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.

24 files changed, 324 insertions, 262 deletions

diff --git a/R/ds_mixed.R b/R/ds_mixed.R
new file mode 100644
index 00000000..c5055712
--- /dev/null
+++ b/R/ds_mixed.R
@@ -0,0 +1,17 @@
+#'  Synthetic data for hierarchical kinetic degradation models
+#'
+#' The R code used to create this data object is installed with this package in
+#' the 'dataset_generation' directory.
+#'
+#' @name ds_mixed
+#' @aliases ds_sfo ds_fomc ds_dfop ds_hs ds_dfop_sfo
+#' @examples
+#' \dontrun{
+#'   sfo_mmkin <- mmkin("SFO", ds_sfo, quiet = TRUE, error_model = "tc", cores = 15)
+#'   sfo_saem <- saem(sfo_mmkin, no_random_effect = "parent_0")
+#'   plot(sfo_saem)
+#' }
+#'
+#' # This is the code used to generate the datasets
+#' cat(readLines(system.file("dataset_generation/ds_mixed.R", package = "mkin")), sep = "\n")
+NULL
diff --git a/_pkgdown.yml b/_pkgdown.yml
index 8aa44ed4..98ed84f2 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -67,7 +67,7 @@ reference:
       - parplot
   - title: Datasets and known results
     contents:
-      - focus_soil_moisture
+      - ds_mixed
       - D24_2014
       - dimethenamid_2018
       - FOCUS_2006_A
@@ -82,6 +82,7 @@ reference:
       - synthetic_data_for_UBA_2014
       - experimental_data_for_UBA_2019
       - test_data_from_UBA_2014
+      - focus_soil_moisture
       - mkinds
       - mkindsg
   - title: NAFTA guidance
diff --git a/data/ds_mixed.rda b/data/ds_mixed.rda
new file mode 100644
index 00000000..cf9f6463
--- /dev/null
+++ b/data/ds_mixed.rda
diff --git a/inst/dataset_generation/ds_mixed.R b/inst/dataset_generation/ds_mixed.R
new file mode 100644
index 00000000..f2ae6e7e
--- /dev/null
+++ b/inst/dataset_generation/ds_mixed.R
@@ -0,0 +1,105 @@
+# Synthetic data for hierarchical kinetic models
+# Refactored version of the code previously in tests/testthat/setup_script.R
+# The number of datasets was 3 for FOMC, and 10 for HS in that script, now it
+# is always 15 for consistency
+
+library(mkin)  # We use mkinmod and mkinpredict
+sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
+n <- 15
+log_sd <- 0.3
+err_1 = list(const = 1, prop = 0.05)
+tc <- function(value) sigma_twocomp(value, err_1$const, err_1$prop)
+const <- function(value) 2
+
+set.seed(123456)
+SFO <- mkinmod(parent = mkinsub("SFO"))
+sfo_pop <- list(parent_0 = 100, k_parent = 0.03)
+sfo_parms <- as.matrix(data.frame(
+    k_parent = rlnorm(n, log(sfo_pop$k_parent), log_sd)))
+set.seed(123456)
+ds_sfo <- lapply(1:n, function(i) {
+  ds_mean <- mkinpredict(SFO, sfo_parms[i, ],
+    c(parent = sfo_pop$parent_0), sampling_times)
+  add_err(ds_mean, tc, n = 1)[[1]]
+})
+attr(ds_sfo, "pop") <- sfo_pop
+attr(ds_sfo, "parms") <- sfo_parms
+
+set.seed(123456)
+FOMC <- mkinmod(parent = mkinsub("FOMC"))
+fomc_pop <- list(parent_0 = 100, alpha = 2, beta = 8)
+fomc_parms <- as.matrix(data.frame(
+    alpha = rlnorm(n, log(fomc_pop$alpha), 0.4),
+    beta = rlnorm(n, log(fomc_pop$beta), 0.2)))
+set.seed(123456)
+ds_fomc <- lapply(1:n, function(i) {
+  ds_mean <- mkinpredict(FOMC, fomc_parms[i, ],
+    c(parent = fomc_pop$parent_0), sampling_times)
+  add_err(ds_mean, tc, n = 1)[[1]]
+})
+attr(ds_fomc, "pop") <- fomc_pop
+attr(ds_fomc, "parms") <- fomc_parms
+
+set.seed(123456)
+DFOP <- mkinmod(parent = mkinsub("DFOP"))
+dfop_pop <- list(parent_0 = 100, k1 = 0.06, k2 = 0.015, g = 0.4)
+dfop_parms <- as.matrix(data.frame(
+  k1 = rlnorm(n, log(dfop_pop$k1), log_sd),
+  k2 = rlnorm(n, log(dfop_pop$k2), log_sd),
+  g = plogis(rnorm(n, qlogis(dfop_pop$g), log_sd))))
+set.seed(123456)
+ds_dfop <- lapply(1:n, function(i) {
+  ds_mean <- mkinpredict(DFOP, dfop_parms[i, ],
+    c(parent = dfop_pop$parent_0), sampling_times)
+  add_err(ds_mean, tc, n = 1)[[1]]
+})
+attr(ds_dfop, "pop") <- dfop_pop
+attr(ds_dfop, "parms") <- dfop_parms
+
+set.seed(123456)
+HS <- mkinmod(parent = mkinsub("HS"))
+hs_pop <- list(parent_0 = 100, k1 = 0.08, k2 = 0.01, tb = 15)
+hs_parms <- as.matrix(data.frame(
+  k1 = rlnorm(n, log(hs_pop$k1), log_sd),
+  k2 = rlnorm(n, log(hs_pop$k2), log_sd),
+  tb = rlnorm(n, log(hs_pop$tb), 0.1)))
+set.seed(123456)
+ds_hs <- lapply(1:n, function(i) {
+  ds_mean <- mkinpredict(HS, hs_parms[i, ],
+    c(parent = hs_pop$parent_0), sampling_times)
+  add_err(ds_mean, const, n = 1)[[1]]
+})
+attr(ds_hs, "pop") <- hs_pop
+attr(ds_hs, "parms") <- hs_parms
+
+set.seed(123456)
+DFOP_SFO <- mkinmod(
+  parent = mkinsub("DFOP", "m1"),
+  m1 = mkinsub("SFO"),
+  quiet = TRUE)
+dfop_sfo_pop <- list(parent_0 = 100,
+  k_m1 = 0.007, f_parent_to_m1 = 0.5,
+  k1 = 0.1, k2 = 0.02, g = 0.5)
+dfop_sfo_parms <- as.matrix(data.frame(
+  k1 = rlnorm(n, log(dfop_sfo_pop$k1), log_sd),
+  k2 = rlnorm(n, log(dfop_sfo_pop$k2), log_sd),
+  g = plogis(rnorm(n, qlogis(dfop_sfo_pop$g), log_sd)),
+  f_parent_to_m1 = plogis(rnorm(n,
+      qlogis(dfop_sfo_pop$f_parent_to_m1), log_sd)),
+  k_m1 = rlnorm(n, log(dfop_sfo_pop$k_m1), log_sd)))
+ds_dfop_sfo_mean <- lapply(1:n,
+  function(i) {
