Type objective_function<Type>::operator() ()
{
DATA_VECTOR(observed);
PARAMETER_VECTOR(population);
PARAMETER(log_process_error);
PARAMETER(log_obs_error);
Type process_error = exp(log_process_error);
Type obs_error = exp(log_obs_error);
int n_obs = observed.size(); // number of observations
Type nll = 0; // negative log likelihood
// likelihood for state transitions
for(int y=1; y<n_obs; y++){
Type m = population[y-1];
nll -= dnorm(population(y), m, process_error, true);
}
// likelihood for observations
for(int y=0; y<n_obs; y++){
nll -= dnorm(observed(y), population(y), obs_error, true);
}
ADREPORT(process_error);
ADREPORT(obs_error);
return nll;
}
Type objective_function<Type>::operator() () {
DATA_VECTOR(E);
DATA_VECTOR(deaths);
DATA_SPARSE_MATRIX(P); // precision matrix
PARAMETER(alpha);
PARAMETER(log_sigma2_V);
PARAMETER_VECTOR(V);
PARAMETER(log_sigma2_U);
PARAMETER_VECTOR(W);
int N = E.size();
vector<Type> log_deaths_pred(N);
vector<Type> mu(N);
Type nll = 0;
Type tau_V = 1 / exp(log_sigma2_V);
Type tau_U = 1 / exp(log_sigma2_U);
nll -= dnorm(alpha, Type(0), Type(10), 1);
nll -= dgamma(tau_V, Type(0.5), Type(2000), 1);
nll -= dgamma(tau_U, Type(0.5), Type(2000), 1);
nll -= dnorm(V, Type(0), exp(0.5 * log_sigma2_V), 1).sum();
vector<Type> tmp = P * W;
nll -= -0.5 * (W * tmp).sum();
vector<Type> U = W * exp(0.5 * log_sigma2_U);
nll -= dnorm(U.sum(), Type(0), Type(0.00001), 1);
for (size_t i = 0; i < N; i++) log_deaths_pred(i) = log(E(i)) + alpha + V(i) + U(i);
for (size_t i = 0; i < N; i++) nll -= dpois(deaths(i), exp(log_deaths_pred(i)), 1);
for (size_t i = 0; i < N; i++) mu(i) = exp(alpha + V(i) + U(i));
vector<Type> deaths_pred = exp(log_deaths_pred);
ADREPORT(U);
ADREPORT(deaths_pred);
ADREPORT(mu);
return nll;
}
Type objective_function<Type>::operator()()
{
DATA_FACTOR(Sex);
DATA_VECTOR(Age);
DATA_VECTOR(Length);
int n = Length.size();
// These are the parameters (three are vectors; one is a scalar)
PARAMETER_VECTOR(Linf);
PARAMETER_VECTOR(Kappa);
PARAMETER_VECTOR(t0);
PARAMETER(LogSigma);
Type Sigma = exp(LogSigma);
vector<Type> LengthPred(n);
// Provide the standard error of Sigma
ADREPORT(Sigma);
// Predictions and likelihoods
for(int i=0;i<n;i++){
Type Temp = Kappa(Sex(i))*(Age(i)-t0(Sex(i)));
LengthPred(i) = Linf(Sex(i))*(1.0-exp(-Temp));
}
Type nll = -sum(dnorm(Length,LengthPred,Sigma,true));
// Prediction for sex 1 and age 10
Type Temp = Kappa(0)*(Type(10)-t0(0));
Type PredLen10 = Linf(0)*(1.0-exp(-Temp));
ADREPORT(PredLen10);
// Predicted growth curve
matrix<Type>LenPred(2,50);
for (int Isex=0;Isex<2;Isex++)
for (int Iage=1;Iage<=50;Iage++)
{
Temp = Kappa(Isex)*(Iage*1.0-t0(Isex));
