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-rw-r--r--tests/testthat/summary_saem_biphasic_s.txt3
-rw-r--r--tests/testthat/test-dmta.R64
-rw-r--r--tests/testthat/test_dmta.R120
-rw-r--r--tests/testthat/test_mixed.R93
-rw-r--r--tests/testthat/test_saemix_parent.R91
5 files changed, 214 insertions, 157 deletions
diff --git a/tests/testthat/summary_saem_biphasic_s.txt b/tests/testthat/summary_saem_biphasic_s.txt
index 995e81c8..4569099f 100644
--- a/tests/testthat/summary_saem_biphasic_s.txt
+++ b/tests/testthat/summary_saem_biphasic_s.txt
@@ -17,7 +17,8 @@ Data:
Model predictions using solution type analytical
-Fitted in test time 0 s using 300, 100 iterations
+Fitted in test time 0 s
+Using 300, 100 iterations and 4 chains
Variance model: Constant variance
diff --git a/tests/testthat/test-dmta.R b/tests/testthat/test-dmta.R
deleted file mode 100644
index 12bbcb8e..00000000
--- a/tests/testthat/test-dmta.R
+++ /dev/null
@@ -1,64 +0,0 @@
-local_edition(3)
-
-# Data
-dmta_ds <- lapply(1:7, function(i) {
- ds_i <- dimethenamid_2018$ds[[i]]$data
- ds_i[ds_i$name == "DMTAP", "name"] <- "DMTA"
- ds_i$time <- ds_i$time * dimethenamid_2018$f_time_norm[i]
- ds_i
-})
-names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title)
-dmta_ds[["Elliot"]] <- rbind(dmta_ds[["Elliot 1"]], dmta_ds[["Elliot 2"]])
-dmta_ds[["Elliot 1"]] <- dmta_ds[["Elliot 2"]] <- NULL
-
-# mkin
-nlm_dfop <- mmkin("DFOP", dmta_ds)
-nlm_dfop_tc <- mmkin("DFOP", dmta_ds, error_model = "tc")
-parms(nlm_dfop_tc)
-
-# nlme
-nlme_dfop_tc <- nlme(nlm_dfop_tc)
-summary(nlme_dfop_tc)
-intervals(nlme_dfop_tc)
-
-# saemix
-saem_saemix_dfop_tc <- saem(nlm_dfop_tc)
-saem_saemix_dfop_tc$so <- saemix::llgq.saemix(saem_saemix_dfop_tc$so)
-summary(saem_saemix_dfop_tc)
-intervals(saem_saemix_dfop_tc)
-AIC(saem_saemix_dfop_tc$so)
-AIC(saem_saemix_dfop_tc$so, "gq")
-AIC(saem_saemix_dfop_tc$so, "lin")
-saemix::plot(saem_saemix_dfop_tc$so, plot.type = "likelihood")
-saemix::plot(saem_saemix_dfop_tc$so, plot.type = "convergence")
-
-saem_saemix_dfop_tc_1k <- saem(nlm_dfop_tc, nbiter.saemix = c(1000, 100))
-AIC(saem_saemix_dfop_tc_1k$so)
-saemix::plot(saem_saemix_dfop_tc_1k$so, plot.type = "convergence")
-saemix::plot(saem_saemix_dfop_tc_1k$so, plot.type = "likelihood")
-intervals(saem_saemix_dfop_tc_1k)
-
-saem_saemix_dfop_tc_1.5k <- saem(nlm_dfop_tc, nbiter.saemix = c(1500, 100))
-saem_saemix_dfop_tc_1.5k$so <- saemix::llgq.saemix(saem_saemix_dfop_tc_1.5k$so)
-saemix::plot(saem_saemix_dfop_tc_1.5k$so, plot.type = "convergence")
-AIC(saem_saemix_dfop_tc_1.5k$so)
-AIC(saem_saemix_dfop_tc_1.5k$so, "gq")
-intervals(saem_saemix_dfop_tc_1.5k)
-
-# nlmixr saem
-saem_nlmixr_dfop_tc <- nlmixr(nlm_dfop_tc, est = "saem",
- control = nlmixr::saemControl(nBurn = 300, nEm = 100, nmc = 9, print = 0))
-intervals(saem_nlmixr_dfop_tc)
-summary(saem_nlmixr_dfop_tc)
-AIC(saem_nlmixr_dfop_tc$nm)
-
-saem_nlmixr_dfop_tc_1k <- nlmixr(nlm_dfop_tc, est = "saem",
- control = nlmixr::saemControl(nBurn = 1000, nEm = 300, nmc = 9, print = 0))
-intervals(saem_nlmixr_dfop_tc_1k)
-summary(saem_nlmixr_dfop_tc_1k)
-AIC(saem_nlmixr_dfop_tc_1k$nm)
-
-focei_nlmixr_dfop_tc <- nlmixr(nlm_dfop_tc, est = "focei")
-intervals(focei_nlmixr_dfop_tc)
