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#undef TMB_OBJECTIVE_PTR
#define TMB_OBJECTIVE_PTR obj
// name of function below **MUST** match filename
template <class Type>
Type tmb_distsamp(objective_function<Type>* obj) {
//Describe input data
DATA_MATRIX(y); //observations
DATA_MATRIX(X_state); //lambda fixed effect design mat
DATA_SPARSE_MATRIX(Z_state); //psi random effect design mat
DATA_VECTOR(offset_state);
DATA_INTEGER(n_group_vars_state); //# of grouping variables for lambda
DATA_IVECTOR(n_grouplevels_state); //# of levels of each grouping variable
DATA_MATRIX(X_det); //same thing but for p
DATA_SPARSE_MATRIX(Z_det);
DATA_VECTOR(offset_det);
DATA_INTEGER(n_group_vars_det);
DATA_IVECTOR(n_grouplevels_det);
DATA_INTEGER(survey_type);
DATA_INTEGER(keyfun_type);
DATA_VECTOR(A); // Area
DATA_VECTOR(db); // distance breaks
DATA_MATRIX(a);
DATA_VECTOR(w);
DATA_MATRIX(u);
PARAMETER_VECTOR(beta_state); //Fixed effect params for lambda
PARAMETER_VECTOR(b_state); //Random intercepts and/or slopes for lambda
PARAMETER_VECTOR(lsigma_state); //Random effect variance(s) for lambda
PARAMETER_VECTOR(beta_det); //Same thing but for det
PARAMETER_VECTOR(b_det);
PARAMETER_VECTOR(lsigma_det);
PARAMETER_VECTOR(beta_scale); //Trick here: this is 0-length array if keyfun != hazard
Type scale = 0; // If not hazard this is ignored later
if(keyfun_type == 3) scale = exp(beta_scale(0)); // If hazard
//Define the log likelihood so that it can be calculated in parallel over sites
parallel_accumulator<Type> loglik(obj);
int M = y.rows(); // # of sites
int J = y.cols(); // # of distance categories per site
//Construct lambda vector
vector<Type> lam = X_state * beta_state + offset_state;
lam = add_ranef(lam, loglik, b_state, Z_state, lsigma_state,
n_group_vars_state, n_grouplevels_state);
lam = exp(lam);
lam = lam.array() * A.array();
//Construct distance parameter (sigma, rate, etc.) vector
vector<Type> dp(M);
if(keyfun_type > 0){ // If keyfun is not uniform
dp = X_det * beta_det + offset_det;
dp = add_ranef(dp, loglik, b_det, Z_det, lsigma_det,
n_group_vars_det, n_grouplevels_det);
dp = exp(dp);
}
//Likelihood
for (int i=0; i<M; i++){
//Not sure if defining this inside loop is necessary for parallel
vector<Type> asub = a.row(i);
vector<Type> usub = u.row(i);
vector<Type> cp = distance_prob(keyfun_type, dp(i), scale, survey_type, db,
w, asub, usub);
vector<Type> ysub = y.row(i);
Type site_lp = 0;
for (int j=0; j<J; j++){
if(R_IsNA(asDouble(ysub(j)))) goto endsite; //If any NAs found skip site
site_lp += dpois(ysub(j), lam(i) * cp(j), true);
}
loglik -= site_lp;
endsite: ;
}
return loglik;
}
#undef TMB_OBJECTIVE_PTR
#define TMB_OBJECTIVE_PTR this
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