LenPred(Isex,Iage-1) = Linf(Isex)*(1.0-exp(-Temp));
}
REPORT(LenPred);
return nll;
}
Type objective_function<Type>::operator()()
{
DATA_FACTOR(group);
DATA_MATRIX(covariates); // #obs x #par
DATA_MATRIX(predCovariates);
PARAMETER_MATRIX(beta); // #par x #group-1
Type nll;
matrix<Type> probtmp = covariates * beta; // #obs x #group-1
matrix<Type> prob(probtmp.rows(),probtmp.cols()+1); // #obs x #group
prob.block(0,0,probtmp.rows(),probtmp.cols()) = probtmp;
prob.col(prob.cols()-1) = prob.col(0)*Type(0);
prob = exp(prob.array()).matrix();
for(int i = 0; i < prob.rows(); ++i){
prob.row(i) = prob.row(i)/prob.row(i).sum();
}
for(int i = 0; i < group.size(); i++){
nll -= log(prob(i,group(i)));
}
REPORT(prob);
ADREPORT(prob);
matrix<Type> probtmpP = predCovariates * beta; // #obs x #group-1
matrix<Type> probPred(probtmpP.rows(),probtmpP.cols()+1); // #obs x #group
probPred.block(0,0,probtmpP.rows(),probtmpP.cols()) = probtmpP;
probPred.col(probPred.cols()-1) = probPred.col(0)*Type(0);
probPred = exp(prob.array()).matrix();//exp(probPred);
for(int i = 0; i < probPred.rows(); ++i){
probPred.row(i) = probPred.row(i)/probPred.row(i).sum();
}
REPORT(probPred);
ADREPORT(probPred);
return nll;
}
Type objective_function<Type>::operator() () {
// data:
DATA_MATRIX(x_ij);
DATA_VECTOR(y_i);
DATA_IVECTOR(k_i); // vector of IDs
DATA_INTEGER(n_k); // number of IDs
// parameters:
PARAMETER_VECTOR(b_j)
PARAMETER_VECTOR(sigma_j);
PARAMETER(log_b0_sigma);
PARAMETER_VECTOR(b0_k);
int n_data = y_i.size(); // get number of data points to loop over
// Linear predictor
vector<Type> linear_predictor_i(n_data);
vector<Type> linear_predictor_sigma_i(n_data);
linear_predictor_i = x_ij*b_j;
linear_predictor_sigma_i = sqrt(exp(x_ij*sigma_j));
Type nll = 0.0; // initialize negative log likelihood
for(int i = 0; i < n_data; i++){
nll -= dnorm(y_i(i), b0_k(k_i(i)) + linear_predictor_i(i) , linear_predictor_sigma_i(i), true);
}
for(int k = 0; k < n_k; k++){
nll -= dnorm(b0_k(k), Type(0.0), exp(log_b0_sigma), true);
}
REPORT( b0_k );
REPORT(b_j );
ADREPORT( b0_k );
ADREPORT( b_j );
return nll;
}
Type objective_function<Type>::operator() ()
{
// Data
DATA_INTEGER( n_data );
DATA_INTEGER( n_factors );
DATA_FACTOR( Factor );
DATA_VECTOR( Y );
DATA_VECTOR_INDICATOR(keep, Y);
// Parameters
PARAMETER( X0 );
PARAMETER( log_SD0 );
PARAMETER( log_SDZ );
PARAMETER_VECTOR( Z );
// Objective funcction
Type jnll = 0;
// Probability of data conditional on fixed and random effect values
for( int i=0; i<n_data; i++){
jnll -= dnorm( Y(i), X0 + Z(Factor(i)), exp(log_SD0), true );
}
// Probability of random coefficients
for( int i=0; i<n_factors; i++){
jnll -= dnorm( Z(i), Type(0.0), exp(log_SDZ), true );
}
