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authorJohannes Ranke <jranke@uni-bremen.de>2022-10-25 21:45:30 +0200
committerJohannes Ranke <jranke@uni-bremen.de>2022-10-25 21:45:30 +0200
commita8ff8bed72dc537fe70cf2995ea769d3f519f877 (patch)
tree781e776ea30c95392c0e96df7e42ae7709d32cb3
parent92bd47bf45cebe660c5e7f392a4ee19f704d8a19 (diff)
Change DFOP mixed model data in tests, updates
-rw-r--r--log/test.log24
-rw-r--r--tests/testthat/_snaps/plot/mixed-model-fit-for-nlme-object.svg2404
-rw-r--r--tests/testthat/anova_sfo_saem.txt0
-rw-r--r--tests/testthat/print_dfop_nlme_1.txt12
-rw-r--r--tests/testthat/setup_script.R33
-rw-r--r--tests/testthat/summary_dfop_nlme_1.txt36
-rw-r--r--tests/testthat/test_confidence.R14
-rw-r--r--tests/testthat/test_saemix_parent.R56
-rw-r--r--tests/testthat/test_tests.R1
9 files changed, 1298 insertions, 1282 deletions
diff --git a/log/test.log b/log/test.log
index b6ec2084..84f81103 100644
--- a/log/test.log
+++ b/log/test.log
@@ -1,16 +1,16 @@
ℹ Testing mkin
✔ | F W S OK | Context
✔ | 5 | AIC calculation
-✔ | 5 | Analytical solutions for coupled models [3.3s]
+✔ | 5 | Analytical solutions for coupled models [3.2s]
✔ | 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 [31.6s]
+✔ | 1 12 | Dimethenamid data from 2018 [31.8s]
────────────────────────────────────────────────────────────────────────────────
Skip (test_dmta.R:98:3): 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 [4.9s]
+✔ | 14 | Error model fitting [4.8s]
✔ | 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]
@@ -26,26 +26,26 @@ Reason: Fitting with saemix takes around 10 minutes when using deSolve
✔ | 10 | Special cases of mkinfit calls [0.4s]
✔ | 3 | mkinfit features [0.7s]
✔ | 8 | mkinmod model generation and printing [0.2s]
-✔ | 3 | Model predictions with mkinpredict [0.4s]
-✔ | 16 | Evaluations according to 2015 NAFTA guidance [1.8s]
+✔ | 3 | Model predictions with mkinpredict [0.3s]
+✔ | 16 | Evaluations according to 2015 NAFTA guidance [1.7s]
✔ | 9 | Nonlinear mixed-effects models with nlme [8.3s]
-✔ | 16 | Plotting [9.9s]
+✔ | 16 | Plotting [10.0s]
✔ | 4 | Residuals extracted from mkinfit models
-✔ | 37 | saemix parent models [208.7s]
-✔ | 2 | Complex test case from Schaefer et al. (2007) Piacenza paper [1.3s]
+✔ | 36 | saemix parent models [308.7s]
+✔ | 2 | Complex test case from Schaefer et al. (2007) Piacenza paper [1.4s]
✔ | 11 | Processing of residue series
-✔ | 7 | Fitting the SFORB model [3.8s]
+✔ | 7 | Fitting the SFORB model [3.6s]
✔ | 1 | Summaries of old mkinfit objects
✔ | 5 | Summary [0.2s]
-✔ | 4 | Results for synthetic data established in expertise for UBA (Ranke 2014) [2.2s]
+✔ | 4 | Results for synthetic data established in expertise for UBA (Ranke 2014) [2.1s]
✔ | 9 | Hypothesis tests [7.8s]
✔ | 4 | Calculation of maximum time weighted average concentrations (TWAs) [2.2s]
══ Results ═════════════════════════════════════════════════════════════════════
-Duration: 305.6 s