+    mkinpredict(DFOP_SFO, dfop_sfo_parms[i, ],
+      c(parent = dfop_sfo_pop$parent_0, m1 = 0), sampling_times)
+  }
+)
+set.seed(123456)
+ds_dfop_sfo <- lapply(ds_dfop_sfo_mean, function(ds) {
+  add_err(ds,
+    sdfunc = function(value) sqrt(err_1$const^2 + value^2 * err_1$prop^2),
+    n = 1, secondary = "m1")[[1]]
+})
+attr(ds_dfop_sfo, "pop") <- dfop_sfo_pop
+attr(ds_dfop_sfo, "parms") <- dfop_sfo_parms
+
+#save(ds_sfo, ds_fomc, ds_dfop, ds_hs, ds_dfop_sfo, file = "data/ds_mixed.rda", version = 2)
diff --git a/log/check.log b/log/check.log
index 7aa4610c..31fc31eb 100644
--- a/log/check.log
+++ b/log/check.log
@@ -5,7 +5,7 @@
 * using options ‘--no-tests --as-cran’
 * checking for file ‘mkin/DESCRIPTION’ ... OK
 * checking extension type ... Package
-* this is package ‘mkin’ version ‘1.2.0’
+* this is package ‘mkin’ version ‘1.2.1’
 * package encoding: UTF-8
 * checking CRAN incoming feasibility ... Note_to_CRAN_maintainers
 Maintainer: ‘Johannes Ranke <johannes.ranke@jrwb.de>’
@@ -41,7 +41,7 @@ Maintainer: ‘Johannes Ranke <johannes.ranke@jrwb.de>’
 * checking S3 generic/method consistency ... OK
 * checking replacement functions ... OK
 * checking foreign function calls ... OK
-* checking R code for possible problems ... [18s/18s] OK
+* checking R code for possible problems ... [17s/17s] OK
 * checking Rd files ... OK
 * checking Rd metadata ... OK
 * checking Rd line widths ... OK
diff --git a/log/test.log b/log/test.log
index 87d24690..d1de270e 100644
--- a/log/test.log
+++ b/log/test.log
@@ -5,19 +5,19 @@
 ✔ |         5 | Calculation of Akaike weights
 ✔ |         3 | Export dataset for reading into CAKE
 ✔ |        12 | Confidence intervals and p-values [1.0s]
-✔ |     1  12 | Dimethenamid data from 2018 [31.5s]
+✔ |     1  12 | Dimethenamid data from 2018 [32.6s]
 ────────────────────────────────────────────────────────────────────────────────
 Skip ('test_dmta.R:98'): Different backends get consistent results for SFO-SFO3+, dimethenamid data
 Reason: Fitting this ODE model with saemix takes about 15 minutes on my system
 ────────────────────────────────────────────────────────────────────────────────
-✔ |        14 | Error model fitting [5.0s]
+✔ |        14 | Error model fitting [5.1s]
 ✔ |         5 | Time step normalisation
 ✔ |         4 | Calculation of FOCUS chi2 error levels [0.6s]
 ✔ |        14 | Results for FOCUS D established in expertise for UBA (Ranke 2014) [0.8s]
 ✔ |         4 | Test fitting the decline of metabolites from their maximum [0.4s]
 ✔ |         1 | Fitting the logistic model [0.2s]
-✔ |        10 | Batch fitting and diagnosing hierarchical kinetic models [24.4s]
-✔ |     1  12 | Nonlinear mixed-effects models [0.3s]
+✔ |        10 | Batch fitting and diagnosing hierarchical kinetic models [24.6s]
+✔ |     1  13 | Nonlinear mixed-effects models [0.4s]
 ────────────────────────────────────────────────────────────────────────────────
 Skip ('test_mixed.R:79'): saemix results are reproducible for biphasic fits
 Reason: Fitting with saemix takes around 10 minutes when using deSolve
@@ -26,32 +26,32 @@ Reason: Fitting with saemix takes around 10 minutes when using deSolve
 ✔ |        10 | Special cases of mkinfit calls [0.5s]
 ✔ |         3 | mkinfit features [0.7s]
 ✔ |         8 | mkinmod model generation and printing [0.2s]
-✔ |         3 | Model predictions with mkinpredict [0.3s]
-✔ |         9 | Multistart method for saem.mmkin models [36.2s]
+✔ |         3 | Model predictions with mkinpredict [0.4s]
+✔ |         9 | Multistart method for saem.mmkin models [37.3s]
 ✔ |        16 | Evaluations according to 2015 NAFTA guidance [2.5s]
-✔ |         9 | Nonlinear mixed-effects models with nlme [8.8s]
-✔ |        16 | Plotting [10.0s]
+✔ |         9 | Nonlinear mixed-effects models with nlme [9.2s]
+✔ |        16 | Plotting [10.1s]
 ✔ |         4 | Residuals extracted from mkinfit models
-✔ |     1  36 | saemix parent models [65.8s]
+✔ |     1  36 | saemix parent models [71.4s]
 ────────────────────────────────────────────────────────────────────────────────
 Skip ('test_saemix_parent.R:143'): We can also use mkin solution methods for saem
 Reason: This still takes almost 2.5 minutes although we do not solve ODEs
 ────────────────────────────────────────────────────────────────────────────────
 ✔ |         2 | Complex test case from Schaefer et al. (2007) Piacenza paper [1.4s]
 ✔ |        11 | Processing of residue series
-✔ |        10 | Fitting the SFORB model [3.7s]
+✔ |        10 | Fitting the SFORB model [3.9s]
 ✔ |         1 | Summaries of old mkinfit objects
 ✔ |         5 | Summary [0.2s]
-✔ |         4 | Results for synthetic data established in expertise for UBA (Ranke 2014) [2.2s]
-✔ |         9 | Hypothesis tests [8.0s]
+✔ |         4 | Results for synthetic data established in expertise for UBA (Ranke 2014) [2.3s]
+✔ |         9 | Hypothesis tests [8.3s]
 ✔ |         4 | Calculation of maximum time weighted average concentrations (TWAs) [2.2s]
 