// Reporting
Type SDZ = exp(log_SDZ);
Type SD0 = exp(log_SD0);
ADREPORT( SDZ );
REPORT( SDZ );
ADREPORT( SD0 );
REPORT( SD0 );
ADREPORT( Z );
REPORT( Z );
ADREPORT( X0 );
REPORT( X0 );
// bias-correction testing
Type MeanZ = Z.sum() / Z.size();
Type SampleVarZ = ( (Z-MeanZ) * (Z-MeanZ) ).sum();
Type SampleSDZ = pow( SampleVarZ + 1e-20, 0.5);
REPORT( SampleVarZ );
REPORT( SampleSDZ );
ADREPORT( SampleVarZ );
ADREPORT( SampleSDZ );
return jnll;
}
Type objective_function<Type>::operator() ()
{
// Data
DATA_INTEGER( n_y );
DATA_INTEGER( n_s );
DATA_IVECTOR( s_i );
DATA_VECTOR( y_i );
// Parameters
PARAMETER( x0 );
//PARAMETER( log_sdz ); //variability in site effect
//PARAMETER_VECTOR( z_s ); //site effect
// Objective funcction
Type jnll = 0;
// Probability of data conditional on fixed and random effect values
vector<Type> ypred_i(n_y);
for( int i=0; i<n_y; i++){
ypred_i(i) = exp( x0 );//+ z_s(s_i(i)) );
jnll -= dpois( y_i(i), ypred_i(i), true );
}
// Probability of random coefficients
//for( int s=0; s<n_s; s++){
// jnll -= dnorm( z_s(s), Type(0.0), exp(log_sdz), true );
//}
// Reporting
//Type sdz = exp(log_sdz);
//REPORT( sdz );
//REPORT( z_s );
REPORT( x0 );
//ADREPORT( sdz );
//ADREPORT( z_s );
ADREPORT( x0 );
return jnll;
}
Type objective_function<Type>::operator() () {
// data:
DATA_MATRIX(x_ij); // fixed effect model matrix
DATA_MATRIX(x_sigma_ij); // fixed effect model matrix
DATA_VECTOR(y_i); // response vector
DATA_IVECTOR(pholder_i); // vector of IDs for strategy
DATA_IVECTOR(strategy_i); // vector of IDs for permit holder
DATA_INTEGER(n_pholder); // number of IDs for pholder
DATA_INTEGER(n_strategy); // number of IDs for strategy
DATA_INTEGER(diversity_column); // fixed effect column position of diversity
DATA_VECTOR(b1_cov_re_i); // predictor data for random slope
DATA_VECTOR(b2_cov_re_i); // predictor data for random slope
/* DATA_VECTOR(b3_cov_re_i); // predictor data for random slope */
DATA_VECTOR(g1_cov_re_i); // predictor data for random slope
DATA_IVECTOR(spec_div_all_1); // indicator for if there is variability in diversity
// parameters:
PARAMETER_VECTOR(b_j);
PARAMETER_VECTOR(sigma_j);
PARAMETER(log_b0_pholder_tau);
// PARAMETER(log_b1_pholder_tau);
PARAMETER(log_b0_strategy_tau);
PARAMETER(log_b1_strategy_tau);
PARAMETER(log_b2_strategy_tau);
/* PARAMETER(log_b3_strategy_tau); */
PARAMETER_VECTOR(b0_pholder);
// PARAMETER_VECTOR(b1_pholder);
PARAMETER_VECTOR(b0_strategy);
PARAMETER_VECTOR(b1_strategy);
PARAMETER_VECTOR(b2_strategy);
/* PARAMETER_VECTOR(b3_strategy); */
//PARAMETER(log_g0_pholder_tau);
PARAMETER(log_g0_strategy_tau);
PARAMETER(log_g1_strategy_tau);
// PARAMETER_VECTOR(g0_pholder);
