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-rw-r--r--R/mkinfit.R25
-rw-r--r--test.log30
-rw-r--r--tests/testthat/DFOP_FOCUS_C_messages.txt270
-rw-r--r--tests/testthat/FOCUS_2006_C_mkinds.txt0
-rw-r--r--tests/testthat/test_from_max_mean.R17
-rw-r--r--tests/testthat/test_mkinds.R6
-rw-r--r--tests/testthat/test_mkinfit_errors.R71
-rw-r--r--tests/testthat/test_plots_summary_twa.R33
8 files changed, 413 insertions, 39 deletions
diff --git a/R/mkinfit.R b/R/mkinfit.R
index 63455300..b5020418 100644
--- a/R/mkinfit.R
+++ b/R/mkinfit.R
@@ -526,10 +526,13 @@ mkinfit <- function(mkinmod, observed,
}
}
- # Return number of iterations for SANN method
+ # Return number of iterations for SANN method and alert user to check if
+ # the approximation to the optimum is sufficient
if (method.modFit == "SANN") {
fit$iter = maxit.modFit
- if(!quiet) cat("Termination of the SANN algorithm does not imply convergence.\n")
+ fit$warning <- paste0("Termination of the SANN algorithm does not imply convergence.\n",
+ "Make sure the approximation of the optimum is adequate.")
+ warning(fit$warning)
}
# We need to return some more data for summary and plotting
@@ -740,6 +743,16 @@ summary.mkinfit <- function(object, data = TRUE, distimes = TRUE, alpha = 0.05,
ans$errmin <- mkinerrmin(object, alpha = 0.05)
+ if (object$calls > 0) {
+ if (!is.null(ans$cov.unscaled)){
+ Corr <- cov2cor(ans$cov.unscaled)
+ rownames(Corr) <- colnames(Corr) <- rownames(ans$par)
+ ans$Corr <- Corr
+ } else {
+ warning("Could not estimate covariance matrix; singular system.")
+ }
+ }
+
ans$bparms.ode <- object$bparms.ode
ep <- endpoints(object)
if (length(ep$ff) != 0)
@@ -811,13 +824,9 @@ print.summary.mkinfit <- function(x, digits = max(3, getOption("digits") - 3), .
if (x$calls > 0) {
cat("\nParameter correlation:\n")
if (!is.null(x$cov.unscaled)){
- Corr <- cov2cor(x$cov.unscaled)
- rownames(Corr) <- colnames(Corr) <- rownames(x$par)
- print(Corr, digits = digits, ...)
+ print(x$Corr, digits = digits, ...)
} else {
- msg <- "Could not estimate covariance matrix; singular system:\n"
- warning(msg)
- cat(msg)
+ cat("Could not estimate covariance matrix; singular system.")
}
}
diff --git a/test.log b/test.log
index 98303e9b..aee7acf6 100644
--- a/test.log
+++ b/test.log
@@ -3,7 +3,8 @@ Testing mkin
✔ | OK F W S | Context
⠏ | 0 | Export dataset for reading into CAKE ⠋ | 1 | Export dataset for reading into CAKE ✔ | 1 | Export dataset for reading into CAKE
⠏ | 0 | Calculation of FOCUS chi2 error levels ⠋ | 1 | Calculation of FOCUS chi2 error levels ⠙ | 2 | Calculation of FOCUS chi2 error levels ✔ | 2 | Calculation of FOCUS chi2 error levels [2.3 s]
- ⠏ | 0 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ⠋ | 1 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ⠙ | 2 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ⠹ | 3 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ⠸ | 4 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ⠼ | 5 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ⠴ | 6 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ⠦ | 7 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ⠧ | 8 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ⠇ | 9 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ⠏ | 10 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ⠋ | 11 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ⠙ | 12 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ⠹ | 13 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ⠸ | 14 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ✔ | 14 | Results for FOCUS D established in expertise for UBA (Ranke 2014) [8.0 s]