 ══ Results ═════════════════════════════════════════════════════════════════════
-Duration: 210.4 s
+Duration: 220.0 s
 
 ── Skipped tests  ──────────────────────────────────────────────────────────────
 • Fitting this ODE model with saemix takes about 15 minutes on my system (1)
 • Fitting with saemix takes around 10 minutes when using deSolve (1)
 • This still takes almost 2.5 minutes although we do not solve ODEs (1)
 
-[ FAIL 0 | WARN 0 | SKIP 3 | PASS 269 ]
+[ FAIL 0 | WARN 0 | SKIP 3 | PASS 270 ]
diff --git a/man/ds_mixed.Rd b/man/ds_mixed.Rd
new file mode 100644
index 00000000..227b8e7f
--- /dev/null
+++ b/man/ds_mixed.Rd
@@ -0,0 +1,24 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/ds_mixed.R
+\name{ds_mixed}
+\alias{ds_mixed}
+\alias{ds_sfo}
+\alias{ds_fomc}
+\alias{ds_dfop}
+\alias{ds_hs}
+\alias{ds_dfop_sfo}
+\title{Synthetic data for hierarchical kinetic degradation models}
+\description{
+The R code used to create this data object is installed with this package in
+the 'dataset_generation' directory.
+}
+\examples{
+\dontrun{
+  sfo_mmkin <- mmkin("SFO", ds_sfo, quiet = TRUE, error_model = "tc", cores = 15)
+  sfo_saem <- saem(sfo_mmkin, no_random_effect = "parent_0")
+  plot(sfo_saem)
+}
+
+# This is the code used to generate the datasets
+cat(readLines(system.file("dataset_generation/ds_mixed.R", package = "mkin")), sep = "\n")
+}
diff --git a/tests/testthat/_snaps/multistart/llhist-for-biphasic-saemix-fit.svg b/tests/testthat/_snaps/multistart/llhist-for-dfop-sfo-fit.svg
index 6015aed8..6015aed8 100644
--- a/tests/testthat/_snaps/multistart/llhist-for-biphasic-saemix-fit.svg
+++ b/tests/testthat/_snaps/multistart/llhist-for-dfop-sfo-fit.svg
diff --git a/tests/testthat/_snaps/multistart/llhist-for-sfo-fit.svg b/tests/testthat/_snaps/multistart/llhist-for-sfo-fit.svg
index 98513d06..028c69de 100644
--- a/tests/testthat/_snaps/multistart/llhist-for-sfo-fit.svg
+++ b/tests/testthat/_snaps/multistart/llhist-for-sfo-fit.svg
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diff --git a/tests/testthat/_snaps/multistart/parplot-for-biphasic-saemix-fit.svg b/tests/testthat/_snaps/multistart/parplot-for-dfop-sfo-fit.svg
index 7017908e..7017908e 100644
--- a/tests/testthat/_snaps/multistart/parplot-for-biphasic-saemix-fit.svg
+++ b/tests/testthat/_snaps/multistart/parplot-for-dfop-sfo-fit.svg
diff --git a/tests/testthat/_snaps/multistart/parplot-for-sfo-fit.svg b/tests/testthat/_snaps/multistart/parplot-for-sfo-fit.svg
index 18eb7fcc..a47a585a 100644
--- a/tests/testthat/_snaps/multistart/parplot-for-sfo-fit.svg
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@@ -87,15 +89,15 @@
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diff --git a/tests/testthat/anova_sfo_saem.txt b/tests/testthat/anova_sfo_saem.txt
index 9e4bf71f..0ccd6d5c 100644
--- a/tests/testthat/anova_sfo_saem.txt
+++ b/tests/testthat/anova_sfo_saem.txt
@@ -1,7 +1,7 @@
-Data: 262 observations of 1 variable(s) grouped in 15 datasets
+Data: 263 observations of 1 variable(s) grouped in 15 datasets
 