PARAMETER_VECTOR(g0_strategy);
PARAMETER_VECTOR(g1_strategy);
int n_data = y_i.size();
// Linear predictor
vector<Type> linear_predictor_i(n_data);
vector<Type> linear_predictor_sigma_i(n_data);
vector<Type> eta(n_data);
vector<Type> eta_sigma(n_data);
linear_predictor_i = x_ij*b_j;
linear_predictor_sigma_i = x_sigma_ij*sigma_j;
/* // set slope deviations that we can't estimate to 0: */
/* for(int i = 0; i < n_data; i++){ */
/* if(spec_div_all_1(strategy_i(i)) == 1) { */
/* b1_strategy(strategy_i(i)) = 0; */
/* g1_strategy(strategy_i(i)) = 0; */
/* } */
/* } */
Type nll = 0.0; // initialize negative log likelihood
for(int i = 0; i < n_data; i++){
eta(i) = b0_pholder(pholder_i(i)) +
// b1_pholder(pholder_i(i)) * b1_cov_re_i(i) +
b0_strategy(strategy_i(i)) +
b1_strategy(strategy_i(i)) * b1_cov_re_i(i) +
b2_strategy(strategy_i(i)) * b2_cov_re_i(i) +
/* b3_strategy(strategy_i(i)) * b3_cov_re_i(i) + */
linear_predictor_i(i);
eta_sigma(i) = sqrt(exp(
// g0_pholder(pholder_i(i)) +
g0_strategy(strategy_i(i)) +
g1_strategy(strategy_i(i)) * g1_cov_re_i(i) +
linear_predictor_sigma_i(i)));
nll -= dnorm(y_i(i), eta(i), eta_sigma(i), true);
}
for(int k = 0; k < n_pholder; k++){
nll -= dnorm(b0_pholder(k), Type(0.0), exp(log_b0_pholder_tau), true);
// nll -= dnorm(g0_pholder(k), Type(0.0), exp(log_g0_pholder_tau), true);
// nll -= dnorm(b1_pholder(k), Type(0.0), exp(log_b1_pholder_tau), true);
}
for(int k = 0; k < n_strategy; k++){
nll -= dnorm(b0_strategy(k), Type(0.0), exp(log_b0_strategy_tau), true);
nll -= dnorm(g0_strategy(k), Type(0.0), exp(log_g0_strategy_tau), true);
// only include these species diversity slope deviations
// if there was sufficient variation in species diversity
// to estimate them:
if(spec_div_all_1(k) == 0) {
nll -= dnorm(b1_strategy(k), Type(0.0), exp(log_b1_strategy_tau), true);
nll -= dnorm(g1_strategy(k), Type(0.0), exp(log_g1_strategy_tau), true);
}
nll -= dnorm(b2_strategy(k), Type(0.0), exp(log_b2_strategy_tau), true);
/* nll -= dnorm(b3_strategy(k), Type(0.0), exp(log_b3_strategy_tau), true); */
}
// Reporting
/* Type b0_pholder_tau = exp(log_b0_pholder_tau); */
/* Type b0_strategy_tau = exp(log_b0_strategy_tau); */
// Type b1_tau = exp(log_b1_tau);
/* Type g0_pholder_tau = exp(log_g0_pholder_tau); */
/* Type g0_strategy_tau = exp(log_g0_strategy_tau); */
// Type g1_tau = exp(log_g1_tau);
vector<Type> combined_b1_strategy(n_strategy);
vector<Type> combined_g1_strategy(n_strategy);
for(int k = 0; k < n_strategy; k++){
// these are fixed-effect slopes + random-effect slopes
combined_b1_strategy(k) = b_j(diversity_column) + b1_strategy(k);
combined_g1_strategy(k) = sigma_j(diversity_column) + g1_strategy(k);
}