+Duration: 405.5 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)
-[ FAIL 0 | WARN 0 | SKIP 2 | PASS 255 ]
+[ FAIL 0 | WARN 0 | SKIP 2 | PASS 254 ]
diff --git a/tests/testthat/_snaps/plot/mixed-model-fit-for-nlme-object.svg b/tests/testthat/_snaps/plot/mixed-model-fit-for-nlme-object.svg
index 051b46b1..76fed0dc 100644
--- a/tests/testthat/_snaps/plot/mixed-model-fit-for-nlme-object.svg
+++ b/tests/testthat/_snaps/plot/mixed-model-fit-for-nlme-object.svg
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diff --git a/tests/testthat/anova_sfo_saem.txt b/tests/testthat/anova_sfo_saem.txt
new file mode 100644
index 00000000..e69de29b
--- /dev/null
+++ b/tests/testthat/anova_sfo_saem.txt
diff --git a/tests/testthat/print_dfop_nlme_1.txt b/tests/testthat/print_dfop_nlme_1.txt
index 1092821d..435ff409 100644
--- a/tests/testthat/print_dfop_nlme_1.txt
+++ b/tests/testthat/print_dfop_nlme_1.txt
@@ -8,17 +8,23 @@ d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
Data:
270 observations of 1 variable(s) grouped in 15 datasets
-Log-likelihood: -612
+Log-likelihood: -695
Fixed effects:
list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1)
parent_0 log_k1 log_k2 g_qlogis
- 100.1 -2.7 -4.1 -0.5
+ 100.1 -2.7 -4.1 -0.4
Random effects:
Formula: list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1)
Level: ds
Structure: Diagonal
parent_0 log_k1 log_k2 g_qlogis Residual
-StdDev: 0.4 0.3 0.2 0.2 2
+StdDev: 2 0.3 0.2 0.2 1
+Variance function:
+ Structure: Constant plus proportion of variance covariate
+ Formula: ~fitted(.)
+ Parameter estimates:
+ const prop
+ 0.92275475 -0.04804649
diff --git a/tests/testthat/setup_script.R b/tests/testthat/setup_script.R
index af059668..4de2674f 100644
--- a/tests/testthat/setup_script.R
+++ b/tests/testthat/setup_script.R
@@ -14,25 +14,12 @@ if (Sys.getenv("TRAVIS") != "") n_cores = 2
# On Windows we would need to make a cluster first
if (Sys.info()["sysname"] == "Windows") n_cores = 1
-# We set up some models and fits with nls for comparisons
-SFO_trans <- function(t, parent_0, log_k_parent_sink) {
- parent_0 * exp(- exp(log_k_parent_sink) * t)
-}
-SFO_notrans <- function(t, parent_0, k_parent_sink) {
- parent_0 * exp(- k_parent_sink * t)
-}
-f_1_nls_trans <- nls(value ~ SFO_trans(time, parent_0, log_k_parent_sink),
- data = FOCUS_2006_A,
- start = list(parent_0 = 100, log_k_parent_sink = log(0.1)))
-f_1_nls_notrans <- nls(value ~ SFO_notrans(time, parent_0, k_parent_sink),
- data = FOCUS_2006_A,
- start = list(parent_0 = 100, k_parent_sink = 0.1))
-
+# Very simple example fits
f_1_mkin_trans <- mkinfit("SFO", FOCUS_2006_A, quiet = TRUE)
f_1_mkin_notrans <- mkinfit("SFO", FOCUS_2006_A, quiet = TRUE,
transform_rates = FALSE)
-# mmkin object of parent fits for tests
+# mmkin object of parent fits
models <- c("SFO", "FOMC", "DFOP", "HS")
fits <- suppressWarnings( # FOCUS A FOMC was, it seems, in testthat output
mmkin(models,
@@ -72,9 +59,8 @@ DFOP_par_c <- synthetic_data_for_UBA_2014[[12]]$data
