Type objective_function<Type>::operator() () {
// data:
DATA_MATRIX(age);
DATA_VECTOR(len);
DATA_SCALAR(CV_e);
DATA_INTEGER(num_reads);
// parameters:
PARAMETER(r0); // reference value
PARAMETER(b); // growth displacement
PARAMETER(k); // growth rate
PARAMETER(m); // slope of growth
PARAMETER(CV_Lt);
PARAMETER(gam_shape);
PARAMETER(gam_scale);
PARAMETER_VECTOR(age_re);
// procedures:
Type n = len.size();
Type nll = 0.0; // Initialize negative log-likelihood
Type eps = 1e-5;
CV_e = CV_e < 0.05 ? 0.05 : CV_e;
for (int i = 0; i < n; i++) {
Type x = age_re(i);
if (!isNA(x) && isFinite(x)) {
Type len_pred = pow(r0 + b * exp(k * x), m);
Type sigma_e = CV_e * x + eps;
Type sigma_Lt = CV_Lt * (len_pred + eps);
nll -= dnorm(len(i), len_pred, sigma_Lt, true);
nll -= dgamma(x + eps, gam_shape, gam_scale, true);
for (int j = 0; j < num_reads; j++) {
if (!isNA(age(j, i)) && isFinite(age(j, i)) && age(j, i) >= 0) {
nll -= dnorm(age(j, i), x, sigma_e, true);
}
}
}
}
return nll;
}
Type objective_function<Type>::operator() () {
// data:
DATA_MATRIX(age);
DATA_VECTOR(len);
DATA_SCALAR(CV_e);
DATA_INTEGER(num_reads);
// parameters:
PARAMETER(a); // upper asymptote
PARAMETER(b); // growth range
PARAMETER(k); // growth rate
PARAMETER(CV_Lt);
PARAMETER(beta);
PARAMETER_VECTOR(age_re);
// procedures:
Type n = len.size();
Type nll = 0.0; // Initialize negative log-likelihood
Type eps = 1e-5;
CV_e = CV_e < 0.05 ? 0.05 : CV_e;
for (int i = 0; i < n; i++) {
Type x = age_re(i);
if (!isNA(x) && isFinite(x)) {
Type len_pred = a / (1 + b * exp(-k * x));
Type sigma_e = CV_e * x + eps;
Type sigma_Lt = CV_Lt * (len_pred + eps);
nll -= dnorm(len(i), len_pred, sigma_Lt, true);
nll -= dexp(x, beta, true);
for (int j = 0; j < num_reads; j++) {
if (!isNA(age(j, i)) && isFinite(age(j, i)) && age(j, i) >= 0) {
nll -= dnorm(age(j, i), x, sigma_e, true);
}
}
}
}
return nll;
}
Type objective_function<Type>::operator()()
{
DATA_VECTOR(y); // Observations
DATA_VECTOR_INDICATOR(keep, y); // For one-step predictions
DATA_SCALAR(huge);
PARAMETER_VECTOR(x);
PARAMETER(mu);
PARAMETER(logsigma);
PARAMETER(logs);
// Initial condition
Type nll = -dnorm(x(0), Type(0), huge, true);
// Increments
for (int i = 1; i < x.size(); ++i)
nll -= dnorm(x(i), x(i - 1) + mu, exp(logsigma), true);
// Observations
for (int i = 0; i < y.size(); ++i)
nll -= keep(i) * dnorm(y(i), x(i), exp(logs), true);
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;
}