                         npar  AIC  BIC  Lik Chisq Df Pr(>Chisq)
-sfo_saem_1_reduced         5 1310 1313 -650                    
-sfo_saem_1_reduced_mkin    5 1310 1313 -650     0  0           
-sfo_saem_1                 6 1312 1316 -650     0  1          1
-sfo_saem_1_mkin            6 1312 1316 -650     0  0           
+sfo_saem_1_reduced         5 1302 1306 -646                    
+sfo_saem_1_reduced_mkin    5 1302 1306 -646     0  0           
+sfo_saem_1                 6 1304 1308 -646     0  1          1
+sfo_saem_1_mkin            6 1303 1308 -646     1  0           
diff --git a/tests/testthat/print_dfop_saemix_1.txt b/tests/testthat/print_dfop_saem_1.txt
index 1d399a52..bdc40065 100644
--- a/tests/testthat/print_dfop_saemix_1.txt
+++ b/tests/testthat/print_dfop_saem_1.txt
@@ -13,11 +13,11 @@ Likelihood computed by importance sampling
 
 Fitted parameters:
           estimate lower  upper
-parent_0    100.00 99.00 100.00
-log_k1       -2.70 -3.00  -2.50
-log_k2       -4.10 -4.30  -4.00
+parent_0     99.92 98.77 101.06
+log_k1       -2.72 -2.95  -2.50
+log_k2       -4.14 -4.27  -4.01
 g_qlogis     -0.35 -0.53  -0.16
-a.1           0.92  0.68   1.20
+a.1           0.92  0.68   1.16
 b.1           0.05  0.04   0.06
 SD.log_k1     0.37  0.23   0.51
 SD.log_k2     0.23  0.14   0.31
diff --git a/tests/testthat/print_fits_synth_const.txt b/tests/testthat/print_fits_synth_const.txt
index 2ea1f133..b4bbe6ca 100644
--- a/tests/testthat/print_fits_synth_const.txt
+++ b/tests/testthat/print_fits_synth_const.txt
@@ -4,7 +4,7 @@ Status of individual fits:
       dataset
 model  1  2  3  4  5  6 
   SFO  OK OK OK OK OK OK
-  FOMC C  C  OK OK OK OK
+  FOMC C  OK OK OK OK C 
 
 C: Optimisation did not converge:
 false convergence (8)
diff --git a/tests/testthat/print_mmkin_sfo_1_mixed.txt b/tests/testthat/print_mmkin_sfo_1_mixed.txt
index 33e5bf5c..c12cfe2b 100644
--- a/tests/testthat/print_mmkin_sfo_1_mixed.txt
+++ b/tests/testthat/print_mmkin_sfo_1_mixed.txt
@@ -3,7 +3,7 @@ Structural model:
 d_parent/dt = - k_parent * parent
 
 Data:
-262 observations of 1 variable(s) grouped in 15 datasets
+263 observations of 1 variable(s) grouped in 15 datasets
 
 <mmkin> object
 Status of individual fits:
@@ -16,4 +16,4 @@ OK: No warnings
 
 Mean fitted parameters:
     parent_0 log_k_parent 
-        99.9         -3.3 
+       100.0         -3.4 
diff --git a/tests/testthat/print_multistart_biphasic.txt b/tests/testthat/print_multistart_dfop_sfo.txt
index b4344f22..b4344f22 100644
--- a/tests/testthat/print_multistart_biphasic.txt
+++ b/tests/testthat/print_multistart_dfop_sfo.txt
diff --git a/tests/testthat/print_sfo_saem_1_reduced.txt b/tests/testthat/print_sfo_saem_1_reduced.txt
index bac8848e..1c7fb588 100644
--- a/tests/testthat/print_sfo_saem_1_reduced.txt
+++ b/tests/testthat/print_sfo_saem_1_reduced.txt
@@ -3,16 +3,16 @@ Structural model:
 d_parent/dt = - k_parent * parent
 
 Data:
-262 observations of 1 variable(s) grouped in 15 datasets
+263 observations of 1 variable(s) grouped in 15 datasets
 
 Likelihood computed by importance sampling
    AIC  BIC logLik
-  1310 1313   -650
+  1302 1306   -646
 
 Fitted parameters:
             estimate lower upper
-parent_0       1e+02 99.08 1e+02
-k_parent       4e-02  0.03 4e-02
-a.1            9e-01  0.75 1e+00
+parent_0       1e+02 99.03 1e+02
+k_parent       3e-02  0.03 4e-02
+a.1            9e-01  0.71 1e+00
 b.1            5e-02  0.04 5e-02
-SD.k_parent    3e-01  0.20 4e-01
+SD.k_parent    2e-01  0.14 3e-01
diff --git a/tests/testthat/setup_script.R b/tests/testthat/setup_script.R
index 362038c3..c554800d 100644
--- a/tests/testthat/setup_script.R
+++ b/tests/testthat/setup_script.R
@@ -81,112 +81,27 @@ fit_obs_1 <- mkinfit(m_synth_SFO_lin, SFO_lin_a, error_model = "obs", quiet = TR
 fit_tc_1 <- mkinfit(m_synth_SFO_lin, SFO_lin_a, error_model = "tc", quiet = TRUE,
   error_model_algorithm = "threestep")
 
