda4) Q[2] = qgld(p=0.75, lambda1, lambda2, lambda3, lambda4) iqr = c(IQR = Q[[2]] - Q[[1]]) # Return Value: iqr } # ------------------------------------------------------------------------------ gldSKEW <- function(lambda1=0, lambda2=-1, lambda3=-1/8, lambda4=-1/8) { # A function implemented by Diethelm Wuertz # Description: # Returns true gld robust gld skewness # Arguments: # lambda1 - a numeric value, the location parameter # lambda2 - a numeric value, the scale parameter # lambda3 - a numeric value, the first shape parameter # lambda4 - a numeric value, the second parameter # FUNCTION: # gld Robust Skewness: Q = numeric() Q[1] = qgld(p=0.25, lambda1, lambda2, lambda3, lambda4) Q[2] = qgld(p=0.50, lambda1, lambda2, lambda3, lambda4) Q[3] = qgld(p=0.75, lambda1, lambda2, lambda3, lambda4) skew = c(SKEW = ( Q[[3]] + Q[[1]] - 2* Q[[2]] ) / (Q[[3]] - Q[[1]] ) ) # Return Value: skew } # ------------------------------------------------------------------------------ gldKURT <- function(lambda1=0, lambda2=-1, lambda3=-1/8, lambda4=-1/8) { # A function implemented by Diethelm Wuertz # Description: # Returns true gld robust gld kurtosis # Arguments: # lambda1 - a numeric value, the location parameter # lambda2 - a numeric value, the scale parameter # lambda3 - a numeric value, the first shape parameter # lambda4 - a numeric value, the second parameter # FUNCTION: # gld Robust Kurtosis: Q = numeric() for (p in (1:7)/8) Q = c(Q, qgld(p, lambda1, lambda2, lambda3, lambda4)) kurt = c(KURT = (Q[[7]] - Q[[5]] + Q[[3]] - Q[[1]]) / (Q[[6]] - Q[[2]])) # Return Value: kurt } ################################################################################ fBasics/R/dist-gldMode.R0000644000176000001440000000364211370220773014523 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # gldMode Computes the Generalized Lambda Distribution mode ################################################################################ gldMode <- function(lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8) { # A function implemented by Diethelm Wuertz # Arguments: # lambda1 - a numeric value, the location parameter # lambda2 - a numeric value, the scale parameter # lambda3 - a numeric value, the first shape parameter # lambda4 - a numeric value, the second parameter # Description: # Computes the Generalized Lambda Distribution mode # FUNCTION: # Find Maximum: min = qgld(0.01, lambda1, lambda2, lambda3, lambda4) max = qgld(0.99, lambda1, lambda2, lambda3, lambda4) ans = optimize(f = dgld, interval = c(min, max), lambda1 = lambda1, lambda2 = lambda2, lambda3 = lambda3, lambda4 = lambda4, maximum = TRUE, tol = .Machine$double.eps)$maximum # Return Value: ans } ################################################################################ fBasics/R/dist-gldFit.R0000644000176000001440000005001211370220773014352 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # gldFit Fits parameters of a GLD # .gldFit.mle Fits parameters of a GLD using maximum log-likelihood # .gldFit.mps Fits parameters of a GLD using maximum product spacings # .gldFit.gof Fits parameters of a GLD using GoF Statistics # .ksGLD Kolmogorov Smirnov Statistics # .cvmGLD Cramer von Mise Statistics # .adGLD Anderson Darling Statistics # .gldFit.hist Fits parameters of a GLD using a histogram fit # type="fd" Freedman-Diaconis binning # type="scott" Scott binning # type="sturges" Sturges binning # .gldFit.rob Fits parameters of a GLD using robust moments fit ################################################################################ gldFit <- function(x, lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8, method = c("mle", "mps", "gof", "hist", "rob"), scale = NA, doplot = TRUE, add = FALSE, span = "auto", trace = TRUE, title = NULL, description = NULL, ...) { # A function implemented by Diethelm Wuertz # Description: # Fits parameters of a GLD using maximum log-likelihood # Arguments: # lambda1 - a numeric value, the location parameter # lambda2 - a numeric value, the scale parameter # lambda3 - a numeric value, the first shape parameter # lambda4 - a numeric value, the second parameter # Note: # The GLD uses RS parameterization and parameters in Region 4, # i.e. lambda[3,4]<0, lambda2<0 # FUNCTION: # Check Parameters: stopifnot(lambda2 < 0) stopifnot(lambda3 < 0) stopifnot(lambda4 < 0) # Settings: method = match.arg(method) # Parameter Fit: if (method == "mle") { ans = .gldFit.mle(x, lambda1, lambda2, lambda3, lambda4, scale, doplot, add, span, trace, title, description, ...) } else if (method == "mps") { ans = .gldFit.mps(x, lambda1, lambda2, lambda3, lambda4, scale, doplot, add, span, trace, title, description, ...) } else if (method == "gof") { ans = .gldFit.gof(x, lambda1, lambda2, lambda3, lambda4, scale, doplot, add, span, trace, title, description, ...) } else if (method == "hist") { ans = .gldFit.hist(x, lambda1, lambda2, lambda3, lambda4, scale, doplot, add, span, trace, title, description, ...) } else if (method == "rob") { ans = .gldFit.rob (x, lambda1, lambda2, lambda3, lambda4, scale, doplot, add, span, trace, title, description, ...) } # Return Value: ans } # ------------------------------------------------------------------------------ .gldFit.mle <- function(x, lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8, scale = NA, doplot = TRUE, add = FALSE, span = "auto", trace = TRUE, title = NULL, description = NULL, ...) { # A function implemented by Diethelm Wuertz # Description: # Fits parameters of a GLD using maximum log-likelihood # Example: # require(fBasics) # set.seed(4711); x=rgld(5000); fit=gldFit.mle(x)@fit$estimate; fit # FUNCTION: # Settings: scale = FALSE # Transform: x.orig = x x = as.vector(x) if (scale) { SD = sd(x) x = x / SD } # Settings: CALL = match.call() # Objective Function: obj <- function(x, y = x, trace) { DGLD = try(dgld(y, x[1], x[2], x[3], x[4]), silent = TRUE) if (class(DGLD) == "try-error") return(1e9) f = -sum(log(DGLD)) # Print Iteration Path: if (trace) { cat("\n Objective Function Value: ", f) cat("\n Parameter Estimates: ", x[1], x[2], x[3], x[4], "\n") } f } # Parameter Estimation: eps = 1e-10 BIG = 100 r = nlminb( start = c(lambda1, lambda2, lambda3, lambda4), objective = obj, lower = c(-BIG, -BIG, -BIG, -BIG), upper = c(+BIG, -eps, -eps, -eps), y = x, trace = trace) names(r$par) <- c("lambda1", "lambda2", "lambda3", "lambda4") # Add Title and Description: if (is.null(title)) title = "GLD Region 4 Parameter Estimation" if (is.null(description)) description = description() # Rescale Result: if (scale) { r$par = r$par / c(SD, SD, 1/SD, 1/SD) r$objective = obj(r$par, y = as.vector(x.orig), trace = trace) } fit = list(estimate = r$par, minimum = -r$objective, code = r$convergence) # Optional Plot: if (doplot) { x = as.vector(x.orig) if (span == "auto") span = seq(min(x), max(x), length = 501) z = density(x, ...) x = z$x[z$y > 0] y = z$y[z$y > 0] y.points = dgld(span, r$par[1], r$par[2], r$par[3], r$par[4]) ylim = log(c(min(y.points), max(y.points))) if (add) { lines(x = span, y = log(y.points), ...) } else { plot(x, log(y), xlim = c(span[1], span[length(span)]), ylim = ylim, type = "p", xlab = "x", ylab = "log f(x)", ...) title(main = title) lines(x = span, y = log(y.points), col = "steelblue") } } # Return Value: new("fDISTFIT", call = as.call(CALL), model = "GLD Region 4 Distribution", data = as.data.frame(x.orig), fit = fit, title = as.character(title), description = description() ) } # ------------------------------------------------------------------------------ .gldFit.mps <- function(x, lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8, type = c("sum", "mean", "max", "median", "var"), scale = NA, doplot = TRUE, add = FALSE, span = "auto", trace = TRUE, title = NULL, description = NULL, ...) { # A function implemented by Diethelm Wuertz # Description: # Fits parameters of a GLD using maximum product spacings # Example: # require(fBasics) # set.seed(4711); x=rgld(5000); fit=.gldFit.mps(x)@fit$estimate; fit # FUNCTION: # Settings: scale = FALSE type = match.arg(type) CALL = match.call() # Transform: x.orig = x x = as.vector(x) if (scale) { SD = sd(x) x = x / SD } # Objective Function: TYPE = toupper(type) if(type == "sum") { typeFun = sum } else if (type == "mean") { typeFun = mean } else if (type == "median") { typeFun = median } else if (type == "max") { typeFun = function(x) -max(x) } else if (type == "var") { typeFun = function(x) -var(x) } obj = function(x, y = x, typeFun, trace) { PGLD = try(pgld(sort(y), x[1], x[2], x[3], x[4]), silent = TRUE) if (class(PGLD) == "try-error") return(1e9) DH = diff(c(0, na.omit(PGLD), 1)) f = try(-typeFun(log(DH[DH > 0])), silent = TRUE) if (class(PGLD) == "try-error") return(1e9) # Print Iteration Path: if (trace) { cat("\n Objective Function Value: ", f) cat("\n Parameter Estimates: ", x[1], x[2], x[3], x[4], "\n") } f } # Parameter Estimation: eps = 1e-10 BIG = 100 r = nlminb( start = c(lambda1, lambda2, lambda3, lambda4), objective = obj, lower = c(-BIG, -BIG, -BIG, -BIG), upper = c(+BIG, -eps, -eps, -eps), y = x, typeFun = typeFun, trace = trace) names(r$par) <- c("lambda1", "lambda2", "lambda3", "lambda4") # Add Title and Description: if (is.null(title)) title = "GLD Region 4 MPS Estimation" if (is.null(description)) description = description() # Rescale Result: if (scale) { r$par = r$par / c(SD, SD, 1/SD, 1/SD) r$objective = obj(r$par, y = as.vector(x.orig), trace = trace) } fit = list(estimate = r$par, minimum = -r$objective, code = r$convergence) # Optional Plot: if (doplot) { x = as.vector(x.orig) if (span == "auto") span = seq(min(x), max(x), length = 501) z = density(x, ...) x = z$x[z$y > 0] y = z$y[z$y > 0] y.points = dgld(span, r$par[1], r$par[2], r$par[3], r$par[4]) ylim = log(c(min(y.points), max(y.points))) if (add) { lines(x = span, y = log(y.points), ...) } else { plot(x, log(y), xlim = c(span[1], span[length(span)]), ylim = ylim, type = "p", xlab = "x", ylab = "log f(x)", ...) title(main = title) lines(x = span, y = log(y.points), col = "steelblue") } } # Return Value: new("fDISTFIT", call = as.call(CALL), model = "GLD Region 4 Distribution", data = as.data.frame(x.orig), fit = fit, title = as.character(title), description = description() ) } # ------------------------------------------------------------------------------ .ksGLD <- function(N, PFGL) { D = 1/(2*N) + max ( abs( PFGL - ((1:N)-0.5)/N ) ) D } .cvmGLD <- function(N, PFGL) { W2 = 1/(12*N) + sum ( PFGL - ((1:N)-0.5)/N )^2 W2 } .adGLD <- function(N, PFGL) { A2 = -N -(1/N) * sum( (2*(1:N)-1) * ( log(PFGL) + log(1-rev(PFGL)) ) ) A2 } # ------------------------------------------------------------------------------ .gldFit.gof <- function(x, lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8, type = c("ad", "ks", "cvm"), scale = NA, doplot = TRUE, add = FALSE, span = "auto", trace = TRUE, title = NULL, description = NULL, ...) { # A function implemented by Diethelm Wuertz # Description: # Fits parameters of a GLD using GoF Statistics # Example: # require(fBasics) # set.seed(4711); x=rgld(5000); fit=.gldFit.gof(x)@fit$estimate; fit # FUNCTION: # Settings: scale = FALSE type = match.arg(type) CALL = match.call() # Transform: x.orig = x x = as.vector(x) if (scale) { SD = sd(x) x = x / SD } # Objective Function: TYPE = toupper(type) ## DJS 20/02/2010 ## typeFun <- match.fun(paste(".", type, sep = "")) typeFun <- match.fun(paste(".", type, "GLD", sep = "")) obj = function(x, y = x, typeFun, trace) { PFGL = try(pgld(sort(y), x[1], x[2], x[3], x[4]), silent = TRUE) if (class(PFGL) == "try-error") return(1e9) PFGL = PFGL[PFGL > 0] PFGL = PFGL[PFGL < 1] N = length(PFGL) f = typeFun(N, PFGL) if (is.na(f)) return(1e9) # Print Iteration Path: if (trace) { cat("\n Objective Function Value: ", f) cat("\n Parameter Estimates: ", x[1], x[2], x[3], x[4],"\n") } f } # Parameter Estimation: eps = 1e-10 BIG = 100 r = nlminb( start = c(lambda1, lambda2, lambda3, lambda4), objective = obj, lower = c(-BIG, -BIG, -BIG, -BIG), upper = c(+BIG, -eps, -eps, -eps), y = x, typeFun = typeFun, trace = trace) names(r$par) <- c("lambda1", "lambda2", "lambda3", "lambda4") # Add Title and Description: if (is.null(title)) title = paste("GLD Region 4", TYPE, "Estimation") if (is.null(description)) description = description() # Rescale Result: if (scale) { r$par = r$par / c(SD, SD, 1/SD, 1/SD) r$objective = obj(r$par, y = as.vector(x.orig), trace = trace) } fit = list(estimate = r$par, minimum = -r$objective, code = r$convergence) # Optional Plot: if (doplot) { x = as.vector(x.orig) if (span == "auto") span = seq(min(x), max(x), length = 501) z = density(x, ...) x = z$x[z$y > 0] y = z$y[z$y > 0] y.points = dgld(span, r$par[1], r$par[2], r$par[3], r$par[4]) ylim = log(c(min(y.points), max(y.points))) if (add) { lines(x = span, y = log(y.points), ...) } else { plot(x, log(y), xlim = c(span[1], span[length(span)]), ylim = ylim, type = "p", xlab = "x", ylab = "log f(x)", ...) title(main = title) lines(x = span, y = log(y.points), col = "steelblue") } } # Return Value: new("fDISTFIT", call = as.call(CALL), model = "GLD Region 4 Distribution", data = as.data.frame(x.orig), fit = fit, title = as.character(title), description = description() ) } # ------------------------------------------------------------------------------ .gldFit.hist <- function(x, lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8, type = c("fd", "sturges", "scott"), scale = NA, doplot = TRUE, add = FALSE, span = "auto", trace = TRUE, title = NULL, description = NULL, ...) { # A function implemented by Diethelm Wuertz # Description: # Fits parameters of a GLD using a histogram fit # Example: # require(fBasics) # set.seed(4711); x=rgld(5000); fit=gldFit.hist(x)@fit$estimate; fit # FUNCTION: # Settings: scale = FALSE CALL = match.call() type = match.arg(type) if (type == "fd") type = "FD" else if (type == "scott") type = "Scott" else if (type == "sturges") type = "Sturges" # Transform: x.orig = x x = as.vector(x) if (scale) { SD = sd(x) x = x / SD } # Histogram: HIST = hist(x, breaks = type, plot = FALSE) # Objective Function: obj = function(x, hist, trace) { DFGL = try(dgld(hist$mids, x[1], x[2], x[3], x[4]), silent = TRUE) if (class(DFGL) == "try-error") return(1e9) DST = (hist$density - DFGL)^2 f = sum(DST) # Print Iteration Path: if (trace) { cat("\n Objective Function Value: ", f) cat("\n Parameter Estimates: ", x[1], x[2], x[3], x[4], "\n") } f } # Parameter Estimation: eps = 1e-10 BIG = 100 r = nlminb( start = c(lambda1, lambda2, lambda3, lambda4), objective = obj, lower = c(-BIG, -BIG, -BIG, -BIG), upper = c(+BIG, -eps, -eps, -eps), hist = HIST, trace = trace) names(r$par) <- c("lambda1", "lambda2", "lambda3", "lambda4") # Add Title and Description: if (is.null(title)) title = "GLD Region 4 Histogram Estimation" if (is.null(description)) description = description() # Rescale Result: if (scale) { r$par = r$par / c(SD, SD, 1/SD, 1/SD) r$objective = obj(r$par, y = as.vector(x.orig), trace = trace) } fit = list(estimate = r$par, minimum = -r$objective, code = r$convergence) # Optional Plot: if (doplot) { x = as.vector(x.orig) if (span == "auto") span = seq(min(x), max(x), length = 501) z = density(x, ...) x = z$x[z$y > 0] y = z$y[z$y > 0] y.points = dgld(span, r$par[1], r$par[2], r$par[3], r$par[4]) ylim = log(c(min(y.points), max(y.points))) if (add) { lines(x = span, y = log(y.points), ...) } else { plot(x, log(y), xlim = c(span[1], span[length(span)]), ylim = ylim, type = "p", xlab = "x", ylab = "log f(x)", ...) title(main = title) lines(x = span, y = log(y.points), col = "steelblue") } } # Return Value: new("fDISTFIT", call = as.call(CALL), model = "GLD Region 4 Distribution", data = as.data.frame(x.orig), fit = fit, title = as.character(title), description = description() ) } # ------------------------------------------------------------------------------ .gldFit.rob <- function(x, lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8, scale = NA, doplot = TRUE, add = FALSE, span = "auto", trace = TRUE, title = NULL, description = NULL, ...) { # A function implemented by Diethelm Wuertz # Description: # Fits parameters of a GLD using robust moments (quantile) fit # Example: # require(fBasics) # set.seed(4711); x=rgld(5000); fit=.gldFit.rob(x)@fit$estimate; fit # FUNCTION: # Settings: scale = FALSE # Transform: x.orig = x x = as.vector(x) if (scale) { SD = sd(x) x = x / SD } # Settings: CALL = match.call() xMED = sampleMED(x) xIQR = sampleIQR(x) xSKEW = sampleSKEW(x) xKURT = sampleKURT(x) # Objective Function: obj = function(x, xMED, xIQR, xSKEW, xKURT, trace) { lambda1 = x[1] lambda2 = x[2] lambda3 = x[3] lambda4 = x[4] ROB = try(sqrt( (xMED -gldMED (lambda1, lambda2, lambda3, lambda4))^2 + (xIQR -gldIQR (lambda1, lambda2, lambda3, lambda4))^2 + (xSKEW-gldSKEW(lambda1, lambda2, lambda3, lambda4))^2 + (xKURT-gldKURT(lambda1, lambda2, lambda3, lambda4))^2 ), silent = TRUE) if(class(ROB) == "try-error") return(1e9) f = ROB # Print Iteration Path: if (trace) { cat("\n Objective Function Value: ", f) cat("\n Parameter Estimates: ", x[1], x[2], x[3], x[4], "\n") } f } # Parameter Estimation: eps = 1e-10 BIG = 100 r = nlminb( start = c(lambda1, lambda2, lambda3, lambda4), objective = obj, lower = c(-BIG, -BIG, -BIG, -BIG), upper = c(+BIG, -eps, -eps, -eps), xMED = xMED, xIQR = xIQR, xSKEW = xSKEW, xKURT = xKURT, trace = trace) names(r$par) <- c("lambda1", "lambda2", "lambda3", "lambda4") # Add Title and Description: if (is.null(title)) title = "GLD Region 4 Robust Moment Estimation" if (is.null(description)) description = description() # Rescale Result: if (scale) { r$par = r$par / c(SD, SD, 1/SD, 1/SD) r$objective = obj(r$par, y = as.vector(x.orig), trace = trace) } fit = list(estimate = r$par, minimum = -r$objective, code = r$convergence) # Optional Plot: if (doplot) { x = as.vector(x.orig) if (span == "auto") span = seq(min(x), max(x), length = 501) z = density(x, ...) x = z$x[z$y > 0] y = z$y[z$y > 0] y.points = dgld(span, r$par[1], r$par[2], r$par[3], r$par[4]) ylim = log(c(min(y.points), max(y.points))) if (add) { lines(x = span, y = log(y.points), ...) } else { plot(x, log(y), xlim = c(span[1], span[length(span)]), ylim = ylim, type = "p", xlab = "x", ylab = "log f(x)", ...) title(main = title) lines(x = span, y = log(y.points), col = "steelblue") } } # Return Value: new("fDISTFIT", call = as.call(CALL), model = "GLD Region 4 Distribution", data = as.data.frame(x.orig), fit = fit, title = as.character(title), description = description() ) } ################################################################################ fBasics/R/dist-gld.R0000644000176000001440000001246511370220773013721 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # dgld Returns density for Generalized Lambda DF # pgld Returns probability for Generalized Lambda DF # qgld Returns quantiles for Generalized Lambda DF # rgld Returns random variates for Generalized Lambda DF ################################################################################ dgld <- function(x, lambda1=0, lambda2=-1, lambda3=-1/8, lambda4=-1/8, log = FALSE) { # A function implemented by Diethelm Wuertz # Description: # Returns density for Generalized Lambda DF # Arguments: # lambda1 - a numeric value, the location parameter # lambda2 - a numeric value, the scale parameter # lambda3 - a numeric value, the first shape parameter # lambda4 - a numeric value, the second parameter # Details: # This a simple wrapper for the GLD using the RS parameterisation # in Region 4. The parameter range is restricted to negative lambda3 # and lambda4 parameters # Note: # Uses gld functions from package gld # Example: # dgld( (-25:25)/10 ) # FUNCTION: # Parameters: if (length(lambda1) == 4) { lambda4 = lambda1[4] lambda3 = lambda1[3] lambda2 = lambda1[2] lambda1 = lambda1[1] } # Check Parameters: stopifnot (lambda2 < 0) stopifnot (lambda3 < 0) stopifnot (lambda4 < 0) # Density: d = .dgld(x, lambda1=lambda1, lambda2=lambda2, lambda3=lambda3, lambda4=lambda4, param="rs") # Log: if(log) d = log(d) # Return Value: d } # ------------------------------------------------------------------------------ pgld <- function(q, lambda1=0, lambda2=-1, lambda3=-1/8, lambda4=-1/8) { # A function implemented by Diethelm Wuertz # Description: # Returns probability for Generalized Lambda DF # Arguments: # lambda1 - a numeric value, the location parameter # lambda2 - a numeric value, the scale parameter # lambda3 - a numeric value, the first shape parameter # lambda4 - a numeric value, the second parameter # Note: # Uses gld functions from package gld # Example: # pgld( (-25:25)/10 ) # FUNCTION: # Check Parameters: stopifnot (lambda2 < 0) stopifnot (lambda3 < 0) stopifnot (lambda4 < 0) # Probability: p = .pgld(q, lambda1=lambda1, lambda2=lambda2, lambda3=lambda3, lambda4=lambda4, param="rs") # Return Value: p } # ------------------------------------------------------------------------------ qgld <- function(p, lambda1=0, lambda2=-1, lambda3=-1/8, lambda4=-1/8) { # A function implemented by Diethelm Wuertz # Description: # Returns quantiles for Generalized Lambda DF # Arguments: # lambda1 - a numeric value, the location parameter # lambda2 - a numeric value, the scale parameter # lambda3 - a numeric value, the first shape parameter # lambda4 - a numeric value, the second parameter # Note: # Uses gld functions from package gld # Example: # qgld((1:99)/100) # FUNCTION: # Check Parameters: stopifnot (lambda2 < 0) stopifnot (lambda3 < 0) stopifnot (lambda4 < 0) # Quantiles: q = .qgld(p, lambda1=lambda1, lambda2=lambda2, lambda3=lambda3, lambda4=lambda4, param="rs") # Return Value: q } # ------------------------------------------------------------------------------ rgld <- function(n = 100, lambda1=0, lambda2=-1, lambda3=-1/8, lambda4=-1/8) { # A function implemented by Diethelm Wuertz # Description: # Returns random variates for Generalized Lambda DF # Arguments: # lambda1 - a numeric value, the location parameter # lambda2 - a numeric value, the scale parameter # lambda3 - a numeric value, the first shape parameter # lambda4 - a numeric value, the second parameter # Note: # Uses gld functions from package gld # FUNCTION: # Check Parameters: stopifnot (lambda2 < 0) stopifnot (lambda3 < 0) stopifnot (lambda4 < 0) # Random Variates: r = .rgld(n, lambda1=lambda1, lambda2=lambda2, lambda3=lambda3, lambda4=lambda4, param="rs") # Return Value: r } ################################################################################ fBasics/R/dist-ghtRobMoments.R0000644000176000001440000001023611370220773015735 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # ghtMED Returns true GHT median # ghtIQR Returns true GHT inter quartal range # ghtSKEW Returns true GHT robust skewness # ghtKURT Returns true GHT robust kurtosis ################################################################################ ghtMED <- function(beta = 0.1, delta = 1, mu = 0, nu = 10) { # A function implemented by Diethelm Wuertz # Description: # Returns true ght median # Arguments: # beta - a numeric value, the location parameter # delta - a numeric value, the scale parameter # mu - a numeric value, the first shape parameter # nu - a numeric value, the second parameter # FUNCTION: # ght Median Q = qght(p=0.5, beta, delta, mu, nu) med = c(MED = Q) # Return Value: med } # ------------------------------------------------------------------------------ ghtIQR <- function(beta = 0.1, delta = 1, mu = 0, nu = 10) { # A function implemented by Diethelm Wuertz # Description: # Returns true ght inter quartal range # Arguments: # beta - a numeric value, the location parameter # delta - a numeric value, the scale parameter # mu - a numeric value, the first shape parameter # nu - a numeric value, the second parameter # FUNCTION: # ght Inter Quartile Range Q = numeric() Q[1] = qght(p=0.25, beta, delta, mu, nu) Q[2] = qght(p=0.75, beta, delta, mu, nu) iqr = c(IQR = Q[[2]] - Q[[1]]) # Return Value: iqr } # ------------------------------------------------------------------------------ ghtSKEW <- function(beta = 0.1, delta = 1, mu = 0, nu = 10) { # A function implemented by Diethelm Wuertz # Description: # Returns true ght robust ght skewness # Arguments: # beta - a numeric value, the location parameter # delta - a numeric value, the scale parameter # mu - a numeric value, the first shape parameter # nu - a numeric value, the second parameter # FUNCTION: # ght Robust Skewness: Q = numeric() Q[1] = qght(p=0.25, beta, delta, mu, nu) Q[2] = qght(p=0.50, beta, delta, mu, nu) Q[3] = qght(p=0.75, beta, delta, mu, nu) skew = c(SKEW = ( Q[[3]] + Q[[1]] - 2* Q[[2]] ) / (Q[[3]] - Q[[1]] ) ) # Return Value: skew } # ------------------------------------------------------------------------------ ghtKURT <- function(beta = 0.1, delta = 1, mu = 0, nu = 10) { # A function implemented by Diethelm Wuertz # Description: # Returns true ght robust ght kurtosis # Arguments: # beta - a numeric value, the location parameter # delta - a numeric value, the scale parameter # mu - a numeric value, the first shape parameter # nu - a numeric value, the second parameter # FUNCTION: # ght Robust Kurtosis: Q = numeric() for (p in (1:7)/8) Q = c(Q, qght(p, beta, delta, mu, nu)) kurt = c(KURT = (Q[[7]] - Q[[5]] + Q[[3]] - Q[[1]]) / (Q[[6]] - Q[[2]])) # Return Value: kurt } ################################################################################ fBasics/R/dist-ghtMoments.R0000644000176000001440000000726411370220773015301 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # ghtMean Returns true GH Student-t mean # ghtVar Returns true GH Student-t variance # ghtSkew Returns true GH Student-t skewness # ghtKurt Returns true GH Student-t kurtosis # FUNCTION: DESCRIPTION: # ghtMoments Returns true GH Student-t moments ################################################################################ ghtMean <- function(beta=0.1, delta=1, mu=0, nu=10) { # A function implemented by Diethelm Wuertz # Descriptions: # Returns true Mean of the GH Student-t distribution # FUNCTION: # GH Parameters: alpha = abs(beta) + 1e-6 lambda = -nu/2 # Return Value: ghMean(alpha, beta, delta, mu, lambda) } # ------------------------------------------------------------------------------ ghtVar <- function(beta=0.1, delta=1, mu=0, nu=10) { # A function implemented by Diethelm Wuertz # Descriptions: # Returns true variance of the GH Student-t distribution # FUNCTION: # GH Parameters: alpha = abs(beta) + 1e-6 lambda = -nu/2 # Return Value: ghVar(alpha, beta, delta, mu, lambda) } # ------------------------------------------------------------------------------ ghtSkew <- function(beta=0.1, delta=1, mu=0, nu=10) { # A function implemented by Diethelm Wuertz # Descriptions: # Returns true Skewness of the GH Student-t distribution # FUNCTION: # GH Parameters: alpha = abs(beta) + 1e-6 lambda = -nu/2 # Return Value: ghSkew(alpha, beta, delta, mu, lambda) } # ------------------------------------------------------------------------------ ghtKurt <- function(beta=0.1, delta=1, mu=0, nu=10) { # A function implemented by Diethelm Wuertz # Descriptions: # Returns true Kurtosis of the GH Student-t distribution # FUNCTION: # GH Parameters: alpha = abs(beta) + 1e-6 lambda = -nu/2 # Return Value: ghKurt(alpha, beta, delta, mu, lambda) } ################################################################################ ghtMoments <- function(order, type = c("raw", "central", "mu"), beta=0.1, delta=1, mu=0, nu=10) { # A function implemented by Diethelm Wuertz # Descriptions: # Returns moments of the GH Student-t distribution # FUNCTION: # Settings: type = match.arg(type) # GH Parameters: alpha = abs(beta) + 1e-6 lambda = -nu/2 # Return Value: ghMoments(order, type, alpha, beta, delta, mu, lambda) } ################################################################################ fBasics/R/dist-ghtMode.R0000644000176000001440000000343111370220773014533 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # ghtMode Computes the generalized hyperbolic Student-t mode ################################################################################ ghtMode <- function(beta = 0.1, delta = 1, mu = 0, nu = 10) { # A function implemented by Diethelm Wuertz # Description: # Computes the mode of the Generalized Hyperbolic Student-t PDF # Arguments: # beta - skewness parameter # delta - scale parameter # mu - location parameter # nu - shape parameter # Example: # ghtMode() # FUNCTION: # Find Maximum: min = qght(0.01, beta, delta, mu, nu) max = qght(0.99, beta, delta, mu, nu) ans = optimize(f = dght, interval = c(min, max), beta = beta, delta = delta, mu = mu, nu = nu, maximum = TRUE, tol = .Machine$double.eps)$maximum # Return Value: ans } ################################################################################ fBasics/R/dist-ghtFit.R0000644000176000001440000000711711370220773014376 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # ghtFit Fits parameters of a generalized hyperbolic density ################################################################################ ghtFit <- function(x, beta = 0.1, delta = 1, mu = 0, nu = 10, scale = TRUE, doplot = TRUE, span = "auto", trace = TRUE, title = NULL, description = NULL, ...) { # A function implemented by Diethelm Wuertz # Description: # Fits parameters of a generalized hyperbolic density # Example: # x = rght(10000); ghtFit(x, 0.2, 0.8, mean(x), 8) # FUNCTION: # Transform: x.orig = x x = as.vector(x) if (scale) { SD = sd(x) x = x / SD } # Settings: CALL = match.call() # Log-likelihood Function: obj <- function(x, y = x, trace){ if (NA %in% x) return(1e99) ## if (abs(x[2]) >= x[1]) return(1e99) f = -sum(log(dght(y, x[1], x[2], x[3], x[4]))) # Print Iteration Path: if (trace) { cat("\n Objective Function Value: ", -f) cat("\n Parameter Estimates: ", x, "\n") } f } # Variable Transformation and Minimization: eps = 1e-10 BIG = 1000 f = obj(x = c(beta, delta, mu, nu), y = x, trace = FALSE) r = nlminb(start = c(beta, delta, mu, nu), objective = obj, lower = c(-BIG, eps, -BIG, -BIG), upper = BIG, y = x, trace = trace) names(r$par) <- c("beta", "delta", "mu", "nu") # Result: if (scale) { r$par = r$par / c(SD, 1/SD, 1/SD, 1) r$objective = obj(r$par, y = as.vector(x.orig), trace = trace) } # Optional Plot: if (doplot) { x = as.vector(x.orig) if (span == "auto") span = seq(min(x), max(x), length = 51) z = density(x, n = 100, ...) x = z$x[z$y > 0] y = z$y[z$y > 0] y.points = dght(span, r$par[1], r$par[2], r$par[3], r$par[4]) ylim = log(c(min(y.points), max(y.points))) plot(x, log(y), xlim = c(span[1], span[length(span)]), ylim = ylim, type = "p", xlab = "x", ylab = "log f(x)", ...) title("GHT Parameter Estimation") lines(x = span, y = log(y.points), col = "steelblue") } # Add Title and Description: if (is.null(title)) title = "Generalized Hyperbolic Parameter Estimation" if (is.null(description)) description = description() # Fit: fit = list( estimate = r$par, minimum = -r$objective, code = r$convergence) # Return Value: new("fDISTFIT", call = as.call(CALL), model = "Generalized Hyperbolic Distribution", data = as.data.frame(x.orig), fit = fit, title = as.character(title), description = description() ) } ################################################################################ fBasics/R/dist-ght.R0000644000176000001440000001102211370220773013721 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # dght Density of the generalized hyperbolic Student-t # pght Probability of the GHT # dght Quantiles of the GHT # rght Random variates of the GHT ################################################################################ dght <- function(x, beta = 0.1, delta = 1, mu = 0, nu = 10, log = FALSE) { # A function implemented by Diethelm Wuertz # Description: # Returns density of the generalized hyperbolic Student-t # Arguments: # Example: # x = (-5):5; dght(x) # FUNCTION: # Parameters: if (length(beta) == 4) { nu = beta[4] mu = beta[3] delta = beta[2] beta = beta[1] } # GH Parameters: alpha = abs(beta) + 1e-6 lambda = -nu/2 # Density: ans = dgh(x, alpha, beta, delta, mu, lambda, log = log) # Return Value: ans } # ------------------------------------------------------------------------------ pght <- function(q, beta = 0.1, delta = 1, mu = 0, nu = 10) { # A function implemented by Diethelm Wuertz # Description: # Returns probabilities of the generalized hyperbolic Student-t # Arguments: # Example: # q = (-5):5; pght(q) # FUNCTION: # Parameters: if (length(beta) == 4) { nu = beta[4] mu = beta[3] delta = beta[2] beta = beta[1] } # Cumulative Probability: ans = NULL for (Q in q) { Integral = integrate(dght, -Inf, Q, stop.on.error = FALSE, beta = beta, delta = delta, mu = mu, nu = nu) ans = c(ans, as.numeric(unlist(Integral)[1]) ) } # Return Value: ans } # ------------------------------------------------------------------------------ qght <- function(p, beta = 0.1, delta = 1, mu = 0, nu = 10) { # A function implemented by Diethelm Wuertz # Description: # Returns quantiles of the generalized hyperbolic Student-t # Arguments: # Example: # p = (1:9)/10; qght(p); round(pght(qght(p)), digits = 4) # FUNCTION: # Parameters: if (length(beta) == 4) { nu = beta[4] mu = beta[3] delta = beta[2] beta = beta[1] } # Internal Functions: .froot <- function(x, beta = beta, delta = delta, mu = mu, nu = nu, p) { pght(q = x, beta = beta, delta = delta, mu = mu, nu = nu) - p } # Loop over all p's: result = NULL for (pp in p) { lower = -1 upper = +1 counter = 0 iteration = NA while (is.na(iteration)) { iteration = fBasics:::.unirootNA(f = .froot, interval = c(lower, upper), beta = beta, delta = delta, mu = mu, nu = nu, p = pp) counter = counter + 1 lower = lower - 2^counter upper = upper + 2^counter } result = c(result, iteration) } # Return Value: ans = result + mu ans } # ------------------------------------------------------------------------------ rght <- function(n, beta = 0.1, delta = 1, mu = 0, nu = 10) { # A function implemented by Diethelm Wuertz # Description: # Returns random Variates of generalized hyperbolic Student-t # Arguments: # Example: # r = rght(10) # FUNCTION: # Parameters: if (length(beta) == 4) { nu = beta[4] mu = beta[3] delta = beta[2] beta = beta[1] } # GH Parameters: alpha = abs(beta) + 1e-6 lambda = -nu/2 # Random Variates: x = rgh(n, alpha, beta = beta, delta = delta, mu = mu, lambda) # Return Value: x } ################################################################################ fBasics/R/dist-ghRobMoments.R0000644000176000001440000001041111370220773015544 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # ghMED Returns true gh median # ghIQR Returns true gh inter quartal range # ghSKEW Returns true gh robust skewness # ghKURT Returns true gh robust kurtosis ################################################################################ ghMED <- function(alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) { # A function implemented by Diethelm Wuertz # Description: # Returns true gh median # Arguments: # beta - a numeric value, the location parameter # delta - a numeric value, the scale parameter # mu - a numeric value, the first shape parameter # nu - a numeric value, the second parameter # FUNCTION: # gh Median Q = qgh(p=0.5, alpha, beta, delta, mu, lambda) med = c(MED = Q) # Return Value: med } # ------------------------------------------------------------------------------ ghIQR <- function(alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) { # A function implemented by Diethelm Wuertz # Description: # Returns true gh inter quartal range # Arguments: # beta - a numeric value, the location parameter # delta - a numeric value, the scale parameter # mu - a numeric value, the first shape parameter # nu - a numeric value, the second parameter # FUNCTION: # gh Inter Quartile Range Q = numeric() Q[1] = qgh(p=0.25, alpha, beta, delta, mu, lambda) Q[2] = qgh(p=0.75, alpha, beta, delta, mu, lambda) iqr = c(IQR = Q[[2]] - Q[[1]]) # Return Value: iqr } # ------------------------------------------------------------------------------ ghSKEW <- function(alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) { # A function implemented by Diethelm Wuertz # Description: # Returns true gh robust gh skewness # Arguments: # beta - a numeric value, the location parameter # delta - a numeric value, the scale parameter # mu - a numeric value, the first shape parameter # nu - a numeric value, the second parameter # FUNCTION: # gh Robust Skewness: Q = numeric() Q[1] = qgh(p=0.25, alpha, beta, delta, mu, lambda) Q[2] = qgh(p=0.50, alpha, beta, delta, mu, lambda) Q[3] = qgh(p=0.75, alpha, beta, delta, mu, lambda) skew = c(SKEW = ( Q[[3]] + Q[[1]] - 2* Q[[2]] ) / (Q[[3]] - Q[[1]] ) ) # Return Value: skew } # ------------------------------------------------------------------------------ ghKURT <- function(alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) { # A function implemented by Diethelm Wuertz # Description: # Returns true gh robust gh kurtosis # Arguments: # beta - a numeric value, the location parameter # delta - a numeric value, the scale parameter # mu - a numeric value, the first shape parameter # nu - a numeric value, the second parameter # FUNCTION: # gh Robust Kurtosis: Q = numeric() for (p in (1:7)/8) Q = c(Q, qgh(p, alpha, beta, delta, mu, lambda)) kurt = c(KURT = (Q[[7]] - Q[[5]] + Q[[3]] - Q[[1]]) / (Q[[6]] - Q[[2]])) # Return Value: kurt } ################################################################################ fBasics/R/dist-ghMoments.R0000644000176000001440000003103711370220773015110 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # ghMean Returns true GH mean # ghVar Returns true GH variance # ghSkew Returns true GH skewness # ghKurt Returns true GH kurtosis # FUNCTION: DESCRIPTION: # ghMoments Returns true GH moments # FUNCTION: UTILITY FUNCTION: # .aRecursionGH Computes the moment coefficients a recursively # .besselZ Computes Bessel/Power Function ratio # FUNCTION: MOMENTS ABOUT MU: # .ghMuMoments Computes mu-moments from formula # .ghMuMomentsIntegrated Computes mu-moments by integration # .checkGHMuMoments Checks mu-moments # FUNCTION: MOMENTS ABOUT ZERO: # .ghRawMoments Computes raw moments from formula # .ghRawMomentsIntegrated Computes raw moments by Integration # .checkGHRawMoments Checks raw moments # FUNCTION: MOMENTS ABOUT MEAN: # .ghCentralMoments Computes central moments from formula ################################################################################ ghMean <- function(alpha=1, beta=0, delta=1, mu=0, lambda=-1/2) { # A function implemented by Diethelm Wuertz # FUNCTION: # Return Value: zeta = delta * sqrt(alpha*alpha-beta*beta) mean = mu + beta * delta^2 * .kappaGH(zeta, lambda) mean } # ------------------------------------------------------------------------------ ghVar <- function(alpha=1, beta=0, delta=1, mu=0, lambda=-1/2) { # A function implemented by Diethelm Wuertz # FUNCTION: # Return Value: var = .ghCentralMoments(k=2, alpha, beta, delta, mu, lambda)[[1]] var } # ------------------------------------------------------------------------------ ghSkew <- function(alpha=1, beta=0, delta=1, mu=0, lambda=-1/2) { # A function implemented by Diethelm Wuertz # FUNCTION: # Moments: k2 = .ghCentralMoments(k=2, alpha, beta, delta, mu, lambda)[[1]] k3 = .ghCentralMoments(k=3, alpha, beta, delta, mu, lambda)[[1]] # Return Value: skew = k3/(k2^(3/2)) skew } # ------------------------------------------------------------------------------ ghKurt <- function(alpha=1, beta=0, delta=1, mu=0, lambda=-1/2) { # A function implemented by Diethelm Wuertz # FUNCTION: # Moments: k2 = .ghCentralMoments(k=2, alpha, beta, delta, mu, lambda)[[1]] k4 = .ghCentralMoments(k=4, alpha, beta, delta, mu, lambda)[[1]] # Return Value: kurt = k4/k2^2 - 3 kurt } ################################################################################ ghMoments <- function(order, type = c("raw", "central", "mu"), alpha=1, beta=0, delta=1, mu=0, lambda=-1/2) { # A function implemented by Diethelm Wuertz # Descriptions: # Returns moments of the GH distribution # FUNCTION: # Settings: type = match.arg(type) # Moments: if (type == "raw") { ans = .ghRawMoments(order, alpha, beta, delta, mu, lambda) } else if (type == "central") { ans = .ghCentralMoments(order, alpha, beta, delta, mu, lambda) } else if (type == "mu") { ans = .ghMuMoments(order, alpha, beta, delta, mu, lambda) } # Return Value: ans } ################################################################################ .aRecursionGH <- function(k = 12, trace = FALSE) { # A function implemented by Diethelm Wuertz # Description: # Computes the moment coefficients recursively # Example: # .aRecursionGH() # [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] # [1,] 1 0 0 0 0 0 0 0 0 0 0 0 # [2,] 1 1 0 0 0 0 0 0 0 0 0 0 # [3,] 0 3 1 0 0 0 0 0 0 0 0 0 # [4,] 0 3 6 1 0 0 0 0 0 0 0 0 # [5,] 0 0 15 10 1 0 0 0 0 0 0 0 # [6,] 0 0 15 45 15 1 0 0 0 0 0 0 # [7,] 0 0 0 105 105 21 1 0 0 0 0 0 # [8,] 0 0 0 105 420 210 28 1 0 0 0 0 # [9,] 0 0 0 0 945 1260 378 36 1 0 0 0 #[10,] 0 0 0 0 945 4725 3150 630 45 1 0 0 #[11,] 0 0 0 0 0 10395 17325 6930 990 55 1 0 #[12,] 0 0 0 0 0 10395 62370 51975 13860 1485 66 1 # FUNCTION: # Setting Start Values: a = matrix(rep(0, times = k*k), ncol = k) a[1, 1] = 1 if (k > 1) a[2, 1] = 1 # Compute all Cofficients by Recursion: if (k > 1) { for (d in 2:k) { for (l in 2:d) { a[d,l] = a[d-1, l-1] + a[d-1, l]*(2*l+1-d) } } } rownames(a) = paste("k=", 1:k, sep = "") colnames(a) = paste("l=", 1:k, sep = "") # Trace: if (trace) { cat("\n") print(a) cat("\n") } for (K in 1:k) { L = trunc((K+1)/2):K M = 2*L-K s1 = as.character(a[K, L]) s2 = paste("delta^", 2*L, sep = "") s3 = paste("beta^", M, sep = "") s4 = paste("Z_lambda+", L, sep = "" ) if (trace) { cat("k =", K, "\n") print(s1) print(s2) print(s3) print(s4) cat("\n") } } if (trace) cat("\n") # Return Value: list(a = a[k, L], delta = 2*L, beta = M, besselOffset = L) } # ------------------------------------------------------------------------------ .besselZ <- function(x, lambda) { # A function implemented by Diethelm Wuertz # Description: # Computes Bessel/Power Function ratio # Ratio: ratio = besselK(x, lambda)/x^lambda # Return Value: ratio } ################################################################################ .ghMuMoments <- function(k = 4, alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) { # A function implemented by Diethelm Wuertz # Description: # Computes mu moments from formula # Note: # mu is not used. # FUNCTION: # Check parameters: if (lambda >= 0) stopifnot(abs(beta) < alpha) if (lambda < 0) stopifnot(abs(beta) <= alpha) if (lambda > 0) stopifnot(delta >= 0) if (lambda <= 0) stopifnot(delta > 0) zeta = delta*sqrt(alpha^2-beta^2) cLambda = 1/.besselZ(zeta, lambda) a = .aRecursionGH(k) coeff = a$a deltaPow = a$delta betaPow = a$beta besselIndex = lambda + a$besselOffset M = coeff * delta^deltaPow * beta^betaPow * .besselZ(zeta, besselIndex) ans = cLambda * sum(M) attr(ans, "M") <- M # Return Value: ans } # ------------------------------------------------------------------------------ .ghMuMomentsIntegrated <- function(k = 4, alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) { # A function implemented by Diethelm Wuertz # Description: # Computes mu moments by integration # FUNCTION: # Check parameters: if (lambda >= 0) stopifnot(abs(beta) < alpha) if (lambda < 0) stopifnot(abs(beta) <= alpha) if (lambda > 0) stopifnot(delta >= 0) if (lambda <= 0) stopifnot(delta > 0) mgh <- function(x, k, alpha, beta, delta, lambda) { x^k * dgh(x, alpha, beta, delta, mu=0, lambda) } M = integrate(mgh, -Inf, Inf, k = k, alpha = alpha, beta = beta, delta = delta, lambda = lambda, subdivisions = 1000, rel.tol = .Machine$double.eps^0.5)[[1]] # Return Value: M } # ------------------------------------------------------------------------------ .checkGHMuMoments <- function(K = 10) { # A function implemented by Diethelm Wuertz # Description: # Checks mu moments # Example: # .checkGHMuMoments() # FUNCTION: # Compute Raw Moments: mu = NULL for (k in 1:K) { mu = c(mu, .ghMuMoments(k, alpha = 1.1, beta = 0.1, delta = 0.9, mu = 0.1)) } names(mu) = 1:K int = NULL for (k in 1:K) { int = c(int, .ghMuMomentsIntegrated(k, alpha = 1.1, beta = 0.1, delta = 0.9, mu = 0.1)) } names(int) = NULL # Return Value: rbind(mu, int) } # ------------------------------------------------------------------------------ .ghRawMoments <- function(k = 4, alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) { # A function implemented by Diethelm Wuertz # Description: # Computes mu-moments from formula # FUNCTION: # Settings: binomCoeff = choose(k, 0:k) # Compute Mu Moments: M = 1 for (l in 1:k) M = c(M, .ghMuMoments(l, alpha, beta, delta, mu, lambda)) muPower = mu^(k:0) muM = binomCoeff * muPower * M # Return Value: sum(muM) } # ------------------------------------------------------------------------------ .ghRawMomentsIntegrated <- function(k = 4, alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) { # A function implemented by Diethelm Wuertz # Description: # Computes mu-moments by integration # FUNCTION: # Check parameters: if (lambda >= 0) stopifnot(abs(beta) < alpha) if (lambda < 0) stopifnot(abs(beta) <= alpha) if (lambda > 0) stopifnot(delta >= 0) if (lambda <= 0) stopifnot(delta > 0) # Compute Mu Moments by Integration: mgh <- function(x, k, alpha, beta, delta, mu, lambda) { x^k * dgh(x, alpha, beta, delta, mu, lambda) } M = integrate(mgh, -Inf, Inf, k = k, alpha = alpha, beta = beta, delta = delta, mu = mu, lambda = lambda, subdivisions = 1000, rel.tol = .Machine$double.eps^0.5)[[1]] # Return Value: M } # ------------------------------------------------------------------------------ .checkGHRawMoments <- function(K = 10) { # A function implemented by Diethelm Wuertz # Description: # Checks raw-moments # Example: # .checkGHRawMoments() # FUNCTION: raw = NULL for (k in 1:K) { raw = c(raw, .ghRawMoments(k, alpha = 1.1, beta = 0.1, delta = 0.9, mu = 0.1)) } names(raw) = 1:K int = NULL for (k in 1:K) { int = c(int, .ghRawMomentsIntegrated(k, alpha = 1.1, beta = 0.1, delta = 0.9, mu = 0.1)) } names(int) = NULL # Return Value: rbind(raw,int) } ################################################################################ .ghCentralMoments <- function (k = 4, alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) { zeta = delta * sqrt(alpha*alpha-beta*beta) mean = mu + beta * delta^2 * .kappaGH(zeta, lambda) M = 1 for (i in 1:k) M = c(M, .ghMuMoments(i, alpha, beta, delta, mu, lambda)) binomCoeff <- choose(k, 0:k) centralPower <- (mu - mean)^(k:0) centralM <- binomCoeff * centralPower * M sum(centralM) } ################################################################################ fBasics/R/dist-ghMode.R0000644000176000001440000000320711370220773014350 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # ghMode Computes mode of the generalized hyperbolic DF ################################################################################ ghMode <- function(alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) { # A function implemented by Diethelm Wuertz # Description: # Computes the mode of the Generalized Hyperbolic PDF # FUNCTION: # Find Maximum: min = qgh(0.01, alpha, beta, delta, mu, lambda) max = qgh(0.99, alpha, beta, delta, mu, lambda) ans = optimize(f = dgh, interval = c(min, max), alpha = alpha, beta = beta, delta = delta, mu = mu, lambda = lambda, maximum = TRUE, tol = .Machine$double.eps)$maximum # Return Value: ans } ################################################################################ fBasics/R/dist-ghFit.R0000644000176000001440000000711011370220773014203 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: GENERALIZED DISTRIBUTION: # ghFit