-
-AIC(saem_nlmixr_dfop_tc$nm)
diff --git a/tests/testthat/test_dmta.R b/tests/testthat/test_dmta.R
new file mode 100644
index 00000000..3437966f
--- /dev/null
+++ b/tests/testthat/test_dmta.R
@@ -0,0 +1,120 @@
+context("Dimethenamid data from 2018, parent fits")
+
+# Data
+dmta_ds <- lapply(1:7, function(i) {
+ ds_i <- dimethenamid_2018$ds[[i]]$data
+ ds_i[ds_i$name == "DMTAP", "name"] <- "DMTA"
+ ds_i$time <- ds_i$time * dimethenamid_2018$f_time_norm[i]
+ ds_i
+})
+names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title)
+dmta_ds[["Elliot"]] <- rbind(dmta_ds[["Elliot 1"]], dmta_ds[["Elliot 2"]])
+dmta_ds[["Elliot 1"]] <- dmta_ds[["Elliot 2"]] <- NULL
+
+# mkin
+dmta_dfop <- mmkin("DFOP", dmta_ds, quiet = TRUE)
+dmta_dfop_tc <- mmkin("DFOP", dmta_ds, error_model = "tc", quiet = TRUE)
+
+test_that("Different backends get consistent results for DFOP tc, dimethenamid data", {
+
+ # nlme
+ expect_warning(
+ nlme_dfop_tc <- nlme(dmta_dfop_tc),
+ "Iteration 3, .* false convergence")
+ ints_nlme <- intervals(nlme_dfop_tc)
+
+ # saemix
+ saem_saemix_dfop_tc <- saem(dmta_dfop_tc)
+ ints_saemix <- intervals(saem_saemix_dfop_tc)
+
+ # saemix mkin transformations
+ saem_saemix_dfop_tc_mkin <- saem(dmta_dfop_tc, transformations = "mkin")
+ ints_saemix_mkin <- intervals(saem_saemix_dfop_tc_mkin)
+
+ # nlmixr saem
+ saem_nlmixr_dfop_tc <- nlmixr(dmta_dfop_tc, est = "saem",
+ control = nlmixr::saemControl(nBurn = 300, nEm = 100, nmc = 9, print = 0))
+ ints_nlmixr_saem <- intervals(saem_nlmixr_dfop_tc)
+
+ # nlmixr focei
+ # We get three warnings about nudged etas, the initial optimization and
+ # gradient problems with initial estimate and covariance
+ # We need to capture output, otherwise it pops up in testthat output
+ expect_warning(tmp <- capture_output(focei_nlmixr_dfop_tc <- nlmixr(
+ dmta_dfop_tc, est = "focei",
+ control = nlmixr::foceiControl(print = 0), all = TRUE)))
+ ints_nlmixr_focei <- intervals(focei_nlmixr_dfop_tc)
+
+ # Fixed effects
+ ## saemix vs. nlme
+ expect_true(all(ints_saemix$fixed[, "est."] >
+ backtransform_odeparms(ints_nlme$fixed[, "lower"], dmta_dfop$mkinmod)))
+ expect_true(all(ints_saemix$fixed[, "est."] <
+ backtransform_odeparms(ints_nlme$fixed[, "upper"], dmta_dfop$mkinmod)))
+
+ ## saemix mkin vs. nlme
+ expect_true(all(ints_saemix_mkin$fixed[, "est."] >
+ backtransform_odeparms(ints_nlme$fixed[, "lower"], dmta_dfop$mkinmod)))
+ expect_true(all(ints_saemix_mkin$fixed[, "est."] <
+ backtransform_odeparms(ints_nlme$fixed[, "upper"], dmta_dfop$mkinmod)))
+
+ ## nlmixr saem vs. nlme
+ expect_true(all(ints_nlmixr_saem$fixed[, "est."] >
+ backtransform_odeparms(ints_nlme$fixed[, "lower"], dmta_dfop$mkinmod)))
+ expect_true(all(ints_nlmixr_saem$fixed[, "est."] <
+ backtransform_odeparms(ints_nlme$fixed[, "upper"], dmta_dfop$mkinmod)))
+
+ ## nlmixr focei vs. nlme
+ expect_true(all(ints_nlmixr_focei$fixed[, "est."] >
+ backtransform_odeparms(ints_nlme$fixed[, "lower"], dmta_dfop$mkinmod)))
+ expect_true(all(ints_nlmixr_focei$fixed[, "est."] <
+ backtransform_odeparms(ints_nlme$fixed[, "upper"], dmta_dfop$mkinmod)))
+
+ # Random effects
+ ## for saemix with saemix transformations, the comparison would be complicated...