-# Mixed models data and fits
-sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
-n <- n_biphasic <- 15
-log_sd <- 0.3
-err_1 = list(const = 1, prop = 0.05)
-tc <- function(value) sigma_twocomp(value, err_1$const, err_1$prop)
-const <- function(value) 2
-
-set.seed(123456)
-SFO <- mkinmod(parent = mkinsub("SFO"))
-k_parent = rlnorm(n, log(0.03), log_sd)
-set.seed(123456)
-ds_sfo <- lapply(1:n, function(i) {
-  ds_mean <- mkinpredict(SFO, c(k_parent = k_parent[i]),
-    c(parent = 100), sampling_times)
-  add_err(ds_mean, tc, n = 1)[[1]]
-})
-
-set.seed(123456)
-FOMC <- mkinmod(parent = mkinsub("FOMC"))
-fomc_pop <- list(parent_0 = 100, alpha = 2, beta = 8)
-fomc_parms <- as.matrix(data.frame(
-    alpha = rlnorm(n, log(fomc_pop$alpha), 0.4),
-    beta = rlnorm(n, log(fomc_pop$beta), 0.2)))
-set.seed(123456)
-ds_fomc <- lapply(1:3, function(i) {
-  ds_mean <- mkinpredict(FOMC, fomc_parms[i, ],
-    c(parent = 100), sampling_times)
-  add_err(ds_mean, tc, n = 1)[[1]]
-})
-
-set.seed(123456)
-DFOP <- mkinmod(parent = mkinsub("DFOP"))
-dfop_pop <- list(parent_0 = 100, k1 = 0.06, k2 = 0.015, g = 0.4)
-dfop_parms <- as.matrix(data.frame(
-  k1 = rlnorm(n, log(dfop_pop$k1), log_sd),
-  k2 = rlnorm(n, log(dfop_pop$k2), log_sd),
-  g = plogis(rnorm(n, qlogis(dfop_pop$g), log_sd))))
-set.seed(123456)
-ds_dfop <- lapply(1:n, function(i) {
-  ds_mean <- mkinpredict(DFOP, dfop_parms[i, ],
-    c(parent = dfop_pop$parent_0), sampling_times)
-  add_err(ds_mean, tc, n = 1)[[1]]
-})
-
-set.seed(123456)
-HS <- mkinmod(parent = mkinsub("HS"))
-hs_pop <- list(parent_0 = 100, k1 = 0.08, k2 = 0.01, tb = 15)
-hs_parms <- as.matrix(data.frame(
-  k1 = rlnorm(n, log(hs_pop$k1), log_sd),
-  k2 = rlnorm(n, log(hs_pop$k2), log_sd),
-  tb = rlnorm(n, log(hs_pop$tb), 0.1)))
-set.seed(123456)
-ds_hs <- lapply(1:10, function(i) {
-  ds_mean <- mkinpredict(HS, hs_parms[i, ],
-    c(parent = hs_pop$parent_0), sampling_times)
-  add_err(ds_mean, const, n = 1)[[1]]
-})
-
-set.seed(123456)
-DFOP_SFO <- mkinmod(
-  parent = mkinsub("DFOP", "m1"),
-  m1 = mkinsub("SFO"),
-  quiet = TRUE)
-dfop_sfo_pop <- list(parent_0 = 100,
-  k_m1 = 0.007, f_parent_to_m1 = 0.5,
-  k1 = 0.1, k2 = 0.02, g = 0.5)
-syn_biphasic_parms <- as.matrix(data.frame(
-  k1 = rlnorm(n_biphasic, log(dfop_sfo_pop$k1), log_sd),
-  k2 = rlnorm(n_biphasic, log(dfop_sfo_pop$k2), log_sd),
-  g = plogis(rnorm(n_biphasic, qlogis(dfop_sfo_pop$g), log_sd)),
-  f_parent_to_m1 = plogis(rnorm(n_biphasic,
-      qlogis(dfop_sfo_pop$f_parent_to_m1), log_sd)),
-  k_m1 = rlnorm(n_biphasic, log(dfop_sfo_pop$k_m1), log_sd)))
-ds_biphasic_mean <- lapply(1:n_biphasic,
-  function(i) {
-    mkinpredict(DFOP_SFO, syn_biphasic_parms[i, ],
-      c(parent = 100, m1 = 0), sampling_times)
-  }
-)
-set.seed(123456)
-ds_biphasic <- lapply(ds_biphasic_mean, function(ds) {
-  add_err(ds,
-    sdfunc = function(value) sqrt(err_1$const^2 + value^2 * err_1$prop^2),
-    n = 1, secondary = "m1")[[1]]
-})
-
 # Mixed model fits
 mmkin_sfo_1 <- mmkin("SFO", ds_sfo, quiet = TRUE, error_model = "tc", cores = n_cores)
 mmkin_dfop_1 <- mmkin("DFOP", ds_dfop, quiet = TRUE, cores = n_cores,
   error_model = "tc")
 
-mmkin_biphasic <- mmkin(list("DFOP-SFO" = DFOP_SFO), ds_biphasic, quiet = TRUE, cores = n_cores,
+DFOP_SFO <- mkinmod(parent = mkinsub("DFOP", "m1"),
+  m1 = mkinsub("SFO"), quiet = TRUE)
+mmkin_dfop_sfo <- mmkin(list("DFOP-SFO" = DFOP_SFO), ds_dfop_sfo, quiet = TRUE, cores = n_cores,
   control = list(eval.max = 500, iter.max = 400),
   error_model = "tc")
 
