dnbinom_tau.cpp

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#include "statsLib.h"
#include <df1b2fun.h>
#include <adrndeff.h> 


dvariable dnbinom_tau(const double& x, const prevariable& mu, const prevariable& tau)
{
  //x is the observed count
  //mu is the predicted mean
  //tau is the overdispersion parameter
  if (value(tau)<1.0)
  {
    cerr<<"tau is <=1.0 in dnbinom_tau()";
    return(0.0);
  }
  RETURN_ARRAYS_INCREMENT();
  dvariable loglike;

  loglike =  log_negbinomial_density(x, mu, tau);

  RETURN_ARRAYS_DECREMENT();
  return(-loglike);
}

df1b2variable dnbinom_tau(const double& x, const df1b2variable& mu, const df1b2variable& tau)
{
  //x is the observed count
  //mu is the predicted mean
  //tau is the overdispersion parameter
  if (value(tau)<1.0)
  {
    cerr<<"tau is <=1.0 in dnbinom_tau()";
    return(0.0);
  }
  RETURN_ARRAYS_INCREMENT();
  df1b2variable loglike;
  loglike = log_negbinomial_density(x, mu, tau);

  RETURN_ARRAYS_DECREMENT();
  return(-loglike);
}

df1b2variable dnbinom_tau(const dvector& x, const df1b2vector& mu, const df1b2variable& tau)
{
  //the observed counts are in x
  //mu is the predicted mean
  //tau is the overdispersion parameter
  if (value(tau)<1.0)
  {
    cerr<<"tau is <=1.0 in dnbinom_tau()";
    return(0.0);
  }
  RETURN_ARRAYS_INCREMENT();
  int i,imin,imax;
  imin=x.indexmin();
  imax=x.indexmax();
  df1b2variable loglike;
  loglike=0.;
  for(i = imin; i<=imax; i++)
  { 
    loglike += log_negbinomial_density(x(i), mu(i), tau);
  }
  RETURN_ARRAYS_DECREMENT();
  return(-loglike);
}
df1b2variable dnbinom_tau(const dvector& x, const df1b2vector& mu, const df1b2vector& tau)
{
  //the observed counts are in x
  //mu is the predicted mean
  //tau is the overdispersion parameter

  RETURN_ARRAYS_INCREMENT();
  int i,imin,imax;
  imin=x.indexmin();
  imax=x.indexmax();
  df1b2variable loglike;
  loglike=0.;
 
  for(i = imin; i<=imax; i++)
  {
    if (value(tau(i))<1.0)
    {
      cerr<<"tau("<<i<<") is <=1.0 in dnbinom_tau()";
      return(0.0);
    }
    loglike += log_negbinomial_density(x(i), mu(i), tau(i));
  }
  RETURN_ARRAYS_DECREMENT();
  return(-loglike);
}
dvariable dnbinom_tau(const dvector& x, const dvar_vector& mu, const prevariable& tau)
{
  //the observed counts are in x
  //mu is the predicted mean
  //tau is the overdispersion parameter
  if (value(tau)<1.0)
  {
    cerr<<"tau is <=1.0 in dnbinom_tau()";
    return(0.0);
  }
  RETURN_ARRAYS_INCREMENT();
  int i,imin,imax;
  imin=x.indexmin();
  imax=x.indexmax();
  dvariable loglike = 0.;

  for(i = imin; i<=imax; i++)
  {
    loglike += log_negbinomial_density(x(i), mu(i), tau);
  }
  RETURN_ARRAYS_DECREMENT();
  return(-loglike);
}
dvariable dnbinom_tau(const dvector& x, const dvar_vector& mu, const dvar_vector& tau)
{
  //the observed counts are in x
  //mu is the predicted mean
  //tau is the overdispersion parameter
  RETURN_ARRAYS_INCREMENT();
  int i,imin,imax;
  imin=x.indexmin();
  imax=x.indexmax();
  dvariable loglike;
  loglike=0.;

  for(i = imin; i<=imax; i++)
  {
    if (value(tau(i))<1.0)
    {
      cerr<<"tau("<<i<<") is <=1.0 in dnbinom_tau()";
      return(0.0);
    }
    
    loglike += log_negbinomial_density(x(i), mu(i), tau(i));
  }
  RETURN_ARRAYS_DECREMENT();
  return(-loglike);
}