Type objective_function<Type>::operator() ()
{
DATA_VECTOR(times);
DATA_VECTOR(obs);
PARAMETER(log_R0);
PARAMETER(m);
PARAMETER(log_theta);
PARAMETER(log_sigma);
Type theta=exp(log_theta);
Type sigma=exp(log_sigma);
Type R0=exp(log_R0);
int n1=times.size();
int n2=2;//mean and variance
vector<Type> Dt(n1-1);
vector<Type> Ex(n1-1);
vector<Type> Vx(n1-1);
Type nll=0;
m=0;
Dt=diff(times);
Ex=theta*(Type(1)-exp(-R0*Dt)) + obs.segment(0, n1-1)*exp(-R0*Dt);
Vx=Type(0.5)*sigma*sigma*(Type(1)-exp(Type(-2)*R0*Dt))/R0;
for(int i=0; i<n1-1; i++)
{
nll-= dnorm(obs[i+1], Ex[i], sqrt(Vx[i]), true);
}
return nll;
}
Type objective_function<Type>::operator() ()
{
DATA_MATRIX(obs);
DATA_MATRIX(coord);
DATA_VECTOR(covObs);
DATA_INTEGER(p);
DATA_VECTOR(h);
PARAMETER(logObsSd);
PARAMETER(logObsTSd);
PARAMETER(logStatSd);
PARAMETER_MATRIX(x);
Type nll = 0.0;
covafill<Type> cf(coord,covObs,h,p);
// Contribution from states
for(int i = 1; i < x.cols(); ++i) {
nll -= dnorm(x(0,i), x(0,i-1), exp(logStatSd),true);
nll -= dnorm(x(1,i), x(1,i-1), exp(logStatSd),true);
}
// contribution from observations
for(int i = 0; i < obs.cols(); ++i) {
nll -= dnorm(obs(0,i), x(0,i), exp(logObsSd),true);
nll -= dnorm(obs(1,i), x(1,i), exp(logObsSd),true);
vector<Type> tmp = x.col(i);
Type val = evalFill((CppAD::vector<Type>)tmp, cf)[0];
nll -= dnorm(obs(2,i), val, exp(logObsTSd),true);
}
return nll;
}
Type objective_function<Type>::operator() ()
{
DATA_VECTOR(Y);
DATA_VECTOR(x);
PARAMETER(a);
PARAMETER(b);
PARAMETER(logSigma);
parallel_accumulator<Type> nll(this);
for(int i=0;i<x.size();i++)nll-=dnorm(Y[i],a+b*x[i],exp(logSigma),true);
return nll;
}
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
DATA_VECTOR( y_i );
DATA_MATRIX( X_ij );
// Parameters
PARAMETER_VECTOR( b_j );
PARAMETER_VECTOR( theta_z );
// Objective funcction
Type zero_prob = 1 / (1 + exp(-theta_z(0)));
Type logsd = exp(theta_z(1));
Type jnll = 0;
int n_data = y_i.size();
// Linear predictor
vector<Type> linpred_i( n_data );
linpred_i = X_ij * b_j;
// Probability of data conditional on fixed effect values
for( int i=0; i<n_data; i++){
if(y_i(i)==0) jnll -= log( zero_prob );
if(y_i(i)!=0) jnll -= log( 1-zero_prob ) + dlognorm( y_i(i), linpred_i(i), logsd, true );
}
// Reporting
REPORT( zero_prob );
REPORT( logsd );
REPORT( linpred_i );
return jnll;
}
Type objective_function<Type>::operator() ()
{
DATA_VECTOR(x);
DATA_VECTOR(y);
int n = y.size();
PARAMETER(b0);
PARAMETER(b1);
PARAMETER(logSigma);
vector<Type> yfit(n);
Type neglogL = 0.0;
yfit = b0 + b1*x;
neglogL = -sum(dnorm(y, yfit, exp(logSigma), true));
return neglogL;
}
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_VECTOR(times);
DATA_VECTOR(obs);
PARAMETER(log_R0);
PARAMETER(log_a);
PARAMETER(log_theta);
PARAMETER(log_sigma);
Type sigma=exp(log_sigma);
Type theta=exp(log_theta);
Type R0=exp(log_R0);
Type a=exp(log_a)+Type(1e-4);
int n1=times.size();
int n2=2;//mean and variance
matrix<Type> xdist(n1,n2); //Ex and Vx
Type m=(a-Type(1))*R0/times(n1-1);
Type pen;
xdist(0,0)=obs[0];
xdist(0,1)=Type(0);
Type nll=0;
Fun<Type> F;
F.setpars(R0, m, theta, sigma);
CppAD::vector<Type> xi(n2);
xi[1]=Type(0); //Bottinger's code started variance at 0 for all lsoda calls
Type ti;
Type tf;
for(int i=0; i<n1-1; i++)
{
xi[0] = Type(obs[i]);
ti=times[i];
tf=times[i+1];
xdist.row(i+1) = vector<Type>(CppAD::Runge45(F, 1, ti, tf, xi));
xdist(i+1,1)=posfun(xdist(i+1,1), Type(1e-3), pen);//to keep the variance positive
nll-= dnorm(obs[i+1], xdist(i+1,0), sqrt(xdist(i+1,1)), true);
}
nll+=pen; //penalty if the variance is near eps
