Adding object to summary function

UofUEpiBio · Dec 12, 2024 · affa7b8 · affa7b8
1 parent 3535438
commit affa7b8
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Showing 2 changed files with 8 additions and 8 deletions.
diff --git a/src/lfmcmc.cpp b/src/lfmcmc.cpp
@@ -148,11 +148,13 @@ SEXP set_summary_fun_cpp(
     LFMCMCSummaryFun<TData_default> fun_call = [fun](
         std::vector< epiworld_double >& res,
         const TData_default& dat,
-        LFMCMC<TData_default>*
+        LFMCMC<TData_default>* lfmcmc_obj
         ) -> void {
 
         auto dat_int = cpp11::doubles(dat);
-        auto res_tmp = cpp11::doubles(fun(dat_int));
+        WrapLFMCMC(lfmcmc_ptr)(lfmcmc_obj, false);
+        auto res_tmp = cpp11::doubles(fun(dat_int, lfmcmc_ptr));
+
 
         if (res.size() == 0u)
             res.resize(res_tmp.size());
@@ -185,10 +187,7 @@ SEXP set_kernel_fun_cpp(
         auto sim_stats_doubles = cpp11::doubles(simulated_stats);
         auto obs_stats_doubles = cpp11::doubles(observed_stats);
 
-        cpp11::external_pointer<LFMCMC<TData_default>> lfmcmc_ptr(
-            lfmcmc_obj,
-            false
-            );
+        WrapLFMCMC(lfmcmc_ptr)(lfmcmc_obj, false);
 
         return cpp11::as_cpp<epiworld_double>(
             fun(sim_stats_doubles, obs_stats_doubles, epsilon, lfmcmc_ptr)

diff --git a/vignettes/likelihood-free-mcmc.Rmd b/vignettes/likelihood-free-mcmc.Rmd
@@ -108,7 +108,7 @@ For our example, since the population distribution is already a summary of the d
 With more complicated use cases, you might instead compute summary statistics such as the mean or standard deviation.
 
 ```{r sumfun}
-summary_fun <- function(data) {
+summary_fun <- function(data, model) {
   return(data)
 }
 ```
@@ -126,8 +126,9 @@ proposal_fun <- function(old_params) {
 The **kernel function** effectively scores the results of the latest simulation run against the observed data, by comparing the summary statistics from `summary_fun` for each.
 LFMCMC uses the kernel score and the Hastings Ratio to determine whether or not to accept the parameters for that run.
 In our example, since `summary_fun` simply passes the data through, `simulated_stats` and `observed_stats` are our simulated and observed data respectively.
+
 ```{r kernfun}
-kernel_fun <- function(simulated_stats, observed_stats, epsilon) {
+kernel_fun <- function(simulated_stats, observed_stats, epsilon, model) {
   dnorm(sqrt(sum((simulated_stats - observed_stats)^2)))
 }
 ```