 # nlme
 dfop_nlme_1 <- suppressWarnings(nlme(mmkin_dfop_1))
-nlme_biphasic <- suppressWarnings(nlme(mmkin_biphasic))
+nlme_dfop_sfo <- suppressWarnings(nlme(mmkin_dfop_sfo))
 
 # saemix
 sfo_saem_1 <- saem(mmkin_sfo_1, quiet = TRUE, transformations = "saemix")
 sfo_saem_1_reduced <- update(sfo_saem_1, no_random_effect = "parent_0")
-dfop_saemix_1 <- saem(mmkin_dfop_1, quiet = TRUE, transformations = "mkin",
+dfop_saem_1 <- saem(mmkin_dfop_1, quiet = TRUE, transformations = "mkin",
   no_random_effect = c("parent_0", "g_qlogis"))
 
-saem_biphasic_m <- saem(mmkin_biphasic, transformations = "mkin", quiet = TRUE)
-saem_biphasic_s <- saem(mmkin_biphasic, transformations = "saemix", quiet = TRUE)
+saem_dfop_sfo_m <- saem(mmkin_dfop_sfo, transformations = "mkin", quiet = TRUE)
+saem_dfop_sfo_s <- saem(mmkin_dfop_sfo, transformations = "saemix", quiet = TRUE)
 
diff --git a/tests/testthat/summary_hfit_sfo_tc.txt b/tests/testthat/summary_hfit_sfo_tc.txt
index 41743091..bb5bf6fb 100644
--- a/tests/testthat/summary_hfit_sfo_tc.txt
+++ b/tests/testthat/summary_hfit_sfo_tc.txt
@@ -8,7 +8,7 @@ Equations:
 d_parent/dt = - k_parent * parent
 
 Data:
-106 observations of 1 variable(s) grouped in 6 datasets
+104 observations of 1 variable(s) grouped in 6 datasets
 
 Model predictions using solution type analytical 
 
@@ -28,15 +28,15 @@ Results:
 
 Likelihood computed by importance sampling
   AIC BIC logLik
-  533 531   -261
+  524 523   -257
 
 Optimised parameters:
-                  est.  lower  upper
-parent_0        101.02  99.58 102.46
-log_k_parent     -3.32  -3.53  -3.11
-a.1               0.91   0.64   1.17
-b.1               0.05   0.04   0.06
-SD.log_k_parent   0.27   0.11   0.42
+                  est. lower  upper
+parent_0        100.68 99.27 102.08
+log_k_parent     -3.38 -3.55  -3.21
+a.1               0.87  0.59   1.14
+b.1               0.05  0.04   0.06
+SD.log_k_parent   0.21  0.09   0.33
 
 Correlation: 
              pr_0
@@ -44,18 +44,18 @@ log_k_parent 0.1
 
 Random effects:
                 est. lower upper
-SD.log_k_parent  0.3   0.1   0.4
+SD.log_k_parent  0.2  0.09   0.3
 
 Variance model:
     est. lower upper
-a.1 0.91  0.64  1.17
+a.1 0.87  0.59  1.14
 b.1 0.05  0.04  0.06
 
 Backtransformed parameters:
           est. lower upper
-parent_0 1e+02 1e+02 1e+02
-k_parent 4e-02 3e-02 4e-02
+parent_0 1e+02 99.27 1e+02
+k_parent 3e-02  0.03 4e-02
 
 Estimated disappearance times:
        DT50 DT90
-parent   19   64
+parent   20   68
diff --git a/tests/testthat/summary_saem_biphasic_s.txt b/tests/testthat/summary_saem_dfop_sfo_s.txt
index 7c337843..7c337843 100644
--- a/tests/testthat/summary_saem_biphasic_s.txt
+++ b/tests/testthat/summary_saem_dfop_sfo_s.txt
diff --git a/tests/testthat/test_mixed.R b/tests/testthat/test_mixed.R
index 2d53c6dd..ab8dfc27 100644
--- a/tests/testthat/test_mixed.R
+++ b/tests/testthat/test_mixed.R
@@ -1,22 +1,23 @@
 context("Nonlinear mixed-effects models")
 
+
 # Round error model parameters as they are not rounded in print methods
 dfop_nlme_1$modelStruct$varStruct$const <-
   signif(dfop_nlme_1$modelStruct$varStruct$const, 3)
 dfop_nlme_1$modelStruct$varStruct$prop <-
   signif(dfop_nlme_1$modelStruct$varStruct$prop, 4)
 
+dfop_sfo_pop <- attr(ds_dfop_sfo, "pop")
+
 test_that("Print methods work", {
   expect_known_output(print(fits[, 2:3], digits = 2), "print_mmkin_parent.txt")
   expect_known_output(print(mixed(mmkin_sfo_1), digits = 2), "print_mmkin_sfo_1_mixed.txt")
   expect_known_output(print(dfop_nlme_1, digits = 1), "print_dfop_nlme_1.txt")
+  expect_known_output(print(sfo_saem_1_reduced, digits = 1), "print_sfo_saem_1_reduced.txt")
 
-  # In order to address the platform dependence of the results, we round to two
-  # significant digits before printing
-  dfop_saemix_1_print <- dfop_saemix_1
-  dfop_saemix_1_print$so@results@conf.int[c("estimate", "lower", "upper")] <-
-    signif(dfop_saemix_1_print$so@results@conf.int[c("estimate", "lower", "upper")], 2)
-  expect_known_output(print(dfop_saemix_1_print, digits = 1), "print_dfop_saemix_1.txt")
+  skip_on_cran() # The following test is platform dependent and fails on
+  # win-builder with current (18 Nov 2022) R-devel and on the Fedora CRAN check systems
+  expect_known_output(print(dfop_saem_1, digits = 1), "print_dfop_saem_1.txt")
 })
 
 test_that("nlme results are reproducible to some degree", {
@@ -36,17 +37,16 @@ test_that("nlme results are reproducible to some degree", {
   # k1 and k2 just fail the first test (lower bound of the ci), so we need to exclude it
   dfop_no_k1_k2 <- c("parent_0", "k_m1", "f_parent_to_m1", "g")
   dfop_sfo_pop_no_k1_k2 <- as.numeric(dfop_sfo_pop[dfop_no_k1_k2])
-  dfop_sfo_pop <- as.numeric(dfop_sfo_pop) # to remove names
 