+ ## saemix mkin vs. nlme
+ expect_true(all(ints_saemix$random[, "est."] >
+ backtransform_odeparms(ints_nlme$reStruct$ds[, "lower"], dmta_dfop$mkinmod)))
+ expect_true(all(ints_saemix$fixed[, "est."] <
+ backtransform_odeparms(ints_nlme$fixed[, "upper"], dmta_dfop$mkinmod)))
+
+ ## nlmixr saem vs. nlme
+ expect_true(all(ints_nlmixr_saem$random[, "est."] >
+ backtransform_odeparms(ints_nlme$reStruct$ds[, "lower"], dmta_dfop$mkinmod)))
+ expect_true(all(ints_nlmixr_saem$random[, "est."] <
+ backtransform_odeparms(ints_nlme$reStruct$ds[, "upper"], dmta_dfop$mkinmod)))
+
+ ## nlmixr focei vs. nlme
+ expect_true(all(ints_nlmixr_focei$random[, "est."] >
+ backtransform_odeparms(ints_nlme$reStruct$ds[, "lower"], dmta_dfop$mkinmod)))
+ expect_true(all(ints_nlmixr_focei$random[, "est."] <
+ backtransform_odeparms(ints_nlme$reStruct$ds[, "upper"], dmta_dfop$mkinmod)))
+
+ # Variance function
+ # saemix vs. nlme
+ expect_true(all(ints_saemix[[3]][, "est."] >
+ ints_nlme$varStruct[, "lower"]))
+ expect_true(all(ints_saemix[[3]][, "est."] <
+ ints_nlme$varStruct[, "upper"]))
+
+ # saemix with mkin transformations vs. nlme
+ expect_true(all(ints_saemix_mkin[[3]][, "est."] >
+ ints_nlme$varStruct[, "lower"]))
+ expect_true(all(ints_saemix_mkin[[3]][, "est."] <
+ ints_nlme$varStruct[, "upper"]))
+
+ # nlmixr saem vs. nlme
+ expect_true(all(ints_nlmixr_saem[[3]][, "est."] >
+ ints_nlme$varStruct[, "lower"]))
+ expect_true(all(ints_nlmixr_saem[[3]][, "est."] <
+ ints_nlme$varStruct[, "upper"]))
+
+ # nlmixr focei vs. nlme
+ # We only test for the proportional part (rsd_high), as the
+ # constant part (sigma_low) obtained with nlmixr/FOCEI is below the lower
+ # bound of the confidence interval obtained with nlme
+ expect_true(ints_nlmixr_focei[[3]]["rsd_high", "est."] >
+ ints_nlme$varStruct["prop", "lower"])
+ expect_true(ints_nlmixr_focei[[3]]["rsd_high", "est."] <
+ ints_nlme$varStruct["prop", "upper"])
+})
diff --git a/tests/testthat/test_mixed.R b/tests/testthat/test_mixed.R
index 40bd3fdf..dbcc66ce 100644
--- a/tests/testthat/test_mixed.R
+++ b/tests/testthat/test_mixed.R
@@ -1,96 +1,5 @@
context("Nonlinear mixed-effects models")
-test_that("Parent fits using saemix are correctly implemented", {
-
- expect_error(saem(fits), "Only row objects")
- # Some fits were done in the setup script
- mmkin_sfo_2 <- update(mmkin_sfo_1, fixed_initials = c(parent = 100))
- expect_error(update(mmkin_sfo_1, models = c("SFOOO")), "Please supply models.*")
-
- sfo_saem_2 <- saem(mmkin_sfo_1, quiet = TRUE, transformations = "mkin")
- sfo_saem_3 <- expect_error(saem(mmkin_sfo_2, quiet = TRUE), "at least two parameters")
- s_sfo_s1 <- summary(sfo_saem_1)
- s_sfo_s2 <- summary(sfo_saem_2)
-
- sfo_nlme_1 <- expect_warning(nlme(mmkin_sfo_1), "not converge")
- s_sfo_n <- summary(sfo_nlme_1)
-
- # Compare with input
