No Random Effects: Example and Test

no_random.cpp

@(@\newcommand{\R}[1]{ {\rm #1} } \newcommand{\B}[1]{ {\bf #1} } \newcommand{\W}[1]{ \; #1 \; }@)@This is cppad_mixed--20220519 documentation: Here is a link to its current documentation . No Random Effects: Example and Test
Model
.

Model
@[@ \B{p}( z_i | \theta ) \sim \B{N} ( \theta_i , 1 ) @]@@[@ \B{p}( \theta_i ) \sim \B{N} ( 0 , 1 ) @]@The corresponding fixed likelihood g(theta) is @[@ g( \theta ) = \frac{1}{2} \sum_{i} \left[ \log ( 2 \pi ) + \theta_i^2 + \log ( 2 \pi ) + ( z_i - \theta_i )^2 \right] @]@ The optimal solution (with no constraints) is @[@ \hat{\theta}_i = z_i / 2 @]@


# include <cppad/cppad.hpp>
# include <cppad/mixed/cppad_mixed.hpp>

namespace {
     using CppAD::log;
     using CppAD::AD;
     //
     using CppAD::mixed::d_sparse_rcv;
     using CppAD::mixed::a1_double;
     using CppAD::mixed::d_vector;
     using CppAD::mixed::a1_vector;
     //
     class mixed_derived : public cppad_mixed {
     private:
          size_t                n_fixed_;
          const d_vector&       z_;
     public:
          // constructor
          mixed_derived(
               size_t                 n_fixed        ,
               size_t                 n_random       ,
               const d_vector&        z              ) :
               cppad_mixed(n_fixed, n_random)  ,
               n_fixed_(n_fixed)               ,
               z_(z)
          {    assert(z.size() == n_fixed); }
          // implementation of fix_likelihood as p(z|theta) * p(theta)
          a1_vector fix_likelihood(
               const a1_vector&         fixed_vec  ) override
          {
               // initialize log-density
               a1_vector vec(1);
               vec[0] = 0.0;

               // compute this factors once
               // sqrt_2pi = CppAD::sqrt( 8.0 * CppAD::atan(1.0) );

               for(size_t j = 0; j < n_fixed_; j++)
               {
                    // Data term p(z|theta)
                    a1_double res  = (z_[j] - fixed_vec[j]);
                    vec[0]    += res * res / 2.0;
                    // following term does not depend on fixed effects
                    // vec[0]    += log(sqrt_2pi );

                    // prior term p(theta)
                    res     = fixed_vec[j];
                    vec[0] += res * res / 2.0;
                    // following term does not depend on fixed effects
                    // vec[0]    += log(sqrt_2pi );
               }
               return vec;
          }
     };
}

bool no_random_xam(void)
{
     bool   ok = true;
     double inf = std::numeric_limits<double>::infinity();
     double tol = 1e-8;

     // fixed effects
     size_t n_fixed  = 3;
     d_vector
          fixed_lower(n_fixed), fixed_in(n_fixed), fixed_upper(n_fixed);
     for(size_t j = 0; j < n_fixed; j++)
     {    fixed_lower[j] = - inf;
          fixed_in[j]    = 0.0;
          fixed_upper[j] = inf;
     }
     //
     // no random effects
     size_t n_random = 0;
     d_vector random_in(0);
     //
     // no constriants
     d_vector fix_constraint_lower(0), fix_constraint_upper(0);
     //
     d_vector z(n_fixed);
     for(size_t i = 0; i < n_fixed; i++)
          z[i] = double(i+1);

     // object that is derived from cppad_mixed
     // (test full netwon method to make sure it works with no random effects).
     mixed_derived mixed_object(n_fixed, n_random, z);
     mixed_object.initialize(fixed_in, random_in);

     // optimize the fixed effects using quasi-Newton method
     std::string fixed_ipopt_options =
          "Integer print_level               0\n"
          "String  sb                        yes\n"
          "String  derivative_test           first-order\n"
          "String  derivative_test_print_all yes\n"
          "Numeric tol                       1e-8\n"
          "Integer max_iter                  15\n"
     ;
     std::string random_ipopt_options =
          "Integer print_level 0\n"
          "String  sb          yes\n"
          "String  derivative_test second-order\n"
     ;
     d_vector random_lower(n_random), random_upper(n_random);
     for(size_t i = 0; i < n_random; i++)
     {    random_lower[i] = -inf;
          random_upper[i] = +inf;
     }
     d_vector fixed_scale = fixed_in;
     CppAD::mixed::fixed_solution solution = mixed_object.optimize_fixed(
          fixed_ipopt_options,
          random_ipopt_options,
          fixed_lower,
          fixed_upper,
          fix_constraint_lower,
          fix_constraint_upper,
          fixed_scale,
          fixed_in,
          random_lower,
          random_upper,
          random_in
     );
     d_vector fixed_out = solution.fixed_opt;
     //
     for(size_t j = 0; j < n_fixed; j++)
          ok &= fabs( fixed_out[j] - z[j] / 2.0 ) <= tol;

     return ok;
}

Input File: example/user/no_random.cpp