TMB Documentation  v1.9.0
register_atomic_parallel.cpp
// Parallel version of 'register_atomic'
#include <TMB.hpp>
template<class Type>
struct univariate {
Type x, n, mu, sd; // Parameter in integrand
Type operator() (Type u) {
Type ans = 0;
ans += dnorm(u, Type(0.), Type(1.), true);
Type p = invlogit(sd * u + mu);
p = squeeze(p);
ans += x * log(p) + (n - x) * log(1. - p);
ans = exp(ans);
return ans;
}
};
/*
This function evaluates the marginal density of x where
u ~ Normal( mu, sd^2 )
x | u ~ Binom ( n , plogis(u) )
*/
template<class Type>
vector<Type> GaussBinomial(vector<Type> input) {
Type x = input[0], n = input[1]; // Data
Type mu = input[2], sd = input[3]; // Parameters
univariate<Type> f = {x, n, mu, sd};
Type a = -5, b = 5;
vector<Type> res(1);
res[0] = romberg::integrate(f, a, b);
res[0] /= exp(lgamma(n+1) - (lgamma(x+1) + lgamma(n-x+1)));
return res;
}
REGISTER_ATOMIC(GaussBinomial)
template<class Type>
Type objective_function<Type>::operator() ()
{
vector<Type> mu = A * b;
PARAMETER(logsd);
Type sd = exp(logsd);
Type ans = 0;
Type tiny = 0.0; // Set to 1e-12 for robustness
for(int i=0; i < x.size(); i++)
PARALLEL_REGION {
vector<Type> input(4);
input << x(i), n(i), mu(i), sd;
ans -= log( GaussBinomial(input)[0] + tiny );
}
return ans;
}
License: GPL v2