-  ci_dfop_sfo_n <- summary(nlme_biphasic)$confint_back
+  ci_dfop_sfo_n <- summary(nlme_dfop_sfo)$confint_back
 
   expect_true(all(ci_dfop_sfo_n[dfop_no_k1_k2, "lower"] < dfop_sfo_pop_no_k1_k2))
-  expect_true(all(ci_dfop_sfo_n[, "upper"] > dfop_sfo_pop))
+  expect_true(all(ci_dfop_sfo_n[, "upper"] > as.numeric(dfop_sfo_pop)))
 })
 
 test_that("saemix results are reproducible for biphasic fits", {
 
-  test_summary <- summary(saem_biphasic_s)
+  test_summary <- summary(saem_dfop_sfo_s)
   test_summary$saemixversion <- "Dummy 0.0 for testing"
   test_summary$mkinversion <- "Dummy 0.0 for testing"
   test_summary$Rversion <- "Dummy R version for testing"
@@ -54,33 +54,33 @@ test_that("saemix results are reproducible for biphasic fits", {
   test_summary$date.summary <- "Dummy date for testing"
   test_summary$time <- c(elapsed = "test time 0")
 
-  expect_known_output(print(test_summary, digits = 1), "summary_saem_biphasic_s.txt")
+  expect_known_output(print(test_summary, digits = 1), "summary_saem_dfop_sfo_s.txt")
 
   dfop_sfo_pop <- as.numeric(dfop_sfo_pop)
   no_k1 <- c(1, 2, 3, 5, 6)
   no_k2 <- c(1, 2, 3, 4, 6)
   no_k1_k2 <- c(1, 2, 3, 6)
 
-  ci_dfop_sfo_s_s <- summary(saem_biphasic_s)$confint_back
+  ci_dfop_sfo_s_s <- summary(saem_dfop_sfo_s)$confint_back
   expect_true(all(ci_dfop_sfo_s_s[, "lower"] < dfop_sfo_pop))
   expect_true(all(ci_dfop_sfo_s_s[, "upper"] > dfop_sfo_pop))
 
   # k2 is not fitted well
-  ci_dfop_sfo_s_m <- summary(saem_biphasic_m)$confint_back
+  ci_dfop_sfo_s_m <- summary(saem_dfop_sfo_m)$confint_back
   expect_true(all(ci_dfop_sfo_s_m[no_k2, "lower"] < dfop_sfo_pop[no_k2]))
   expect_true(all(ci_dfop_sfo_s_m[no_k1, "upper"] > dfop_sfo_pop[no_k1]))
 
   # I tried to only do few iterations in routine tests as this is so slow
   # but then deSolve fails at some point (presumably at the switch between
   # the two types of iterations)
-  #saem_biphasic_2 <- saem(mmkin_biphasic, solution_type = "deSolve",
+  #saem_dfop_sfo_2 <- saem(mmkin_biphasic, solution_type = "deSolve",
   # control = list(nbiter.saemix = c(10, 5), nbiter.burn = 5), quiet = TRUE)
 
   skip("Fitting with saemix takes around 10 minutes when using deSolve")
-  saem_biphasic_2 <- saem(mmkin_biphasic, solution_type = "deSolve", quiet = TRUE)
+  saem_dfop_sfo_2 <- saem(mmkin_dfop_sfo, solution_type = "deSolve", quiet = TRUE)
 
   # As with the analytical solution, k1 and k2 are not fitted well
-  ci_dfop_sfo_s_d <- summary(saem_biphasic_2)$confint_back
+  ci_dfop_sfo_s_d <- summary(saem_dfop_sfo_2)$confint_back
   expect_true(all(ci_dfop_sfo_s_d[no_k2, "lower"] < dfop_sfo_pop[no_k2]))
   expect_true(all(ci_dfop_sfo_s_d[no_k1, "upper"] > dfop_sfo_pop[no_k1]))
 })
diff --git a/tests/testthat/test_multistart.R b/tests/testthat/test_multistart.R
index 502cee98..3a511e06 100644
--- a/tests/testthat/test_multistart.R
+++ b/tests/testthat/test_multistart.R
@@ -9,26 +9,22 @@ test_that("multistart works for saem.mmkin models", {
     best(saem_sfo_s_multi),
     test = TRUE
   )
-  # On winbuilder, sfo_saem_1 gives an AIC of 1310.8, while we get 1311.7
-  # locally (using saemix 3.2, which likely makes the difference due to the
-  # error parameter patch) on Linux and Windows. The other, well-determined
-  # fits both give 1309.7.
-  expect_equal(round(anova_sfo, 1)["sfo_saem_1_reduced", "AIC"], 1309.7)
-  expect_equal(round(anova_sfo, 1)["best(saem_sfo_s_multi)", "AIC"], 1309.7)
-  expect_true(anova_sfo[3, "Pr(>Chisq)"] > 0.2) # Local: 1, CRAN: 0.4
+  expect_equal(round(anova_sfo, 1)["sfo_saem_1_reduced", "AIC"], 1302.2)
+  expect_equal(round(anova_sfo, 1)["best(saem_sfo_s_multi)", "AIC"], 1302.2)
+  expect_true(anova_sfo[3, "Pr(>Chisq)"] > 0.2) # Local: 1, win-builder: 0.4
 
   set.seed(123456)
-  saem_biphasic_m_multi <- multistart(saem_biphasic_m, n = 8,
+  saem_dfop_sfo_m_multi <- multistart(saem_dfop_sfo_m, n = 8,
     cores = n_cores)
-  expect_known_output(print(saem_biphasic_m_multi),
-    file = "print_multistart_biphasic.txt")
+  expect_known_output(print(saem_dfop_sfo_m_multi),
+    file = "print_multistart_dfop_sfo.txt")
 
