/**
* Description not yet available.
* \param
*/
dvar_vector posfun(const dvar_vector&x,double eps,const prevariable& _pen)
{
int mmin=x.indexmin();
int mmax=x.indexmax();
dvar_vector tmp(mmin,mmax);
for (int i=mmin;i<=mmax;i++)
{
tmp(i)=posfun(x(i),eps,_pen);
}
return tmp;
}
Type objective_function<Type>::operator() ()
{
DATA_VECTOR(times);
DATA_VECTOR(obs);
PARAMETER(log_R0);
PARAMETER(log_a);
PARAMETER(log_theta);
PARAMETER(log_sigma);
Type sigma=exp(log_sigma);
Type theta=exp(log_theta);
Type R0=exp(log_R0);
Type a=exp(log_a)+Type(1e-4);
int n1=times.size();
int n2=2;//mean and variance
matrix<Type> xdist(n1,n2); //Ex and Vx
Type m=(a-Type(1))*R0/times(n1-1);
Type pen;
xdist(0,0)=obs[0];
xdist(0,1)=Type(0);
Type nll=0;
Fun<Type> F;
F.setpars(R0, m, theta, sigma);
CppAD::vector<Type> xi(n2);
xi[1]=Type(0); //Bottinger's code started variance at 0 for all lsoda calls
Type ti;
Type tf;
for(int i=0; i<n1-1; i++)
{
xi[0] = Type(obs[i]);
ti=times[i];
tf=times[i+1];
xdist.row(i+1) = vector<Type>(CppAD::Runge45(F, 1, ti, tf, xi));
xdist(i+1,1)=posfun(xdist(i+1,1), Type(1e-3), pen);//to keep the variance positive
nll-= dnorm(obs[i+1], xdist(i+1,0), sqrt(xdist(i+1,1)), true);
}
nll+=pen; //penalty if the variance is near eps
return nll;
}
void LRGS::population_dynamics()
{
// cout<<"In population dynamics"<<endl;
int i;
dvariable btmp;
m_fpen.initialize();
for(i=m_syr;i<=m_nyr;i++)
{
// m_ft(i) = -log((-m_ct(i)+m_bt(i))/m_bt(i));
m_ft(i) = m_ct(i) / m_bt(i);
if(i-m_syr > m_agek)
{
m_rt(i) = m_a*m_bt(i-m_agek)/(1.+m_b*m_bt(i-m_agek)) * exp(m_wt(i));
}
btmp = m_s*m_bt(i) + m_rt(i) - m_ct(i);
m_bt(i+1) = posfun(btmp,0.1,m_fpen);
// m_ft(i) = -log( (m_bt(i)*(1.-m_s) + m_bt(i+1) - m_rt(i))/m_bt(i) );
}
// sd_dep = m_bt(nyr)/m_bo;
}