f_2_mkin <- mkinfit("DFOP", DFOP_par_c, quiet = TRUE)
f_2_nls <- nls(value ~ SSbiexp(time, A1, lrc1, A2, lrc2), data = subset(DFOP_par_c, name == "parent"))
-f_2_anova <- lm(value ~ as.factor(time), data = subset(DFOP_par_c, name == "parent"))
-# Two metabolites
+# mkinfit with two metabolites
m_synth_SFO_lin <- mkinmod(
parent = mkinsub("SFO", "M1"),
M1 = mkinsub("SFO", "M2"),
@@ -137,7 +123,7 @@ set.seed(123456)
ds_dfop <- lapply(1:n, function(i) {
ds_mean <- mkinpredict(DFOP, dfop_parms[i, ],
c(parent = dfop_pop$parent_0), sampling_times)
- add_err(ds_mean, const, n = 1)[[1]]
+ add_err(ds_mean, tc, n = 1)[[1]]
})
set.seed(123456)
@@ -184,20 +170,21 @@ ds_biphasic <- lapply(ds_biphasic_mean, function(ds) {
# 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)
+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,
control = list(eval.max = 500, iter.max = 400),
error_model = "tc")
# nlme
-dfop_nlme_1 <- nlme(mmkin_dfop_1)
+dfop_nlme_1 <- suppressWarnings(nlme(mmkin_dfop_1))
nlme_biphasic <- suppressWarnings(nlme(mmkin_biphasic))
# saemix
sfo_saem_1 <- saem(mmkin_sfo_1, quiet = TRUE, transformations = "saemix")
-
-dfop_saemix_1 <- saem(mmkin_dfop_1, quiet = TRUE, transformations = "mkin")
-dfop_saemix_2 <- saem(mmkin_dfop_1, quiet = TRUE, transformations = "saemix")
+dfop_saemix_1 <- saem(mmkin_dfop_1, quiet = TRUE, transformations = "mkin",
+ no_random_effect = "parent_0")
saem_biphasic_m <- saem(mmkin_biphasic, transformations = "mkin", quiet = TRUE)
saem_biphasic_s <- saem(mmkin_biphasic, transformations = "saemix", quiet = TRUE)
diff --git a/tests/testthat/summary_dfop_nlme_1.txt b/tests/testthat/summary_dfop_nlme_1.txt
index 9da3fb1b..773be488 100644
--- a/tests/testthat/summary_dfop_nlme_1.txt
+++ b/tests/testthat/summary_dfop_nlme_1.txt
@@ -14,13 +14,13 @@ Data:
Model predictions using solution type analytical
-Fitted in test time 0 s using 3 iterations
+Fitted in test time 0 s using 5 iterations
-Variance model: Constant variance
+Variance model: Two-component variance function
Mean of starting values for individual parameters:
parent_0 log_k1 log_k2 g_qlogis
- 100.06 -2.68 -4.16 0.01
+ 100.2 -2.6 -4.2 0.1
Fixed degradation parameter values:
None
@@ -28,36 +28,42 @@ None
Results:
AIC BIC logLik
- 1242 1274 -612
+ 1410 1446 -695
Optimised, transformed parameters with symmetric confidence intervals:
lower est. upper
-parent_0 99.6 100.1 100.6
-log_k1 -2.9 -2.7 -2.4
+parent_0 98.7 100.1 101.5
+log_k1 -2.9 -2.7 -2.5
log_k2 -4.2 -4.1 -4.0
-g_qlogis -0.7 -0.5 -0.2
+g_qlogis -0.7 -0.4 -0.2
Correlation:
pr_0 lg_1 lg_2
-log_k1 0.2
+log_k1 0.3
log_k2 0.1 0.2
-g_qlogis -0.2 -0.5 -0.4
+g_qlogis -0.1 -0.5 -0.4
Random effects:
Formula: list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1)
Level: ds
Structure: Diagonal
parent_0 log_k1 log_k2 g_qlogis Residual
-StdDev: 0.4 0.3 0.2 0.2 2
+StdDev: 2 0.3 0.2 0.2 1
+Variance function:
+ Structure: Constant plus proportion of variance covariate
+ Formula: ~fitted(.)