Fits parameters of a generalized hyperbolic DF ################################################################################ ghFit <- function(x, alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2, scale = TRUE, doplot = TRUE, span = "auto", trace = TRUE, title = NULL, description = NULL, ...) { # A function implemented by Diethelm Wuertz # Description: # Fits parameters of a generalized hyperbolic density # FUNCTION: # Transform: x.orig = x x = as.vector(x) if (scale) { SD = sd(x) x = x / SD } # Settings: CALL = match.call() # Log-likelihood Function: obj = function(x, y = x, trace){ if (NA %in% x) return(1e99) if (abs(x[2]) >= x[1]) return(1e99) f = -sum(dgh(y, x[1], x[2], x[3], x[4], x[5], log = TRUE)) # Print Iteration Path: if (trace) { cat("\n Objective Function Value: ", -f) cat("\n Parameter Estimates: ", x, "\n") } f } # Minimization: r = # Variable Transformation and Minimization: eps = 1e-10 BIG = 1000 f = obj(x = c(alpha, beta, delta, mu, lambda), y = x, trace = FALSE) r = nlminb(start = c(alpha, beta, delta, mu, lambda), objective = obj, lower = c(eps, -BIG, eps, -BIG, -BIG), upper = BIG, y = x, trace = trace) names(r$par) <- c("alpha", "beta", "delta", "mu", "lambda") # Result: if (scale) { r$par = r$par / c(SD, SD, 1/SD, 1/SD, 1) r$objective = obj(r$par, y = as.vector(x.orig), trace = trace) } # Optional Plot: if (doplot) { x = as.vector(x.orig) if (span == "auto") span = seq(min(x), max(x), length = 51) z = density(x, n = 100, ...) x = z$x[z$y > 0] y = z$y[z$y > 0] y.points = dnig(span, r$par[1], r$par[2], r$par[3], r$par[4]) ylim = log(c(min(y.points), max(y.points))) plot(x, log(y), xlim = c(span[1], span[length(span)]), ylim = ylim, type = "p", xlab = "x", ylab = "log f(x)", ...) title("GH Parameter Estimation") lines(x = span, y = log(y.points), col = "steelblue") } # Add Title and Description: if (is.null(title)) title = "Generalized Hyperbolic Parameter Estimation" if (is.null(description)) description = description() # Fit: fit = list(estimate = r$par, minimum = -r$objective, code = r$convergence) # Return Value: new("fDISTFIT", call = as.call(CALL), model = "Generalized Hyperbolic Distribution", data = as.data.frame(x.orig), fit = fit, title = as.character(title), description = description() ) } ################################################################################ fBasics/R/dist-gh.R0000644000176000001440000001245311370220773013546 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # dgh Returns density for generalized hyperbolic DF # pgh Returns probability for generalized hyperbolic DF # qgh Returns quantiles for generalized hyperbolic DF # rgh Returns random variates for generalized hyperbolic DF ################################################################################ dgh <- function(x, alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2, log = FALSE) { # A function implemented by Diethelm Wuertz # Description: # Returns density for the generalized hyperbolic distribution # FUNCTION: # Parameters: if (length(alpha) == 4) { mu = alpha[4] delta = alpha[3] beta = alpha[2] alpha = alpha[1] } # Checks: if (alpha <= 0) stop("alpha must be greater than zero") if (delta <= 0) stop("delta must be greater than zero") if (abs(beta) >= alpha) stop("abs value of beta must be less than alpha") # Density: arg = delta*sqrt(alpha^2-beta^2) a = (lambda/2)*log(alpha^2-beta^2) - ( log(sqrt(2*pi)) + (lambda-0.5)*log(alpha) + lambda*log(delta) + log(besselK(arg, lambda, expon.scaled = TRUE)) - arg ) f = ((lambda-0.5)/2)*log(delta^2+(x - mu)^2) # Use exponential scaled form to prevent from overflows: arg = alpha * sqrt(delta^2+(x-mu)^2) k = log(besselK(arg, lambda-0.5, expon.scaled = TRUE)) - arg e = beta*(x-mu) # Put all together: ans = a + f + k + e if(!log) ans = exp(ans) # Return Value: ans } # ------------------------------------------------------------------------------ pgh <- function(q, alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) { # A function implemented by Diethelm Wuertz # Description: # Returns probability for the generalized hyperbolic distribution # FUNCTION: # Checks: if (alpha <= 0) stop("alpha must be greater than zero") if (delta <= 0) stop("delta must be greater than zero") if (abs(beta) >= alpha) stop("abs value of beta must be less than alpha") # Probability: ans = NULL for (Q in q) { Integral = integrate(dgh, -Inf, Q, stop.on.error = FALSE, alpha = alpha, beta = beta, delta = delta, mu = mu, lambda = lambda) ans = c(ans, as.numeric(unlist(Integral)[1])) } # Return Value: ans } # ------------------------------------------------------------------------------ qgh <- function(p, alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) { # A function implemented by Diethelm Wuertz # Description: # Returns quantiles for the generalized hyperbolic distribution # FUNCTION: # Checks: if (alpha <= 0) stop("alpha must be greater than zero") if (delta <= 0) stop("delta must be greater than zero") if (abs(beta) >= alpha) stop("abs value of beta must be less than alpha") # Internal Function: .froot <- function(x, alpha, beta, delta, mu, lambda, p) { pgh(q = x, alpha = alpha, beta = beta, delta = delta, mu = mu, lambda = lambda) - p } # Quantiles: result = NULL for (pp in p) { lower = -1 upper = +1 counter = 0 iteration = NA while (is.na(iteration)) { iteration = .unirootNA(f = .froot, interval = c(lower, upper), alpha = alpha, beta = beta, delta = delta, mu = mu, lambda = lambda, p = pp) counter = counter + 1 lower = lower - 2^counter upper = upper + 2^counter } result = c(result, iteration) } # Return Value: result } # ------------------------------------------------------------------------------ rgh <- function(n, alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) { # A function implemented by Diethelm Wuertz # Description: # Returns random variates for the generalized hyperbolic distribution # FUNCTION: # Checks: if (alpha <= 0) stop("alpha must be greater than zero") if (delta <= 0) stop("delta must be greater than zero") if (abs(beta) >= alpha) stop("abs value of beta must be less than alpha") # Settings: theta = c(lambda, alpha, beta, delta, mu) # Random Numbers: ans = .rghyp(n, theta) # Attributes: attr(ans, "control") = c(dist = "gh", alpha = alpha, beta = beta, delta = delta, mu = mu, lambda = lambda) # Return Value: ans } ################################################################################ fBasics/R/dist-fDISTFIT.R0000644000176000001440000000415711415310241014414 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # 'fDISTFIT' S4 Class representation # show.fDISTFIT Prints Results from a Fitted Distribution ################################################################################ setClass("fDISTFIT", representation( call = "call", model = "character", data = "data.frame", fit = "list", title = "character", description = "character" ) ) # ------------------------------------------------------------------------------ setMethod("show", "fDISTFIT", function(object) { # A function implemented by Diethelm Wuertz # Description: # Prints Results from a Fitted Distribution # FUNCTION: # Title: cat("\nTitle:\n ") cat(object@title, "\n") # Call: cat("\nCall:\n ") cat(paste(deparse(object@call), sep = "\n", collapse = "\n"), "\n", sep = "") # Model: cat("\nModel:\n ", object@model, "\n", sep = "") # Estimate: cat("\nEstimated Parameter(s):\n") print(object@fit$estimate) # Description: cat("\nDescription:\n ") cat(object@description, "\n\n") # Return Value: invisible() }) # ------------------------------------------------------------------------------ ################################################################################ fBasics/R/dist-distPlot.R0000644000176000001440000000334111415310241014734 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ .distFitPlot <- function(fit, x, FUN = "dnig", main = "Parameter Estimation", span = "auto", add = FALSE, ...) { x = as.vector(x) if (span == "auto") span = seq(min(x), max(x), length = 501) z = density(x, n = 100, ...) x = z$x[z$y > 0] y = z$y[z$y > 0] # The Density function must accept multiple parameters # from the first parameter dFun = match.fun(FUN) y.points = dnig(span, fit$par) ylim = log(c(min(y.points), max(y.points))) if (add) { lines(x = span, y = log(y.points), col = "steelblue") } else { plot(x, log(y), xlim = c(span[1], span[length(span)]), ylim = ylim, type = "p", xlab = "x", ylab = "log f(x)", ...) title(main) lines(x = span, y = log(y.points), col = "steelblue") } } ################################################################################ fBasics/R/dist-distErrors.R0000644000176000001440000000306711415310241015277 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ .distStandardErrors <- function(fit, obj, x) { # Add Standard Errors and t-Values: hessian = tsHessian(x = fit$par, fun = obj, y = x, trace = FALSE) colnames(hessian) = rownames(hessian) = names(fit$par) fit$cvar = solve(hessian) fit$se.coef = sqrt(diag(fit$cvar)) if (fit$scale) fit$se.coef = fit$se.coef / fit$scaleParams fit$tval = fit$par/fit$se.coef fit$matcoef = cbind(fit$par, fit$se.coef, fit$tval, 2*(1-pnorm(abs(fit$tval)))) dimnames(fit$matcoef) = list(names(fit$tval), c(" Estimate", " Std. Error", " t value", "Pr(>|t|)")) # Return Value: fit } ################################################################################ fBasics/R/dist-distCheck.R0000644000176000001440000001060511613257742015054 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # distCheck Checks consistency of distributions ################################################################################ distCheck <- function(fun = "norm", n = 1000, robust = TRUE, subdivisions = 100, ...) { # A function implemented by Diethelm Wuertz # Description: # Checks consistency of distributions # Arguments: # fun - a character string denoting the name of the distribution # n - an integer specifying the number of random variates to be # generated # robust - a logical flag, should robust estimates be used? By # default \code{TRUE} # subdivisions - an integer specifying the numbers of subdivisions # in integration __ NB: only used in one place, *not* in the other.. hmm # ... - the distributional parameters and passed to integrate() ### FIXME (MM): add args.integrate = list() to be passed to integrate(), ### ----- and pass the others to distrib.functions # Examples: # .distCheck("norm", mean = 1, sd = 1) # .distCheck("t", df = 4) # .distCheck("exp", rate = 2) # .distCheck("weibull", shape = 1) # FUNCTION: # Distribution Functions: cat("\nDistribution Check for:", fun, "\n ") CALL = match.call() cat("Call: ") cat(paste(deparse(CALL), sep = "\n", collapse = "\n"), "\n", sep = "") dfun = match.fun(paste("d", fun, sep = "")) pfun = match.fun(paste("p", fun, sep = "")) qfun = match.fun(paste("q", fun, sep = "")) rfun = match.fun(paste("r", fun, sep = "")) # Range: xmin = qfun(p = 0.01, ...) xmax = qfun(p = 0.99, ...) # Check 1 - Normalization: NORM = integrate(dfun, lower = -Inf, upper = Inf, subdivisions = subdivisions, stop.on.error = FALSE, ...) cat("\n1. Normalization Check:\n NORM ") print(NORM) normCheck = (abs(NORM[[1]]-1) < 0.01) # Check 2: cat("\n2. [p-pfun(qfun(p))]^2 Check:\n ") p = c(0.001, 0.01, 0.1, 0.5, 0.9, 0.99, 0.999) P = pfun(qfun(p, ...), ...) pP = round(rbind(p, P), 3) print(pP) RMSE = sd(p-P) print(c(RMSE = RMSE)) rmseCheck = (abs(RMSE) < 0.0001) # Check 3: cat("\n3. r(", n, ") Check:\n", sep = "") r = rfun(n = n, ...) if (!robust) { SAMPLE.MEAN = mean(r) SAMPLE.VAR = var(r) } else { robustSample = MASS::cov.mcd(r, quantile.used = floor(0.95*n)) SAMPLE.MEAN = robustSample$center SAMPLE.VAR = robustSample$cov[1,1] } SAMPLE = data.frame(t(c(MEAN = SAMPLE.MEAN, "VAR" = SAMPLE.VAR)), row.names = "SAMPLE") print(signif(SAMPLE, 3)) fun1 = function(x, ...) { x * dfun(x, ...) } fun2 = function(x, M, ...) { x^2 * dfun(x, ...) } MEAN = integrate(fun1, lower = -Inf, upper = Inf, subdivisions = 5000, stop.on.error = FALSE,...) cat(" X ") print(MEAN) VAR = integrate(fun2, lower = -Inf, upper = Inf, subdivisions = 5000, stop.on.error = FALSE, ...) cat(" X^2 ") print(VAR) EXACT = data.frame(t(c(MEAN = MEAN[[1]], "VAR" = VAR[[1]] - MEAN[[1]]^2)), row.names = "EXACT ") print(signif(EXACT, 3)) meanvarCheck = (abs(SAMPLE.VAR-EXACT$VAR)/EXACT$VAR < 0.1) cat("\n") # Done: ans = list( normCheck = normCheck, rmseCheck = rmseCheck, meanvarCheck = meanvarCheck) # Return Value: unlist(ans) } # ------------------------------------------------------------------------------ .distCheck <- distCheck # Keep for older Rmetrics Versions ################################################################################ fBasics/R/builtin-varpieA2R.R0000644000176000001440000000165311415310241015434 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: # varpie ################################################################################ fBasics/R/builtin-testNortest.R0000644000176000001440000000166011415310241016175 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: # testNortest ################################################################################ fBasics/R/builtin-ssdenGss.R0000644000176000001440000017765012137277416015467 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: ################################################################################ # YC (2013-04-28): In an effor to remove built-in code from # thrid-party packages, gss package is now imported. For the time # being, we keep the hidden functions as wrapper to avoid # back-compatibily. # Code borrowed from # R's contributed package "gss" written by Chong Gu. # Rmetrics: # Note that gss is not available on Debian as of 2009-04-28. # To run these functions under Debian/Rmetrics we have them # implemented here as a builtin. ## Package: gss ## Version: 2.0-9 ## Date: 2012-01-24 ## Title: General Smoothing Splines ## Author: Chong Gu ## Maintainer: Chong Gu ## Depends: R (>= 2.14.0) ## Description: A comprehensive package for structural multivariate ## function estimation using smoothing splines. ## License: GPL (>= 2) ## Packaged: 2012-01-24 14:36:56 UTC; chong ## Repository: CRAN ## Date/Publication: 2012-01-24 16:52:40 ## ----------------------------------------------------------------------------- ## file: gss/R/ssden.R ## Fit density model .ssden <- gss::ssden ## .ssden <- function(formula,type=NULL,data=list(),alpha=1.4, ## weights=NULL,subset,na.action=na.omit, ## id.basis=NULL,nbasis=NULL,seed=NULL, ## domain=as.list(NULL),quad=NULL, ## qdsz.depth=NULL,bias=NULL, ## prec=1e-7,maxiter=30,skip.iter=FALSE) ## { ## ## Obtain model frame and model terms ## mf <- match.call() ## mf$type <- mf$alpha <- NULL ## mf$id.basis <- mf$nbasis <- mf$seed <- NULL ## mf$domain <- mf$quad <- mf$qdsz.depth <- mf$bias <- NULL ## mf$prec <- mf$maxiter <- mf$skip.iter <- NULL ## mf[[1]] <- as.name("model.frame") ## mf <- eval(mf,parent.frame()) ## cnt <- model.weights(mf) ## mf$"(weights)" <- NULL ## ## Generate sub-basis ## nobs <- dim(mf)[1] ## if (is.null(id.basis)) { ## if (is.null(nbasis)) nbasis <- max(30,ceiling(10*nobs^(2/9))) ## if (nbasis>=nobs) nbasis <- nobs ## if (!is.null(seed)) set.seed(seed) ## id.basis <- sample(nobs,nbasis,prob=cnt) ## } ## else { ## if (max(id.basis)>nobs|min(id.basis)<1) ## stop("gss error in .ssden: id.basis out of range") ## nbasis <- length(id.basis) ## } ## ## Set domain and/or generate quadrature ## if (is.null(quad)) { ## ## Set domain and type ## fac.list <- NULL ## for (xlab in names(mf)) { ## x <- mf[[xlab]] ## if (is.factor(x)) { ## fac.list <- c(fac.list,xlab) ## domain[[xlab]] <- NULL ## } ## else { ## if (!is.vector(x)) ## stop("gss error in .ssden: no default quadrature") ## if (is.null(domain[[xlab]])) { ## mn <- min(x) ## mx <- max(x) ## domain[[xlab]] <- c(mn,mx)+c(-1,1)*(mx-mn)*.05 ## } ## else domain[[xlab]] <- c(min(domain[[xlab]]),max(domain[[xlab]])) ## if (is.null(type[[xlab]])) ## type[[xlab]] <- list("cubic",domain[[xlab]]) ## else { ## if (length(type[[xlab]])==1) ## type[[xlab]] <- list(type[[xlab]][[1]],domain[[xlab]]) ## } ## } ## } ## ## Generate numerical quadrature ## domain <- data.frame(domain) ## mn <- domain[1,] ## mx <- domain[2,] ## dm <- ncol(domain) ## if (dm==1) { ## ## Gauss-Legendre or uniform quadrature ## xlab <- names(domain) ## if (type[[xlab]][[1]]%in%c("per","cubic.per","linear.per")) { ## quad <- list(pt=mn+(1:200)/200*(mx-mn), ## wt=rep((mx-mn)/200,200)) ## } ## else quad <- .gauss.quad(200,c(mn,mx)) ## quad$pt <- data.frame(quad$pt) ## colnames(quad$pt) <- colnames(domain) ## } ## else { ## ## Smolyak cubature ## if (is.null(qdsz.depth)) qdsz.depth <- switch(min(dm,6)-1,18,14,10,9,7) ## quad <- .smolyak.quad(dm,qdsz.depth) ## for (i in 1:ncol(domain)) { ## xlab <- colnames(domain)[i] ## wk <- mf[[xlab]] ## jk <- .ssden(~wk,domain=data.frame(wk=domain[,i]),alpha=2, ## id.basis=id.basis,weights=cnt) ## quad$pt[,i] <- .qssden(jk,quad$pt[,i]) ## quad$wt <- quad$wt/.dssden(jk,quad$pt[,i]) ## } ## jk <- wk <- NULL ## quad$pt <- data.frame(quad$pt) ## colnames(quad$pt) <- colnames(domain) ## } ## ## Incorporate factors in quadrature ## if (!is.null(fac.list)) { ## for (i in 1:length(fac.list)) { ## wk <- ## expand.grid(levels(mf[[fac.list[i]]]),1:length(quad$wt)) ## quad$wt <- quad$wt[wk[,2]] ## col.names <- c(fac.list[i],colnames(quad$pt)) ## quad$pt <- data.frame(wk[,1],quad$pt[wk[,2],]) ## colnames(quad$pt) <- col.names ## } ## } ## quad <- list(pt=quad$pt,wt=quad$wt) ## } ## else { ## for (xlab in names(mf)) { ## x <- mf[[xlab]] ## if (is.vector(x)&!is.factor(x)) { ## mn <- min(x,quad$pt[[xlab]]) ## mx <- max(x,quad$pt[[xlab]]) ## range <- c(mn,mx)+c(-1,1)*(mx-mn)*.05 ## if (is.null(type[[xlab]])) ## type[[xlab]] <- list("cubic",range) ## else { ## if (length(type[[xlab]])==1) ## type[[xlab]] <- list(type[[xlab]][[1]],range) ## else { ## mn0 <- min(type[[xlab]][[2]]) ## mx0 <- max(type[[xlab]][[2]]) ## if ((mn0>mn)|(mx0alpha,(alpha.wk-alpha)*fit$wk[2],0) ## cv+adj ## } ## ## initialization ## mu.r <- apply(qd.wt*t(qd.r),2,sum)/sum(qd.wt) ## v.r <- apply(qd.wt*t(qd.r^2),2,sum)/sum(qd.wt) ## if (nnull) { ## mu.s <- apply(qd.wt*t(qd.s),2,sum)/sum(qd.wt) ## v.s <- apply(qd.wt*t(qd.s^2),2,sum)/sum(qd.wt) ## } ## if (is.null(s)) theta <- 0 ## else theta <- log10(sum(v.s-mu.s^2)/nnull/sum(v.r-mu.r^2)*nxi) / 2 ## log.la0 <- log10(sum(v.r-mu.r^2)/sum(diag(q))) + theta ## ## lambda search ## cd <- rep(0,nxi+nnull) ## la <- log.la0 ## repeat { ## mn <- la-1 ## mx <- la+1 ## zz <- .nlm0(cv,c(mn,mx)) ## if (min(zz$est-mn,mx-zz$est)>=1e-3) break ## else la <- zz$est ## } ## ## return ## jk1 <- cv(zz$est) ## int <- sum(qd.wt*exp(t(rbind(10^theta*qd.r,qd.s))%*%cd)) ## c <- cd[1:nxi] ## if (nnull) d <- cd[nxi+(1:nnull)] ## else d <- NULL ## list(lambda=zz$est,theta=theta,c=c,d=d,int=int,cv=jk1) ## } ## Fit multiple smoothing parameter density ## .mspdsty <- function(s,r,id.basis,cnt,qd.s,qd.r,qd.wt,prec,maxiter,alpha,bias,skip.iter) ## { ## nxi <- dim(r)[1] ## nobs <- dim(r)[2] ## nqd <- length(qd.wt) ## nq <- dim(r)[3] ## if (!is.null(s)) nnull <- dim(s)[1] ## else nnull <- 0 ## nxis <- nxi+nnull ## if (is.null(cnt)) cnt <- 0 ## ## cv function ## cv <- function(theta) { ## ind.wk <- theta!=theta.old ## if (sum(ind.wk)==nq) { ## r.wk0 <- qd.r.wk0 <- 0 ## for (i in 1:nq) { ## r.wk0 <- r.wk0 + 10^theta[i]*r[,,i] ## qd.r.wk0 <- qd.r.wk0 + 10^theta[i]*qd.r[,,i] ## } ## assign("r.wk",r.wk0+0,inherits=TRUE) ## assign("qd.r.wk",qd.r.wk0+0,inherits=TRUE) ## assign("theta.old",theta+0,inherits=TRUE) ## } ## else { ## r.wk0 <- r.wk ## qd.r.wk0 <- qd.r.wk ## for (i in (1:nq)[ind.wk]) { ## theta.wk <- (10^(theta[i]-theta.old[i])-1)*10^theta.old[i] ## r.wk0 <- r.wk0 + theta.wk*r[,,i] ## qd.r.wk0 <- qd.r.wk0 + theta.wk*qd.r[,,i] ## } ## } ## q.wk <- r.wk0[,id.basis] ## fit <- .Fortran("dnewton", ## cd=as.double(cd), as.integer(nxis), ## as.double(10^lambda*q.wk), as.integer(nxi), ## as.double(rbind(r.wk0,s)), as.integer(nobs), ## as.integer(sum(cnt)), as.integer(cnt), ## as.double(t(rbind(qd.r.wk0,qd.s))), as.integer(nqd), ## as.integer(bias$nt), as.double(bias$wt), ## as.double(t(qd.wt*bias$qd.wt)), ## as.double(prec), as.integer(maxiter), ## as.double(.Machine$double.eps), integer(nxis), ## wk=double(2*((nqd+1)*bias$nt+nobs)+nxis*(2*nxis+4)+max(nxis,3)), ## info=integer(1),PACKAGE="fBasics") ## if (fit$info==1) stop("gss error in .ssden: Newton iteration diverges") ## if (fit$info==2) warning("gss warning in .ssden: Newton iteration fails to converge") ## assign("cd",fit$cd,inherits=TRUE) ## cv <- alpha*fit$wk[2]-fit$wk[1] ## alpha.wk <- max(0,theta-log.th0-5)*(3-alpha) + alpha ## alpha.wk <- min(alpha.wk,3) ## adj <- ifelse (alpha.wk>alpha,(alpha.wk-alpha)*fit$wk[2],0) ## cv+adj ## } ## cv.wk <- function(theta) cv.scale*cv(theta)+cv.shift ## ## initialization ## theta <- -log10(apply(r[,id.basis,],3,function(x)sum(diag(x)))) ## r.wk <- qd.r.wk <- 0 ## for (i in 1:nq) { ## r.wk <- r.wk + 10^theta[i]*r[,,i] ## qd.r.wk <- qd.r.wk + 10^theta[i]*qd.r[,,i] ## } ## ## theta adjustment ## z <- .sspdsty(s,r.wk,r.wk[,id.basis],cnt,qd.s,qd.r.wk,qd.wt,prec,maxiter,alpha,bias) ## theta <- theta + z$theta ## r.wk <- qd.r.wk <- 0 ## for (i in 1:nq) { ## theta[i] <- 2*theta[i] + log10(t(z$c)%*%r[,id.basis,i]%*%z$c) ## r.wk <- r.wk + 10^theta[i]*r[,,i] ## qd.r.wk <- qd.r.wk + 10^theta[i]*qd.r[,,i] ## } ## mu <- apply(qd.wt*t(qd.r.wk),2,sum)/sum(qd.wt) ## v <- apply(qd.wt*t(qd.r.wk^2),2,sum)/sum(qd.wt) ## log.la0 <- log10(sum(v-mu^2)/sum(diag(r.wk[,id.basis]))) ## log.th0 <- theta-log.la0 ## ## lambda search ## z <- .sspdsty(s,r.wk,r.wk[,id.basis],cnt,qd.s,qd.r.wk,qd.wt,prec,maxiter,alpha,bias) ## lambda <- z$lambda ## log.th0 <- log.th0 + z$lambda ## theta <- theta + z$theta ## cd <- c(z$c,z$d) ## int <- z$int ## ## early return ## if (skip.iter) { ## z$theta <- theta ## return(z) ## } ## ## theta search ## counter <- 0 ## r.wk <- qd.r.wk <- 0 ## for (i in 1:nq) { ## r.wk <- r.wk + 10^theta[i]*r[,,i] ## qd.r.wk <- qd.r.wk + 10^theta[i]*qd.r[,,i] ## } ## theta.old <- theta ## ## scale and shift cv ## tmp <- abs(cv(theta)) ## cv.scale <- 1 ## cv.shift <- 0 ## if (tmp<1&tmp>10^(-4)) { ## cv.scale <- 10/tmp ## cv.shift <- 0 ## } ## if (tmp<10^(-4)) { ## cv.scale <- 10^2 ## cv.shift <- 10 ## } ## repeat { ## zz <- nlm(cv.wk,theta,stepmax=1,ndigit=7) ## if (zz$code<=3) break ## theta <- zz$est ## counter <- counter + 1 ## if (counter>=5) { ## warning("gss warning in .ssden: CV iteration fails to converge") ## break ## } ## } ## ## return ## jk1 <- cv(zz$est) ## qd.r.wk <- 0 ## for (i in 1:nq) qd.r.wk <- qd.r.wk + 10^zz$est[i]*qd.r[,,i] ## int <- sum(qd.wt*exp(t(rbind(qd.r.wk,qd.s))%*%cd)) ## c <- cd[1:nxi] ## if (nnull) d <- cd[nxi+(1:nnull)] ## else d <- NULL ## list(lambda=lambda,theta=zz$est,c=c,d=d,int=int,cv=jk1) ## } ## ----------------------------------------------------------------------------- ## file: gss/R/dssden.R .pssden <- gss::pssden ## .pssden <- ## Compute cdf for univariate density estimate ## function(object,q) { ## if (!(".ssden"%in%class(object))) stop("gss error in .pssden: not a ssden object") ## if (dim(object$mf)[2]!