RXR-3163: minimal change to undo breaking change in PR#59

R/get_map_estimates.R

@@ -299,18 +299,14 @@ get_map_estimates <- function(
       likelihood = like
     )
   } else {
-    # When residuals=TRUE, the FOCE Jacobian computed in calc_residuals
-    # provides both CWRES and the Hessian/vcov, so we can skip the more
-    # expensive numDeriv::hessian here.
-    skip_hessian_mle <- skip_hessian || residuals
     output <- tryCatch({
       fit <- mle_wrapper(
         ll_func,
         start = omega$eta,
         method = method,
         optimizer = optimizer,
         control = control,
-        skip_hessian = skip_hessian_mle,
+        skip_hessian = skip_hessian,
         data = list(
           data = data,
           sim_object = sim_object,
@@ -436,14 +432,11 @@ get_map_estimates <- function(
   }
 
   ## Add variance-covariance matrix to output object.
-  ## When residuals were computed, the FOCE vcov from the Jacobian is
-  ## preferred over numDeriv::hessian (faster, same FOCE approximation).
-  vcov_source <- obj$fit$vcov
-  if (!is.null(obj$foce_vcov)) {
-    vcov_source <- obj$foce_vcov
-  }
+  ## foce_vcov (computed from the FOCE Jacobian) is stored for diagnostics
+  ## but not used as the primary vcov source — it has known issues with
+  ## observation weights and proportional error that need to be fixed first.
   obj$vcov_full <- get_varcov_matrix(
-    vcov_source,
+    obj$fit$vcov,
     fallback = omega$full
   )
   if(inherits(obj$vcov_full, "matrix")) {

tests/testthat/test-get_map_estimates.R

@@ -596,6 +596,7 @@ test_that("FOCE vcov is positive definite even when numDeriv Hessian would fail"
   expect_false(is.null(fit$foce_vcov))
   expect_true(PKPDsim::is_positive_definite(fit$foce_vcov))
   expect_true(PKPDsim::is_positive_definite(fit$vcov_full))
+  skip("Skipping until weights are added to FOCE")
   expect_equal(fit$vcov_full, fit$foce_vcov)
 })