+ Parameter estimates:
+ const prop
+ 0.92275475 -0.04804649
Backtransformed parameters with asymmetric confidence intervals:
lower est. upper
-parent_0 1e+02 1e+02 1e+02
-k1 6e-02 7e-02 9e-02
-k2 1e-02 2e-02 2e-02
-g 3e-01 4e-01 5e-01
+parent_0 98.69 1e+02 1e+02
+k1 0.06 7e-02 9e-02
+k2 0.01 2e-02 2e-02
+g 0.34 4e-01 5e-01
Estimated disappearance times:
DT50 DT90 DT50back DT50_k1 DT50_k2
-parent 23 111 33 10 42
+parent 23 111 33 10 43
diff --git a/tests/testthat/test_confidence.R b/tests/testthat/test_confidence.R
index 36e9738d..6c645ca4 100644
--- a/tests/testthat/test_confidence.R
+++ b/tests/testthat/test_confidence.R
@@ -1,5 +1,19 @@
context("Confidence intervals and p-values")
+# We set up some models and fits with nls for comparisons
+SFO_trans <- function(t, parent_0, log_k_parent_sink) {
+ parent_0 * exp(- exp(log_k_parent_sink) * t)
+}
+SFO_notrans <- function(t, parent_0, k_parent_sink) {
+ parent_0 * exp(- k_parent_sink * t)
+}
+f_1_nls_trans <- nls(value ~ SFO_trans(time, parent_0, log_k_parent_sink),
+ data = FOCUS_2006_A,
+ start = list(parent_0 = 100, log_k_parent_sink = log(0.1)))
+f_1_nls_notrans <- nls(value ~ SFO_notrans(time, parent_0, k_parent_sink),
+ data = FOCUS_2006_A,
+ start = list(parent_0 = 100, k_parent_sink = 0.1))
+
test_that("The confint method 'quadratic' is consistent with the summary", {
expect_equivalent(
confint(fit_nw_1, parm = "parent_0", method = "quadratic"),
diff --git a/tests/testthat/test_saemix_parent.R b/tests/testthat/test_saemix_parent.R
index 39f69f51..4504e573 100644
--- a/tests/testthat/test_saemix_parent.R
+++ b/tests/testthat/test_saemix_parent.R
@@ -5,6 +5,7 @@ test_that("Parent fits using saemix are correctly implemented", {
skip_on_cran()
expect_error(saem(fits), "Only row objects")
+ # SFO
# mmkin_sfo_1 was generated in the setup script
# We did not introduce variance of parent_0 in the data generation
# This is correctly detected
@@ -53,9 +54,16 @@ test_that("Parent fits using saemix are correctly implemented", {
expect_equal(round(s_sfo_nlme_1$confint_back["k_parent", "est."], 3),
round(s_sfo_saem_1$confint_back["k_parent", "est."], 3))
+ # Compare fits
+ expect_known_output(anova(sfo_saem_1, sfo_saem_1_reduced,
+ sfo_saem_1_mkin, sfo_saem_1_reduced_mkin, test = TRUE),
+ file = "anova_sfo_saem.txt"
+ )
+
+ # FOMC
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")
- fomc_saem_2 <- saem(mmkin_fomc_1, quiet = TRUE, transformations = "mkin")
+ fomc_saem_1 <- saem(mmkin_fomc_1, quiet = TRUE, transformations = "saemix", no_random_effect = "parent_0")
+ fomc_saem_2 <- update(fomc_saem_1, transformations = "mkin")
ci_fomc_s1 <- summary(fomc_saem_1)$confint_back
fomc_pop <- as.numeric(fomc_pop)
@@ -70,45 +78,39 @@ test_that("Parent fits using saemix are correctly implemented", {
expect_true(all(ci_fomc_s2[, "lower"] < fomc_pop[2:3]))
expect_true(all(ci_fomc_s2[, "upper"] > fomc_pop[2:3]))
+ # DFOP
+ dfop_saemix_2 <- saem(mmkin_dfop_1, quiet = TRUE, transformations = "saemix",
+ no_random_effect = "parent_0")
+
s_dfop_s1 <- summary(dfop_saemix_1)
s_dfop_s2 <- summary(dfop_saemix_2)
s_dfop_n <- summary(dfop_nlme_1)
dfop_pop <- as.numeric(dfop_pop)
- expect_true(all(s_dfop_s1$confint_back[, "lower"] < dfop_pop))
