Type objective_function<Type>::operator() ()
{
DATA_ARRAY(wtage);
DATA_ARRAY(wtcv);
// matrix<Type> yfit(n);
int nr = wtage.dim(0);
int nc = wtage.dim(1);
PARAMETER(log_sd_coh);
PARAMETER(log_sd_yr );
PARAMETER_VECTOR(mnwt );
PARAMETER_VECTOR(coh_eff); // (styr-nages-age_st+1,endyr-age_st+3,3);
PARAMETER_VECTOR( yr_eff); // yr_eff(styr,endyr+3,3);
Type nll = 0.0;
Type sigma_coh = exp(log_sd_coh);
Type sigma_yr = exp(log_sd_yr );
Type wt_pre;
= mnwt*exp(sigma_yr*yr_eff(i));
Type objective_function<Type>::operator() ()
{
DATA_VECTOR (a);
DATA_ARRAY (b);
DATA_MATRIX (c);
DATA_SPARSE_MATRIX (d);
PARAMETER (p);
REPORT(a);
REPORT(b);
REPORT(c);
REPORT(d);
REPORT(p);
//// Vector of anything:
vector<matrix<Type> > voa(2);
voa[0] = c;
voa[1] = c;
REPORT(voa);
return 0;
}
Type objective_function<Type>::operator() ()
{
// Data objects
DATA_VECTOR(v1);
DATA_MATRIX(m1);
DATA_ARRAY(a1);
// Parameter objects
PARAMETER(p) // Not used in this example
// Obtaining dimensions of objects
REPORT(a1.dim);
vector<int> d2(2);
d2(0) = m1.rows();
d2(1) = m1.cols();
REPORT(d2);
int d3 = v1.size();
REPORT(d3);
// Matrix multiplication versus elementwise multiplication (similar for addition, subtraction,...)
matrix<Type> m1m1 = m1*m1; // Matrix multiplication of matrices
REPORT(m1m1);
array<Type> a1a1 = a1*a1; // Element-wise multiplication of arrays
REPORT(a1a1);
matrix<Type> m1m1_by_element = (m1.array()*m1.array()).matrix(); // Elementwise multiplication of matrices
REPORT(m1m1_by_element);
array<Type> a1a1_matrix_mult(2,2);
a1a1_matrix_mult = (a1.matrix()*a1.matrix()).array(); // Matrix multiplication of arrays
REPORT(a1a1_matrix_mult);
// Matrix-vector multiplication
REPORT(m1*v1); // matrix-vector product (linear algebra style)
Type v1_norm2 = (v1*v1).sum(); // Inner product v1*v1
REPORT(v1_norm2);
// Indexing objects
m1(1,1); // Element (1,1) of matrix m1
m1.row(1); // 2nd row of matrix m1
m1.col(1); // 2nd col of matrix m1
a1(1,1); // Element (1,1) of array a1
a1.transpose().col(1); // 2nd row of array a1
a1.col(1); //2nd col of array a1
v1(1); // 2nd element of vector v1
// Subsetting matrices and vectors
v1.head(1); // First element of v1
v1.tail(1); // Last element of v1
m1.block(0,0,1,1); // Block of m1 consisting of m1(1,1)
// Subsetting arrays
// See R help pages for "template", i.e. "help(template)" in R
// Generic matrix operations that we must ensure compiles
m1.transpose();
m1.diagonal();
m1.asDiagonal();
Type ans;
return ans;
}
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;
}