- expect_equal(round(s_sfo_s2$confint_ranef["SD.log_k_parent", "est."], 1), 0.3)
- # k_parent is a bit different from input 0.03 here
- expect_equal(round(s_sfo_s1$confint_back["k_parent", "est."], 3), 0.035)
- expect_equal(round(s_sfo_s2$confint_back["k_parent", "est."], 3), 0.035)
-
- # But the result is pretty unanimous between methods
- expect_equal(round(s_sfo_s1$confint_back["k_parent", "est."], 3),
- round(s_sfo_s2$confint_back["k_parent", "est."], 3))
- expect_equal(round(s_sfo_s1$confint_back["k_parent", "est."], 3),
- round(s_sfo_n$confint_back["k_parent", "est."], 3))
-
- mmkin_fomc_1 <- mmkin("FOMC", ds_fomc, quiet = TRUE, error_model = "tc", cores = n_cores)
- fomc_saem_1 <- saem(mmkin_fomc_1, quiet = TRUE)
- ci_fomc_s1 <- summary(fomc_saem_1)$confint_back
-
- fomc_pop <- as.numeric(fomc_pop)
- expect_true(all(ci_fomc_s1[, "lower"] < fomc_pop))
- expect_true(all(ci_fomc_s1[, "upper"] > fomc_pop))
-
- mmkin_fomc_2 <- update(mmkin_fomc_1, state.ini = 100, fixed_initials = "parent")
- fomc_saem_2 <- saem(mmkin_fomc_2, quiet = TRUE, transformations = "mkin")
- ci_fomc_s2 <- summary(fomc_saem_2)$confint_back
-
- expect_true(all(ci_fomc_s2[, "lower"] < fomc_pop[2:3]))
- expect_true(all(ci_fomc_s2[, "upper"] > fomc_pop[2:3]))
-
- s_dfop_s1 <- summary(dfop_saemix_1)
- s_dfop_s2 <- summary(dfop_saemix_2)
- s_dfop_n <- summary(dfop_nlme_1)
-
- dfop_pop <- as.numeric(dfop_pop)
- expect_true(all(s_dfop_s1$confint_back[, "lower"] < dfop_pop))
- expect_true(all(s_dfop_s1$confint_back[, "upper"] > dfop_pop))
- expect_true(all(s_dfop_s2$confint_back[, "lower"] < dfop_pop))
- expect_true(all(s_dfop_s2$confint_back[, "upper"] > dfop_pop))
-
- dfop_mmkin_means_trans_tested <- mean_degparms(mmkin_dfop_1, test_log_parms = TRUE)
- dfop_mmkin_means_trans <- apply(parms(mmkin_dfop_1, transformed = TRUE), 1, mean)
-
- dfop_mmkin_means_tested <- backtransform_odeparms(dfop_mmkin_means_trans_tested, mmkin_dfop_1$mkinmod)
- dfop_mmkin_means <- backtransform_odeparms(dfop_mmkin_means_trans, mmkin_dfop_1$mkinmod)
-
- # We get < 20% deviations for parent_0 and k1 by averaging the transformed parameters
- # If we average only parameters passing the t-test, the deviation for k2 is also < 20%
- rel_diff_mmkin <- (dfop_mmkin_means - dfop_pop) / dfop_pop
- rel_diff_mmkin_tested <- (dfop_mmkin_means_tested - dfop_pop) / dfop_pop
- expect_true(all(rel_diff_mmkin[c("parent_0", "k1")] < 0.20))
- expect_true(all(rel_diff_mmkin_tested[c("parent_0", "k1", "k2")] < 0.20))
-
- # We get < 15% deviations with transformations made in mkin
- rel_diff_1 <- (s_dfop_s1$confint_back[, "est."] - dfop_pop) / dfop_pop
- expect_true(all(rel_diff_1 < 0.15))
-
- # We get < 20% deviations with transformations made in saemix
- rel_diff_2 <- (s_dfop_s2$confint_back[, "est."] - dfop_pop) / dfop_pop
- expect_true(all(rel_diff_2 < 0.2))
-
- mmkin_hs_1 <- mmkin("HS", ds_hs, quiet = TRUE, error_model = "const", cores = n_cores)