=1) stop("gss error in .pssden: not a 1-D density") ## mn <- min(object$domain) ## mx <- max(object$domain) ## order.q <- rank(q) ## p <- q <- sort(q) ## q.dup <- duplicated(q) ## p[q<=mn] <- 0 ## p[q>=mx] <- 1 ## qd.hize <- 200 ## qd <- .gauss.quad(2*qd.hize,c(mn,mx)) ## d.qd <- .dssden(object,qd$pt) ## gap <- diff(qd$pt) ## g.wk <- gap[qd.hize]/2 ## for (i in 1:(qd.hize-2)) g.wk <- c(g.wk,gap[qd.hize+i]-g.wk[i]) ## g.wk <- 2*g.wk ## g.wk <- c(g.wk,(mx-mn)/2-sum(g.wk)) ## gap[qd.hize:1] <- gap[qd.hize+(1:qd.hize)] <- g.wk ## brk <- cumsum(c(mn,gap)) ## kk <- (1:length(q))[q>mn&q=1] <- mx ## qd.hize <- 200 ## qd <- .gauss.quad(2*qd.hize,c(mn,mx)) ## d.qd <- .dssden(object,qd$pt) ## gap <- diff(qd$pt) ## g.wk <- gap[qd.hize]/2 ## for (i in 1:(qd.hize-2)) g.wk <- c(g.wk,gap[qd.hize+i]-g.wk[i]) ## g.wk <- 2*g.wk ## g.wk <- c(g.wk,(mx-mn)/2-sum(g.wk)) ## gap[qd.hize:1] <- gap[qd.hize+(1:qd.hize)] <- g.wk ## brk <- cumsum(c(mn,gap)) ## p.wk <- cumsum(d.qd*qd$wt) ## kk <- (1:length(p))[p>0&p<1] ## for (i in kk) { ## if (p.dup[i]) { ## q[i] <- q.dup ## next ## } ## ind <- (1:(2*qd.hize))[p.wk=interval[2]) ## warning("gss warning in .gauss.quad: interval limits swapped") ## z <- .Fortran("gaussq", ## as.integer(1), ## as.integer(size), ## as.double(0), as.double(0), ## as.integer(0), ## as.double(c(-1,1)), double(size), ## t=double(size), w=double(size), ## PACKAGE="fBasics") ## mn <- min(interval[1:2]) ## range <- abs(interval[1]-interval[2]) ## pt <- mn+range*(z$t+1)/2 ## wt <- range*z$w/2 ## list(pt=pt,wt=wt) ## } ## ----------------------------------------------------------------------------- ## file: gss/R/smolyak.R ## .smolyak.quad <- ## Generate delayed Smolyak cubature ## function(d, k) { ## size <- .C("size_smolyak", ## as.integer(d), ## as.integer(d+k), ## size=integer(1), ## PACKAGE="fBasics")$size ## z <- .C("quad_smolyak", ## as.integer(d), ## as.integer(d+k), ## pt=double(d*size), ## wt=as.double(1:size), ## PACKAGE="fBasics") ## list(pt=t(matrix(z$pt,d,size)),wt=z$wt) ## } ## .smolyak.size <- ## Get the size of delayed Smolyak cubature ## function(d, k) { ## .C("size_smolyak", ## as.integer(d), ## as.integer(d+k), ## size=integer(1), ## PACKAGE="fBasics")$size ## } ## ----------------------------------------------------------------------------- ## file: gss/R/mkterm.R ## Make phi and rk for model terms ## .mkterm <- function(mf,type) ## { ## ## Obtain model terms ## mt <- attr(mf,"terms") ## xvars <- as.character(attr(mt,"variables"))[-1] ## xfacs <- attr(mt,"factors") ## term.labels <- labels(mt) ## if (attr(attr(mf,"terms"),"intercept")) ## term.labels <- c("1",term.labels) ## ## Backward compatibility ## vlist <- xvars[as.logical(apply(xfacs,1,sum))] ## if (!is.null(type)&&!is.list(type)&&(type%in%c("cubic","linear","tp"))) { ## type.wk <- type ## type <- NULL ## for (xlab in vlist) type[[xlab]] <- type.wk ## } ## ## Set types for marginals ## var.type <- NULL ## for (xlab in vlist) { ## x <- mf[,xlab] ## if (!is.null(type[[xlab]])) { ## ## Check consistency and set default parameters ## type.wk <- type[[xlab]][[1]] ## if ## (!(type.wk%in%c("ordinal","nominal","cubic","linear","per","trig", ## "cubic.per","linear.per","tp","sphere","custom"))) ## stop("gss error in .mkterm: unknown type") ## if (type.wk%in%c("ordinal","nominal")) { ## par.wk <- NULL ## if (!is.factor(x)) ## stop("gss error in .mkterm: wrong type") ## } ## if (type.wk%in%c("cubic","linear")) { ## if (length(type[[xlab]])==1) { ## mn <- min(x) ## mx <- max(x) ## par.wk <- c(mn,mx)+c(-1,1)*.05*(mx-mn) ## } ## else par.wk <- type[[xlab]][[2]] ## if (is.factor(x)|!is.vector(x)) ## stop("gss error in .mkterm: wrong type") ## } ## if (type.wk%in%c("per","cubic.per","linear.per","trig")) { ## if (type.wk=="per") type.wk <- "cubic.per" ## if (length(type[[xlab]])==1) ## stop("gss error in .mkterm: missing domain of periodicity") ## else par.wk <- type[[xlab]][[2]] ## if (is.factor(x)|!is.vector(x)) ## stop("gss error in .mkterm: wrong type") ## } ## if (type.wk=="tp") { ## if (length(type[[xlab]])==1) ## par.wk <- list(order=2,mesh=x,weight=1) ## else { ## par.wk <- par.wk1 <- type[[xlab]][[2]] ## if (length(par.wk1)==1) ## par.wk <- list(order=par.wk1,mesh=x,weight=1) ## if (is.null(par.wk$mesh)) par.wk$mesh <- x ## if (is.null(par.wk$weight)) par.wk$weight <- 1 ## } ## if (dim(as.matrix(x))[2]!=dim(as.matrix(par.wk$mesh))[2]) ## stop("gss error in .mkterm: wrong dimension in normalizing mesh") ## } ## if (type.wk=="sphere") { ## if (length(type[[xlab]])==1) ## par.wk <- 2 ## else par.wk <- type[[xlab]][[2]] ## if (!(par.wk%in%(2:4))) ## stop("gss error in .mkterm: spherical order not implemented") ## } ## if (type.wk=="custom") par.wk <- type[[xlab]][[2]] ## } ## else { ## ## Set default types ## if (is.factor(x)) { ## ## categorical variable ## if (is.ordered(x)) type.wk <- "ordinal" ## else type.wk <- "nominal" ## par.wk <- NULL ## } ## else { ## ## numerical variable ## if (is.vector(x)) { ## type.wk <- "cubic" ## mn <- min(x) ## mx <- max(x) ## par.wk <- c(mn,mx)+c(-1,1)*.05*(mx-mn) ## } ## else { ## type.wk <- "tp" ## par.wk <- list(order=2,mesh=x,weight=1) ## } ## } ## } ## var.type[[xlab]] <- list(type.wk,par.wk) ## } ## ## Create the phi and rk functions ## term <- list(labels=term.labels) ## iphi.wk <- 1 ## irk.wk <- 1 ## for (label in term.labels) { ## iphi <- irk <- phi <- rk <- NULL ## if (label=="1") { ## ## the constant term ## iphi <- iphi.wk ## iphi.wk <- iphi.wk + 1 ## term[[label]] <- list(iphi=iphi,nphi=1,nrk=0) ## next ## } ## vlist <- xvars[as.logical(xfacs[,label])] ## x <- mf[,vlist] ## dm <- length(vlist) ## if (dm==1) { ## type.wk <- var.type[[vlist]][[1]] ## if (type.wk%in%c("nominal","ordinal")) { ## ## factor variable ## if (type.wk=="nominal") fun.env <- .mkrk.nominal(levels(x)) ## else fun.env <- .mkrk.ordinal(levels(x)) ## if (nlevels(x)>2) { ## ## phi ## nphi <- 0 ## ## rk ## rk.fun <- function(x,y,nu=1,env,outer.prod=FALSE) { ## env$fun(x,y,env$env,outer.prod) ## } ## nrk <- 1 ## irk <- irk.wk ## irk.wk <- irk.wk + nrk ## rk <- list(fun=rk.fun,env=fun.env) ## } ## else { ## ## phi ## phi.fun <- function(x,nu=1,env) { ## wk <- as.factor(names(env$env$code)[1]) ## env$fun(x,wk,env$env) ## } ## nphi <- 1 ## iphi <- iphi.wk ## iphi.wk <- iphi.wk + nphi ## phi <- list(fun=phi.fun,env=fun.env) ## ## rk ## nrk <- 0 ## } ## } ## if (type.wk=="cubic") { ## ## cubic splines ## range <- var.type[[vlist]][[2]] ## ## phi ## phi.env <- .mkphi.cubic(range) ## phi.fun <- function(x,nu=1,env) env$fun(x,nu,env$env) ## nphi <- 1 ## iphi <- iphi.wk ## iphi.wk <- iphi.wk + nphi ## phi <- list(fun=phi.fun,env=phi.env) ## ## rk ## rk.env <- .mkrk.cubic(range) ## rk.fun <- function(x,y,nu=1,env,outer.prod=FALSE) { ## env$fun(x,y,env$env,outer.prod) ## } ## nrk <- 1 ## irk <- irk.wk ## irk.wk <- irk.wk + nrk ## rk <- list(fun=rk.fun,env=rk.env) ## } ## if (type.wk%in%c("cubic.per","linear","linear.per","sphere")) { ## ## cubic periodic, linear, and linear periodic splines ## range <- var.type[[vlist]][[2]] ## ## phi ## nphi <- 0 ## ## rk ## if (type.wk=="cubic.per") rk.env <- .mkrk.cubic.per(range) ## if (type.wk=="linear") rk.env <- .mkrk.linear(range) ## if (type.wk=="linear.per") rk.env <- .mkrk.linear.per(range) ## if (type.wk=="sphere") rk.env <- .mkrk.sphere(range) ## rk.fun <- function(x,y,nu=1,env,outer.prod=FALSE) { ## env$fun(x,y,env$env,outer.prod) ## } ## nrk <- 1 ## irk <- irk.wk ## irk.wk <- irk.wk + nrk ## rk <- list(fun=rk.fun,env=rk.env) ## } ## if (type.wk=="trig") { ## ## trigonometric splines ## range <- var.type[[vlist]][[2]] ## ## phi ## phi.env <- .mkphi.trig(range) ## phi.fun <- function(x,nu=1,env) env$fun(x,nu,env$env) ## nphi <- 2 ## iphi <- iphi.wk ## iphi.wk <- iphi.wk + nphi ## phi <- list(fun=phi.fun,env=phi.env) ## ## rk ## rk.env <- .mkrk.trig(range) ## rk.fun <- function(x,y,nu=1,env,outer.prod=FALSE) { ## env$fun(x,y,env$env,outer.prod) ## } ## nrk <- 1 ## irk <- irk.wk ## irk.wk <- irk.wk + nrk ## rk <- list(fun=rk.fun,env=rk.env) ## } ## if (type.wk=="tp") { ## ## thin-plate splines ## par <- var.type[[vlist]][[2]] ## order <- par$order ## mesh <- par$mesh ## weight <- par$weight ## if (is.vector(x)) xdim <- 1 ## else xdim <- dim(x)[2] ## ## phi ## phi.env <- .mkphi.tp(xdim,order,mesh,weight) ## phi.fun <- function(x,nu=1,env) { ## env$fun(x,nu,env$env) ## } ## nphi <- choose(xdim+order-1,xdim)-1 ## iphi <- iphi.wk ## iphi.wk <- iphi.wk + nphi ## phi <- list(fun=phi.fun,env=phi.env) ## ## rk ## rk.env <- .mkrk.tp(xdim,order,mesh,weight) ## rk.fun <- function(x,y,nu=1,env,outer.prod=FALSE) { ## env$fun(x,y,env$env,outer.prod) ## } ## nrk <- 1 ## irk <- irk.wk ## irk.wk <- irk.wk + nrk ## rk <- list(fun=rk.fun,env=rk.env) ## } ## if (type.wk=="custom") { ## ## user-defined ## par <- var.type[[vlist]][[2]] ## nphi <- par$nphi ## if (nphi>0) { ## phi.env <- par$mkphi(par$env) ## phi.fun <- function(x,nu=1,env) { ## env$fun(x,nu,env$env) ## } ## iphi <- iphi.wk ## iphi.wk <- iphi.wk + nphi ## phi <- list(fun=phi.fun,env=phi.env) ## } ## rk.env <- par$mkrk(par$env) ## rk.fun <- function(x,y,nu=1,env,outer.prod=FALSE) { ## env$fun(x,y,env$env,outer.prod) ## } ## nrk <- 1 ## irk <- irk.wk ## irk.wk <- irk.wk + nrk ## rk <- list(fun=rk.fun,env=rk.env) ## } ## } ## else { ## bin.fac <- n.phi <- phi.list <- rk.list <- NULL ## for (i in 1:dm) { ## type.wk <- var.type[[vlist[i]]][[1]] ## if (type.wk%in%c("nominal","ordinal")) { ## ## factor variable ## if (type.wk=="nominal") ## rk.wk <- .mkrk.nominal(levels(x[[i]])) ## else rk.wk <- .mkrk.ordinal(levels(x[[i]])) ## phi.wk <- rk.wk ## n.phi <- c(n.phi,0) ## bin.fac <- c(bin.fac,!(nlevels(x[[i]])>2)) ## } ## if (type.wk=="cubic") { ## ## cubic splines ## range <- var.type[[vlist[i]]][[2]] ## ## phi ## phi.wk <- .mkphi.cubic(range) ## n.phi <- c(n.phi,1) ## ## rk ## rk.wk <- .mkrk.cubic(range) ## bin.fac <- c(bin.fac,0) ## } ## if (type.wk%in%c("cubic.per","linear","linear.per","sphere")) { ## ## cubic periodic, linear, or linear periodic splines ## range <- var.type[[vlist[i]]][[2]] ## n.phi <- c(n.phi,0) ## phi.wk <- NULL ## if (type.wk=="cubic.per") rk.wk <- .mkrk.cubic.per(range) ## if (type.wk=="linear") rk.wk <- .mkrk.linear(range) ## if (type.wk=="linear.per") rk.wk <- .mkrk.linear.per(range) ## if (type.wk=="sphere") rk.wk <- .mkrk.sphere(range) ## bin.fac <- c(bin.fac,0) ## } ## if (type.wk=="trig") { ## ## trigonometric splines ## range <- var.type[[vlist[i]]][[2]] ## ## phi ## phi.wk <- .mkphi.trig(range) ## n.phi <- c(n.phi,2) ## ## rk ## rk.wk <- .mkrk.trig(range) ## bin.fac <- c(bin.fac,0) ## } ## if (type.wk=="tp") { ## ## thin-plate splines ## par <- var.type[[vlist[i]]][[2]] ## order <- par$order ## mesh <- par$mesh ## weight <- par$weight ## if (is.vector(x[[i]])) xdim <- 1 ## else xdim <- dim(x[[i]])[2] ## phi.wk <- .mkphi.tp(xdim,order,mesh,weight) ## n.phi <- c(n.phi,choose(xdim+order-1,xdim)-1) ## rk.wk <- .mkrk.tp(xdim,order,mesh,weight) ## bin.fac <- c(bin.fac,0) ## } ## if (type.wk=="custom") { ## ## user-defined ## par <- var.type[[vlist[i]]][[2]] ## n.phi <- c(n.phi,par$nphi) ## if (par$nphi>0) phi.wk <- par$mkphi(par$env) ## else phi.wk <- NULL ## rk.wk <- par$mkrk(par$env) ## bin.fac <- c(bin.fac,0) ## } ## phi.list <- c(phi.list,list(phi.wk)) ## rk.list <- c(rk.list,list(rk.wk)) ## } ## ## phi ## if (!all(as.logical(n.phi+bin.fac))) nphi <- 0 ## else { ## phi.env <- list(dim=dm,phi=phi.list,n.phi=n.phi,bin.fac=bin.fac) ## phi.fun <- function(x,nu=1,env) { ## ind <- nu - 1 ## z <- 1 ## for (i in 1:env$dim) { ## if (env$bin.fac[i]) { ## wk <- as.factor(names(env$phi[[i]]$env$code)[1]) ## z <- z * env$phi[[i]]$fun(x[[i]],wk,env$phi[[i]]$env) ## } ## else { ## code <- ind%%env$n.phi[i] + 1 ## ind <- ind%/%env$n.phi[i] ## z <- z * env$phi[[i]]$fun(x[[i]],code,env$phi[[i]]$env) ## } ## } ## z ## } ## nphi <- prod(n.phi+bin.fac) ## iphi <- iphi.wk ## iphi.wk <- iphi.wk + nphi ## phi <- list(fun=phi.fun,env=phi.env) ## } ## ## rk ## rk.env <- list(dim=dm,n.phi=n.phi,nphi=nphi, ## phi=phi.list,rk=rk.list) ## rk.fun <- function(x,y,nu=1,env,outer.prod=FALSE) { ## n.rk <- ifelse(env$n.phi,2,1) ## ind <- nu - !env$nphi ## z <- 1 ## for (i in 1:env$dim) { ## code <- ind%%n.rk[i] + 1 ## ind <- ind%/%n.rk[i] ## if (code==n.rk[i]) { ## z <- z * env$rk[[i]]$fun(x[[i]],y[[i]], ## env$rk[[i]]$env,outer.prod) ## } ## else { ## z.wk <- 0 ## for (j in 1:env$n.phi[i]) { ## phix <- env$phi[[i]]$fun(x[[i]],j,env$phi[[i]]$env) ## phiy <- env$phi[[i]]$fun(y[[i]],j,env$phi[[i]]$env) ## if (outer.prod) z.wk <- z.wk + outer(phix,phiy) ## else z.wk <- z.wk + phix * phiy ## } ## z <- z * z.wk ## } ## } ## z ## } ## n.rk <- ifelse(n.phi,2,1) ## nrk <- prod(n.rk) - as.logical(nphi) ## irk <- irk.wk ## irk.wk <- irk.wk + nrk ## rk <- list(fun=rk.fun,env=rk.env) ## } ## term[[label]] <- list(vlist=vlist, ## iphi=iphi,nphi=nphi,phi=phi, ## irk=irk,nrk=nrk,rk=rk) ## } ## term ## } ## ----------------------------------------------------------------------------- ## file: gss/R/mkfun.factor.R ## Make RK for nominal shrinkage ## .mkrk.nominal <- function(levels) ## { ## k <- length(levels) ## if (k<2) stop("gss error: factor should have at least two levels") ## code <- 1:k ## names(code) <- as.character(levels) ## ## Create the environment ## env <- list(code=code,table=diag(k)-1/k) ## ## Create the rk function ## fun <- function(x, y, env, outer.prod = FALSE) { ## if (!(is.factor(x)&is.factor(y))) { ## stop("gss error in rk: inputs are of wrong types") ## } ## x <- as.numeric(env$code[as.character(x)]) ## y <- as.numeric(env$code[as.character(y)]) ## if (any(is.na(c(x,y)))) { ## stop("gss error in rk: unknown factor levels") ## } ## if (outer.prod) env$table[x, y] ## else env$table[cbind(x,y)] ## } ## ## Return the function and the environment ## list(fun=fun,env=env) ## } ## Make RK for ordinal shrinkage ## .mkrk.ordinal <- function(levels) ## { ## k <- length(levels) ## if (k<2) stop("gss error: factor should have at least two levels") ## code <- 1:k ## names(code) <- as.character(levels) ## ## penalty matrix ## if (k==2) { ## B <- diag(.25,2) ## B[1,2] <- B[2,1] <- -.25 ## } ## else { ## B <- diag(2,k) ## B[1,1] <- B[k,k] <- 1 ## diag(B[-1,-k]) <- diag(B[-k,-1]) <- -1 ## ## Moore-Penrose inverse ## B <- eigen(B) ## B <- B$vec[,-k] %*% diag(1/B$val[-k]) %*% t(B$vec[,-k]) ## tol <- sqrt(.Machine$double.eps) ## B <- ifelse(abs(B)env$max)) { ## stop("gss error in rk: inputs are out of range") ## } ## ##% Scale the inputs ## x <- (x-env$min)/(env$max-env$min) ## y <- (y-env$min)/(env$max-env$min) ## ##% Return the result ## rk <- function(x,y) { ## k2 <- function(x) ((x-.5)^2-1/12)/2 ## k4 <- function(x) ((x-.5)^4-(x-.5)^2/2+7/240)/24 ## k2(x)*k2(y)-k4(abs(x-y)) ## } ## if (outer.prod) outer(x,y,rk) ## else rk(x,y) ## } ## ## Return the function and the environment ## list(fun=fun,env=env) ## } ## Make phi function for cubic splines ## .mkphi.cubic <- function(range) ## { ## ## Create the environment ## env <- list(min=min(range), max=max(range)) ## ## Create the phi function ## fun <- function(x,nu,env) { ## ##% Check the input ## if (!is.vector(x)) { ## stop("gss error in phi: inputs are of wrong types") ## } ## if ((min(x)env$max)) { ## stop("gss error in phi: inputs are out of range") ## } ## ##% Return the result ## (x-env$min)/(env$max-env$min)-.5 ## } ## ## Return the function and the environment ## list(fun=fun,env=env) ## } ## Make RK for periodic cubic splines ## .mkrk.cubic.per <- function(range) ## { ## ## Create the environment ## env <- list(min=min(range), max=max(range)) ## ## Create the rk function ## fun <- function(x,y,env,outer.prod=FALSE) { ## ##% Check the inputs ## if (!(is.vector(x)&is.vector(y))) { ## stop("gss error in rk: inputs are of wrong types") ## } ## if ((min(x,y)env$max)) { ## stop("gss error in rk: inputs are out of range") ## } ## ##% Scale the inputs ## x <- (x-env$min)/(env$max-env$min) ## y <- (y-env$min)/(env$max-env$min) ## ##% Return the result ## rk <- function(x,y) { ## k4 <- function(x) ((x-.5)^4-(x-.5)^2/2+7/240)/24 ## -k4(abs(x-y)) ## } ## if (outer.prod) outer(x,y,rk) ## else rk(x,y) ## } ## ## Return the function and the environment ## list(fun=fun,env=env) ## } ## Make RK for trigonometric splines ## .mkrk.trig <- function(range) ## { ## ## Create the environment ## env <- list(min=min(range), max=max(range)) ## ## Create the rk function ## fun <- function(x,y,env,outer.prod=FALSE) { ## ##% Check the inputs ## if (!(is.vector(x)&is.vector(y))) { ## stop("gss error in rk: inputs are of wrong types") ## } ## if ((min(x,y)env$max)) { ## stop("gss error in rk: inputs are out of range") ## } ## ##% Scale the inputs ## x <- (x-env$min)/(env$max-env$min) ## y <- (y-env$min)/(env$max-env$min) ## ##% Return the result ## rk <- function(x,y) { ## k4 <- function(x) ((x-.5)^4-(x-.5)^2/2+7/240)/24 ## -k4(abs(x-y))-2*cos(2*pi*(x-y))/(2*pi)^4 ## } ## if (outer.prod) outer(x,y,rk) ## else rk(x,y) ## } ## ## Return the function and the environment ## list(fun=fun,env=env) ## } ## Make phi function for trigonometric splines ## .mkphi.trig <- function(range) ## { ## ## Create the environment ## env <- list(min=min(range), max=max(range)) ## ## Create the phi function ## fun <- function(x,nu,env) { ## ##% Check the input ## if (!is.vector(x)) { ## stop("gss error in phi: inputs are of wrong types") ## } ## if ((min(x)env$max)) { ## stop("gss error in phi: inputs are out of range") ## } ## ##% Return the result ## xx <- (x-env$min)/(env$max-env$min) ## switch(nu,cos(2*pi*xx),sin(2*pi*xx)) ## } ## ## Return the function and the environment ## list(fun=fun,env=env) ## } ## Make RK for linear splines ## .mkrk.linear <- function(range) ## { ## ## Create the environment ## env <- list(min=min(range), max=max(range)) ## ## Create the rk function ## fun <- function(x,y,env,outer.prod=FALSE) { ## ##% Check the inputs ## if (!(is.vector(x)&is.vector(y))) { ## stop("gss error in rk: inputs are of wrong types") ## } ## if ((min(x,y)env$max)) { ## stop("gss error in rk: inputs are out of range") ## } ## ##% Scale the inputs ## x <- (x-env$min)/(env$max-env$min) ## y <- (y-env$min)/(env$max-env$min) ## ##% Return the result ## rk <- function(x,y) { ## k1 <- function(x) (x-.5) ## k2 <- function(x) ((x-.5)^2-1/12)/2 ## k1(x)*k1(y)+k2(abs(x-y)) ## } ## if (outer.prod) outer(x,y,rk) ## else rk(x,y) ## } ## ## Return the function and the environment ## list(fun=fun,env=env) ## } ## Make RK for periodic linear splines ## .mkrk.linear.per <- function(range) ## { ## ## Create the environment ## env <- list(min=min(range), max=max(range)) ## ## Create the rk function ## fun <- function(x,y,env,outer.prod=FALSE) { ## ##% Check the inputs ## if (!(is.vector(x)&is.vector(y))) { ## stop("gss error in rk: inputs are of wrong types") ## } ## if ((min(x,y)env$max)) { ## stop("gss error in rk: inputs are out of range") ## } ## ##% Scale the inputs ## x <- (x-env$min)/(env$max-env$min) ## y <- (y-env$min)/(env$max-env$min) ## ##% Return the result ## rk <- function(x,y) { ## k2 <- function(x) ((x-.5)^2-1/12)/2 ## k2(abs(x-y)) ## } ## if (outer.prod) outer(x,y,rk) ## else rk(x,y) ## } ## ## Return the function and the environment ## list(fun=fun,env=env) ## } ## ----------------------------------------------------------------------------- ## file: gss/R/mkfun.tp.R ## Make RK for thin-plate splines ## .mkrk.tp <- function(dm,order,mesh,weight=1) ## { ## ## Check inputs ## if (!((2*order>dm)&(dm>=1))) { ## stop("gss error: thin-plate spline undefined for the parameters") ## } ## if (xor(is.vector(mesh),dm==1) ## |xor(is.matrix(mesh),dm>=2)) { ## stop("gss error in .mkrk.tp: mismatched inputs") ## } ## if ((min(weight)<0)|(max(weight)<=0)) { ## stop("gss error in .mkrk.tp: negative weights") ## } ## ## Set weights ## if (is.vector(mesh)) N <- length(mesh) ## else N <- dim(mesh)[1] ## weight <- rep(weight,len=N) ## weight <- sqrt(weight/sum(weight)) ## ## Obtain orthonormal basis ## phi.p <- .mkphi.tp.p(dm,order) ## nnull <- choose(dm+order-1,dm) ## s <- NULL ## for (nu in 1:nnull) s <- cbind(s,phi.p$fun(mesh,nu,phi.p$env)) ## s <- qr(weight*s) ## if (s$rankdm)&(dm>=1))) { ## stop("gss error: thin-plate spline undefined for the parameters") ## } ## if (xor(is.vector(mesh),dm==1) ## |xor(is.matrix(mesh),dm>=2)) { ## stop("gss error in .mkphi.tp: mismatched inputs") ## } ## if ((min(weight)<0)|(max(weight)<=0)) { ## stop("gss error in .mkphi.tp: negative weights") ## } ## ## Set weights ## if (is.vector(mesh)) N <- length(mesh) ## else N <- dim(mesh)[1] ## weight <- rep(weight,len=N) ## weight <- sqrt(weight/sum(weight)) ## ## Create the environment ## phi.p <- .mkphi.tp.p(dm,order) ## nnull <- choose(dm+order-1,dm) ## s <- NULL ## for (nu in 1:nnull) s <- cbind(s,phi.p$fun(mesh,nu,phi.p$env)) ## s <- qr(weight*s) ## if (s$rankdm)&(dm>=1))) { ## stop("gss error: thin-plate spline undefined for the parameters") ## } ## ## Create the environment ## if (dm%%2) { ## theta <- gamma(dm/2-order)/2^(2*order)/pi^(dm/2)/gamma(order) ## } ## else { ## theta <- (-1)^(dm/2+order+1)/2^(2*order-1)/pi^(dm/2)/ ## gamma(order)/gamma(order-dm/2+1) ## } ## env <- list(dim=dm,order=order,theta=theta) ## ## Create the rk.p function ## fun <- function(x,y,env,outer.prod=FALSE) { ## ## Check inputs ## if (env$dim==1) { ## if (!(is.vector(x)&is.vector(y))) { ## stop("gss error in rk: inputs are of wrong types") ## } ## } ## else { ## if (is.vector(x)) x <- t(as.matrix(x)) ## if (env$dim!=dim(x)[2]) { ## stop("gss error in rk: inputs are of wrong dimensions") ## } ## if (is.vector(y)) y <- t(as.matrix(y)) ## if (env$dim!=dim(y)[2]) { ## stop("gss error in rk: inputs are of wrong dimensions") ## } ## } ## ## Return the results ## if (outer.prod) { ## if (env$dim==1) { ## fn1 <- function(x,y) abs(x-y) ## d <- outer(x,y,fn1) ## } ## else { ## fn2 <- function(x,y) sqrt(sum((x-y)^2)) ## d <- NULL ## for (i in 1:dim(y)[1]) d <- cbind(d,apply(x,1,fn2,y[i,])) ## } ## } ## else { ## if (env$dim==1) d <- abs(x-y) ## else { ## N <- max(dim(x)[1],dim(y)[1]) ## x <- t(matrix(t(x),env$dim,N)) ## y <- t(matrix(t(y),env$dim,N)) ## fn <- function(x) sqrt(sum(x^2)) ## d <- apply(x-y,1,fn) ## } ## } ## power <- 2*env$order-env$dim ## switch(1+env$dim%%2, ## env$theta*d^power*log(ifelse(d>0,d,1)), ## env$theta*d^power) ## } ## ## Return the function and the environment ## list(fun=fun,env=env) ## } ## Make pseudo phi function for thin-plate splines ## .mkphi.tp.p <- function(dm,order) ## { ## ## Check inputs ## if (!