dnbinom.cpp

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#include <df1b2fun.h>
#include <adrndeff.h> 
dvariable dnbinom(const double& x, const prevariable& mu, const prevariable& k)
{
  //x is the observed count
  //mu is the predicted mean
  //k is the overdispersion parameter
  if (value(k)<0.0)
  {
    cerr<<"k is <=0.0 in dnbinom()";
    return(0.0);
  }
  RETURN_ARRAYS_INCREMENT();
  dvariable loglike;

  loglike = gammln(k+x)-gammln(k)-gammln(x+1)+k*log(k)-k*log(mu+k)+x*log(mu)-x*log(mu+k);

  RETURN_ARRAYS_DECREMENT();
  return(-loglike);
}

df1b2variable dnbinom(const double& x, const df1b2variable& mu, const df1b2variable& k)
{
  //x is the observed count
  //mu is the predicted mean
  //k is the overdispersion parameter
  if (value(k)<0.0)
  {
    cerr<<"k is <=0.0 in dnbinom()";
    return(0.0);
  }
  RETURN_ARRAYS_INCREMENT();
  df1b2variable loglike;
  loglike = gammln(k+x)-gammln(k)-gammln(x+1)+k*log(k)-k*log(mu+k)+x*log(mu)-x*log(mu+k);

  RETURN_ARRAYS_DECREMENT();
  return(-loglike);
}

df1b2variable dnbinom(const dvector& x, const df1b2vector& mu, const df1b2variable& k)
{
  //the observed counts are in x
  //mu is the predicted mean
  //k is the overdispersion parameter
  if (value(k)<0.0)
  {
    cerr<<"k is <=0.0 in dnbinom()";
    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 += gammln(k+x(i))-gammln(k)-gammln(x(i)+1)+k*log(k)-k*log(mu(i)+k)+x(i)*log(mu(i))-x(i)*log(mu(i)+k);
  }
  RETURN_ARRAYS_DECREMENT();
  return(-loglike);
}
df1b2variable dnbinom(const dvector& x, const df1b2vector& mu, const df1b2vector& k)
{
  //the observed counts are in x
  //mu is the predicted mean
  //k 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(k(i))<0.0)
    {
      cerr<<"k("<<i<<") is <=0.0 in dnbinom()";
      return(0.0);
    }
    loglike += gammln(k(i)+x(i))-gammln(k(i))-gammln(x(i)+1)+k(i)*log(k(i))-k(i)*log(mu(i)+k(i))+x(i)*log(mu(i))-x(i)*log(mu(i)+k(i));
  }
  RETURN_ARRAYS_DECREMENT();
  return(-loglike);
}
dvariable dnbinom(const dvector& x, const dvar_vector& mu, const prevariable& k)
{
  //the observed counts are in x
  //mu is the predicted mean
  //k is the overdispersion parameter
  if (value(k)<0.0)
  {
    cerr<<"k is <=0.0 in dnbinom()";
    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 += gammln(k+x(i))-gammln(k)-gammln(x(i)+1)+k*log(k)-k*log(mu(i)+k)+x(i)*log(mu(i))-x(i)*log(mu(i)+k);
  }
  RETURN_ARRAYS_DECREMENT();
  return(-loglike);
}
dvariable dnbinom(const dvector& x, const dvar_vector& mu, const dvar_vector& k)
{
  //the observed counts are in x
  //mu is the predicted mean
  //k 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(k(i))<0.0)
    {
      cerr<<"k("<<i<<") is <=0.0 in dnbinom()";
      return(0.0);
    }
    
    loglike += gammln(k(i)+x(i))-gammln(k(i))-gammln(x(i)+1)+k(i)*log(k(i))-k(i)*log(mu(i)+k(i))+x(i)*log(mu(i))-x(i)*log(mu(i)+k(i));
  }
  RETURN_ARRAYS_DECREMENT();
  return(-loglike);
}