- hs_saem_1 <- saem(mmkin_hs_1, quiet = TRUE)
- ci_hs_s1 <- summary(hs_saem_1)$confint_back
-
- hs_pop <- as.numeric(hs_pop)
- # expect_true(all(ci_hs_s1[, "lower"] < hs_pop)) # k1 is overestimated
- expect_true(all(ci_hs_s1[, "upper"] > hs_pop))
-
- mmkin_hs_2 <- update(mmkin_hs_1, state.ini = 100, fixed_initials = "parent")
- hs_saem_2 <- saem(mmkin_hs_2, quiet = TRUE)
- ci_hs_s2 <- summary(hs_saem_2)$confint_back
-
- #expect_true(all(ci_hs_s2[, "lower"] < hs_pop[2:4])) # k1 again overestimated
- expect_true(all(ci_hs_s2[, "upper"] > hs_pop[2:4]))
-
- # HS would likely benefit from implemenation of transformations = "saemix"
-})
-
test_that("Print methods work", {
expect_known_output(print(fits[, 2:3], digits = 2), "print_mmkin_parent.txt")
expect_known_output(print(mmkin_biphasic_mixed, digits = 2), "print_mmkin_biphasic_mixed.txt")
@@ -122,7 +31,7 @@ test_that("nlme results are reproducible to some degree", {
expect_true(all(ci_dfop_sfo_n[, "upper"] > dfop_sfo_pop))
})
-test_that("saem results are reproducible for biphasic fits", {
+test_that("saemix results are reproducible for biphasic fits", {
test_summary <- summary(saem_biphasic_s)
test_summary$saemixversion <- "Dummy 0.0 for testing"
diff --git a/tests/testthat/test_saemix_parent.R b/tests/testthat/test_saemix_parent.R
new file mode 100644
index 00000000..2f05c175
--- /dev/null
+++ b/tests/testthat/test_saemix_parent.R
@@ -0,0 +1,91 @@
+test_that("Parent fits using saemix are correctly implemented", {
+
+ expect_error(saem(fits), "Only row objects")
+ # Some fits were done in the setup script
+ mmkin_sfo_2 <- update(mmkin_sfo_1, fixed_initials = c(parent = 100))
+ expect_error(update(mmkin_sfo_1, models = c("SFOOO")), "Please supply models.*")
+
+ sfo_saem_2 <- saem(mmkin_sfo_1, quiet = TRUE, transformations = "mkin")
+ sfo_saem_3 <- expect_error(saem(mmkin_sfo_2, quiet = TRUE), "at least two parameters")
+ s_sfo_s1 <- summary(sfo_saem_1)
+ s_sfo_s2 <- summary(sfo_saem_2)
+
+ sfo_nlme_1 <- expect_warning(nlme(mmkin_sfo_1), "not converge")
+ s_sfo_n <- summary(sfo_nlme_1)
+
+ # Compare with input
+ expect_equal(round(s_sfo_s2$confint_ranef["SD.log_k_parent", "est."], 1), 0.3)
+ # k_parent is a bit different from input 0.03 here
+ expect_equal(round(s_sfo_s1$confint_back["k_parent", "est."], 3), 0.035)
+ expect_equal(round(s_sfo_s2$confint_back["k_parent", "est."], 3), 0.035)
+
+ # But the result is pretty unanimous between methods
+ expect_equal(round(s_sfo_s1$confint_back["k_parent", "est."], 3),
+ round(s_sfo_s2$confint_back["k_parent", "est."], 3))
+ expect_equal(round(s_sfo_s1$confint_back["k_parent", "est."], 3),
+ round(s_sfo_n$confint_back["k_parent", "est."], 3))
+
+ mmkin_fomc_1 <- mmkin("FOMC", ds_fomc, quiet = TRUE, error_model = "tc", cores = n_cores)
+ fomc_saem_1 <- saem(mmkin_fomc_1, quiet = TRUE)