((2*order>dm)&(dm>=1))) { ## stop("gss error: thin-plate spline undefined for the parameters") ## } ## ## Create the environment ## pol.code <- NULL ## for (i in 0:(order^dm-1)) { ## ind <- i; code <- NULL ## for (j in 1:dm) { ## code <- c(code,ind%%order) ## ind <- ind%/%order ## } ## if (sum(code)90)|(max(abs(x[,2]),abs(y[,2]))>180)) { ## stop("gss error in rk: inputs are out of range") ## } ## ##% Convert to radian ## lat.x <- x[,1]/180*pi; lon.x <- x[,2]/180*pi ## lat.y <- y[,1]/180*pi; lon.y <- y[,2]/180*pi ## ##% Return the result ## rk <- function(lat.x,lon.x,lat.y,lon.y,order) { ## z <- cos(lat.x)*cos(lat.y)*cos(lon.x-lon.y)+sin(lat.x)*sin(lat.y) ## W <- ifelse(z<1-10^(-10),(1-z)/2,0) ## A <- ifelse(W>0,log(1+1/sqrt(W)),0) ## C <- ifelse(W>0,2*sqrt(W),0) ## switch(order-1, ## (A*4*W*(3*W-1)+6*W*(1-C)+1)/2, ## (W*W*(A*((840*W-720)*W+72)+420*W*(1-C)+220*C-150)-4*W+3)/12, ## (W*W*W*(A*(((27720*W-37800)*W+12600)*W-600)+ ## (13860*(1-C)*W+14280*C-11970)*W-2772*C+1470)+ ## 15*W*W-3*W+5)/30) - 1/(2*order-1) ## } ## if (outer.prod) { ## zz <- NULL ## for (i in 1:length(lat.y)) ## zz <- cbind(zz,rk(lat.x,lon.x,lat.y[i],lon.y[i],env$order)) ## } ## else zz <- rk(lat.x,lon.x,lat.y,lon.y,env$order) ## zz ## } ## ## Return the function and the environment ## list(fun=fun,env=env) ## } ## ----------------------------------------------------------------------------- ## file: gss/R/nlm0.R ## minimization of univariate function on finite intervals ## using 3-point quadratic fit with golden-section safe-guard ## .nlm0 <- function(fun,range,prec=1e-7) ## { ## ratio <- 2/(sqrt(5)+1) ## ll.x <- min(range) ## uu.x <- max(range) ## if (uu.x-ll.xuu.x)|(deltarange.l) nn.x <- uu.x - ratio*range.u ## else nn.x <- ll.x + ratio*range.l ## } ## ## Update middle points ## nn.fit <- fun(nn.x) ## neval <- neval + 1 ## if (nn.x # Description: Model-robust standard error estimators for cross-sectional, # time series and longitudinal data. # LazyLoad: yes # LazyData: yes # Depends: R (>= 2.0.0), stats, zoo # Suggests: car, lmtest, strucchange, AER, survival, MASS # Imports: stats # License: GPL-2 # Copyright: (C) 2004 Thomas Lumley and Achim Zeileis # ------------------------------------------------------------------------------ .kweights <- function(x, kernel = c("Truncated", "Bartlett", "Parzen", "Tukey-Hanning", "Quadratic Spectral"), normalize = FALSE) { kernel <- match.arg(kernel) if(normalize) { ca <- switch(kernel, "Truncated" = 2, "Bartlett" = 2/3, "Parzen" = .539285, "Tukey-Hanning" = 3/4, "Quadratic Spectral" = 1) } else ca <- 1 switch(kernel, "Truncated" = { ifelse(ca * x > 1, 0, 1) }, "Bartlett" = { ifelse(ca * x > 1, 0, 1 - abs(ca * x)) }, "Parzen" = { ifelse(ca * x > 1, 0, ifelse(ca * x < 0.5, 1 - 6 * (ca * x)^2 + 6 * abs(ca * x)^3, 2 * (1 - abs(ca * x))^3)) }, "Tukey-Hanning" = { ifelse(ca * x > 1, 0, (1 + cos(pi * ca * x))/2) }, "Quadratic Spectral" = { y <- 6 * pi * x/5 ifelse(x < 1e-4, 1, 3 * (1/y)^2 * (sin(y)/y - cos(y))) }) } ################################################################################ fBasics/R/builtin-hypHyperbolicDist.R0000644000176000001440000004764511370220773017333 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: # .rghyp # .rgigjd # .rgigjd1 # .dhyp1 # .dhyp2 # .dhyp3 # .dhyp4 # .phyp1 # .phyp2 # .phyp3 # .phyp4 # .qhyp1 # .qhyp2 # .qhyp3 # .qhyp4 # .rhyp1 # .rhyperb # .hyperb.change.pars # .rhyp2 # .rhyp3 # .rhyp4 ################################################################################ # This is code borrowed from # David Scott's website. # The functions are also part of previous versions of the R contributed # package Hyperbolic Dist. # Rmetrics: # Note that these functions are not packaged and available on Debian # as of 2007-06-23. # To run these functions under Debian/Rmetrics we have them # implemented here as a builtin. ################################################################################ .rghyp <- function(n, theta) { # A function implemented by Diethelm Wuertz # Author: # Original Version by David Scott # FUNCTION: # Settings: lambda = theta[1] alpha = theta[2] beta = theta[3] delta = theta[4] mu = theta[5] chi = delta^2 psi = alpha^2 - beta^2 # Ckecks: if (alpha <= 0) stop("alpha must be greater than zero") if (delta <= 0) stop("delta must be greater than zero") if (abs(beta) >= alpha) stop("abs value of beta must be less than alpha") # Random Numbers: if (lambda == 1){ X = .rgigjd1(n, c(lambda, chi, psi)) } else{ X = .rgigjd(n, c(lambda, chi, psi)) } # Result: sigma = sqrt(X) Z = rnorm(n) Y = mu + beta*sigma^2 + sigma*Z # Return Value: Y } # ------------------------------------------------------------------------------ .rgigjd <- function(n, theta) { # A function implemented by Diethelm Wuertz # Author: # Original Version by David Scott # FUNCTION: # Settings: lambda = theta[1] chi = theta[2] psi = theta[3] # Checks: if (chi < 0) stop("chi can not be negative") if (psi < 0) stop("psi can not be negative") if ((lambda >= 0)&(psi==0)) stop("When lambda >= 0, psi must be > 0") if ((lambda <= 0)&(chi==0)) stop("When lambda <= 0, chi must be > 0") if (chi == 0) stop("chi = 0, use rgamma") if (psi == 0) stop("algorithm only valid for psi > 0") alpha = sqrt(psi/chi) beta = sqrt(psi*chi) m = (lambda-1+sqrt((lambda-1)^2+beta^2))/beta g = function(y){ 0.5*beta*y^3 - y^2*(0.5*beta*m+lambda+1) + y*((lambda-1)*m-0.5*beta) + 0.5*beta*m } upper = m while (g(upper) <= 0) upper = 2*upper yM = uniroot(g, interval=c(0,m))$root yP = uniroot(g, interval=c(m,upper))$root a = (yP-m)*(yP/m)^(0.5*(lambda-1))*exp(-0.25*beta*(yP+1/yP-m-1/m)) b = (yM-m)*(yM/m)^(0.5*(lambda-1))*exp(-0.25*beta*(yM+1/yM-m-1/m)) c = -0.25*beta*(m+1/m) + 0.5*(lambda-1)*log(m) output = numeric(n) for(i in 1:n){ need.value = TRUE while(need.value==TRUE){ R1 = runif (1) R2 = runif (1) Y = m + a*R2/R1 + b*(1-R2)/R1 if (Y>0){ if (-log(R1)>=-0.5*(lambda-1)*log(Y)+0.25*beta*(Y+1/Y)+c){ need.value = FALSE } } } output[i] = Y } # Return Value: output/alpha } # ------------------------------------------------------------------------------ .rgigjd1 <- function(n, theta) { # A function implemented by Diethelm Wuertz # Description: # Modified version of rgigjd to generate random observations # from a generalised inverse Gaussian distribution in the # special case where lambda = 1. # Author: # Original Version by David Scott # FUNCTION: if (length(theta) == 2) theta = c(1, theta) # Settings: lambda = 1 chi = theta[2] psi = theta[3] # Checks: if (chi < 0) stop("chi can not be negative") if (psi < 0) stop("psi can not be negative") if (chi == 0) stop("chi = 0, use rgamma") if (psi == 0) stop("When lambda >= 0, psi must be > 0") alpha = sqrt(psi/chi) beta = sqrt(psi*chi) m = abs(beta)/beta g = function(y){ 0.5*beta*y^3 - y^2*(0.5*beta*m+lambda+1) + y*(-0.5*beta) + 0.5*beta*m } upper = m while (g(upper)<=0) upper = 2*upper yM = uniroot(g,interval=c(0,m))$root yP = uniroot(g,interval=c(m,upper))$root a = (yP-m)*exp(-0.25*beta*(yP+1/yP-m-1/m)) b = (yM-m)*exp(-0.25*beta*(yM+1/yM-m-1/m)) c = -0.25*beta*(m+1/m) output = numeric(n) for(i in 1:n){ need.value = TRUE while(need.value==TRUE){ R1 = runif (1) R2 = runif (1) Y = m + a*R2/R1 + b*(1-R2)/R1 if (Y>0){ if (-log(R1)>=0.25*beta*(Y+1/Y)+c){ need.value = FALSE } } } output[i] = Y } # Return Value: output/alpha } # ------------------------------------------------------------------------------ .dhyp1 <- function(x, alpha = 1, beta = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Returns Hyperbolic Density Function PDF # Arguments: # alpha, beta - Shape Parameter, |beta| <= alpha # delta - Scale Parameter, 0 <= delta # mu - Location Parameter # FUNCTION: # Density: efun = exp( -alpha*sqrt(delta^2 + (x-mu)^2) + beta*(x-mu) ) sqr = sqrt(alpha^2-beta^2) bK1 = besselK(delta*sqr, nu = 1) prefac = sqr / ( 2 * alpha * delta * bK1) ans = prefac * efun # Return Value: ans } # ------------------------------------------------------------------------------ .dhyp2 <- function(x, zeta = 1, rho = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Returns Hyperbolic density in the 2nd parameterization # FUNCTION: # Arguments: # alpha, beta - Shape Parameter, |beta| <= alpha # delta - Scale Parameter, 0 <= delta # mu - Location Parameter # Parameter Change: alpha = zeta / ( delta * sqrt(1 - rho*rho) ) beta = alpha * rho # Return Value: ans = dhyp(x, alpha, beta, delta, mu) ans } # ------------------------------------------------------------------------------ .dhyp3 <- function(x, xi = 1/sqrt(2), chi = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Returns Hyperbolic density in the 2nd parameterization # Arguments: # alpha, beta - Shape Parameter, |beta| <= alpha # delta - Scale Parameter, 0 <= delta # mu - Location Parameter # FUNCTION: # Parameter Change: rho = chi / xi zeta = 1/xi^2 - 1 alpha = zeta / ( delta * sqrt(1 - rho*rho) ) beta = alpha * rho # Return Value: ans = dhyp(x, alpha, beta, delta, mu) ans } # ------------------------------------------------------------------------------ .dhyp4 <- function(x, a.bar = 1, b.bar = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Returns Hyperbolic density in the 2nd parameterization # Arguments: # alpha, beta - Shape Parameter, |beta| <= alpha # delta - Scale Parameter, 0 <= delta # mu - Location Parameter # FUNCTION: # Parameter Change: alpha = a.bar / delta beta = b.bar / delta # Return Value: ans = dhyp(x, alpha, beta, delta, mu) ans } # ------------------------------------------------------------------------------ .phyp1 <- function(q, alpha = 1, beta = 0, delta = 1, mu = 0, ...) { # A function implemented by Diethelm Wuertz # Description: # Return cumulative probability of Hyperbolic PDF # Arguments: # alpha, beta - Shape Parameter, |beta| <= alpha # delta - Scale Parameter, 0 <= delta # mu - Location Parameter # FUNCTION: # Cumulative Probability: ans = NULL for (Q in q) { Integral = integrate(dhyp, -Inf, Q, stop.on.error = FALSE, alpha = alpha, beta = beta, delta = delta, mu = mu, ...) # Works in both, R and SPlus: ans = c(ans, as.numeric(unlist(Integral)[1]) ) } # Return Value: ans } # ------------------------------------------------------------------------------ .phyp2 <- function(q, zeta = 1, rho = 0, delta = 1, mu = 0, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns cumulative probability in the 2nd parameterization # Arguments: # zeta, rho - Shape Parameter, resulting |beta| <= alpha # delta - Scale Parameter, 0 <= delta # mu - Location Parameter # FUNCTION: # Parameter Change: alpha = zeta / ( delta * sqrt(1 - rho*rho) ) beta = alpha * rho # Return Value: ans = phyp(q, alpha, beta, delta, mu, ...) ans } # ------------------------------------------------------------------------------ .phyp3 <- function(q, xi = 1/sqrt(2), chi = 0, delta = 1, mu = 0, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns cumulative probability in the 3rd parameterization # Arguments: # xi, xhi - Shape Parameter, resulting |beta| <= alpha # delta - Scale Parameter, 0 <= delta # mu - Location Parameter # FUNCTION: # Parameter Change: rho = chi / xi zeta = 1/xi^2 - 1 alpha = zeta / ( delta * sqrt(1 - rho*rho) ) beta = alpha * rho # Return Value: ans = phyp(q, alpha, beta, delta, mu, ...) ans } # ------------------------------------------------------------------------------ .phyp4 <- function(q, a.bar = 1, b.bar = 0, delta = 1, mu = 0, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns cumulative probability in the 4th parameterization # Arguments: # a.bar, b.bar - Shape Parameter, resulting |beta| <= alpha # delta - Scale Parameter, 0 <= delta # mu - Location Parameter # FUNCTION: # Parameter Change: alpha = a.bar / delta beta = b.bar / delta # Return Value: ans = phyp(q, alpha, beta, delta, mu, ...) ans } # ------------------------------------------------------------------------------ .qhyp1 <- function(p, alpha = 1, beta = 0, delta = 1, mu = 0, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns quantiles of Hyperbolic PDF # Arguments: # alpha, beta - Shape Parameter, |beta| <= alpha # delta - Scale Parameter, 0 <= delta # mu - Location Parameter # Note: # This procedure will not run under Splus. # FUNCTION: # Internal Functions: .froot <- function(x, alpha, beta, delta, p) { phyp(q = x, alpha = alpha, beta = beta, delta = delta, mu = 0) - p } # Loop over all p's: result = NULL for (pp in p) { lower = -1 upper = +1 counter = 0 iteration = NA while (is.na(iteration)) { iteration = .unirootNA(f = .froot, interval = c(lower, upper), alpha = alpha, beta = beta, delta = delta, p = pp) counter = counter + 1 lower = lower-2^counter upper = upper+2^counter } result = c(result, iteration) } # Return Value: ans = result + mu ans } # ------------------------------------------------------------------------------ .qhyp2 <- function(p, zeta = 1, rho = 0, delta = 1, mu = 0, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns quantiles of Hyperbolic PDF in the 2nd parameterization # Arguments: # zeta, rho - Shape Parameter, resulting |beta| <= alpha # delta - Scale Parameter, 0 <= delta # mu - Location Parameter # FUNCTION: # Parameter Change: alpha = zeta / ( delta * sqrt(1 - rho*rho) ) beta = alpha * rho # Return Value: ans = qhyp(p, alpha, beta, delta, mu, ...) ans } # ------------------------------------------------------------------------------ .qhyp3 <- function(p, xi = 1/sqrt(2), chi = 0, delta = 1, mu = 0, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns quantiles of Hyperbolic PDF in the 3rd parameterization # Arguments: # zeta, chi - Shape Parameter, resulting |beta| <= alpha # delta - Scale Parameter, 0 <= delta # mu - Location Parameter # FUNCTION: # Parameter Change: rho = chi / xi zeta = 1/xi^2 - 1 alpha = zeta / ( delta * sqrt(1 - rho*rho) ) beta = alpha * rho # Return Value: ans = qhyp(p, alpha, beta, delta, mu, ...) ans } # ------------------------------------------------------------------------------ .qhyp4 <- function(p, a.bar = 1, b.bar = 0, delta = 1, mu = 0, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns quantiles of Hyperbolic PDF in the 4th parameterization # Arguments: # a.bar, b.bar - Shape Parameter, resulting |beta| <= alpha # delta - Scale Parameter, 0 <= delta # mu - Location Parameter # FUNCTION: # Parameter Change: alpha = NA beta = NA # Return Value: ans = qhyp(p, alpha, beta, delta, mu, ...) ans } # ------------------------------------------------------------------------------ .rhyp1 <- function(n, alpha = 1, beta = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Returns random deviates of Hyperbolic PDF # Arguments: # n - number of random deviates to be generated # alpha, beta - Shape Parameter, |beta| <= alpha # delta - Scale Parameter, 0 <= delta # mu - Location Parameter # Notes: # I have removed my original Fortran program and replaced it by # the dhyperb() function from the HyperbolicDist Package, written # by David Scott, Ai-Wei Lee, Jennifer Tso, Richard Trendall. # License: GPL # FUNCTION: # Result - Use Standard Parameterization: Zeta = delta * sqrt(alpha^2 - beta^2) hyp.Pi = beta / sqrt(alpha^2 - beta^2) theta = c(hyp.Pi, Zeta, delta, mu) # Return Value: ans = .rhyperb(n = n, theta = theta) ans } # ------------------------------------------------------------------------------ .rhyperb <- function(n, theta) { # FUNCTION: # Internal Function: hyp.pi = theta[1] zeta = theta[2] delta = theta[3] mu = theta[4] alpha = as.numeric(.hyperb.change.pars(1, 2, theta))[1] * delta beta = as.numeric(.hyperb.change.pars(1, 2, theta))[2] * delta phi = as.numeric(.hyperb.change.pars(1, 3, theta))[1] * delta gamma = as.numeric(.hyperb.change.pars(1, 3, theta))[2] * delta theta.start = -sqrt(phi * gamma) t = -sqrt(gamma/phi) w = sqrt(phi/gamma) delta1 = exp(theta.start)/phi delta2 = (w - t) * exp(theta.start) delta3 = exp(-gamma * w)/gamma k = 1/(delta1 + delta2 + delta3) r = k * delta1 v = 1 - k * delta3 output = numeric(n) need.value = TRUE for (i in 1:n) { while (need.value == TRUE) { U = runif(1) E = rexp(1) if (U <= r) { x = 1/phi * log(phi * U/k) if (E >= alpha * (sqrt(1 + x^2) + x)) { need.value = FALSE } } if ((U > r) & (U <= v)) { x = t - 1/phi + U * exp(-theta.start)/k if (E >= alpha * sqrt(1 + x^2) - beta * x + theta.start) { need.value = FALSE } } if (U > v) { x = 1/gamma * log(k/gamma) - 1/gamma * log(1 - U) if (E >= alpha * (sqrt(1 + x^2) - x)) { need.value = FALSE } } } output[i] = delta * x + mu need.value = TRUE } # Return Value: output } # ------------------------------------------------------------------------------ .hyperb.change.pars <- function(from, to, theta) { # FUNCTION: # Internal Function: delta <- theta[3] mu <- theta[4] hyperb.pi <- theta[1] zeta <- theta[2] if (from == 1 && to == 2) { alpha <- zeta * sqrt(1 + hyperb.pi^2)/delta beta <- zeta * hyperb.pi/delta output = c(alpha = alpha, beta = beta, delta = delta, mu = mu) } if (from == 1 && to == 3) { phi <- zeta/delta * (sqrt(1 + hyperb.pi^2) + hyperb.pi) gamma <- zeta/delta * (sqrt(1 + hyperb.pi^2) - hyperb.pi) output = c(phi = phi, gamma = gamma, delta = delta, mu = mu) } # Return Value: output } # ------------------------------------------------------------------------------ .rhyp2 <- function(n, zeta = 1, rho = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Returns random deviates of Hyperbolic PDF in the 2nd parameterization # Arguments: # zeta, rho - Shape Parameter, resulting |beta| <= alpha # delta - Scale Parameter, 0 <= delta # mu - Location Parameter # FUNCTION: # Parameter Change: alpha = zeta / ( delta * sqrt(1 - rho*rho) ) beta = alpha * rho ans = rhyp(n, alpha, beta, delta, mu) # Return Value: ans } # ------------------------------------------------------------------------------ .rhyp3 <- function(n, xi = 1/sqrt(2), chi = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Returns random deviates of Hyperbolic PDF in the 3rd parameterization # Arguments: # zeta, chi - Shape Parameter, resulting |beta| <= alpha # delta - Scale Parameter, 0 <= delta # mu - Location Parameter # FUNCTION: # Parameter Change: rho = chi / xi zeta = 1/xi^2 - 1 alpha = zeta / ( delta * sqrt(1 - rho*rho) ) beta = alpha * rho ans = rhyp(n, alpha, beta, delta, mu) # Return Value: ans } # ------------------------------------------------------------------------------ .rhyp4 <- function(n, a.bar = 1, b.bar = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Returns random deviates of Hyperbolic PDF in the 4th parameterization # Arguments: # a.bar, b.bar - Shape Parameter, resulting |beta| <= alpha # delta - Scale Parameter, 0 <= delta # mu - Location Parameter # FUNCTION: # Parameter Change: alpha = a.bar / delta beta = b.bar / delta ans = rhyp(n, alpha, beta, delta, mu) # Return Value: ans } ################################################################################ fBasics/R/builtin-gmmGmm.R0000644000176000001440000004521511370220773015076 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: # .gmm # .HAC # .weightsAndrews2 # .bwAndrews2 # .summary.gmm # .lintest ################################################################################ # Code borrowed from # R's contributed package "gmm" written by Pierre Chausse. # Rmetrics: # Note that gmm is not available on Debian as of 2009-04-28. # To run these functions under Debian/Rmetrics we have them # implemented here as a builtin. # We also made modifications for tailored usage with Rmetrics. # Note that the dependences in the original package requires zoo # which may create conflicts with Rmetrics timeDate/timeSeries. # Package: gmm # Version: 1.0-4 # Date: 2009-03-10 # Title: Generalized Method of Moments and Generalized Empirical Likelihood # Author: Pierre Chausse # Maintainer: Pierre Chausse # Description: It is a complete suite to estimate models based on moment # conditions. It includes the two step Generalized method of moments # (GMM) of Hansen(1982), the iterated GMM and continuous updated # estimator (CUE) of Hansen-Eaton-Yaron(1996) and several methods that # belong to the Generalized Empirical Likelihood (GEL) family of estimators, # as presented by Smith(1997), Kitamura(1997), Newey-Smith(2004) and # Anatolyev(2005). # Depends: R (>= 2.0.0), sandwich, tseries, mvtnorm # Imports: stats # License: GPL (>= 2) # ------------------------------------------------------------------------------ .gmm <- function(g, x, t0=NULL, gradv=NULL, type=c("twoStep","cue","iterative"), wmatrix = c("optimal","ident"), vcov=c("HAC","iid"), kernel=c("Quadratic Spectral","Truncated", "Bartlett", "Parzen", "Tukey-Hanning"),crit=10e-7,bw = .bwAndrews2, prewhite = FALSE, ar.method = "ols", approx="AR(1)",tol = 1e-7, itermax=100,intercept=TRUE,optfct=c("optim","optimize"), ...) { type <- match.arg(type) kernel <- match.arg(kernel) vcov <- match.arg(vcov) wmatrix <- match.arg(wmatrix) optfct <- match.arg(optfct) typeg=0 if (is(g,"formula")) { typeg=1 dat <- .get_dat(g,x,intercept=intercept) x <- dat$x g <- function(tet,x,ny=dat$ny,nh=dat$nh,k=dat$k) { tet <- matrix(tet,ncol=k) if (intercept) { e <- x[,1:ny] - x[,(ny+1):(ny+k)]%*%t(tet) gt <- e for (i in 2:nh) { gt <- cbind(gt,e*x[,(ny+k+i)]) } } if (!intercept) e <- x[,1:ny] - x[,(ny+1):(ny+k)]%*%t(tet) gt <- e*x[,ny+k+1] if (nh > 1) { for (i in 2:nh) { gt <- cbind(gt,e*x[,(ny+k+i)]) } } return(gt) } gradv <- function(x,ny=dat$ny,nh=dat$nh,k=dat$k) { dgb <- -(t(x[,(ny+k+1):(ny+k+nh)])%*%x[,(ny+1):(ny+k)])%x% diag(rep(1,ny))/nrow(x) return(dgb) } tetlin <- function(x,w,ny=dat$ny,nh=dat$nh,k=dat$k) { n <- nrow(x) ym <- as.matrix(x[,1:ny]) xm <- as.matrix(x[,(ny+1):(ny+k)]) hm <- as.matrix(x[,(ny+k+1):(ny+k+nh)]) whx <- solve(w,(crossprod(hm,xm)%x%diag(ny))) wvecyh <- solve(w,matrix(crossprod(ym,hm),ncol=1)) dg <- gradv(x) xx <- crossprod(dg,whx) par <- solve(xx,crossprod(dg,wvecyh)) gb <- matrix(colSums(g(par,x))/n,ncol=1) value <- crossprod(gb,solve(w,gb)) res <- list(par=par,value=value) return(res) } } if (optfct == "optimize") { n = nrow(g(t0[1],x)) q = ncol(g(t0[1],x)) k = 1 k2 <- k df <- q-k } else { if (typeg) { k <- dat$k k2 <- k*dat$ny n <- nrow(x) q <- dat$ny*dat$nh df <- q-k*dat$ny } else { n = nrow(g(t0,x)) q = ncol(g(t0,x)) k = length(t0) k2 <- k df <- q-k } } obj1 <- function(thet) { gt <- g(thet,x) gbar <- as.vector(colMeans(gt)) obj <- crossprod(gbar,solve(w,gbar)) return(obj) } iid <- function(thet) { gt <- g(thet,x) v <- crossprod(gt,gt)/n return(v) } Gf <- function(thet) { myenv <- new.env() assign('x',x,envir=myenv) assign('thet',thet,envir=myenv) barg <- function(x,thet) { gt <- g(thet,x) gbar <- as.vector(colMeans(gt)) return(gbar) } G <- attr(numericDeriv(quote(barg(x,thet)),"thet",myenv),"gradient") return(G) } if (q == k2 | wmatrix == "ident") { if (typeg) { w <- diag(q) res <- tetlin(x,w) z = list(par=res$par,objective=res$value) } else { w=diag(rep(1,q)) if (optfct == "optim") res <- optim(t0,obj1, ...) else { res <- optimize(obj1,t0, ...) res$par <- res$minimum res$value <- res$objective } z = list(par=res$par,objective=res$value) } } else { if (type=="twoStep") { w=diag(rep(1,q)) if (typeg) { res1 <- tetlin(x,w) } else { if (optfct == "optim") res1 <- optim(t0,obj1, ...) else { res1 <- optimize(obj1,t0, ...) res1$par <- res1$minimum res1$value <- res1$objective } } if (vcov == "iid") w <- iid(res1$par) if (vcov == "HAC") w <- .HAC(g(res1$par,x), kernel=kernel, bw=bw, prewhite=prewhite,ar.method=ar.method,approx=approx,tol=tol) if (typeg) { res2 <- tetlin(x,w) } else { if (optfct == "optim") res2 <- optim(res1$par,obj1, ...) else { res2 <- optimize(obj1,t0, ...) res2$par <- res2$minimum res2$value <- res2$objective } } z = list(par=res2$par,objective=res2$value) } if (type=="cue") { obj_cue <- function(thet) { gt <- g(thet,x) gbar <- as.vector(colMeans(gt)) if (vcov == "iid") w2 <- iid(thet) if (vcov == "HAC") w2 <- .HAC(g(thet,x), kernel=kernel, bw=bw, prewhite=prewhite,ar.method=ar.method,approx=approx,tol=tol) obj <- crossprod(gbar,solve(w2,gbar)) return(obj) } if (typeg) { if (is.null(t0)) t0 <- tetlin(x,diag(rep(1,q)))$par if (optfct == "optim") { res2 <- optim(t0,obj_cue, ...) } else { res2 <- optimize(obj_cue,t0, ...) res2$par <- res2$minimum res2$value <- res2$objective } } else { if (optfct == "optim") res2 <- optim(t0,obj_cue, ...) else { res2 <- optimize(obj_cue,t0, ...) res2$par <- res2$minimum res2$value <- res2$objective } } z = list(par=res2$par,objective=res2$value) } if (type=="iterative") { w=diag(rep(1,q)) if (typeg) res <- tetlin(x,w) else { if (optfct == "optim") res <- optim(t0,obj1, ...) else { res <- optimize(obj1,t0, ...) res$par <- res$minimum res$value <- res$objective } } ch <- 100000 j <- 1 while(ch>crit) { tet <- res$par if (vcov == "iid") w2 <- iid(tet) if (vcov == "HAC") w2 <- .HAC(g(tet,x), kernel=kernel, bw=bw, prewhite=prewhite,ar.method=ar.method,approx=approx,tol=tol) if (typeg) res <- tetlin(x,w2) else { if (optfct == "optim") res <- optim(tet,obj1, ...) else { res <- optimize(obj1,t0, ...) res$par <- res$minimum res$value <- res$objective } } ch <- crossprod(abs(tet-res$par)/tet,abs(tet-res$par)/tet) if (j>itermax) { cat("No convergence after ",itermax," iterations") ch <- crit } j <- j+1 } z = list(par=res$par,objective=res$value) } } if (!is.function(gradv)) G <- Gf(z$par) else if (typeg) G <- gradv(x) else G <- gradv(z$par,x) if (vcov == "iid") v <- iid(z$par)/n else v <- .HAC(g(z$par,x), kernel=kernel, bw=bw,prewhite=prewhite, ar.method=ar.method,approx=approx,tol=tol)/n if (wmatrix == "optimal") { z$vcov <- solve(crossprod(G,solve(v,G))) } else { GGG <- solve(crossprod(G),t(G)) z$vcov <- GGG%*%v%*%t(GGG) } z$gt <- g(z$par,x) if (typeg==0) names(z$par) <- paste("Theta[",1:k,"]",sep="") if (typeg == 1) { namex <- colnames(dat$x[,(dat$ny+1):(dat$ny+dat$k)]) nameh <- colnames(dat$x[,(dat$ny+dat$k+1):(dat$ny+dat$k+dat$nh)]) if (dat$ny > 1) { namey <- colnames(dat$x[,1:dat$ny]) names(z$par) <- paste(rep(namey,dat$k),"_",rep(namex,rep(dat$ny,dat$k)),sep="") colnames(z$gt) <- paste(rep(namey,dat$nh),"_",rep(nameh,rep(dat$ny,dat$nh)),sep="") } if (dat$ny == 1) { names(z$par) <- namex