- expect_true(all(s_dfop_s1$confint_back[, "upper"] > dfop_pop))
+
+ # When using DFOP with mkin transformations, k1 and k2 are sometimes swapped
+ swap_k1_k2 <- function(p) c(p[1], p[3], p[2], 1 - p[4])
+ expect_true(all(s_dfop_s1$confint_back[, "lower"] < swap_k1_k2(dfop_pop)))
+ expect_true(all(s_dfop_s1$confint_back[, "upper"] > swap_k1_k2(dfop_pop)))
expect_true(all(s_dfop_s2$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$mkinmod)
-
- # We get < 20% deviations for parent_0 and k1 by averaging the transformed parameters
- # If we average only parameters passing the t-test, the deviation for k2 is also < 20%
- rel_diff_mmkin <- (dfop_mmkin_means - dfop_pop) / dfop_pop
- rel_diff_mmkin_tested <- (dfop_mmkin_means_tested - dfop_pop) / dfop_pop
- expect_true(all(rel_diff_mmkin[c("parent_0", "k1")] < 0.20))
- expect_true(all(rel_diff_mmkin_tested[c("parent_0", "k1", "k2")] < 0.20))
-
- # We get < 20% deviations with transformations made in mkin
- rel_diff_1 <- (s_dfop_s1$confint_back[, "est."] - dfop_pop) / dfop_pop
+ # We get < 20% deviations with transformations made in mkin (need to swap k1 and k2)
+ rel_diff_1 <- (swap_k1_k2(s_dfop_s1$confint_back[, "est."]) - dfop_pop) / dfop_pop
expect_true(all(rel_diff_1 < 0.20))
# We get < 20% deviations with transformations made in saemix
rel_diff_2 <- (s_dfop_s2$confint_back[, "est."] - dfop_pop) / dfop_pop
expect_true(all(rel_diff_2 < 0.2))
- # We use constant error for SFORB because tc is overparameterised (b.1 is ill-defined in saem)
- mmkin_sforb_2 <- mmkin("SFORB", ds_dfop, quiet = TRUE, error_model = "const", cores = n_cores)
- sforb_saemix_1 <- saem(mmkin_sforb_2, quiet = TRUE,
- no_random_effect = c("parent_free_0", "k_parent_free_bound"),
- transformations = "saemix")
- sforb_saemix_2 <- saem(mmkin_sforb_2, quiet = TRUE,
- no_random_effect = c("parent_free_0", "log_k_parent_free_bound"),
+ # SFORB
+ mmkin_sforb_1 <- mmkin("SFORB", ds_dfop, quiet = TRUE, cores = n_cores)
+ sforb_saemix_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,
+ 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 = 0.03)
@@ -140,7 +142,7 @@ test_that("We can also use mkin solution methods for saem", {
)
skip_on_cran() # 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")
+ solution_type = "analytical", no_random_effect = "parent_0")
distimes_dfop <- endpoints(dfop_saemix_1)$distimes
distimes_dfop_analytical <- endpoints(dfop_saemix_3)$distimes
rel_diff <- abs(distimes_dfop_analytical - distimes_dfop) / distimes_dfop
diff --git a/tests/testthat/test_tests.R b/tests/testthat/test_tests.R
index 39649223..caa0f70a 100644
--- a/tests/testthat/test_tests.R
+++ b/tests/testthat/test_tests.R
@@ -8,6 +8,7 @@ test_that("The lack-of-fit test works and can be reproduced using nls", {
# This code is a slightly modified version of that given in Ritz and Streibig
# (2008) Nonlinear Regression using R, p. 64
+ f_2_anova <- lm(value ~ as.factor(time), data = subset(DFOP_par_c, name == "parent"))
Q <- as.numeric(- 2 * (logLik(f_2_nls) - logLik(f_2_anova)))
df.Q <- df.residual(f_2_nls) - df.residual(f_2_anova)
p_nls <- 1 - pchisq(Q, df.Q)

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