TMB Documentation  v1.9.1
validation/rickervalidation.cpp
// Estimate and validate a Ricker model based on data simulated from the logistic map
//
// Compare Thygesen et al (submitted, 2016): Validation of state space models
// fitted as mixed effects models
//
// This file implements the "Theta logistic population model" from
// Pedersen et al 2012, Ecol. Modelling. With theta=1, this is the Ricker
// model.
//
// Uffe Høgsbro Thygesen and Kasper Kristensen, 2016
#include <TMB.hpp>
template <class Type>
Type objective_function<Type>::operator()()
{
/* Data section */
DATA_VECTOR(Y); // Counted abundance
DATA_VECTOR_INDICATOR(keep, Y); // For one-step predictions
/* Parameter section */
PARAMETER_VECTOR(X); // Latent states. As last as long as Y;
// extra elements are not used
PARAMETER(logr); // Growth rate
PARAMETER(logtheta); // With theta=1, the Ricker model
PARAMETER(logK); // Carrying capacity
PARAMETER(logQ); // Process noise
PARAMETER(logS); // Sample size controlling measurement noise
/* Procedure section */
Type r = exp(logr);
Type theta = exp(logtheta);
Type K = exp(logK);
Type Q = exp(logQ);
Type S = exp(logS);
int timeSteps = Y.size();
Type nll = 0;
// Contributions from state transitions
for (int i = 1; i < timeSteps; i++) {
Type m = X[i - 1] + r * (1.0 - pow(exp(X[i - 1]) / K, theta));
nll -= dnorm(X[i], m, sqrt(Q), true);
}
// Contributions from observations
for (int i = 0; i < timeSteps; i++) {
nll -= keep(i) * dpois(Y[i], S * exp(X[i]),
true); // keep(i) for one-step predictions
}
return nll;
}
License: GPL v2