colnames(z$gt) <- nameh } } dimnames(z$vcov) <- list(names(z$par),names(z$par)) z$df <- df z$k <- k z$n <- n z$met <- type z$kernel <- kernel class(z) <- "gmm" z } # ------------------------------------------------------------------------------ .HAC <- function(x, weights = .weightsAndrews2, bw = .bwAndrews2, prewhite = FALSE, ar.method = "ols", kernel=c("Quadratic Spectral", "Truncated", "Bartlett", "Parzen", "Tukey-Hanning"), approx="AR(1)", tol = 1e-7) { n.orig <- n <- nrow(x) k <- ncol(x) kernel=match.arg(kernel) if(prewhite > 0) { var.fit <- ar(x, order.max = prewhite, demean = FALSE, aic = FALSE, method = ar.method) if(k > 1) D <- solve(diag(ncol(x)) - apply(var.fit$ar, 2:3, sum)) else D <- as.matrix(1/(1 - sum(var.fit$ar))) x <- as.matrix(na.omit(var.fit$resid)) n <- n - prewhite } weights <- weights(x, ar.method = ar.method,kernel=kernel,bw=bw, approx = approx, prewhite = 1, tol = tol) if (length(weights) > n) { warning("more weights than observations, only first n used") weights <- weights[1:n] } utu <- 0.5 * crossprod(x) * weights[1] wsum <- n * weights[1]/2 w2sum <- n * weights[1]^2/2 if (length(weights) > 1) { for (ii in 2:length(weights)) { utu <- utu + weights[ii] * crossprod(x[1:(n - ii + 1), , drop = FALSE], x[ii:n, , drop = FALSE]) wsum <- wsum + (n - ii + 1) * weights[ii] w2sum <- w2sum + (n - ii + 1) * weights[ii]^2 } } utu <- utu + t(utu) if(prewhite > 0) { utu <- crossprod(t(D), utu) %*% t(D) } wsum <- 2 * wsum w2sum <- 2 * w2sum bc <- n^2/(n^2 - wsum) df <- n^2/w2sum rval <- utu/n.orig return(rval) } # ------------------------------------------------------------------------------ .weightsAndrews2 <- function(x, bw = .bwAndrews2, kernel = c("Quadratic Spectral", "Truncated", "Bartlett", "Parzen", "Tukey-Hanning"), approx = c("AR(1)", "ARMA(1,1)"), prewhite = 1, ar.method = "ols", tol = 1e-7, verbose = FALSE) { kernel <- match.arg(kernel) approx=match.arg(approx) if (is.function(bw)) bw <- bw(x, kernel = kernel, prewhite = prewhite, ar.method = ar.method, approx=approx) n <- NROW(x) weights <- .kweights(0:(n - 1)/bw, kernel = kernel) weights <- weights[1:max(which(abs(weights) > tol))] return(weights) } # ------------------------------------------------------------------------------ .bwAndrews2 <- function(x, kernel = c("Quadratic Spectral", "Truncated", "Bartlett", "Parzen", "Tukey-Hanning"), approx = c("AR(1)", "ARMA(1,1)"), prewhite = 1, ar.method = "ols") { kernel <- match.arg(kernel) approx <- match.arg(approx) n <- nrow(x) k <- ncol(x) if (approx == "AR(1)") { fitAR1 <- function(x) { rval <- ar(x, order.max = 1, aic = FALSE, method = "ols") rval <- c(rval$ar, sqrt(rval$var.pred)) names(rval) <- c("rho", "sigma") return(rval) } ar.coef <- apply(x, 2, fitAR1) denum <- sum((ar.coef["sigma", ]/(1 - ar.coef["rho", ]))^4) alpha2 <- sum(4 * ar.coef["rho", ]^2 * ar.coef["sigma", ]^4/(1 - ar.coef["rho", ])^8)/denum alpha1 <- sum(4 * ar.coef["rho", ]^2 * ar.coef["sigma", ]^4/((1 - ar.coef["rho", ])^6 * (1 + ar.coef["rho", ])^2))/denum } else { fitARMA11 <- function(x) { rval <- arima(x, order = c(1, 0, 1), include.mean = FALSE) rval <- c(rval$coef, sqrt(rval$sigma2)) names(rval) <- c("rho", "psi", "sigma") return(rval) } arma.coef <- apply(x, 2, fitARMA11) denum <- sum(((1 + arma.coef["psi", ]) * arma.coef["sigma", ]/(1 - arma.coef["rho", ]))^4) alpha2 <- sum(4 * ((1 + arma.coef["rho", ] * arma.coef["psi", ]) * (arma.coef["rho", ] + arma.coef["psi", ]))^2 * arma.coef["sigma", ]^4/(1 - arma.coef["rho", ])^8)/denum alpha1 <- sum(4 * ((1 + arma.coef["rho", ] * arma.coef["psi", ]) * (arma.coef["rho", ] + arma.coef["psi", ]))^2 * arma.coef["sigma", ]^4/((1 - arma.coef["rho", ])^6 * (1 + arma.coef["rho", ])^2))/denum } rval <- switch(kernel, Truncated = { 0.6611 * (n * alpha2)^(1/5) }, Bartlett = { 1.1447 * (n * alpha1)^(1/3) }, Parzen = { 2.6614 * (n * alpha2)^(1/5) }, "Tukey-Hanning" = { 1.7462 * (n * alpha2)^(1/5) }, "Quadratic Spectral" = { 1.3221 * (n * alpha2)^(1/5) }) return(rval) } # ------------------------------------------------------------------------------ .summary.gmm <- function(object, interval=FALSE, ...) { z <- object se <- sqrt(diag(z$vcov)) par <- z$par tval <- par/se j <- z$objective*z$n ans <- list(met=z$met,kernel=z$kernel,algo=z$algo) names(ans$met) <- "GMM method" names(ans$kernel) <- "kernel for cov matrix" ans$par <- round(cbind(par,se, tval, 2 * pnorm(abs(tval), lower.tail = FALSE)),5) dimnames(ans$par) <- list(names(z$par), c("Estimate", "Std. Error", "t value", "Pr(>|t|)")) ans$J_test <- noquote(paste("Test-J degrees of freedom is ",z$df,sep="")) ans$j <- noquote(cbind(j,ifelse(z$df>0,pchisq(j,z$df,lower.tail = FALSE),"*******"))) dimnames(ans$j) <- list("Test E(g)=0: ",c("J-test","Pz(>j)")) if (interval != FALSE) { zs <- qnorm((1-interval)/2,lower.tail=FALSE) ch <- zs*se ans$interval <- cbind(par-ch,par+ch) dimnames(ans$interval) <- list(names(par),c("Theta_lower","Theta_upper")) } class(ans) <- "summary.gmm" ans } # ------------------------------------------------------------------------------ .lintest <- function(object,R,c) { z <- object dl <- nrow(R) rest <- R%*%z$par - c if (class(z)=="gmm") vcov_par <- z$vcov else vcov_par <- z$vcov_par vcov <- R%*%vcov_par%*%t(R) h0 <- matrix(rep(NA,nrow(R)),ncol=1) for (i in 1:nrow(R)) { testnames <- names(z$par)[R[i,]!=0] rn <- R[i,][R[i,]!=0] if (rn[1] != 1) h0[i] <- paste(rn[1],"*",testnames[1],sep="") else h0[i] <- testnames[1] if (length(rn) > 1) { for (j in 2:length(rn)) { if (rn[j] >= 0) { if (rn[j] != 1) h0[i] <- paste(h0[i]," + ",rn[j],"*",testnames[j],sep="") else h0[i] <- paste(h0[i]," + ",testnames[j],sep="") } else { if (abs(rn[j]) != 1) h0[i] <- paste(h0[i]," - ",abs(rn[j]),"*",testnames[j],sep="") else h0[i] <- paste(h0[i]," - ",testnames[j],sep="") } } } h0[i] <- paste(h0[i]," = ",c[i],sep="") } rh <- solve(vcov,rest) ans <- list(description <- "Wald test for H0: R(Theta)=c") ans$H0 <- noquote(h0) colnames(ans$H0) <- "Null Hypothesis" wt <- crossprod(rest,rh) pv <- pchisq(wt,dl,lower.tail=FALSE) res <- cbind(wt,pv) dimnames(res) <- list("Wald test", c("Statistics","P-Value")) ans$result <- res ans } ################################################################################ fBasics/R/builtin-glGld.R0000644000176000001440000005675511415310241014707 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: # .pgld # .qgld # .qgl.fmkl # .qgl.fm5 # .qgl.rs # .qdgld # .qdgl.rs # .qdgl.fmkl # .qdgl.fm5 # .rgld # .gl.parameter.tidy # .gl.check.lambda ################################################################################ # Code borrowed from # R's contributed package "gld" written by Robert King # Rmetrics: # Note that gmm is not available on Debian as of 2009-11-11. # To run these functions under Debian/Rmetrics we have them # implemented here as a builtin. # Package: gld # Version: 1.8.4 # Date: 2008/10/01 # Title: Estimation and use of the generalised (Tukey) lambda distribution # Author: Robert King # Maintainer: Robert King # Description: The generalised lambda distribution, or Tukey lambda # distribution, provides a wide variety of shapes with one # functional form. This package provides random numbers, # quantiles, probabilities, densities and plots. It also # includes an implementation of the starship estimation method # for the distribution. # License: GPL (>= 2) # URL: http://tolstoy.newcastle.edu.au/~rking/publ/rprogs/information.html # Packaged: 2009-10-14 ################################################################################ .dgld <- function(x, lambda1=0, lambda2=NULL, lambda3=NULL, lambda4=NULL, param="fmkl", lambda5=NULL, inverse.eps=1e-8, max.iterations=500) { # Tidy the parameters so gl.check.lambda will work lambdas <- .gl.parameter.tidy(lambda1, lambda2, lambda3, lambda4, param, lambda5) # Check the parameters if(!.gl.check.lambda(lambdas, param=param, vect=TRUE)) { stop(paste("The parameter values", lambdas, "\ndo not produce a proper distribution with the", param, "parameterisation - see \ndocumentation for .gl.check.lambda")) } # calculate u=F(x) numerically, then use qdgl # Unless x is outside the range, then density should be zero extreme <- .qgld(c(0, 1), lambda1=lambdas, param=param) # It may be better to change this to simply # (x <= extreme[2])*(x >= extreme[1]) outside.range <- !as.logical((x<=extreme[2])*(x>=extreme[1])) u <- .pgld(x, lambdas, param=param, inverse.eps=inverse.eps, max.iterations=max.iterations) dens <- .qdgld(u, lambda1=lambdas, param=param) dens[outside.range] <- 0 dens } ################################################################################ .pgld <- function(q, lambda1=0, lambda2=NULL, lambda3=NULL, lambda4=NULL, param="fmkl", lambda5=NULL, inverse.eps=1e-8, max.iterations=500) { # Thanks to Steve Su, , for improvements to this code # If lambda1 is a vector, the default value for lambda2 will cause a # problem. # I did have a warning about this, but it will occur too often to make # up for the benefit, so I've deleted it. # Tidy the parameters so gl.check.lambda will work lambdas <- .gl.parameter.tidy(lambda1, lambda2, lambda3, lambda4, param, lambda5) # Check the parameters if(!.gl.check.lambda(lambdas, param=param, vect=TRUE)) { stop(paste("The parameter values", lambda1, lambda2, lambda3, lambda4, "\ndo not produce a proper distribution with the", param, "parameterisation - see \ndocumentation for gl.check.lambda")) } jr <- q; jr[sort.list(q)] <- seq(along=q) order.x<-order(q) xx<-sort(q) # xx has the sorted data, and jr & order.x the information to get back # to theoriginal order. extreme <- .qgld(c(inverse.eps, 1-inverse.eps), lambda1=lambdas, param=param) max.e<-extreme[2] min.e<-extreme[1] ind.min<-xx<=min.e ind.max<-xx>=max.e # This simpler comparison works here because we are using inverse.eps as our # tolerance q<-xx[as.logical((xxmin.e))] # We only want to calculate the probabilities for q values inside # the support length.of.vector <- length(q) # Need a blank u to send to C u <- 0*q result <- switch(param, freimer=, # allows for alternate expressions frm=, # allows for alternate expressions FMKL=, # Notes on .C call - the "numerics", lambdas and inverse.eps # don't need the as.??? call as they are implicitly double fmkl=.C( "gl_fmkl_distfunc", lambdas[1], lambdas[2], lambdas[3], lambdas[4], as.double(0), as.double(1), inverse.eps, as.integer(max.iterations), as.double(q), as.double(u), as.integer(length.of.vector), PACKAGE="fBasics"), ramberg=, # Ramberg & Schmeiser ram=, RS=, rs=.C( "gl_rs_distfunc", lambdas[1], lambdas[2], lambdas[3], lambdas[4], as.double(0), as.double(1), inverse.eps, as.integer(max.iterations), as.double(q), as.double(u), as.integer(length.of.vector), PACKAGE="fBasics"), fm5=.C("gl_fm5_distfunc", lambdas[1], lambdas[2], lambdas[3], lambdas[4], lambdas[5], as.double(0), as.double(1), inverse.eps, as.integer(max.iterations), as.double(q), as.double(u), as.integer(length.of.vector), PACKAGE="fBasics"), stop("Error: Parameterisation must be one of fmkl, fm5 or rs") ) # closes "switch" if (!(is.numeric(result[[1]]))){ stop("Values for quantiles outside range. This shouldn't happen") } if (param=="fm5") { u <- result[[11]] } else { u <- result[[10]] } xx[as.logical((xxmin.e))]<-u xx[ind.min]<-0 xx[ind.max]<-1 # Revert to the original order of the dataset: xx[jr] } ################################################################################ .qgld <- function(p, lambda1, lambda2=NULL, lambda3=NULL, lambda4=NULL, param="fmkl", lambda5=NULL) { lambdas <- .gl.parameter.tidy(lambda1, lambda2, lambda3, lambda4, param, lambda5) # Check the values are OK if(!.gl.check.lambda(lambdas, param=param, vect=TRUE)) { stop(paste("The parameter values", paste(lambdas, collapse=" "), "\ndo not produce a proper distribution for the", param, "parameterisation \n - see documentation for gl.check.lambda")) } result <- switch(param, freimer=, # allows for alternate expressions frm=, # allows for alternate expressions FMKL=, fmkl=.qgl.fmkl(p, lambdas), ramberg=, # Ramberg & Schmeiser ram=, RS=, rs=.qgl.rs(p, lambdas), fm5 = .qgl.fm5(p, lambdas), stop("Error: Parameterisation must be fmkl, fm5 or rs") ) # closes "switch" result } # ------------------------------------------------------------------------------ .qgl.fmkl <- function(p, lambdas) { # No checking - use qgl if you want that lambda4 = lambdas[4] lambda3 = lambdas[3] lambda2 = lambdas[2] lambda1 = lambdas[1] p <- as.double(p) # abandoned this for the simpler one below # outside.range <- !as.logical(((p<1)*(p>0))| # (sapply(p, all.equal, 1)=="TRUE")| (sapply(p, all.equal, 0)=="TRUE")) outside.range <- !as.logical((p<=1)*(p>=0)) # u gets only the probabilities in [0, 1] u <- p[!outside.range] # If OK, determine special cases if (lambda3 == 0) { if (lambda4 == 0) { # both log quants <- lambda1 + (log(u) - log(1 - u))/lambda2 } else { # l3 zero, l4 non-zero quants <- lambda1 + (log(u) - ((1 - u)^lambda4 - 1)/lambda4)/lambda2 } } else { # lambda3 non-zero if (lambda4 == 0) { # non-zero, l4 zero quants <- lambda1 + ((u^lambda3 - 1)/lambda3 - log(1 - u))/lambda2 } else { # both non-zero - use usual formula quants <- lambda1 + ( ( u ^ lambda3 - 1) / lambda3 - ( (1-u)^lambda4 - 1) /lambda4 ) / lambda2 } } # Now we have the quantiles for p values inside [0, 1], put them in # the right place in the result vector result <- p*NaN result[!outside.range] <- quants # The remaining "quantiles" are NaN result } # ------------------------------------------------------------------------------ .qgl.fm5 <- function(p, lambdas) { # No parameter value checking. If you want that, use qgl! lambda5 = as.double(lambdas[5]) lambda4 = as.double(lambdas[4]) lambda3 = as.double(lambdas[3]) lambda2 = as.double(lambdas[2]) lambda1 = as.double(lambdas[1]) p <- as.double(p) # abandoned this for the simpler # outside.range <- !as.logical(((p<1)*(p>0))| # (sapply(p, all.equal, 1)=="TRUE")| (sapply(p, all.equal, 0)=="TRUE")) outside.range <- !as.logical((p<=1)*(p>=0)) # u gets only the probabilities in [0, 1] u <- p[!outside.range] # If OK, determine special cases if (lambda3 == 0) { if (lambda4 == 0) { # both log quants <- lambda1 + ((1-lambda5)*log(u) - (1+lambda5)*log(1 - u))/lambda2 } else { # l3 zero, l4 non-zero quants <- lambda1 + ((1-lambda5)*log(u) - (1+lambda5)*((1 - u)^lambda4 - 1)/lambda4)/lambda2 } } else { # lambda3 non-zero if (lambda4 == 0) { # non-zero, l4 zero quants <- lambda1 + ((1-lambda5)*(u^lambda3 - 1)/lambda3 - (1+lambda5)*log(1 - u))/lambda2 } else { # both non-zero - use usual formula quants <- lambda1 + ((1-lambda5)* ( u ^ lambda3 - 1) / lambda3 - (1+lambda5)*( (1-u)^lambda4 - 1) /lambda4 ) / lambda2 } } # Now we have the quantiles for p values inside [0, 1], put them in # the right place in the result vector result <- p*NaN result[!outside.range] <- quants # The remaining "quantiles" are NaN result } # ------------------------------------------------------------------------------ .qgl.rs <- function(p, lambdas) { u <- p # No parameter value checking - use qgl! lambda4 = lambdas[4] lambda3 = lambdas[3] lambda2 = lambdas[2] lambda1 = lambdas[1] # At present, I'm rejecting zero values for l3 and l4, though I think # there are limit results, so one functional form. quants <- lambda1 + ( u ^ lambda3 - (1-u)^lambda4 ) / lambda2 quants } # ------------------------------------------------------------------------------ .qdgld <- function(p, lambda1, lambda2=NULL, lambda3=NULL, lambda4=NULL, param="fmkl", lambda5=NULL) { # Don't allow characters in lambda5 - # common error with parameterisation stuff if(is.character(lambda5)) { stop(paste("lambda5=", lambda5, "It should be a number between -1 and 1")) } lambdas <- .gl.parameter.tidy(lambda1, lambda2, lambda3, lambda4, param, lambda5) # Check the values are OK if(!.gl.check.lambda(lambdas, param=param, vect=TRUE)) { stop(paste("The parameter values", paste(lambdas, collapse=" "), "\ndo not produce a proper distribution for the", param, "parameterisation \n - see documentation for gl.check.lambda")) } result <- switch(param, # Different tests apply for each parameterisation freimer=, # allows for alternate expressions frm=, # allows for alternate expressions FMKL=, fmkl=.qdgl.fmkl(p, lambdas), ramberg=, # Ramberg & Schmeiser ram=, RS=, rs=.qdgl.rs(p, lambdas), fm5 = .qdgl.fm5(p, lambdas), stop("Error: Parameterisation must be fmkl, fm5 or rs") ) # closes "switch" result } # ------------------------------------------------------------------------------ .qdgl.rs <- function(p, lambdas) { # Check the values are OK) if(!.gl.check.lambda(lambdas, param="rs", vect=TRUE)) { stop(paste("The parameter values", paste(lambdas, collapse=" "), "\ndo not produce a proper distribution with the RS parameterisation", " - see \ndocumentation for gl.check.lambda")) } outside.range <- !as.logical((p<=1)*(p>=0)) # u gets only the probabilities in [0, 1] u <- p[!outside.range] dens <- lambdas[2]/(lambdas[3] * (p^(lambdas[3] -1)) + lambdas[4] * ((1 - p)^(lambdas[4] -1))) dens } # ------------------------------------------------------------------------------ .qdgl.fmkl <- function(p, lambdas) { # Check the values are OK) if(!.gl.check.lambda(lambdas, param="fmkl", vect=TRUE)) { stop(paste("The parameter values", paste(lambdas, collapse=" "), "\ndo not produce a proper distribution with the FMKL", "parameterisation - see \ndocumentation for gl.check.lambda")) } outside.range <- !as.logical((p<=1)*(p>=0)) # u gets only the probabilities in [0, 1] u <- p[!outside.range] # The density is given by 1/Q'(u) dens <- lambdas[2]/(p^(lambdas[3] - 1) + (1 - p)^(lambdas[4] - 1)) dens } # ------------------------------------------------------------------------------ .qdgl.fm5 <- function(p, lambdas) { # Check the values are OK) if(!.gl.check.lambda(lambdas, param="fm5", vect=TRUE)) { stop(paste("The parameter values", paste(lambdas, collapse=" "), "\ndo not produce a proper distribution with the FM5", "parameterisation - see \ndocumentation for gl.check.lambda")) } outside.range <- !as.logical((p<=1)*(p>=0)) # u gets only the probabilities in [0, 1] u <- p[!outside.range] # The density is given by 1/Q'(u) dens <- lambdas[2]/((1-lambdas[5])*(u^(lambdas[3] - 1)) + (1+lambdas[5])*((1 - u)^(lambdas[4] - 1)) ) dens } ################################################################################ .rgld <- function(n, lambda1=0, lambda2=NULL, lambda3=NULL, lambda4=NULL, param="fmkl", lambda5=NULL) { # Check the parameters lambdas <- .gl.parameter.tidy(lambda1, lambda2, lambda3, lambda4, param, lambda5) # Check the values are OK if(!.gl.check.lambda(lambdas, param=param, vect=TRUE)) { stop(paste("The parameter values", lambdas, "\ndo not produce a proper distribution for the", param, "parameterisation \n - see documentation for gl.check.lambda")) } # Produce the uniform data p <- runif(n) # convert to gl res <- .qgld(p, lambda1=lambdas, param=param) res } ################################################################################ .gl.parameter.tidy <- function(lambda1, lambda2=NULL, lambda3=NULL, lambda4=NULL, param="fmkl", lambda5=NULL) { # Don't allow characters in lambda5 - common error with parameterisation stuff if(is.character(lambda5)) {stop(paste("lambda5=", lambda5, "It should be a number between -1 and 1"))} # Don't allow numbers in parameterisation - # included as a warning here, so the main one is a stop. if(!is.character(param)) {warning(paste("param=", param, "It shouldn't be a number, ", "it should be a string describing the parameterisation")) } if(length(lambda1) > 1) { # using a vector for the parameters. # Check that there aren't values in the individual lambda arguments if (!(is.null(lambda2) & is.null(lambda3)& is.null(lambda4) & is.null(lambda5)) ) { stop("Call includes vector version of the lambda parameters", "as well as the \nscalar version") } if ((length(lambda1) < 4) | (length(lambda1) > 5 ) ) { stop(paste("argument lambda1 has length", length(lambda1), "\nThis should be 1 (lambda parameters given as seperate ", "arguments), 4 (vector argument \n for RS or FMKL ", "parameterisation) or 5 (vector argument for fm5", "parameterisation")) } if (length(lambda1)== 5) { if (param != "fm5") { stop(paste("argument lambda1 has length", length(lambda1), "which is not valid for the", param, "\nparameterisation")) } # else --- fm5, in vector form, ready for gl.check.lambda } if (length(lambda1)== 4) { if (param == "fm5" ) { stop(paste("argument lambda1 has length 4, which is not", " valid for the fm5 \nparameterisation")) } # else --- 4 parameter versions in vector form, ready for gl.check.lambda } } else { # single parameter arguments - check they are there, # then collect them together if (is.null(lambda2)) { stop("No value for lambda2") } if (is.null(lambda3)) { stop("No value for lambda3") } if (is.null(lambda4)) { stop("No value for lambda4") } if ((is.null(lambda5)) & param=="fm5" ) { stop("No value for lambda5") } if (!(is.null(lambda5)) & param!="fm5") { stop(paste("lambda5=", lambda5, " but there is no lambda 5 for the\n", param, "parameterisation")) } if (param != "fm5") { # A 4 parameter version lambda1 <- c(lambda1, lambda2, lambda3, lambda4) } else { # fm5 lambda1 <- c(lambda1, lambda2, lambda3, lambda4, lambda5) } } # There is now an error if there is the wrong number of parameters, and # lambda1 returned as a vector with 4 or 5 elements # as.double is needed to remove data.frame attributes if lambda1 was # extracted from a data.frame as.double(lambda1) } # ------------------------------------------------------------------------------ .gl.check.lambda <- function(lambdas, lambda2=NULL, lambda3=NULL, lambda4=NULL, param="fmkl", lambda5=NULL, vect=FALSE) { # Checks to see that the lambda values given are allowed. # There is a function called .gl.parameter.tidy that does the tidying # around of parameters. It return a single vector, which contains the # parameters. # If you call this after .gl.parameter.tidy, let it know with the vect=TRUE # argument # If vect=TRUE, we don't need to tidy if (vect) { if (!is.null(lambda3)) { warning("lambda3 should be null because you claim the", " parameters are in a vector") } } else { lambdas <- .gl.parameter.tidy(lambdas, lambda2, lambda3, lambda4, param, lambda5) } if(param=="fm5"){lambda5 = lambdas[5]} lambda4 = lambdas[4] lambda3 = lambdas[3] lambda2 = lambdas[2] lambda1 = lambdas[1] # I did have a check for finite lambdas, but that caused a # problem with data frames, # so I removed it - still need to include the limit results param <- switch(param, # Different tests apply for each parameterisation freimer=, # allows for alternate expressions frm=, # allows for alternate expressions FMKL=, fmkl={ if (lambda2<=0) {return(FALSE)} else {return(TRUE)} }, ramberg=, # Ramberg & Schmeiser ram=, RS=, rs={ if (lambda3*lambda4>0) { # regions 3 and 4 # all values of lambda 3 and lambda 4 OK # check lambda 2 if ((lambda3>0)&(lambda4>0)) { # region 3 - l2 >0 if (lambda2<=0) { ret <- FALSE } else { ret <- TRUE } } if ((lambda3<0)&(lambda4<0)) { # region 4 - l2 <0 if (lambda2>=0) { ret <- FALSE } else { ret <- TRUE } } } else { # other quadrants - lambda 2 must be negative, and lambda3 # lambda 4 values need checking. if (lambda2>=0) {return(FALSE)} # Rectangular regions where RS is not defined if ((lambda3>0)&(lambda3<1)&(lambda4<0)) {return(FALSE)} if ((lambda4>0)&(lambda4<1)&(lambda3<0)) {return(FALSE)} # Different here because there are a # number of ways in which the parameters can fail. # # Curved regions where RS is not defined # change to shorter var names lc <- lambda3 ld <- lambda4 if ((lambda3>-1)&(lambda3<0)&(lambda4>1)) { # region 5 or not? if ( ((1-lc)^(1-lc)*(ld-1)^(ld-1))/((ld-lc)^(ld-lc)) > -lc/ld ) {return(FALSE)} else {return(TRUE)} } # Second curved region if ((lambda4>-1)&(lambda4<0)&(lambda3>1)) { # region 6 or not? if ( ((1-ld)^(1-ld)*(lc-1)^(lc-1))/((lc-ld)^(lc-ld)) > -ld/lc ) {return(FALSE)} else {return(TRUE)} } # There may be some limit results that mean these are not correct, but # I'll check that later # This is not the place where the possible l3, l4 zero values should appear if (lambda3 == 0) { warning('lambda 3 = 0 with RS parameterisation - possible problem') if (lambda4 == 0) {return(FALSE)} else {return(TRUE)} } if (lambda4 == 0) { warning('lambda 5 = 0 with RS parameterisation - possible problem') if (lambda4 == 0) {return(FALSE)} else {return(TRUE)} } # If we get here, then the parameters are OK. ret <- TRUE } }, fm5={ # make lambda5 - in here so it doesn't stuff up the # other parameterisations lambda5 <- lambdas[5] if (lambda2<=0) {ret <- FALSE} else { #Check lambda5 - should be between -1 and 1, but # I haven't checked this against a piece of paper if ((lambda5 >= -1) & (lambda5 <= 1)) { ret <- TRUE } else { ret <- FALSE } } }, stop("Error when checking validity of parameters.