/* REPORT(b0_pholder); */
REPORT(b0_strategy);
REPORT(eta);
REPORT(b1_strategy);
REPORT(b_j);
/* REPORT(g0_pholder); */
REPORT(g0_strategy);
REPORT(g1_strategy);
/* REPORT(b0_tau); */
// REPORT(b1_tau);
/* REPORT(g0_tau); */
// REPORT(g1_tau);
REPORT(combined_b1_strategy);
REPORT(combined_g1_strategy);
// /* ADREPORT(b0_pholder); */
// ADREPORT(b0_strategy);
// ADREPORT(b1_strategy);
// ADREPORT(b_j);
// /* ADREPORT(g0_pholder); */
// ADREPORT(g0_strategy);
// ADREPORT(g1_strategy);
// /* ADREPORT(b0_tau); */
// ADREPORT(b1_tau);
// /* ADREPORT(g0_tau); */
// ADREPORT(g1_tau);
ADREPORT(combined_b1_strategy);
ADREPORT(combined_g1_strategy);
return nll;
}
Type objective_function<Type>::operator() ()
{
DATA_INTEGER(minAge);
DATA_INTEGER(maxAge);
DATA_INTEGER(minYear);
DATA_INTEGER(maxYear);
DATA_ARRAY(catchNo);
DATA_ARRAY(stockMeanWeight);
DATA_ARRAY(propMature);
DATA_ARRAY(M);
DATA_INTEGER(minAgeS);
DATA_INTEGER(maxAgeS);
DATA_INTEGER(minYearS);
DATA_INTEGER(maxYearS);
DATA_SCALAR(surveyTime);
DATA_ARRAY(Q1);
PARAMETER_VECTOR(logN1Y);
PARAMETER_VECTOR(logN1A);
PARAMETER_VECTOR(logFY);
PARAMETER_VECTOR(logFA);
PARAMETER_VECTOR(logVarLogCatch);
PARAMETER_VECTOR(logQ);
PARAMETER(logVarLogSurvey);
int na=maxAge-minAge+1;
int ny=maxYear-minYear+1;
int nas=maxAgeS-minAgeS+1;
int nys=maxYearS-minYearS+1;
// setup F
matrix<Type> F(ny,na);
for(int y=0; y<ny; ++y){
for(int a=0; a<na; ++a){
F(y,a)=exp(logFY(y))*exp(logFA(a));
}
}
// setup logN
matrix<Type> logN(ny,na);
for(int a=0; a<na; ++a){
logN(0,a)=logN1Y(a);
}
for(int y=1; y<ny; ++y){
logN(y,0)=logN1A(y-1);
for(int a=1; a<na; ++a){
logN(y,a)=logN(y-1,a-1)-F(y-1,a-1)-M(y-1,a-1);
if(a==(na-1)){
logN(y,a)=log(exp(logN(y,a))+exp(logN(y,a-1)-F(y-1,a)-M(y-1,a)));
}
}
}
matrix<Type> predLogC(ny,na);
for(int y=0; y<ny; ++y){
for(int a=0; a<na; ++a){
predLogC(y,a)=log(F(y,a))-log(F(y,a)+M(y,a))+log(Type(1.0)-exp(-F(y,a)-M(y,a)))+logN(y,a);
}
}
Type ans=0;
for(int y=0; y<ny; ++y){
for(int a=0; a<na; ++a){
if(a==0){
ans+= -dnorm(log(catchNo(y,a)),predLogC(y,a),exp(Type(0.5)*logVarLogCatch(0)),true);
}else{
ans+= -dnorm(log(catchNo(y,a)),predLogC(y,a),exp(Type(0.5)*logVarLogCatch(1)),true);
}
}
}
matrix<Type> predLogS(nys,nas);
for(int y=0; y<nys; ++y){
for(int a=0; a<nas; ++a){
int sa = a+(minAgeS-minAge);
int sy = y+(minYearS-minYear);
predLogS(y,a) = logQ(a)-(F(sy,sa)+M(sy,sa))*surveyTime+logN(sy,sa);
ans += -dnorm(log(Q1(y,a)),predLogS(y,a),exp(Type(0.5)*logVarLogSurvey),true);
}
}
vector<Type> ssb(ny);
ssb.setZero();
for(int y=0; y<=ny; ++y){
for(int a=0; a<na; ++a){
std::cout<<y<<" "<<a<<" "<<"\n";
ssb(y)+=exp(logN(y,a))*stockMeanWeight(y,a)*propMature(y,a);
}
}
ADREPORT(ssb);
return ans;
}
