lognormal_lpdf.hpp

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#ifndef LOGNORMAL_LPDF
#define LOGNORMAL_LPDF
 
#include "density_components_base.hpp"
#include "../../common/fims_vector.hpp"
#include "../../common/def.hpp"
 
namespace fims_distributions {
template <typename Type>
struct LogNormalLPDF : public DensityComponentBase<Type> {
  fims::Vector<Type> log_sd;
 
  LogNormalLPDF() : DensityComponentBase<Type>() {}
 
  virtual ~LogNormalLPDF() {}
 
  virtual const Type evaluate() {
    // set vector size based on input type (prior, process, or data)
    size_t n_x = this->get_n_x();
    // get expected value vector size
    size_t n_expected = this->get_n_expected();
    // setup vector for recording the log probability density function values
    this->lpdf_vec.resize(n_x);
    std::fill(this->lpdf_vec.begin(), this->lpdf_vec.end(),
              static_cast<Type>(0));
    this->lpdf = static_cast<Type>(0);
 
    // Dimension checks
    // TODO: fix dimension check as expected values no longer used for data
    if (n_x != n_expected) {
      if (n_expected == 1) {
        n_expected = n_x;
      } else if (n_x > n_expected) {
        n_x = n_expected;
      }
    }
 
    if (this->log_sd.size() > 1 && n_x != this->log_sd.size()) {
      throw std::invalid_argument(
          "LognormalLPDF::Vector index out of bounds. The size of observed "
          "data does not equal the size of the log_sd vector. The observed "
          "data vector is of size " +
          fims::to_string(n_x) + " and the log_sd vector is of size " +
          fims::to_string(this->log_sd.size()));
    }
 
    for (size_t i = 0; i < n_x; i++) {
#ifdef TMB_MODEL
      if (this->input_type == "data") {
        // if data, check if there are any NA values and skip lpdf calculation
        // if there are See Deroba and Miller, 2016
        // (https://doi.org/10.1016/j.fishres.2015.12.002) for the use of
        // lognormal constant
        if (this->get_observed(i) != this->data_observed_values->na_value) {
          this->lpdf_vec[i] =
              dnorm(log(this->get_observed(i)), this->get_expected(i),
                    fims_math::exp(log_sd.get_force_scalar(i)), true) -
              log(this->get_observed(i));
        } else {
          this->lpdf_vec[i] = 0;
        }
      } else {
        if (this->input_type == "random_effects") {
          // if random effects, no lognormal constant needs to be applied
          this->lpdf_vec[i] =
              dnorm(log(this->get_observed(i)), this->get_expected(i),
                    fims_math::exp(log_sd.get_force_scalar(i)), true);
        } else {
          this->lpdf_vec[i] =
              dnorm(log(this->get_observed(i)), this->get_expected(i),
                    fims_math::exp(log_sd.get_force_scalar(i)), true) -
              log(this->get_observed(i));
        }
      }
 
      this->lpdf += this->lpdf_vec[i];
      if (this->simulate_flag) {
        FIMS_SIMULATE_F(this->of) {  // preprocessor definition in interface.hpp
                                     // this simulates data that is mean biased
          if (this->input_type == "data") {
            this->data_observed_values->at(i) = fims_math::exp(
                rnorm(this->get_expected(i),
                      fims_math::exp(log_sd.get_force_scalar(i))));
          }
          if (this->input_type == "random_effects") {
            (*this->re)[i] = fims_math::exp(
                rnorm(this->get_expected(i),
                      fims_math::exp(log_sd.get_force_scalar(i))));
          }
          if (this->input_type == "prior") {
            (*(this->priors[i]))[0] = fims_math::exp(
                rnorm(this->get_expected(i),
                      fims_math::exp(log_sd.get_force_scalar(i))));
          }
        }
      }
#endif
    }
#ifdef TMB_MODEL
    vector<Type> lognormal_observed_values = this->observed_values.to_tmb();
    //  FIMS_REPORT_F(lognormal_observed_values, this->of);
#endif
    return (this->lpdf);
  }
};
}  // namespace fims_distributions
#endif