\n", " Parameterisation must be fmkl, rs or fm5") ) # closes "switch" ret } ################################################################################ fBasics/R/builtin-gelGmm.R0000644000176000001440000004574411370220773015074 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: # .rho # .get_lamb # .gel # .smooth_g # .bwNeweyWest2 # .summary.gel # .get_dat ################################################################################ # Code borrowed from # R's contributed package "gmm" written by Pierre Chausse. # Rmetrics: # Note that gmm is not available on Debian as of 2009-04-28. # To run these functions under Debian/Rmetrics we have them # implemented here as a builtin. # We also made modifications for tailored usage with Rmetrics. # Note that the dependences in the original package requires zoo # which may create conflicts with Rmetrics timeDate/timeSeries. # Package: gmm # Version: 1.0-4 # Date: 2009-03-10 # Title: Generalized Method of Moments and Generalized Empirical Likelihood # Author: Pierre Chausse # Maintainer: Pierre Chausse # Description: It is a complete suite to estimate models based on moment # conditions. It includes the two step Generalized method of moments # (GMM) of Hansen(1982), the iterated GMM and continuous updated # estimator (CUE) of Hansen-Eaton-Yaron(1996) and several methods that # belong to the Generalized Empirical Likelihood (GEL) family of estimators, # as presented by Smith(1997), Kitamura(1997), Newey-Smith(2004) and # Anatolyev(2005). # Depends: R (>= 2.0.0), sandwich, tseries, mvtnorm # Imports: stats # License: GPL (>= 2) # ------------------------------------------------------------------------------ .rho <- function(x, lamb, derive = 0, type = c("EL", "ET", "CUE"), drop = TRUE) { type <- match.arg(type) lamb <- matrix(lamb,ncol=1) gml <- x%*%lamb ch <- 0 if (derive==0) { if (type == "EL") { ch <- sum(gml>=1) if (drop) { gml <- (gml<1)*gml rhomat <- log(1-gml) } else { if (ch>0) rhomat <- NaN else rhomat <- log(1-gml) } } if (type == "ET") rhomat <- -exp(gml) if (type == "CUE") rhomat <- -gml -0.5*gml^2 } if (derive==1) { if (type == "EL") rhomat <- -1/(1-gml) if (type == "ET") rhomat <- -exp(gml) if (type == "CUE") rhomat <- -1 -gml } if (derive==2) { if (type == "EL") rhomat <- -1/(1-gml)^2 if (type == "ET") rhomat <- -exp(gml) if (type == "CUE") rhomat <- -1 } rhom <-list(ch = ch, rhomat=rhomat) return(rhom) } # ------------------------------------------------------------------------------ .get_lamb <- function(g, tet, x,type = c('EL', 'ET', 'CUE'), tol_lam=1e-12, maxiterlam=1000, tol_obj = 1e-7) { type <- match.arg(type) gt <- g(tet,x) n <- nrow(gt) tol_cond=1e-12 gb <- colMeans(gt) khat <- crossprod(gt)/n lamb0 <- -solve(khat,gb) conv_mes <- "Normal convergence" singular <-0 crit <-1e30 crit0 <- crit dcrit <- 10 dgblam <- -10 gblam0 <- NULL j <- 1 while ((crit > tol_lam*( 1+sqrt( crossprod(lamb0) ) ) ) & (j<=maxiterlam)) { rho2 <- as.numeric(.rho(gt,lamb0,derive=2,type=type)$rhomat) rho1 <- as.numeric(.rho(gt,lamb0,derive=1,type=type)$rhomat) gblam <- colMeans(rho1*gt) klam <- crossprod(rho2*gt,gt)/n chklam <- sum(abs(klam)) if (!is.null(gblam0)) dgblam <- crossprod(gblam)-crossprod(gblam0) if (is.na(chklam) | chklam == 0 | chklam == Inf | dgblam>0 | dgblam == Inf | is.na(dgblam) | dcrit < 0) { lamb1 <- rep(sqrt(1/n),length(lamb0)) crit <- 0 singular=2 conv_mes <- "The algorithm produced singular system, NaN or Inf" } else { if (rcond(klam)>tol_cond) { lamb1 <- lamb0-solve(klam,gblam) crit <- sqrt(crossprod(lamb0-lamb1)) lamb0 <- lamb1 } else { lamb1 <- rep(sqrt(1/n),length(lamb0)) crit <- 0 singular=2 conv_mes <- "The algorithm produced singular system" } } gblam0 <- gblam j <- j+1 dcrit<- crit0-crit crit0 <- crit } z <- list("lambda"=lamb1,singular=singular,conv_mes=conv_mes) if (j>maxiterlam | max(abs(gblam))>tol_obj) { singular <- 1 conv_mes <- "No convergence after 'maxiterlam' iterations" z$singular <- singular } z$obj <- crossprod(gblam) return(z) } # ------------------------------------------------------------------------------ .gel <- function(g,x,tet0,gradv=NULL,smooth=FALSE,type=c("EL","ET","CUE","ETEL"), vcov=c("HAC","iid"), kernel = c("Bartlett", "Parzen", "Truncated", "Tukey-Hanning"), bw = .bwAndrews2, approx = c("AR(1)", "ARMA(1,1)"), prewhite = 1, ar.method = "ols", tol_weights = 1e-7, tol_lam=1e-9, tol_obj = 1e-9, tol_mom = 1e-9,maxiterlam=1000, constraint = FALSE, intercept = TRUE, optfct = c("optim", "optimize"), optlam = c("iter", "numeric"), ...) { vcov=match.arg(vcov) type <- match.arg(type) optfct <- match.arg(optfct) optlam <- match.arg(optlam) weights = .weightsAndrews2 if (type == "ETEL") { typel <- "ET" typet <- "EL" } else { typel <- type typet <- type } approx <- match.arg(approx) kernel <- match.arg(kernel) k <- length(tet0) typeg=0 if (is(g,"formula")) { typeg=1 dat <- .get_dat(g, x, intercept = intercept) x <- dat$x g <- function(tet,x,ny=dat$ny,nh=dat$nh,k=dat$k) { tet <- matrix(tet,ncol=k) if (intercept) { e <- x[,1:ny] - x[,(ny+1):(ny+k)]%*%t(tet) gt <- e for (i in 2:nh) { gt <- cbind(gt,e*x[,(ny+k+i)]) } } if (!intercept) e <- x[,1:ny] - x[,(ny+1):(ny+k)]%*%t(tet) gt <- e*x[,ny+k+1] if (nh > 1) { for (i in 2:nh) { gt <- cbind(gt,e*x[,(ny+k+i)]) } } return(gt) } gradv <- function(tet,x,ny=dat$ny,nh=dat$nh,k=dat$k) { tet <- matrix(tet,ncol=k) dgb <- -(t(x[,(ny+k+1):(ny+k+nh)])%*%x[,(ny+1):(ny+k)]) %x% diag(rep(1,ny))/nrow(x) return(dgb) } } if (typeg) n <- nrow(x) else n = nrow(g(tet0,x)) if (smooth) { g1 <- g rgmm <- .gmm(g,x,tet0,wmatrix="ident") if (is.function(weights)) w <- weights(g(rgmm$par,x), kernel=kernel, bw=bw, prewhite = prewhite,ar.method=ar.method,approx=approx, tol=tol_weights) else w <- weights sg <- function(thet,x) { gf <- g1(thet,x) gt <- .smooth_g(gf, weights=w)$smoothx return(gt) } g <- sg } lll <- 1 thetf <- function(tet) { if (optlam == "iter") { lamblist <- .get_lamb(g,tet,x,type=typel,tol_lam=tol_lam, maxiterlam=maxiterlam,tol_obj=tol_obj) lamb <- lamblist$lambda gt <- g(tet,x) pt <- -.rho(gt,lamb,type=typet,derive=1)$rhomat/nrow(gt) checkmom <- sum(as.numeric(pt)*gt) if (lamblist$singular==0) p <- sum(.rho(gt,lamb,type=typet)$rhomat) + abs(checkmom)/tol_mom if (lamblist$singular==1) p <- sum(.rho(gt,lamb,type=typet)$rhomat) + abs(checkmom)/tol_mom + lamblist$obj/tol_mom if (lamblist$singular==2) p <- 1e50*lll lll <- lll+1 } else { gt <- g(tet,x) rhofct <- function(lamb) { rhof <- -sum(.rho(gt,lamb,type=typet)$rhomat) return(rhof) } if (ncol(gt)>1) rlamb <- optim(rep(0,ncol(gt)),rhofct,...) else { rlamb <- optimize(rhofct,c(-1,1)) rlamb$par <- rlamb$minimum rlamb$value <- rlamb$objective } lamb <- rlamb$par pt <- -.rho(gt,lamb,type=typet,derive=1)$rhomat/nrow(gt) checkmom <- sum(as.numeric(pt)*gt) p <- -rlamb$value + (checkmom)^2/tol_mom + (sum(as.numeric(pt))-1)^2/tol_mom } return(p) } if (!constraint) if (optfct == "optim") res <- optim(tet0,thetf,...) else { res <- optimize(thetf,tet0,...) res$par <- res$minimum res$convergence <- "Pas applicable" } else res <- constrOptim(tet0,thetf,...) rlamb <- .get_lamb(g,res$par,x,type=typel,tol_lam=tol_lam,maxiterlam=maxiterlam,tol_obj=tol_obj) z <- list(par=res$par,lambda=rlamb$lam,conv_lambda=rlamb$conv_mes,conv_par=res$convergence) z$foc_lambda <- rlamb$obj z$type <- type z$gt <- g(z$par,x) rhom <- .rho(z$gt,z$lambda,type=typet) z$pt <- -.rho(z$gt,z$lambda,type=typet,derive=1)$rhomat/n z$conv_moment <- colSums(as.numeric(z$pt)*z$gt) z$conv_pt <- sum(as.numeric(z$pt)) z$objective <- sum(as.numeric(rhom$rhomat)-.rho(1,0,type=typet)$rhomat)/n if (type == "EL") { z$badrho <- rhom$ch names(z$badrho) <- "Number_of_bad_rho" } Gf <- function(thet) { myenv <- new.env() assign('x',x,envir=myenv) assign('thet',thet,envir=myenv) barg <- function(x,thet) { gt <- g(thet,x) gbar <- as.vector(colMeans(gt)) return(gbar) } G <- attr(numericDeriv(quote(barg(x,thet)),"thet",myenv),"gradient") return(G) } if (!is.function(gradv)) G <- Gf(z$par) else G <- gradv(z$par,x) if (vcov == "iid") khat <- crossprod(z$gt) else khat <- .HAC(g(z$par,x), kernel=kernel, bw=bw,prewhite=prewhite, ar.method=ar.method,approx=approx,tol=tol_weights) kg <- solve(khat,G) z$vcov_par <- solve(crossprod(G,kg))/n z$vcov_lambda <- ((solve(khat)-kg%*%z$vcov_par%*%t(kg)))/n if (smooth) z$weights<-w if (typeg ==0) { names(z$par) <- paste("Theta[",1:k,"]",sep="") colnames(z$gt) <- paste("gt[",1:ncol(z$gt),"]",sep="") names(z$lambda) <- paste("Lambda[",1:ncol(z$gt),"]",sep="") } if (typeg == 1) { namex <- colnames(dat$x[,(dat$ny+1):(dat$ny+dat$k)]) nameh <- colnames(dat$x[,(dat$ny+dat$k+1):(dat$ny+dat$k+dat$nh)]) if (dat$ny > 1) { namey <- colnames(dat$x[,1:dat$ny]) names(z$par) <- paste(rep(namey,dat$k),"_",rep(namex,rep(dat$ny,dat$k)),sep="") colnames(z$gt) <- paste(rep(namey,dat$nh),"_",rep(nameh,rep(dat$ny,dat$nh)),sep="") names(z$lambda) <- paste("Lam(",rep(namey,dat$nh),"_",rep(nameh,rep(dat$ny,dat$nh)),")",sep="") } if (dat$ny == 1) { names(z$par) <- namex colnames(z$gt) <- nameh names(z$lambda) <- nameh } } dimnames(z$vcov_par) <- list(names(z$par),names(z$par)) dimnames(z$vcov_lambda) <- list(names(z$lambda),names(z$lambda)) class(z) <- "gel" return(z) } # ------------------------------------------------------------------------------ .smooth_g <- function(x, bw = .bwAndrews2, prewhite = 1, ar.method = "ols", weights=.weightsAndrews2, kernel=c("Bartlett","Parzen","Truncated","Tukey-Hanning"), approx = c("AR(1)","ARMA(1,1)"), tol = 1e-7) { kernel <- match.arg(kernel) approx <- match.arg(approx) n <- nrow(x) if (is.function(weights)) { w <- weights(x, bw = bw, kernel = kernel, prewhite = prewhite, ar.method = ar.method, tol = tol, verbose = FALSE, approx = approx) } else w <- weights rt <- length(w) if (rt >= 2) { w <- c(w[rt:2],w) w <- w / sum(w) rt <- rt-1 sgt <- function(t) crossprod(x[(t-rt):(t+rt),],w) x[(rt+1):(n-rt),] <- t(sapply((rt+1):(n-rt),sgt)) sx <- list("smoothx"=x,"kern_weights"=w) return(sx) } else sx <- list("smoothx"=x,"kern_weights"=1) return(sx) } # ------------------------------------------------------------------------------ .bwNeweyWest2 <- function(x, kernel = c("Bartlett", "Parzen", "Quadratic Spectral", "Truncated", "Tukey-Hanning"), prewhite = 1, ar.method = "ols",...) { kernel <- match.arg(kernel) if (kernel %in% c("Truncated", "Tukey-Hanning")) stop(paste("Automatic bandwidth selection only available for ", dQuote("Bartlett"), ", ", dQuote("Parzen"), " and ", dQuote("Quadratic Spectral"), " kernel. Use ", sQuote(".bwAndrews2"), " instead.", sep = "")) prewhite <- as.integer(prewhite) n <- nrow(x) k <- ncol(x) weights <- rep(1, k) if (length(weights) < 2) weights <- 1 mrate <- switch(kernel, Bartlett = 2/9, Parzen = 4/25, "Quadratic Spectral" = 2/25) m <- floor(ifelse(prewhite > 0, 3, 4) * (n/100)^mrate) if (prewhite > 0) { x <- as.matrix(na.omit(ar(x, order.max = prewhite, demean = FALSE, aic = FALSE, method = ar.method)$resid)) n <- n - prewhite } hw <- x %*% weights sigmaj <- function(j) sum(hw[1:(n - j)] * hw[(j + 1):n])/n sigma <- sapply(0:m, sigmaj) s0 <- sigma[1] + 2 * sum(sigma[-1]) s1 <- 2 * sum(1:m * sigma[-1]) s2 <- 2 * sum((1:m)^2 * sigma[-1]) qrate <- 1/(2 * ifelse(kernel == "Bartlett", 1, 2) + 1) rval <- switch(kernel, Bartlett = { 1.1447 * ((s1/s0)^2)^qrate }, Parzen = { 2.6614 * ((s2/s0)^2)^qrate }, "Quadratic Spectral" = { 1.3221 * ((s2/s0)^2)^qrate }) rval <- rval * (n + prewhite)^qrate return(rval) } # ------------------------------------------------------------------------------ .summary.gel <- function(object,interval=FALSE, ...) { z <- object n <- nrow(z$gt) khat <- crossprod(z$gt)/n gbar <- colMeans(z$gt) se_par <- sqrt(diag(z$vcov_par)) par <- z$par tval <- par/se_par se_parl <- sqrt(diag(z$vcov_lambda)) lamb <- z$lambda tvall <- lamb/se_parl LR_test <- 2*z$objective*n LM_test <- n*crossprod(z$lambda,crossprod(khat,z$lambda)) J_test <- n*crossprod(gbar,solve(khat,gbar)) test <- c(LR_test,LM_test,J_test) vptest <- pchisq(test,(ncol(z$gt)-length(z$par)),lower.tail=FALSE) ans <- list(type=z$type) names(ans$type) <-"Type of GEL" ans$par <- round(cbind(par,se_par, tval, 2 * pnorm(abs(tval), lower.tail = FALSE)),5) ans$lambda <- round(cbind(lamb,se_parl, tvall, 2 * pnorm(abs(tvall), lower.tail = FALSE)),5) dimnames(ans$par) <- list(names(z$par), c("Estimate", "Std. Error", "t value", "Pr(>|t|)")) dimnames(ans$lambda) <- list(names(z$lambda), c("Estimate", "Std. Error", "t value", "Pr(>|t|)")) ans$test <- cbind(test,vptest) dimnames(ans$test) <- list(c("LR test","LM test","J test"),c("statistics","p-value")) if (interval != FALSE) { zs <- qnorm((1-interval)/2,lower.tail=FALSE) ch <- zs*se_par ans$interval_par <- cbind(par-ch,par+ch) dimnames(ans$interval_par) <- list(names(par),c("Theta_lower","Theta_upper")) chl <- zs*se_parl ans$interval_lam <- cbind(lamb-chl,lamb+chl) dimnames(ans$interval_lam) <- list(names(lamb),c("Lambda_lower","Lambda_upper")) } if (z$type == "EL") ans$badrho <- z$badrho if (!is.null(z$weights)) { ans$weights <- z$weights } ans$conv_par <- z$conv_par ans$conv_pt <- z$conv_pt ans$conv_moment <- cbind(z$conv_moment) ans$conv_lambda <- z$conv_lambda names(ans$conv_par) <- "Convergence_code_theta" names(ans$conv_pt) <- "Sum_of_pt" names(ans$conv_lambda) <- "Convergence_code_for_lambda" dimnames(ans$conv_moment) <- list(names(z$gt),"Sample_moment_with_pt") class(ans) <- "summary.gel" ans } # ------------------------------------------------------------------------------ .get_dat <- function(formula,h,intercept=TRUE) { cl <- match.call() mf <- match.call(expand.dots = FALSE) m <- match(c("formula", "data"), names(mf), 0L) mf <- mf[c(1L, m)] mf$drop.unused.levels <- TRUE mf[[1L]] <- as.name("model.frame") mf <- eval(mf, parent.frame()) mt <- attr(mf, "terms") h <- cbind(rep(1,nrow(h)),h) colnames(h) <- c("Intercept",paste("h",1:(ncol(h)-1),sep="")) y <- as.matrix(model.response(mf, "numeric")) x <- as.matrix(model.matrix(mt, mf, NULL)) if (!intercept) { x <- as.matrix(x[,2:ncol(x)]) h <- as.matrix(h[,2:ncol(h)]) } ny <- ncol(y) k <- ncol(x) nh <- ncol(h) if (nrow(y) != nrow(x) | nrow(x) != nrow(h) | nrow(y)!=nrow(h)) stop("The number of observations of X, Y and H must be the same") if (nh1) colnames(y) <- paste("y",1:ncol(y),sep="") if (ny == 1) colnames(y) <- "y" } x <- cbind(y,x,h) return(list(x=x,nh=nh,ny=ny,k=k)) } ################################################################################ fBasics/R/00fBasics-Package.R0000644000176000001440000000213311370220773015244 0ustar ripleyusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file fBasics/NAMESPACE0000644000176000001440000001707712137277730013120 0ustar ripleyusers################################################ ## useDynLib ################################################ useDynLib("fBasics") ################################################ ## import name space ################################################ import("stabledist") ## FIXME: use importFrom() for the larger packages! import("MASS") import("methods") import("timeDate") import("timeSeries") if(getRversion() >= "2.14.0") { importFrom(stats, "getCall") ## exportMethods("getCall" ) } else { export("getCall") } ################################################ ## S4 classes ################################################ exportClasses("fDISTFIT", "fHTEST" ) exportMethods("show" ) ################################################ ## S3 classes ################################################ S3method("stdev", "default") S3method("termPlot", "default") S3method("volatility", "default") ################################################ ## functions ################################################ export( ".HAC", ".aRecursionGH", ".acfPlot", ".adGLD", ".ansari2Test", ".ansariTest", ".asRGB", ".bartlett2Test", ".besselZ", ".blue2greenPalette", ".blue2redPalette", ".blue2yellowPalette", ".bwAndrews2", ".bwNeweyWest2", ".ccfPlot", ".chcode", ".checkGHMuMoments", ".checkGHRawMoments", ".circlesPlot", ".cnvrt.coords", ".contourPlot", ".counts1Plot", ".counts2Plot", ".cvmGLD", ".cyan2magentaPalette", ".dec.to.hex", ".deltaKappaGH", ".dgld", ".dhyp1", ".dhyp2", ".dhyp3", ".dhyp4", ".distCheck", ".distFitPlot", ".distStandardErrors", ".dssden", ".emptyPlot", ".firePlot", ".fligner2Test", ".gel", ".get_dat", ".get_lamb", ".ghCentralMoments", ".ghMuMoments", ".ghMuMomentsIntegrated", ".ghRawMoments", ".ghRawMomentsIntegrated", ".gl.check.lambda", ".gl.parameter.tidy", ".gldFit.gof", ".gldFit.hist", ".gldFit.mle", ".gldFit.mps", ".gldFit.rob", ".gmm", ".green2redPalette", ".heavisideSlider", ".hex.to.dec", ".histStack", ".hyp1Mode", ".hyp2Mode", ".hyp3Mode", ".hyp4Mode", ".hyperb.change.pars", ".is.positive.definite", ".jb.test", ".jbALM", ".jbLM", ".jbPlot", ".jbTable", ".kappaGH", ".ksGLD", ".kurtosis.test", ".kw2Test", ".kweights", ".lintest", ".lmacfPlot", ".logpdfPlot", ".make.positive.definite", ".maxddStats", ".mleStableFit", ".moodTest", ".mrlPlot", ".nigFit.gmm", ".nigFit.mle", ".nigFit.mps", ".nigFit.vmps", ".normFit", ".omnibus.test", ".pacfPlot", ".paramGH", ".perspPlot", ".pgld", ".phiStable", ".phyp1", ".phyp2", ".phyp3", ".phyp4", ".pjb", ".plot", ".plot.histogram", ".pnigC", ".predict", ".print", ".print.ssd", ".psnigC", ".pssden", ".purple2greenPalette", ".qStableFit", ".qdgl.fm5", ".qdgl.fmkl", ".qdgl.rs", ".qdgld", ".qgl.fm5", ".qgl.fmkl", ".qgl.rs", ".qgld", ".qhyp1", ".qhyp2", ".qhyp3", ".qhyp4", ".qjb", ".qnigC", ".qqgaussPlot", ".qsnigC", ".qssden", ".recessionLines", ".residualsPlot", ".responsesPlot", ".rghyp", ".rgigjd", ".rgigjd1", ".rgld", ".rho", ".rhyp1", ".rhyp2", ".rhyp3", ".rhyp4", ".rhyperb", ".rmetricsPackages", ".rmetricsUnitTest", ".runitTest", ".skewness.test", ".sliderMenu", ".smooth_g", ".ssden", ".stablePlot", ".stylizedFactsGUI", ".subPlot", ".subStars", ".summary", ".summary.gel", ".summary.gmm", ".tTest", ".tdSliderMenu", ".tslag1", ".unirootNA", ".varfTest", ".varpieDemo", ".varpiePlot", ".weightsAndrews2", ".xrug", ".yrug", "Boxcar", "Delta", "Heaviside", "Ramp", "Sign", "Triang", "acfPlot", "adTest", "akimaInterp", "akimaInterpp", "basicStats", "boxL", "boxPercentilePlot", "boxPlot", "box_", "characterTable", "cmPalette", "colIds", "colIds<-", "colVec", "colorLocator", "colorMatrix", "colorTable", "copyright", "correlationTest", "countFunctions", "cumulatedPlot", "cvmTest", "dagoTest", "decor", "densityPlot", "dgh", "dght", "dgld", "dhyp", "distCheck", "divPalette", "dmaxdd", "dnig", "drawdownPlot", "dsgh", "dsght", "dsnig", "dssd", "focusPalette", "get.lcgseed", "getArgs", "getDescription", "getModel", "getSlot", "getTitle", "ghFit", "ghIQR", "ghKURT", "ghKurt", "ghMED", "ghMean", "ghMode", "ghMoments", "ghSKEW", "ghSkew", "ghSlider", "ghVar", "ghtFit", "ghtIQR", "ghtKURT", "ghtKurt", "ghtMED", "ghtMean", "ghtMode", "ghtMoments", "ghtSKEW", "ghtSkew", "ghtVar", "gldFit", "gldIQR", "gldKURT", "gldMED", "gldMode", "gldSKEW", "greyPalette", "gridVector", "heatPalette", "hgrid", "hilbert", "histPlot", "hypFit", "hypIQR", "hypKURT", "hypKurt", "hypMED", "hypMean", "hypMode", "hypMoments", "hypSKEW", "hypSkew", "hypSlider", "hypVar", "interactivePlot", "inv", "isPositiveDefinite", "jarqueberaTest", "jbTest", "kendallTest", "krigeInterp", "kron", "ks2Test", "ksnormTest", "lacfPlot", "lillieTest", "linearInterp", "linearInterpp", "listDescription", "listFunctions", "listIndex", "locationTest", "logDensityPlot", "makePositiveDefinite", "maxddStats", "monoPalette", "nFit", "nigFit", "nigIQR", "nigKURT", "nigKurt", "nigMED", "nigMean", "nigMode", "nigMoments", "nigSKEW", "nigShapeTriangle", "nigSkew", "nigSlider", "nigVar", "norm", "normIQR", "normKURT", "normMED", "normSKEW", "normalTest", "pacfPlot", "pascal", "pchiTest", "pdl", "pearsonTest", "pgh", "pght", "pgld", "phyp", "pmaxdd", "pnig", "print.control", "psgh", "psght", "psnig", "pssd", "qgh", "qght", "qgld", "qhyp", "qnig", "qqghtPlot", "qqgldPlot", "qqnigPlot", "qqnormPlot", "qsgh", "qsght", "qsnig", "qssd", "qualiPalette", "rainbowPalette", "rampPalette", "returnPlot", "returnSeriesGUI", "rgh", "rght", "rgld", "rhyp", "rk", "rmaxdd", "rnig", "rnorm.lcg", "rowAvgs", "rowIds", "rowIds<-", "rowKurtosis", "rowMaxs", "rowMins", "rowProds", "rowQuantiles", "rowSds", "rowSkewness", "rowStats", "rowStdevs", "rowVars", "rowVec", "rsgh", "rsght", "rsnig", "rssd", "rt.lcg", "runif.lcg", "sampleIQR", "sampleKURT", "sampleLmoments", "sampleMED", "sampleSKEW", "scaleTest", "scalinglawPlot", "seqPalette", "seriesPlot", "set.lcgseed", "sfTest", "sghFit", "shapiroTest", "snigFit", "spearmanTest", "ssdFit", "stableFit", "stableSlider", "stdev", "symbolTable", "tFit", "teffectPlot", "termPlot", "terrainPalette", "timPalette", "topoPalette", "tr", "triang", "tsHessian", "tslag", "varianceTest", "vec", "vech", "vgrid", "volatility" ) fBasics/DESCRIPTION0000644000176000001440000000243412140125335013361 0ustar ripleyusersPackage: fBasics Version: 3010.86 Revision: 5503 Date: 2013-04-30 Title: Rmetrics - Markets and Basic Statistics Author: Diethelm Wuertz and Rmetrics core team members, uses code builtin from the following R contributed packages: gmm from Pierre Chauss, gld from Robert King, gss from Chong Gu, nortest from Juergen Gross, HyperbolicDist from David Scott, sandwich from Thomas Lumley and Achim Zeileis, fortran/C code from Kersti Aas and akima (0.5-1: R code under GPL) from Albrecht Gebhardt Maintainer: Yohan Chalabi Depends: stats, MASS, methods, timeDate, timeSeries (>= 2100.84) Imports: stabledist, gss Suggests: akima, spatial, RUnit, tcltk Description: Environment for teaching "Financial Engineering and Computational Finance" NOTE: SEVERAL PARTS ARE STILL PRELIMINARY AND MAY BE CHANGED IN THE FUTURE. THIS TYPICALLY INCLUDES FUNCTION AND ARGUMENT NAMES, AS WELL AS DEFAULTS FOR ARGUMENTS AND RETURN VALUES. Please donate, www.rmetrics.org, to support future activities of the Rmetrics association. LazyData: yes License: GPL (>= 2) URL: http://www.rmetrics.org Packaged: 2013-04-30 20:02:52 UTC; yankee NeedsCompilation: yes Repository: CRAN Date/Publication: 2013-05-01 07:40:45 fBasics/ChangeLog0000644000176000001440000006234612140021553013432 0ustar ripleyusers2013-04-30 chalabi * DESCRIPTION, NAMESPACE, R/builtin-ssdenGss.R, R/zzz.R, src/gss_a.f, src/gss_b.c: gss is now an import package 2012-09-21 chalabi * ChangeLog, DESCRIPTION: Updated ChangeLog and DESC files 2012-09-20 mmaechler * DESCRIPTION, NAMESPACE, R/dist-stable-deprecated.R, inst/unitTests/runit.DistributionFits.R, man/fBasics-deprecated.Rd: no longer re-export the [dpqr]stable() functions from "stabledist"; and more reasonable unitTests for those 2012-09-14 chalabi * ChangeLog, DESCRIPTION: updated DESC and ChangeLog * DESCRIPTION: updated version number * R/utils-Sys.putenv.R: Removed deprecated putenv patch 2012-08-22 chalabi * ChangeLog, DESCRIPTION: updated DESC and ChangeLog * DESCRIPTION, NAMESPACE, R/stats-interpAkima.R, R/stats-interpLinear.R: Removed code that was copied from akiam (0.5-1) to avoid external .Fortran calls to akima package. * DESCRIPTION: updated version number * ChangeLog: updated Changelog * DESCRIPTION: added reference to code from akima (0.5-1) package 2012-08-12 chalabi * DESCRIPTION: updated version number * NAMESPACE, R/test-normalityTest.R: shapiroTest calls now base R function shapiro.test * DESCRIPTION: added stats pkg in Depends 2012-03-21 chalabi * R/utils-getS4.R, man/utils-getS4.Rd: updated manual page and getArgs() 2012-03-20 chalabi * NAMESPACE, R/builtin-ssdenGss.R, src/gss.f, src/gss_a.f, src/gss_b.c: removed fixed warnings generated by fortran routine from gss package * DESCRIPTION: updated DESC 2012-03-19 chalabi * R/builtin-ssdenGss.R, R/gui-sliderMenu.R, R/gui-stableSlider.R, R/utils-listFunctions.R: fixed partial argument names 2012-02-16 mmaechler * NAMESPACE, R/builtin-ssdenGss.R, R/dist-nig.R, R/gui-sliderMenu.R, R/utils-colorLocator.R, man/utils-colorLocator.Rd: more cosmetic; notably first round of "partial argument match" fixes * src/nig.c: add #include .. (thanks to Jan Deleeuw) 2012-01-14 mmaechler * DESCRIPTION, NAMESPACE, R/utils-getS4.R, man/00fBasics-package.Rd, man/utils-getS4.Rd: do not export getCall() -- which conflicts with R >= 2.14, in that case * src/gld.c, src/nig.c: fix C code {stderr, exit} 2011-09-23 mmaechler * DESCRIPTION: remove deprecated "LazyLoad" entry 2011-08-02 chalabi * ChangeLog, DESCRIPTION: updated DESC and ChangeLog * R/utils-getS4.R, man/fBasics-deprecated.Rd, man/stableSlider.Rd, man/utils-getS4.Rd: fixed warnings with R CMD check 2011-06-22 mmaechler * NAMESPACE, R/dist-distCheck.R, R/utils-getS4.R, man/utils-getS4.Rd: getCall() -- adapt to future R 2.14.x ff 2011-06-22 chalabi * NAMESPACE: updated NAMESPACE * man/dist-distCheck.Rd, man/dist-ghRobMoments.Rd, man/dist-ghtRobMoments.Rd, man/dist-gldMode.Rd, man/dist-hypRobMoments.Rd, man/dist-nigRobMoments.Rd, man/dist-normRobMoments.Rd, man/dist-sght.Rd, man/fBasics-deprecated.Rd, man/stableSlider.Rd, man/utils-hessian.Rd: updated man pages with new functions * R/dist-hypRobMoments.R, R/dist-stable-deprecated.R, R/utils-colorRGB.R, R/utils-getS4.R: getSlot() is now a wrapper of slot() function 2011-05-04 chalabi * man/test-normalityTests.Rd: added credits to Paul Johnson for earlier funcions based on his Fortran code. 2011-02-03 mmaechler * DESCRIPTION, NAMESPACE, R/dist-distCheck.R, man/dist-distCheck.Rd: rename package "stable" to "stabledist" - as "stable" has existed for many years in Jim Lindsey`s off-CRAN collection 2011-01-31 mmaechler * DESCRIPTION: update Version ('stable' needs it for unit tests) * man/dist-distCheck.Rd: update: forgot new arg.s in usage * man/fBasics-deprecated.Rd: man for keep deprecated [dpqr]stable() * R/dist-stable-deprecated.R: keep "back compatibility" version of stable * R/dist-distCheck.R, man/dist-distCheck.Rd: (changes from a week ago:) "control" attributes only in the fBasics "deprecated" versions; add utils; start re-enabling the unit tests 2011-01-24 mmaechler * DESCRIPTION, NAMESPACE, R/dist-stable.R, R/dist-stableMode.R, R/gui-stableSlider.R, inst/unitTests/runit.StableDistribution.R, man/dist-stable.Rd, man/dist-stableMode.Rd, man/stableSlider.Rd: from fBasics, factor out a new "stable" package; with distributions, but not (yet?) stableFit() etc 2010-10-26 chalabi * NAMESPACE: updated NAMESPACE 2010-07-23 chalabi * inst/DocCopying.pdf: removed DocCopying.pdf license is already specified in DESCRIPTION file 2010-05-29 wuertz * R/utils-getS4.R, man/utils-getS4.Rd: man page updated * R/utils-getArgs.R: getArgs moved to getS4 * NAMESPACE, R/builtin-glGld.R, R/builtin-posdefCorpcor.R, R/builtin-testNortest.R, R/builtin-varpieA2R.R, R/dist-distCheck.R, R/dist-distErrors.R, R/dist-distPlot.R, R/dist-fDISTFIT.R, R/gui-HeavisideSlider.R, R/gui-ghSlider.R, R/gui-nigSlider.R, R/gui-returnSeries.R, R/utils-Sys.putenv.R, R/utils-getArgs.R, R/utils-getS4.R, R/utils-listDescription.R, R/utils-listFunctions.R, R/utils-listIndex.R, R/utils-printControl.R, R/utils-randLCG.R, R/utils-unitTest.R, man/utils-getS4.Rd: getArgs added, minor mods in some functions, namespace adapted 2010-03-24 chalabi * NAMESPACE, R/builtin-xlsGdata.R, R/read-download.R, R/read-lines.R, R/read-lynx.R, R/read-split.R, R/read-xls.R, inst/perl, man/read-download.Rd, man/read-lines.Rd, man/read-lynx.Rd, man/read-split.Rd, man/read-xls.Rd: moved new read.<> functions from fBasics to fImport 2010-03-09 dscott * man/test-correlationTest.Rd, man/test-fHTEST.Rd, man/test-ks2Test.Rd, man/test-locationTest.Rd, man/test-normalityTests.Rd, man/test-scaleTest.Rd, man/test-varianceTest.Rd: Fixed spelling mistakes * src/nig.c: changed XMAX for 64 bit compatiblitiy and removed unused headers 2010-03-02 wuertz * R/builtin-xlsGdata.R: xls byuiltin added 2010-02-23 wuertz * NAMESPACE: namespace adapted * NAMESPACE: namespace adapted * R/builtin-xlsGdata.R, R/read-download.R, R/read-lines.R, R/read-lynx.R, R/read-split.R, R/read-xls.R, inst/perl, inst/perl/IO, inst/perl/IO/AtomicFile.pm, inst/perl/IO/InnerFile.pm, inst/perl/IO/Lines.pm, inst/perl/IO/Scalar.pm, inst/perl/IO/Scalar.pm.html, inst/perl/IO/ScalarArray.pm, inst/perl/IO/Stringy.pm, inst/perl/IO/Wrap.pm, inst/perl/IO/WrapTie.pm, inst/perl/OLE, inst/perl/OLE/Storage_Lite.pm, inst/perl/Spreadsheet, inst/perl/Spreadsheet/ParseExcel, inst/perl/Spreadsheet/ParseExcel.pm, inst/perl/Spreadsheet/ParseExcel/Dump.pm, inst/perl/Spreadsheet/ParseExcel/FmtDefault.pm, inst/perl/Spreadsheet/ParseExcel/FmtJapan.pm, inst/perl/Spreadsheet/ParseExcel/FmtJapan2.pm, inst/perl/Spreadsheet/ParseExcel/FmtUnicode.pm, inst/perl/Spreadsheet/ParseExcel/SaveParser.pm, inst/perl/Spreadsheet/ParseExcel/Utility.pm, inst/perl/html-parser, inst/perl/html-parser/html-ascii.pl, inst/perl/html-parser/html-parser.html, inst/perl/html-parser/html-to-ascii.pl, inst/perl/html-parser/html-to-rfc.pl, inst/perl/html-parser/parse-html.pl, inst/perl/html-parser/rfc.pl, inst/perl/html-parser/tformat.pl, inst/perl/xls2csv.pl, man/00fBasics-package.Rd, man/read-download.Rd, man/read-lines.Rd, man/read-lynx.Rd, man/read-split.Rd, man/read-xls.Rd: read tools added for reading files from internet 2010-02-21 dscott * ChangeLog, NAMESPACE, R/builtin-glGld.R, R/dist-gld.R, R/dist-gldFit.R: Some fixes to gld functions 2010-02-09 chalabi * DESCRIPTION: updated version number 2010-02-08 chalabi * DESCRIPTION: updated DESC files * man/utils-colorLocator.Rd: update Rd files 2009-12-08 wuertz * DESCRIPTION: description file updated 2009-12-07 wuertz * man/00fBasics-package.Rd: text updated 2009-12-06 wuertz * DistributionsStatus: no longer needed * DESCRIPTION: description updated * DESCRIPTION, man/00fBasics-package.Rd: description file and manual package info updated * man/plot-interactivePlot.Rd, man/print.Rd, man/rand-lcg.Rd, man/returnSeriesGUI.Rd: obsolete man files removed * src/Makevars, src/gld.c, src/gss.f: src files added * R/builtin-posdefCorpcor.R, R/builtin-testNortest.R, R/builtin-varpieA2R.R, R/lines-recessionLines.R, R/list-listDescription.R, R/list-listFunctions.R, R/list-listIndex.R, R/print.R, R/rand-lcg.R, R/utils-listDescription.R, R/utils-listFunctions.R, R/utils-listIndex.R, R/utils-printControl.R, R/utils-randLCG.R, R/utils-recessionLines.R, man/00fBasics-package.Rd: builtin and util scripts added * R/test-correlationTest.R, R/test-fHTEST.R, R/test-jbALM.R, R/test-jbLM.R, R/test-jbTable.R, R/test-ks2Test.R, R/test-locationTest.R, R/test-scaleTest.R, R/test-varianceTest.R: tests updated * R/test-NormalityTests.R, R/test-normalTest.R, R/test-normalityTest.R: normality tests merged * man/00fUtilities-package.Rd, man/colorLocator.Rd, man/colorPalette.Rd, man/colorTable.Rd, man/dist-ssd.Rd, man/interp-akimaInterp.Rd, man/interp-krigeInterp.Rd, man/interp-linearInterp.Rd, man/listDescription.Rd, man/listFunctions.Rd, man/listIndex.Rd, man/plot-returnSeriesGUI.Rd, man/stats-interpAkima.Rd, man/stats-interpKrige.Rd, man/stats-interpLinear.Rd, man/utils-colorLocator.Rd, man/utils-colorPalette.Rd, man/utils-colorTable.Rd, man/utils-getS4.Rd, man/utils-interactivePlot.Rd, man/utils-listDescription.Rd, man/utils-listFunctions.Rd, man/utils-listIndex.Rd, man/utils-printControl.Rd, man/utils-randLCG.Rd: man pages updated * man/00fBasics-package.Rd, man/baseMethods.Rd: man updated * R/builtin-HyperbolicDist.R, R/builtin-gelGMM.R, R/builtin-gelGmm.R, R/builtin-gelGmm2.R, R/builtin-gld.R, R/builtin-gmmGMM.R, R/builtin-gmmGmm.R, R/builtin-gmmGmm2.R, R/builtin-gss.R, R/builtin-sandwichGMM.R, R/color-Locator.R, R/color-Palettes.R, R/color-RGB.R, R/color-Table.R, R/dist-ghFit.R, R/dist-ghMode.R, R/dist-nig.R, R/dist-ssd.R, R/dist-stableFit.R, R/interp-akimaInterp.R, R/interp-krigeInterp.R, R/interp-linearInterp.R, R/matrix-colVec.R, R/matrix-gridVector.R, R/matrix-inv.R, R/matrix-kron.R, R/matrix-norm.R, R/matrix-pascal.R, R/matrix-pdl.R, R/matrix-posDefinite.R, R/matrix-rk.R, R/matrix-tr.R, R/matrix-triang.R, R/matrix-tslag.R, R/matrix-vech.R, R/plot-boxPlot.R, R/plot-interactivePlot.R, R/plot-qqPlot.R, R/plot-seriesPlot.R, R/stats-basicStats.R, R/stats-interpAkima.R, R/stats-interpKrige.R, R/stats-interpLinear.R, R/stats-maxdd.R, R/stats-rowStats.R, R/utils-Heaviside.R, R/utils-colorLocator.R, R/utils-colorPalettes.R, R/utils-colorRGB.R, R/utils-colorTable.R, R/utils-decor.R, R/utils-getS4.R, R/utils-interactivePlot.R, R/utils-symbolTable.R, R/utils-unitrootNA.R: functions sorted and partly scripts renamed * R/builtin-gelGmm2.R, R/builtin-glGld.R, R/builtin-gmmGmm2.R, R/builtin-hypHyperbolicDist.R, R/builtin-kweightsSandwhich.R, R/builtin-ssdenGss.R: builtin functions renamed 2009-12-03 wuertz * NAMESPACE, R/dist-distErrors.R, R/dist-distPlot.R, R/dist-gh.R, R/dist-ghFit.R, R/dist-ghMode.R, R/dist-ghMoments.R, R/dist-ghRobMoments.R, R/dist-nigFit.R, man/00fBasics-package.Rd, man/dist-gh.Rd, man/dist-ghFit.Rd, man/dist-ghMode.Rd, man/dist-ghMoments.Rd, man/dist-ght.Rd, man/dist-ghtFit.Rd, man/dist-ghtMode.Rd, man/dist-ghtMoments.Rd, man/dist-hypMoments.Rd, man/dist-nigMoments.Rd, man/dist-ssd.Rd, man/dist-ssdFit.Rd, man/dist-stable.Rd, man/dist-stableMode.Rd: man pages, NAMESPACE updated and som minor new functionalities added * R/dist-ghMoments.R, R/dist-ghtMoments.R, R/dist-hypMoments.R, R/dist-nigMoments.R, R/lines-recessionLines.R: true moments added * R/dist-nigFit.R, R/utils-hessian.R: standard errors for nigFit added 2009-12-02 wuertz * DistributionsStatus, NAMESPACE, R/dist-ghMoments.R, R/dist-ght.R, R/dist-hypMoments.R, R/dist-nigMoments.R, R/dist-sght.R: further moment functions added * R/dist-ghMoments.R: gh central moments added 2009-12-01 wuertz * NAMESPACE, R/dist-gh.R, R/dist-ghMoments.R, R/dist-ght.R, R/dist-ghtMode.R, R/dist-gld.R, R/dist-hyp.R, R/dist-hypMoments.R, R/dist-hypRobMoments.R, R/dist-nig.R, R/dist-nigMoments.R, R/dist-norm.R, R/dist-normFit.R, R/dist-normRobMoments.R, R/dist-sgh.R, R/dist-sghFit.R, R/dist-snig.R, R/dist-ssd.R, R/dist-stable.R, inst/unitTests/runit.HyperbolicDistribution.R, man/dist-StableDistribution.Rd, man/dist-ght.Rd, man/dist-gld.Rd, man/dist-hyp.Rd, man/dist-stable.Rd: distribution functionality upgraded, some man pages are still missing 2009-11-24 wuertz * DistributionsStatus, NAMESPACE, R/dist-nFit.R, R/dist-normFit.R: nFit renamed to normFit 2009-11-23 wuertz * R/dist-nigFit.R: log(var... updated in nigFit * R/dist-gldFit.R, man/dist-gldFit.Rd: gld mps modified 2009-11-22 wuertz * R/dist-gldFit.R, man/dist-gld.Rd: gld fit and distribution updated * DistributionsStatus, NAMESPACE, R/dist-gldFit.R, R/dist-nigFit.R, man/dist-nigFit.Rd: Yohan's var(MPS) for the nig added * NAMESPACE, R/builtin-gss.R, R/dist-gld.R, R/dist-gldFit.R, R/dist-ssd.R, R/dist-ssdFit.R, R/plot-varpie.R: namespace updated 2009-11-21 wuertz * R/plot-acfPlot.R, R/plot-boxPlot.R, R/plot-counts.R, R/plot-histPlot.R, R/plot-interactivePlot.R, R/plot-scalinglawPlot.R, R/plot-seriesPlot.R, R/plot-subPlot.R, R/plot-subStars.R, R/plot-varpie.R, R/plot.R: cod beautified and descriptions added for plot functions * R/plot-qqPlot.R, man/plot-qqPlot.Rd: qq plot for GLD distribution added * DESCRIPTION: builtin packages and authors added * man/dist-nigMode.Rd: nigMode manual page added * man/dist-ghtMode.Rd, man/dist-nigMoments.Rd: manual page information added * R/builtin-HyperbolicDist.R, R/builtin-gelGMM.R, R/builtin-gld.R, R/builtin-gmmGMM.R, R/builtin-gss.R, R/builtin-sandwichGMM.R, R/dist-hyp.R: builtin function documentation improved * R/builtin-HyperbolicDist.R: code borrowed from David Scott no moved to script file builtin-HyperbolicDist * man/dist-gld.Rd: examples modified * R/dist-nigFit.R: nig mps fit objective function renamed * NAMESPACE: namespace updated * DistributionsStatus: file distribution status added * R/dist-ghFit.R, R/dist-ghMode.R, R/dist-ghMoments.R, R/dist-ght.R, R/dist-ghtFit.R, R/dist-ghtMode.R, R/dist-gldMode.R, R/dist-hyp.R, R/dist-hypFit.R, R/dist-hypMode.R, R/dist-nFit.R, R/dist-nig.R, R/dist-nigFit.R, R/dist-nigMode.R, R/dist-nigMoments.R, R/dist-nigRobMoments.R, R/dist-nigStats.R, R/dist-sgh.R, R/dist-sghFit.R, R/dist-snig.R, R/dist-snigFit.R, R/dist-ssdFit.R, R/dist-stable.R, R/dist-stableFit.R, R/dist-stableMode.R, R/dist-tFit.R: distribution Functions updated, status list added * R/builtin-gelGMM.R, R/builtin-gld.R, R/builtin-gmmGMM.R, R/builtin-gss.R, R/color-Locator.R, R/color-Palettes.R, R/color-RGB.R, R/color-Table.R, R/dist-distCheck.R, R/dist-fDISTFIT.R, R/dist-gh.R, R/dist-ghRobMoments.R, R/dist-ght.R, R/dist-ghtFit.R, R/dist-ghtRobMoments.R, R/dist-gld.R, R/dist-gldFit.R, R/dist-gldMode.R, R/dist-gldRobMoments.R, R/dist-hyp.R, R/dist-hypFit.R, R/dist-hypMode.R, R/dist-nFit.R, R/dist-nig.R, R/dist-nigFit.R, R/dist-nigMode.R, R/dist-nigRobMoments.R, R/dist-nigStats.R, R/dist-sgh.R, R/dist-sghFit.R, R/dist-snig.R, R/dist-snigFit.R, R/dist-ssd.R, R/dist-ssdFit.R, R/dist-stable.R, R/dist-tFit.R, R/gui-HeavisideSlider.R, R/gui-hypSlider.R, R/gui-nigSlider.R, R/gui-returnSeries.R, R/gui-sliderMenu.R, R/gui-stableSlider.R, R/gui-stylizedFacts.R, R/interp-akimaInterp.R, R/interp-krigeInterp.R, R/interp-linearInterp.R, R/list-listFunctions.R, R/list-listIndex.R, R/matrix-colVec.R, R/matrix-gridVector.R, R/matrix-hilbert.R, R/matrix-inv.R, R/matrix-kron.R, R/matrix-norm.R, R/matrix-pascal.R, R/matrix-pdl.R, R/matrix-posDefinite.R, R/matrix-rk.R, R/matrix-tr.R, R/matrix-triang.R, R/matrix-tslag.R, R/matrix-vech.R, R/plot-acfPlot.R, R/plot-boxPlot.R, R/plot-histPlot.R, R/plot-interactivePlot.R, R/plot-qqPlot.R, R/plot-scalinglawPlot.R, R/plot-seriesPlot.R, R/plot-subPlot.R, R/plot-subStars.R, R/plot.R, R/print.R, R/rand-lcg.R, R/stats-maxdd.R, R/stats-rowStats.R, R/stats-sampleLmoments.R, R/test-NormalityTests.R, R/test-correlationTest.R, R/test-fHTEST.R, R/test-jbALM.R, R/test-jbLM.R, R/test-jbTable.R, R/test-ks2Test.R, R/test-locationTest.R, R/test-normalTest.R, R/test-scaleTest.R, R/test-varianceTest.R, R/utils-Heaviside.R, R/utils-Sys.putenv.R, R/utils-as.matrix.ts.R, R/utils-baseMethods.R, R/utils-characterTable.R, R/utils-colIds.R, R/utils-getS4.R, R/utils-symbolTable.R, R/utils-unitTest.R, R/utils-unitrootNA.R: code (header) beautified script files * NAMESPACE: namespace updated * R/builtin-gld.R, R/builtin-gss.R, R/dist-gh.R, R/dist-ghFit.R, R/dist-ghMoments.R, R/dist-ghRobMoments.R, R/dist-ghtRobMoments.R, R/dist-gld.R, R/dist-gldFit.R, R/dist-gldMode.R, R/dist-gldRobMoments.R, R/dist-gldRobust.R, R/dist-nigRobMoments.R, R/dist-nigRobust.R, R/dist-ssd.R, R/dist-ssdFit.R, R/stats-robMoments.R, R/stats-sampleLmoments.R, R/stats-sampleRobMoments.R: gh, gld and ssden distributions (new and extensions) added, Lmoments, robust stuff added * man/utils-characterTable.Rd: man page added * man/correlationTest.Rd, man/dist-gldRobMoments.Rd, man/test-correlationTest.Rd: same scripts renamed * man/Heaviside.Rd, man/NormalityTests.Rd, man/acfPlot.Rd, man/akimaInterp.Rd, man/basicStats.Rd, man/boxPlot.Rd, man/characterTable.Rd, man/colIds.Rd, man/colVec.Rd, man/decor.Rd, man/dist-distCheck.Rd, man/dist-gld.Rd, man/dist-gldFit.Rd, man/dist-nigRobMoments.Rd, man/distCheck.Rd, man/fHTEST.Rd, man/getS4.Rd, man/gridVector.Rd, man/hilbert.Rd, man/histPlot.Rd, man/interactivePlot.Rd, man/interp-linearInterp.Rd, man/inv.Rd, man/krigeInterp.Rd, man/kron.Rd, man/ks2Test.Rd, man/lcg.Rd, man/linearInterp.Rd, man/locationTest.Rd, man/matrix-tr.Rd, man/matrix-vech.Rd, man/matrixNorm.Rd, man/maxdd.Rd, man/pascal.Rd, man/pdl.Rd, man/positiveDefinite.Rd, man/qqPlot.Rd, man/rand-lcg.Rd, man/rk.Rd, man/rowStats.Rd, man/scaleTest.Rd, man/scalinglawPlot.Rd, man/seriesPlot.Rd, man/symbolTable.Rd, man/test-fHTEST.Rd, man/tr.Rd, man/triang.Rd, man/tslag.Rd, man/utild-Heaviside.Rd, man/utild-colIds.Rd, man/utild-decor.Rd, man/utild-getS4.Rd, man/utils-Heaviside.Rd, man/utils-colIds.Rd, man/utils-decor.Rd, man/utils-getS4.Rd, man/varianceTest.Rd, man/vec.Rd: some scripts renamed * man/utild-Heaviside.Rd, man/utild-colIds.Rd, man/utild-decor.Rd, man/utild-getS4.Rd, man/utils-symbolTable.Rd: utils script renames * man/stats-basicStats.Rd, man/stats-maxdd.Rd, man/stats-rowStats.Rd, man/stats-sampleLMoments.Rd, man/stats-sampleRobMoments.Rd: stats scripts renames * man/plot-acfPlot.Rd, man/plot-boxPlot.Rd, man/plot-histPlot.Rd, man/plot-interactivePlot.Rd, man/plot-qqPlot.Rd, man/plot-scalinglawPlot.Rd, man/plot-seriesPlot.Rd: plot script renames * man/interp-akimaInterp.Rd, man/interp-krigeInterp.Rd: interp script renames * man/test-ks2Test.Rd, man/test-locationTest.Rd, man/test-normalityTests.Rd, man/test-scaleTest.Rd, man/test-varianceTest.Rd: test-script renames * man/matrix-colVec.Rd, man/matrix-gridVector.Rd, man/matrix-hilbert.Rd, man/matrix-inv.Rd, man/matrix-kron.Rd, man/matrix-norm.Rd, man/matrix-pascal.Rd, man/matrix-pdl.Rd, man/matrix-posDefinite.Rd, man/matrix-rk.Rd, man/matrix-triang.Rd, man/matrix-tslag.Rd: matrix script renames 2009-11-13 wuertz * NAMESPACE, R/stats-sampleLmoments.R: namespace Lmoments added * R/stats-sampleLmoments.R: function to compute sample-Lmoments added, not yet documented * NAMESPACE: namespace robust moments added * R/dist-nigRobust.R: NIG robust moments added, not yet documented * R/dist-gldRobust.R: GLD robust moments added, not yet documented * NAMESPACE, R/dist-gld.R: gld functions added, not yet documented * R/dist-nigFit.R: misprint removed * NAMESPACE, R/stats-robMoments.R: functions for robust sample moments added, not yet documented * R/dist-nigFit.R: fitting methods added for nig 2009-09-28 chalabi * DESCRIPTION: updated version number * ChangeLog, DESCRIPTION: updated DESCR and ChangeLog 2009-09-24 wuertz * R/dist-sgh.R, R/dist-snig.R: C implementation for NIG Distribution updated * man/00fBasics-package.Rd: text added to manual page 2009-09-11 wuertz * R/dist-nigFit.R, man/dist-nigFit.Rd: nigFit method argument added 2009-09-02 wuertz * R/dist-nigFit.R: maximum product spacings estimation added 2009-08-27 wuertz * src/nig.c: Bug in Kjersti Ass NIG Code fixed by Nikolai Eurich 2009-08-22 wuertz * NAMESPACE, R/dist-ghMoments.R, R/dist-nigFit.R: nig estimators added * R/builtin-gelGMM.R, R/builtin-gmmGMM.R: tabs replaced by spaces 2009-08-21 wuertz * R/dist-nigShapeTriangle.R, R/dist-nigStats.R: nig shape triangle added to stats script file * R/dist-nigStats.R: nig moments added * R/dist-nig.R: nig true moments added * NAMESPACE, R/dist-ghtMode.R, R/dist-nig.R, R/dist-nigMode.R, R/dist-stable.R, R/dist-stableMode.R: missing mode functions added * NAMESPACE: new NAMESPACE structure which should ease maintenance of packages. 2009-06-25 chalabi * DESCRIPTION, NAMESPACE: Merge branch 'devel-timeSeries' Conflicts: pkg/timeSeries/R/base-Extract.R pkg/timeSeries/R/timeSeries.R 2009-06-01 wuertz * NAMESPACE: nasmespace for recession lines updated * R/lines-recessionLines.R: function .recessionLines added 2009-05-18 wuertz * NAMESPACE, R/dist-stableFit.R, man/dist-DistributionFits.Rd: stable dist fit improved * man/as.matrix.ts.Rd.bak: .bak file removed * R/dist-stable.R, R/dist-stableFit.R, man/dist-DistributionFits.Rd: stable distribution nlminb added * R/plot-counts.R: a useful counts plot * R/plot-varpie.R: .varpiePlot, a useful plot for portfolio weights ... 2009-05-13 wuertz * R/list-listDescription.R: 2009-04-29 wuertz * NAMESPACE: NAMESPACE updated * R/dist-ghSlider.R, R/dist-hypSlider.R, R/dist-nigSlider.R, R/dist-stableSlider.R, R/gui-ghSlider.R, R/gui-hypSlider.R, R/gui-nigSlider.R, R/gui-stableSlider.R, R/list-listDescription.R, R/list-listFunctions.R, R/list-listIndex.R, R/listDescription.R, R/listFunctions.R, R/listIndex.R: gui and list functioms renamed in gui-foo and list-foo * R/Heaviside.R, R/HeavisideSlider.R, R/Sys.putenv.R, R/as.matrix.ts.R, R/baseMethods.R, R/basicStats.R, R/characterTable.R, R/colIds.R, R/colVec.R, R/decor.R, R/dist-distCheck.R, R/distCheck.R, R/getS4.R, R/gridVector.R, R/gui-HeavisideSlider.R, R/gui-returnSeries.R, R/gui-sliderMenu.R, R/gui-stylizedFacts.R, R/interactivePlot.R, R/jbALM.R, R/jbLM.R, R/jbTable.R, R/ks2Test.R, R/lcg.R, R/matrix-colVec.R, R/matrix-gridVector.R, R/matrix-tslag.R, R/maxdd.R, R/plot-interactivePlot.R, R/rand-lcg.R, R/returnSeriesGUI.R, R/rowStats.R, R/sliderMenu.R, R/stats-basicStats.R, R/stats-maxdd.R, R/stats-rowStats.R, R/stylizedFactsGUI.R, R/symbolTable.R, R/test-jbALM.R, R/test-jbLM.R, R/test-jbTable.R, R/test-ks2Test.R, R/tslag.R, R/unitTest.R, R/unitrootNA.R, R/utils-Heaviside.R, R/utils-Sys.putenv.R, R/utils-as.matrix.ts.R, R/utils-baseMethods.R, R/utils-characterTable.R, R/utils-colIds.R, R/utils-decor.R, R/utils-getS4.R, R/utils-symbolTable.R, R/utils-unitTest.R, R/utils-unitrootNA.R: utils and stats script renamed as stat-foo and utils-foo * R/akimaInterp.R, R/interp-akimaInterp.R, R/interp-krigeInterp.R, R/interp-linearInterp.R, R/krigeInterp.R, R/linearInterp.R, R/locationTest.R, R/test-locationTest.R: interpolation scripts renamed in interp-foo * R/color-Locator.R, R/color-Palettes.R, R/color-RGB.R, R/color-Table.R, R/colorLocator.R, R/colorPalette.R, R/colorTable.R, R/matrix-pdl.R, R/pdl.R, R/rgb.R: color script files renamed in color-foo * R/NormalityTests.R, R/correlationTest.R, R/dist-fDISTFIT.R, R/fDISTFIT.R, R/fHTEST.R, R/matrix-vech.R, R/normalTest.R, R/scaleTest.R, R/test-NormalityTests.R, R/test-correlationTest.R, R/test-fHTEST.R, R/test-normalTest.R, R/test-scaleTest.R, R/test-varianceTest.R, R/varianceTest.R, R/vec.R: test function scripts renamed in test-foo * R/hilbert.R, R/inv.R, R/kron.R, R/matrix-hilbert.R, R/matrix-inv.R, R/matrix-kron.R, R/matrix-norm.R, R/matrix-pascal.R, R/matrix-posDefinite.R, R/matrix-rk.R, R/matrix-tr.R, R/matrix-triang.R, R/matrixNorm.R, R/pascal.R, R/positiveDefinite.R, R/rk.R, R/tr.R, R/triang.R: matrix scripts renamed in matrix-foo.R to find them better * R/acfPlot.R, R/boxPlot.R, R/histPlot.R, R/qqPlot.R, R/scalinglawPlot.R, R/seriesPlot.R, R/subPlot.R, R/subStars.R: old plot files deleted * R/plot-acfPlot.R, R/plot-boxPlot.R, R/plot-histPlot.R, R/plot-qqPlot.R, R/plot-scalinglawPlot.R, R/plot-seriesPlot.R, R/plot-subPlot.R, R/plot-subStars.R: plot files renamed as plot-foo to find them better * R/builtin-gelGMM.R, R/builtin-gmmGMM.R, R/builtin-sandwichGMM.R: gmm builtin added for parameter estimation 2009-04-19 chalabi * DESCRIPTION: added explicit version number in Depends field for key packages 2009-04-02 chalabi * DESCRIPTION: more explicit depends and suggests field in DESC file. 2009-04-01 chalabi * DESCRIPTION: updated DESC file * R/qqPlot.R: 2009-02-09 wuertz * R/qqPlot.R: 2009-01-28 chalabi * DESCRIPTION: updated version number * DESCRIPTION: changed version number to match version on CRAN + 1 2009-01-12 chalabi * man/NormalityTests.Rd, man/correlationTest.Rd, man/fHTEST.Rd, man/ks2Test.Rd, man/locationTest.Rd, man/scaleTest.Rd, man/varianceTest.Rd: fixed warning with new Rd parser