-  anova_biphasic <- anova(saem_biphasic_m,
-    best(saem_biphasic_m_multi))
+  anova_dfop_sfo <- anova(saem_dfop_sfo_m,
+    best(saem_dfop_sfo_m_multi))
 
   # With the new starting parameters we do not improve
   # with multistart any more
-  expect_equal(anova_biphasic[2, "AIC"], anova_biphasic[1, "AIC"],
+  expect_equal(anova_dfop_sfo[2, "AIC"], anova_dfop_sfo[1, "AIC"],
     tolerance = 1e-4)
   skip_on_travis() # Plots are platform dependent
 
@@ -37,10 +33,10 @@ test_that("multistart works for saem.mmkin models", {
   vdiffr::expect_doppelganger("llhist for sfo fit", llhist_sfo)
   vdiffr::expect_doppelganger("parplot for sfo fit", parplot_sfo)
 
-  llhist_biphasic <- function() llhist(saem_biphasic_m_multi)
-  parplot_biphasic <- function() parplot(saem_biphasic_m_multi,
+  llhist_dfop_sfo <- function() llhist(saem_dfop_sfo_m_multi)
+  parplot_dfop_sfo <- function() parplot(saem_dfop_sfo_m_multi,
     ylim = c(0.5, 2))
 
-  vdiffr::expect_doppelganger("llhist for biphasic saemix fit", llhist_biphasic)
-  vdiffr::expect_doppelganger("parplot for biphasic saemix fit", parplot_biphasic)
+  vdiffr::expect_doppelganger("llhist for dfop sfo fit", llhist_dfop_sfo)
+  vdiffr::expect_doppelganger("parplot for dfop sfo fit", parplot_dfop_sfo)
 })
diff --git a/tests/testthat/test_plot.R b/tests/testthat/test_plot.R
index 13058c00..01b0c1ee 100644
--- a/tests/testthat/test_plot.R
+++ b/tests/testthat/test_plot.R
@@ -44,24 +44,24 @@ test_that("Plotting mkinfit, mmkin and mixed model objects is reproducible", {
 
   f_uba_dfop_sfo_saem <- saem(f_uba_mmkin["DFOP-SFO", ], quiet = TRUE, transformations = "saemix")
 
-  plot_biphasic_mmkin <- function() plot(f_uba_dfop_sfo_mixed, pop_curve = TRUE)
-  vdiffr::expect_doppelganger("mixed model fit for mmkin object", plot_biphasic_mmkin)
+  plot_dfop_sfo_mmkin <- function() plot(f_uba_dfop_sfo_mixed, pop_curve = TRUE)
+  vdiffr::expect_doppelganger("mixed model fit for mmkin object", plot_dfop_sfo_mmkin)
 
-  plot_biphasic_saem_s <- function() plot(f_uba_dfop_sfo_saem)
-  vdiffr::expect_doppelganger("mixed model fit for saem object with saemix transformations", plot_biphasic_saem_s)
+  plot_dfop_sfo_saem_s <- function() plot(f_uba_dfop_sfo_saem)
+  vdiffr::expect_doppelganger("mixed model fit for saem object with saemix transformations", plot_dfop_sfo_saem_s)
 
   skip_on_travis()
 
-  plot_biphasic_nlme <- function() plot(dfop_nlme_1)
-  vdiffr::expect_doppelganger("mixed model fit for nlme object", plot_biphasic_nlme)
+  plot_dfop_sfo_nlme <- function() plot(dfop_nlme_1)
+  vdiffr::expect_doppelganger("mixed model fit for nlme object", plot_dfop_sfo_nlme)
 
-  #plot_biphasic_mmkin <- function() plot(mixed(mmkin_biphasic))
+  #plot_dfop_sfo_mmkin <- function() plot(mixed(mmkin_dfop_sfo))
   # Biphasic fits with lots of data and fits have lots of potential for differences
-  plot_biphasic_nlme <- function() plot(nlme_biphasic)
-  #plot_biphasic_saem_s <- function() plot(saem_biphasic_s)
-  plot_biphasic_saem_m <- function() plot(saem_biphasic_m)
+  plot_dfop_sfo_nlme <- function() plot(nlme_dfop_sfo)
+  #plot_dfop_sfo_saem_s <- function() plot(saem_dfop_sfo_s)
+  plot_dfop_sfo_saem_m <- function() plot(saem_dfop_sfo_m)
 
-  vdiffr::expect_doppelganger("mixed model fit for saem object with mkin transformations", plot_biphasic_saem_m)
+  vdiffr::expect_doppelganger("mixed model fit for saem object with mkin transformations", plot_dfop_sfo_saem_m)
 
   # different results when working with eigenvalues
   plot_errmod_fit_D_obs_eigen <- function() plot_err(fit_D_obs_eigen, sep_obs = FALSE)
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))
 })