https://github.com/cran/RandomFields
Tip revision: cb8a32f1ddcfbcce003da5312030ddb4d1d04dc7 authored by Martin Schlather on 10 July 2012, 00:00:00 UTC
version 2.0.57
version 2.0.57
Tip revision: cb8a32f
addownfctns.cc
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include "RF.h"
#include "Covariance.h"
#include <unistd.h>
/*
// see CovFct.cc for examples of possible definitions.
typedef int (*checkfct)(cov_model *cov);
// The function checks whether the intrinsic parameters
// etc, the dimension dim, and the method match or work together.
// if fails with ERR("msg") when inconsistencies detected.
typedef void (*rangefct)(cov_model *cov, range_arraytype* ra);
// RF.h for definition of cov_model and range_arraytype
typedef void (*covfct)(double *x, cov_model *cov, double *v);
// first two parameters are input parameters:
// x : vector of length 1 for ISOTROPIC, of length 2 for SPACEISOTROPIC
// and of length dim for STATIONARY or NONSTATIONARY
// p : p[KAPPA1], p[KAPPA2], etc
// IMPORTANT! covfct expect the standard model definition with variance 1 and
// scale=1. (These parameters are set elsewhere.)
// The function returns the function value for a covariance model and
// - gamma(h) for a variogram model
typedef double (*natscalefct)(cov_model *cov, int scaling);
// all parameters are input parameters:
// scaling : NATSCALE_EXACT, NATSCALE_APPROX, NATSCALE_MLE
// natscalefct returns the scale parameter such that,
// for x=1, the covariance function value is 0.05. if case
// the scale parameter is not known then the function natscalefct
// should return 0.0 if scaling=NATSCALE_EXACT;
// if scaling=NATSCALE_APPROX or scaling=NATSCALE_MLE values are return
// as approximation or of interest in MLE of parameters to put
// the parameters p[KAPPA1], etc and p[SCALE] into a somehow orthogonal
// direction.
typedef double (*randommeasure)(cov_model *cov);
// the function returns a random draw from the spectral measure in the
// two dimensional spectral turning bands method
nr = IncludePrim(
char *name, // name of the model appearing in R
int kappas, // number of specific parameters
checkfct, // see above
int isotropic, // values are: ISOTROPIC, SPACEISOTROPIC, STATIONARY,
// NONSTATIONARY
bool variogram,// Is the model a variogramm, e.g. gamma(h)=|h| ?
// If so, then the covariance function definition must
// be C(h) = -gamma(h); the derivatives accordingly
rangefct range // see above
);
// below, the function parameters can be set also to NULL. Then
// the corresponding methods will not be available
addCov(int nr, // the number returned by IncludeModel
covfct cov, // see above
cov_model D, // the derivative of a ISOTROPIC model
// or the derivative w.r.t. the spatial
// component in case of a SPACEISOTROPIC model;
// used in TBM3 method for product models; see
// also typedef of isofct
natscalefct naturalscale // see above
);
addTBM(int nr, // the number returned by IncludeModel
cov_model cov_tbm2, // the solved Abel intregral for TBM2
cov_model cov_tbm3, // d(hC(h))/dh -- may become obsolete,
// since it can be composed from cov and
// derivative. The call of cov_tbm3, however,
// can be much faster
randommeasure spectral // see above
);
// other addons exist, but are rarely used
*/
/* gencauchy */
void gCauchy(double *x, cov_model *cov, double *v){
double kappa = cov->p[0][0], delta=cov->p[1][0];
*v = pow(1.0 + pow(*x, kappa), -delta/kappa);
}
double ScalegCauchy(cov_model *cov) {
double kappa = cov->p[0][0], delta=cov->p[1][0];
return pow(pow(0.05, -kappa / delta) - 1.0, -1.0 / kappa);
}
void DgCauchy(double *x, cov_model *cov, double *v){
double kappa = cov->p[0][0], delta=cov->p[1][0], ha, y=*x;
if (y ==0.0) {
*v = ((kappa > 1.0) ? 0.0 : (kappa < 1.0) ? -INFTY : -delta);
} else {
ha=pow(y, kappa - 1.0);
*v = -delta * ha * pow(1.0 + ha * y, -delta / kappa - 1.0);
}
}
void DDgCauchy(double *x, cov_model *cov, double *v){
double kappa = cov->p[0][0], delta=cov->p[1][0], ha, y=*x;
if (y ==0.0) {
*v = ((kappa==2.0) ? delta * (delta + 1.0) : INFTY);
} else {
ha=pow(y, kappa);
*v = delta * ha / (y * y) * (1.0 - kappa + (1.0 + delta) * ha)
* pow(1.0 + ha, -delta / kappa - 2.0);
}
}
void checkgCauchy(cov_model *cov){
if (cov->tsdim > 2)
cov->pref[CircEmbedCutoff] = cov->pref[CircEmbedIntrinsic] = 0;
}
void rangegCauchy(cov_model *cov, range_arraytype* ra){
range_type *range = ra->ranges;
range->min[0] = 0.0;
range->max[0] = 2.0;
range->pmin[0] = 0.05;
range->pmax[0] = 2.0;
range->openmin[0] = true;
range->openmax[0] = false;
range->min[1] = 0.0;
range->max[1] = RF_INF;
range->pmin[1] = 0.05;
range->pmax[1] = 10.0;
range->openmin[1] = true;
range->openmax[1] = true;
}
void addusersfunctions() {
// replace this function by something similar to the code
// found below in the comment
// Only the call of IncludeModel is obligatory.
}
/*
void addusersfunctions() {
int nr;
nr=IncludeModel("gencauchy2", 2, checkgCauchy, ISOTROPIC, false,
rangegCauchy);
addCov(nr,gCauchy, DgCauchy, ScalegCauchy);
addTBM(nr, NULL, NULL, NULL);
}
*/