+ ⠏ | 0 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ⠋ | 1 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ⠙ | 2 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ⠹ | 3 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ⠸ | 4 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ⠼ | 5 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ⠴ | 6 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ⠦ | 7 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ⠧ | 8 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ⠇ | 9 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ⠏ | 10 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ⠋ | 11 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ⠙ | 12 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ⠹ | 13 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ✔ | 13 | Results for FOCUS D established in expertise for UBA (Ranke 2014) [7.9 s]
+ ⠏ | 0 | Test fitting the decline of metabolites from their maximum ⠋ | 1 | Test fitting the decline of metabolites from their maximum ⠙ | 2 | Test fitting the decline of metabolites from their maximum ⠹ | 3 | Test fitting the decline of metabolites from their maximum ✔ | 3 | Test fitting the decline of metabolites from their maximum [0.6 s]
⠏ | 0 | Iteratively reweighted least squares (IRLS) fitting ⠋ | 1 | Iteratively reweighted least squares (IRLS) fitting ⠙ | 2 | Iteratively reweighted least squares (IRLS) fitting ⠹ | 2 1 | Iteratively reweighted least squares (IRLS) fitting ✔ | 2 1 | Iteratively reweighted least squares (IRLS) fitting [15.9 s]
────────────────────────────────────────────────────────────────────────────────
test_irls.R:48: skip: Reweighting method 'tc' works
@@ -14,25 +15,26 @@ Too much trouble with datasets that are randomly generated
test_logistic.R:41: skip: The logistic fit can be done via differential equation
Skip slow fit of logistic model using deSolve without compilation
────────────────────────────────────────────────────────────────────────────────
- ⠏ | 0 | Model predictions with mkinpredict ⠋ | 1 | Model predictions with mkinpredict ⠙ | 2 | Model predictions with mkinpredict ⠹ | 3 | Model predictions with mkinpredict ✔ | 3 | Model predictions with mkinpredict [0.3 s]
- ⠏ | 0 | Evaluations according to 2015 NAFTA guidance ⠋ | 1 | Evaluations according to 2015 NAFTA guidance ⠙ | 2 | Evaluations according to 2015 NAFTA guidance ⠹ | 3 | Evaluations according to 2015 NAFTA guidance ⠸ | 4 | Evaluations according to 2015 NAFTA guidance ⠼ | 5 | Evaluations according to 2015 NAFTA guidance ⠴ | 6 | Evaluations according to 2015 NAFTA guidance ⠦ | 7 | Evaluations according to 2015 NAFTA guidance ⠧ | 8 | Evaluations according to 2015 NAFTA guidance ⠇ | 9 | Evaluations according to 2015 NAFTA guidance ⠏ | 9 1 | Evaluations according to 2015 NAFTA guidance ⠏ | 10 | Evaluations according to 2015 NAFTA guidance ⠋ | 10 1 | Evaluations according to 2015 NAFTA guidance ⠙ | 11 1 | Evaluations according to 2015 NAFTA guidance ⠹ | 12 1 | Evaluations according to 2015 NAFTA guidance ⠸ | 13 1 | Evaluations according to 2015 NAFTA guidance ⠼ | 14 1 | Evaluations according to 2015 NAFTA guidance ⠴ | 15 1 | Evaluations according to 2015 NAFTA guidance ⠦ | 16 1 | Evaluations according to 2015 NAFTA guidance ✖ | 16 1 | Evaluations according to 2015 NAFTA guidance [1.5 s]
+ ⠏ | 0 | Test dataset class mkinds used in gmkin ⠋ | 1 | Test dataset class mkinds used in gmkin ✔ | 1 | Test dataset class mkinds used in gmkin
+ ⠏ | 0 | Special cases of mkinfit calls ⠋ | 1 | Special cases of mkinfit calls ⠙ | 2 | Special cases of mkinfit calls ⠹ | 3 | Special cases of mkinfit calls ⠸ | 4 | Special cases of mkinfit calls ⠼ | 5 | Special cases of mkinfit calls ⠴ | 6 | Special cases of mkinfit calls ⠦ | 7 | Special cases of mkinfit calls ⠧ | 8 | Special cases of mkinfit calls ⠇ | 9 | Special cases of mkinfit calls ⠏ | 10 | Special cases of mkinfit calls ⠋ | 11 | Special cases of mkinfit calls ⠙ | 12 | Special cases of mkinfit calls ⠹ | 12 1 | Special cases of mkinfit calls ⠸ | 13 1 | Special cases of mkinfit calls ⠼ | 14 1 | Special cases of mkinfit calls ⠴ | 15 1 | Special cases of mkinfit calls ⠦ | 16 1 | Special cases of mkinfit calls ✔ | 16 1 | Special cases of mkinfit calls [3.0 s]
────────────────────────────────────────────────────────────────────────────────
-test_nafta.R:50: failure: Test data from Appendix D are correctly evaluated
-round(res$distimes[, 1:2]) not equivalent to `dtx_sop`.
