Make mixed model test data permanent to ensure reproducibility

To ensure that tests on different platforms work on the same data, the
mixed modelling test data previosly generated in
tests/testthat/setup_script.R were generated once using the script in
inst/dataset/generation/ds_mixed.R, and are now distributed with the
package.

1 files changed, 20 insertions, 20 deletions

diff --git a/tests/testthat/test_saemix_parent.R b/tests/testthat/test_saemix_parent.R
index 20889c6c..31605931 100644
--- a/tests/testthat/test_saemix_parent.R
+++ b/tests/testthat/test_saemix_parent.R
@@ -38,11 +38,11 @@ test_that("Parent fits using saemix are correctly implemented", {
   s_sfo_nlme_1 <- summary(sfo_nlme_1)
 
   # Compare with input
-  expect_equal(round(s_sfo_saem_1$confint_ranef["SD.k_parent", "est."], 1), 0.3)
-  expect_equal(round(s_sfo_saem_1_mkin$confint_ranef["SD.log_k_parent", "est."], 1), 0.3)
+  expect_equal(round(s_sfo_saem_1$confint_ranef["SD.k_parent", "est."], 1), 0.3, tol = 0.1)
+  expect_equal(round(s_sfo_saem_1_mkin$confint_ranef["SD.log_k_parent", "est."], 1), 0.3, tol = 0.1)
   # k_parent is a bit different from input 0.03 here
-  expect_equal(round(s_sfo_saem_1$confint_back["k_parent", "est."], 3), 0.035)
-  expect_equal(round(s_sfo_saem_1_mkin$confint_back["k_parent", "est."], 3), 0.035)
+  expect_equal(round(s_sfo_saem_1$confint_back["k_parent", "est."], 3), 0.033)
+  expect_equal(round(s_sfo_saem_1_mkin$confint_back["k_parent", "est."], 3), 0.033)
 
   # But the result is pretty unanimous between methods
   expect_equal(round(s_sfo_saem_1_reduced$confint_back["k_parent", "est."], 3),
@@ -74,7 +74,7 @@ test_that("Parent fits using saemix are correctly implemented", {
   mmkin_fomc_1 <- mmkin("FOMC", ds_fomc, quiet = TRUE, error_model = "tc", cores = n_cores)
   fomc_saem_1 <- saem(mmkin_fomc_1, quiet = TRUE, transformations = "saemix", no_random_effect = "parent_0")
 
-  fomc_pop <- as.numeric(fomc_pop)
+  fomc_pop <- as.numeric(attr(ds_fomc, "pop"))
   ci_fomc_s1 <- summary(fomc_saem_1)$confint_back
   expect_true(all(ci_fomc_s1[, "lower"] < fomc_pop))
   expect_true(all(ci_fomc_s1[, "upper"] > fomc_pop))
@@ -87,14 +87,14 @@ test_that("Parent fits using saemix are correctly implemented", {
   expect_equal(endpoints(fomc_saem_1), endpoints(fomc_saem_2), tol = 0.01)
 
   # DFOP
-  dfop_saemix_2 <- saem(mmkin_dfop_1, quiet = TRUE, transformations = "saemix",
+  dfop_saem_2 <- saem(mmkin_dfop_1, quiet = TRUE, transformations = "saemix",
     no_random_effect = "parent_0")
 
-  s_dfop_s1 <- summary(dfop_saemix_1) # mkin transformations
-  s_dfop_s2 <- summary(dfop_saemix_2) # saemix transformations
+  s_dfop_s1 <- summary(dfop_saem_1) # mkin transformations
+  s_dfop_s2 <- summary(dfop_saem_2) # saemix transformations
   s_dfop_n <- summary(dfop_nlme_1)
 
-  dfop_pop <- as.numeric(dfop_pop)
+  dfop_pop <- as.numeric(attr(ds_dfop, "pop"))
 
   expect_true(all(s_dfop_s1$confint_back[, "lower"] < dfop_pop))
   expect_true(all(s_dfop_s1$confint_back[, "upper"] > dfop_pop))
@@ -111,18 +111,18 @@ test_that("Parent fits using saemix are correctly implemented", {
 
   # SFORB
   mmkin_sforb_1 <- mmkin("SFORB", ds_dfop, quiet = TRUE, cores = n_cores)
-  sforb_saemix_1 <- saem(mmkin_sforb_1, quiet = TRUE,
+  sforb_saem_1 <- saem(mmkin_sforb_1, quiet = TRUE,
     no_random_effect = c("parent_free_0"),
     transformations = "mkin")
-  sforb_saemix_2 <- saem(mmkin_sforb_1, quiet = TRUE,
+  sforb_saem_2 <- saem(mmkin_sforb_1, quiet = TRUE,
     no_random_effect = c("parent_free_0"),
     transformations = "saemix")
   expect_equal(
-    log(endpoints(dfop_saemix_1)$distimes[1:2]),
-    log(endpoints(sforb_saemix_1)$distimes[1:2]), tolerance = 0.01)
+    log(endpoints(dfop_saem_1)$distimes[1:2]),
+    log(endpoints(sforb_saem_1)$distimes[1:2]), tolerance = 0.01)
   expect_equal(
-    log(endpoints(sforb_saemix_1)$distimes[1:2]),
-    log(endpoints(sforb_saemix_2)$distimes[1:2]), tolerance = 0.01)
+    log(endpoints(sforb_saem_1)$distimes[1:2]),
+    log(endpoints(sforb_saem_2)$distimes[1:2]), tolerance = 0.01)
 
   mmkin_hs_1 <- mmkin("HS", ds_hs, quiet = TRUE, error_model = "const", cores = n_cores)
   hs_saem_1 <- saem(mmkin_hs_1, quiet = TRUE, no_random_effect = "parent_0")
@@ -131,7 +131,7 @@ test_that("Parent fits using saemix are correctly implemented", {
   expect_equal(endpoints(hs_saem_1), endpoints(hs_saem_2), tol = 0.01)
   ci_hs_s1 <- summary(hs_saem_1)$confint_back
 
-  hs_pop <- as.numeric(hs_pop)
+  hs_pop <- as.numeric(attr(ds_hs, "pop"))
   #expect_true(all(ci_hs_s1[, "lower"] < hs_pop)) # k1 is overestimated
   expect_true(all(ci_hs_s1[, "upper"] > hs_pop))
 })
@@ -141,10 +141,10 @@ test_that("We can also use mkin solution methods for saem", {
     "saemix transformations is only supported if an analytical solution is implemented"
   )
   skip("This still takes almost 2.5 minutes although we do not solve ODEs")
-  dfop_saemix_3 <- saem(mmkin_dfop_1, quiet = TRUE, transformations = "mkin",
-    solution_type = "analytical", no_random_effect = "parent_0")
-  distimes_dfop <- endpoints(dfop_saemix_1)$distimes
-  distimes_dfop_analytical <- endpoints(dfop_saemix_3)$distimes
+  dfop_saem_3 <- saem(mmkin_dfop_1, quiet = TRUE, transformations = "mkin",
+    solution_type = "analytical", no_random_effect = c("parent_0", "g_qlogis"))
+  distimes_dfop <- endpoints(dfop_saem_1)$distimes
+  distimes_dfop_analytical <- endpoints(dfop_saem_3)$distimes
   rel_diff <- abs(distimes_dfop_analytical - distimes_dfop) / distimes_dfop
   expect_true(all(rel_diff < 0.01))
 })