return nll;
}
Type objective_function<Type>::operator() ()
{
DATA_VECTOR(x);
PARAMETER(mu);
PARAMETER(logSigma);
Type f;
f = -sum(dnorm(x,mu,exp(logSigma), true));
return f;
}
Type objective_function<Type>::operator() ()
{
DATA_VECTOR(obs);
DATA_FACTOR(group);
PARAMETER_VECTOR(mu);
PARAMETER_VECTOR(sd);
Type res=0;
for(int i=0;i<obs.size();i++){
res -= dnorm(obs[i],mu[group[i]],sd[group[i]],true);
}
return res;
}
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_VECTOR(x);
PARAMETER(mu);
PARAMETER(logSigma);
Type f;
f = -sum(dnorm(x,mu,exp(logSigma), true));
f /= f / 0.0;
std::cout << "f " << f << std::endl;
return f;
}
Type objective_function<Type>::operator() ()
{
DATA_VECTOR(x);
DATA_VECTOR(y);
int n = y.size();
PARAMETER(b0);
PARAMETER(b1);
PARAMETER(logSigma);
vector<Type> yfit(n);
Type neglogL = 0.0;
//Call triple function
yfit = b0 + b1*x;
neglogL = -sum(dnorm(y, yfit, exp(logSigma), true));
// JIM THORSON JUST ROCK'N TMB
std::cout << b0<<" "<<b1<<"\n ";
return neglogL;
}
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_VECTOR(height);
DATA_VECTOR(times);
DATA_IVECTOR(timeidx);
DATA_IARRAY(trackinfo);
DATA_VECTOR(weights);
PARAMETER(logSigma);
PARAMETER(logSigmaRW);
PARAMETER(logitp);
PARAMETER_VECTOR(u);
int timeSteps=times.size();
int obsDim=height.size();
int noTracks=trackinfo.dim[0];
Type p=ilogit(logitp);
Type ans=0;
Type sdRW=exp(logSigmaRW);
for(int i=1;i<timeSteps;i++)
ans += -dnorm(u(i),u(i-1),sdRW*sqrt(times(i)-times(i-1)),true);
Type sdObs=exp(logSigma);
for(int t=0;t<noTracks;t++){
vector<Type> sub=height.segment(trackinfo(t,0),trackinfo(t,2));
vector<Type> subw=weights.segment(trackinfo(t,0),trackinfo(t,2));
for(int i=0;i<trackinfo(t,2);i++){
ans += nldens(sub(i),u(timeidx(trackinfo(t,0))-1),sdObs/sqrt(subw(i)),p);
}
}
return ans;
}
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(x);
DATA_MATRIX(D);
PARAMETER(phi);
PARAMETER(kappa);
matrix<Type> C(D);
for(int i=0; i<C.rows(); i++)
for(int j=0; j<C.cols(); j++)
C(i,j) = matern(D(i,j), phi, kappa);
Type nll = density::MVNORM_t<Type>(C)(x);
return nll;
}
Type objective_function<Type>::operator() ()
{
DATA_VECTOR(y);
PARAMETER(phi);
PARAMETER(shape1);
PARAMETER(shape2);
PARAMETER(sd);
PARAMETER_VECTOR(u);
Type res = 0;
res += density::AR1(phi)(u);
vector<Type> unif = pnorm(u, Type(0), Type(1));
vector<Type> x = qbeta(unif, shape1, shape2);
res -= dnorm(y, x, sd, true).sum();
return res;
}
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 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;
}
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
DATA_VECTOR( y_i ); //capital letters - macro - Kasper built into C++ code
// Parameters
PARAMETER( mean );
PARAMETER( log_sd );
// Objective funcction
Type sd = exp(log_sd);
Type jnll = 0;
int n_data = y_i.size();
// Probability of data conditional on fixed effect values
for( int i=0; i<n_data; i++){
jnll -= dnorm( y_i(i), mean, sd, true );
}
// Reporting
return jnll;
}
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_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_VECTOR(y);
DATA_MATRIX(X)
DATA_MATRIX(dd)
PARAMETER_VECTOR(b);
PARAMETER(a);
PARAMETER(log_sigma);
int n = dd.rows();
// Construct joint negative log-likelihood
joint_nll<Type> jnll(y, X, dd, b, a, log_sigma);
// Random effect initial guess
vector<Type> u(n);
u.setZero();
// Calculate Laplace approx (updates u)
DATA_INTEGER(niter);
Type res = laplace(jnll, u, niter);
ADREPORT(u)
return res;
}
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
DATA_INTEGER( like ); // define likelihood type, 1==delta lognormal, 2==delta gamma