-1/6 mismatches
-[5] 5192060 - 5192066 == -6
+test_mkinfit_errors.R:52: skip: mkinfit warns if the user chooses the SANN method
+The SANN algorithm takes very long with the default maximum number of iterations of 10000
────────────────────────────────────────────────────────────────────────────────
- ⠏ | 0 | Fitting of parent only models ⠋ | 1 | Fitting of parent only models ⠙ | 2 | Fitting of parent only models ⠹ | 3 | Fitting of parent only models ⠸ | 4 | Fitting of parent only models ⠼ | 5 | Fitting of parent only models ⠴ | 6 | Fitting of parent only models ⠦ | 7 | Fitting of parent only models ⠧ | 8 | Fitting of parent only models ⠇ | 9 | Fitting of parent only models ⠏ | 10 | Fitting of parent only models ⠋ | 11 | Fitting of parent only models ⠙ | 12 | Fitting of parent only models ⠹ | 13 | Fitting of parent only models ⠸ | 14 | Fitting of parent only models ⠼ | 15 | Fitting of parent only models ⠴ | 16 | Fitting of parent only models ⠦ | 17 | Fitting of parent only models ⠧ | 18 | Fitting of parent only models ⠇ | 19 | Fitting of parent only models ⠏ | 20 | Fitting of parent only models ⠋ | 21 | Fitting of parent only models ✔ | 21 | Fitting of parent only models [23.9 s]
- ⠏ | 0 | Calculation of maximum time weighted average concentrations (TWAs) ⠋ | 1 | Calculation of maximum time weighted average concentrations (TWAs) ⠙ | 2 | Calculation of maximum time weighted average concentrations (TWAs) ⠹ | 3 | Calculation of maximum time weighted average concentrations (TWAs) ⠸ | 4 | Calculation of maximum time weighted average concentrations (TWAs) ⠼ | 5 | Calculation of maximum time weighted average concentrations (TWAs) ⠴ | 6 | Calculation of maximum time weighted average concentrations (TWAs) ⠦ | 7 | Calculation of maximum time weighted average concentrations (TWAs) ⠧ | 8 | Calculation of maximum time weighted average concentrations (TWAs) ✔ | 8 | Calculation of maximum time weighted average concentrations (TWAs) [7.4 s]
+ ⠏ | 0 | Model predictions with mkinpredict ⠋ | 1 | Model predictions with mkinpredict ⠙ | 2 | Model predictions with mkinpredict ⠹ | 3 | Model predictions with mkinpredict ✔ | 3 | Model predictions with mkinpredict [0.3 s]
+ ⠏ | 0 | Evaluations according to 2015 NAFTA guidance ⠋ | 1 | Evaluations according to 2015 NAFTA guidance ⠙ | 2 | Evaluations according to 2015 NAFTA guidance ⠹ | 3 | Evaluations according to 2015 NAFTA guidance ⠸ | 4 | Evaluations according to 2015 NAFTA guidance ⠼ | 5 | Evaluations according to 2015 NAFTA guidance ⠴ | 6 | Evaluations according to 2015 NAFTA guidance ⠦ | 7 | Evaluations according to 2015 NAFTA guidance ⠧ | 8 | Evaluations according to 2015 NAFTA guidance ⠇ | 9 | Evaluations according to 2015 NAFTA guidance ⠏ | 9 1 | Evaluations according to 2015 NAFTA guidance ⠏ | 10 | Evaluations according to 2015 NAFTA guidance ⠋ | 11 | Evaluations according to 2015 NAFTA guidance ⠙ | 12 | Evaluations according to 2015 NAFTA guidance ⠹ | 13 | Evaluations according to 2015 NAFTA guidance ⠸ | 14 | Evaluations according to 2015 NAFTA guidance ⠼ | 15 | Evaluations according to 2015 NAFTA guidance ⠴ | 16 | Evaluations according to 2015 NAFTA guidance ⠦ | 17 | Evaluations according to 2015 NAFTA guidance ✔ | 17 | Evaluations according to 2015 NAFTA guidance [1.5 s]