+ ci_fomc_s1 <- summary(fomc_saem_1)$confint_back
+
+ fomc_pop <- as.numeric(fomc_pop)
+ expect_true(all(ci_fomc_s1[, "lower"] < fomc_pop))
+ expect_true(all(ci_fomc_s1[, "upper"] > fomc_pop))
+
+ mmkin_fomc_2 <- update(mmkin_fomc_1, state.ini = 100, fixed_initials = "parent")
+ fomc_saem_2 <- saem(mmkin_fomc_2, quiet = TRUE, transformations = "mkin")
+ ci_fomc_s2 <- summary(fomc_saem_2)$confint_back
+
+ expect_true(all(ci_fomc_s2[, "lower"] < fomc_pop[2:3]))
+ expect_true(all(ci_fomc_s2[, "upper"] > fomc_pop[2:3]))
+
+ s_dfop_s1 <- summary(dfop_saemix_1)
+ s_dfop_s2 <- summary(dfop_saemix_2)
+ s_dfop_n <- summary(dfop_nlme_1)
+
+ dfop_pop <- as.numeric(dfop_pop)
+ expect_true(all(s_dfop_s1$confint_back[, "lower"] < dfop_pop))
+ expect_true(all(s_dfop_s1$confint_back[, "upper"] > dfop_pop))
+ expect_true(all(s_dfop_s2$confint_back[, "lower"] < dfop_pop))
+ expect_true(all(s_dfop_s2$confint_back[, "upper"] > dfop_pop))
+
+ dfop_mmkin_means_trans_tested <- mean_degparms(mmkin_dfop_1, test_log_parms = TRUE)
+ dfop_mmkin_means_trans <- apply(parms(mmkin_dfop_1, transformed = TRUE), 1, mean)
+
+ dfop_mmkin_means_tested <- backtransform_odeparms(dfop_mmkin_means_trans_tested, mmkin_dfop_1$mkinmod)
+ dfop_mmkin_means <- backtransform_odeparms(dfop_mmkin_means_trans, mmkin_dfop_1$mkinmod)
+
+ # We get < 20% deviations for parent_0 and k1 by averaging the transformed parameters
+ # If we average only parameters passing the t-test, the deviation for k2 is also < 20%
+ rel_diff_mmkin <- (dfop_mmkin_means - dfop_pop) / dfop_pop
+ rel_diff_mmkin_tested <- (dfop_mmkin_means_tested - dfop_pop) / dfop_pop
+ expect_true(all(rel_diff_mmkin[c("parent_0", "k1")] < 0.20))
+ expect_true(all(rel_diff_mmkin_tested[c("parent_0", "k1", "k2")] < 0.20))
+
+ # We get < 15% deviations with transformations made in mkin
+ rel_diff_1 <- (s_dfop_s1$confint_back[, "est."] - dfop_pop) / dfop_pop
+ expect_true(all(rel_diff_1 < 0.15))
+
+ # We get < 20% deviations with transformations made in saemix
+ rel_diff_2 <- (s_dfop_s2$confint_back[, "est."] - dfop_pop) / dfop_pop
+ expect_true(all(rel_diff_2 < 0.2))
+
+ mmkin_hs_1 <- mmkin("HS", ds_hs, quiet = TRUE, error_model = "const", cores = n_cores)
+ hs_saem_1 <- saem(mmkin_hs_1, quiet = TRUE)
+ ci_hs_s1 <- summary(hs_saem_1)$confint_back
+
+ hs_pop <- as.numeric(hs_pop)
+ # expect_true(all(ci_hs_s1[, "lower"] < hs_pop)) # k1 is overestimated
+ expect_true(all(ci_hs_s1[, "upper"] > hs_pop))
+
+ mmkin_hs_2 <- update(mmkin_hs_1, state.ini = 100, fixed_initials = "parent")
+ hs_saem_2 <- saem(mmkin_hs_2, quiet = TRUE)
+ ci_hs_s2 <- summary(hs_saem_2)$confint_back
+
+ #expect_true(all(ci_hs_s2[, "lower"] < hs_pop[2:4])) # k1 again overestimated
+ expect_true(all(ci_hs_s2[, "upper"] > hs_pop[2:4]))
+
+ # HS would likely benefit from implemenation of transformations = "saemix"
+})
+

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