DATA_VECTOR( y_i ); // observations
DATA_MATRIX( X_ij ); // covariate design matrix
DATA_VECTOR( include ); //0== include in NLL, 1== exclude from NLL
// Parameters
PARAMETER_VECTOR( b_j ); // betas to generate expected values
PARAMETER_VECTOR( theta_z ); // variances
// Transformations
Type zero_prob = 1 / (1 + exp(-theta_z(0)));
Type sd = exp(theta_z(1)); //standard deviation (lognormal), scale parameter theta (gamma)
int n_data = y_i.size();
Type jnll = 0;
Type pred_jnll = 0;
vector<Type> jnll_i(n_data);
// linear predictor
vector<Type> logpred_i( n_data );
logpred_i = X_ij * b_j;
// Delta lognormal
if(like==1){
for( int i=0; i<n_data; i++){
if(y_i(i)==0) jnll_i(i) -= log( zero_prob );
if(y_i(i)!=0) jnll_i(i) -= log( 1-zero_prob ) + dlognorm( y_i(i), logpred_i(i), sd, true );
// Running counter
if( include(i)==0 ) jnll += jnll_i(i);
if( include(i)==1 ) pred_jnll += jnll_i(i);
}
}
// Delta gamma
if(like==2){
for(int i=0; i<n_data; i++){
if(y_i(i)==0) jnll_i(i) -= log( zero_prob );
if(y_i(i)!=0) jnll_i(i) -= log( 1-zero_prob ) + dgamma( y_i(i), pow(sd,-2), exp(logpred_i(i))*pow(sd,2), true );
// Running counter
if( include(i)==0 ) jnll += jnll_i(i);
if( include(i)==1 ) pred_jnll += jnll_i(i);
}
}
// Reporting
REPORT( zero_prob );
REPORT( sd );
REPORT( logpred_i );
REPORT( b_j );
REPORT( pred_jnll );
REPORT( jnll_i );
return jnll;
}
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
DATA_INTEGER(n_j); // number of IDs
DATA_VECTOR(b1_cov_re_i); // predictor data for random slope
DATA_VECTOR(sigma1_cov_re_i); // predictor data for random slope
//DATA_VECTOR(sigma2_cov_re_i); // predictor data for random slope
// parameters:
PARAMETER_VECTOR(b_j)
PARAMETER_VECTOR(sigma_j);
PARAMETER(log_b0_sigma);
PARAMETER_VECTOR(b0_k);
PARAMETER(log_b1_sigma);
PARAMETER_VECTOR(b1_k);
PARAMETER(log_sigma0_sigma);
PARAMETER(log_sigma1_sigma);
PARAMETER_VECTOR(sigma0_k);
PARAMETER_VECTOR(sigma1_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 = 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)) + b1_k(k_i(i)) * b1_cov_re_i(i) +
linear_predictor_i(i),
sqrt(exp(
sigma0_k(k_i(i)) +
sigma1_k(k_i(i)) * sigma1_cov_re_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);
nll -= dnorm(b1_k(k), Type(0.0), exp(log_b1_sigma), true);
nll -= dnorm(sigma0_k(k), Type(0.0), exp(log_sigma0_sigma), true);
nll -= dnorm(sigma1_k(k), Type(0.0), exp(log_sigma1_sigma), true);
//nll -= dnorm(sigma2_k(k), Type(0.0), exp(log_sigma2_sigma), true);
}
// Reporting
Type b0_sigma = exp(log_b0_sigma);
Type b1_sigma = exp(log_b1_sigma);
Type sigma0_sigma = exp(log_sigma0_sigma);
Type sigma1_sigma = exp(log_sigma1_sigma);
//Type sigma2_sigma = exp(log_sigma2_sigma);
vector<Type> b1_b1_k(n_k);
vector<Type> sigma1_sigma1_k(n_k);
for(int k = 0; k < n_k; k++){
// these are fixed-effect slopes + random-effect slopes
b1_b1_k(k) = b_j(n_j) + b1_k(k);
sigma1_sigma1_k(k) = sigma_j(n_j) + sigma1_k(k);
}
REPORT( b0_k );
REPORT( b1_k );
REPORT( b_j );
REPORT( sigma0_k );
REPORT( sigma1_k );
//REPORT( sigma2_k );
REPORT(b0_sigma);
REPORT(b1_sigma);
REPORT(sigma0_sigma);
REPORT(sigma1_sigma);
//REPORT(sigma2_sigma);
REPORT(b1_b1_k);
REPORT(sigma1_sigma1_k);
//ADREPORT( b0_k );
//ADREPORT( b1_k );
//ADREPORT( b_j );
//ADREPORT( sigma0_k );
//ADREPORT( sigma1_k );
//ADREPORT( sigma2_k );
//ADREPORT(b0_sigma);
//ADREPORT(b1_sigma);
//ADREPORT(sigma0_sigma);
//ADREPORT(sigma1_sigma);
//ADREPORT(sigma2_sigma);
//ADREPORT(b1_b1_k);
//ADREPORT(sigma1_sigma1_k);
return nll;
}
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;
}