+ ⠏ | 0 | Fitting of parent only models ⠋ | 1 | Fitting of parent only models ⠙ | 2 | Fitting of parent only models ⠹ | 3 | Fitting of parent only models ⠸ | 4 | Fitting of parent only models ⠼ | 5 | Fitting of parent only models ⠴ | 6 | Fitting of parent only models ⠦ | 7 | Fitting of parent only models ⠧ | 8 | Fitting of parent only models ⠇ | 9 | Fitting of parent only models ⠏ | 10 | Fitting of parent only models ⠋ | 11 | Fitting of parent only models ⠙ | 12 | Fitting of parent only models ⠹ | 13 | Fitting of parent only models ⠸ | 14 | Fitting of parent only models ⠼ | 15 | Fitting of parent only models ⠴ | 16 | Fitting of parent only models ⠦ | 17 | Fitting of parent only models ⠧ | 18 | Fitting of parent only models ⠇ | 19 | Fitting of parent only models ⠏ | 20 | Fitting of parent only models ⠋ | 21 | Fitting of parent only models ✔ | 21 | Fitting of parent only models [23.8 s]
+ ⠏ | 0 | Calculation of maximum time weighted average concentrations (TWAs) ⠋ | 1 | Calculation of maximum time weighted average concentrations (TWAs) ⠙ | 2 | Calculation of maximum time weighted average concentrations (TWAs) ⠹ | 3 | Calculation of maximum time weighted average concentrations (TWAs) ⠸ | 4 | Calculation of maximum time weighted average concentrations (TWAs) ✔ | 4 | Calculation of maximum time weighted average concentrations (TWAs) [3.7 s]
⠏ | 0 | Summary ⠋ | 1 | Summary ✔ | 1 | Summary
⠏ | 0 | Plotting ⠋ | 1 | Plotting ⠙ | 2 | Plotting ⠹ | 3 | Plotting ⠸ | 4 | Plotting ✔ | 4 | Plotting [0.3 s]
- ⠏ | 0 | Complex test case from Schaefer et al. (2007) Piacenza paper ⠋ | 1 | Complex test case from Schaefer et al. (2007) Piacenza paper ⠙ | 2 | Complex test case from Schaefer et al. (2007) Piacenza paper ✔ | 2 | Complex test case from Schaefer et al. (2007) Piacenza paper [5.9 s]
+ ⠏ | 0 | AIC calculation ⠋ | 1 | AIC calculation ⠙ | 2 | AIC calculation ✔ | 2 | AIC calculation
+ ⠏ | 0 | Complex test case from Schaefer et al. (2007) Piacenza paper ⠋ | 1 | Complex test case from Schaefer et al. (2007) Piacenza paper ⠙ | 2 | Complex test case from Schaefer et al. (2007) Piacenza paper ✔ | 2 | Complex test case from Schaefer et al. (2007) Piacenza paper [5.8 s]
⠏ | 0 | Results for synthetic data established in expertise for UBA (Ranke 2014) ⠋ | 1 | Results for synthetic data established in expertise for UBA (Ranke 2014) ⠙ | 2 | Results for synthetic data established in expertise for UBA (Ranke 2014) ⠹ | 3 | Results for synthetic data established in expertise for UBA (Ranke 2014) ⠸ | 4 | Results for synthetic data established in expertise for UBA (Ranke 2014) ✔ | 4 | Results for synthetic data established in expertise for UBA (Ranke 2014) [7.4 s]
══ Results ═════════════════════════════════════════════════════════════════════
-Duration: 74.6 s
+Duration: 74.2 s
-OK: 79
-Failed: 1
+OK: 97
+Failed: 0
Warnings: 0
-Skipped: 2
+Skipped: 3
diff --git a/tests/testthat/DFOP_FOCUS_C_messages.txt b/tests/testthat/DFOP_FOCUS_C_messages.txt
new file mode 100644
index 00000000..7abde0b6
--- /dev/null
+++ b/tests/testthat/DFOP_FOCUS_C_messages.txt
@@ -0,0 +1,270 @@
+parent_0 log_k1 log_k2 g_ilr
+85.1 -2.302585 -4.60517 0
+Model cost at call 1 : 7391.39
+85.1 -2.302585 -4.60517 0
+85.1 -2.302585 -4.60517 0
+Model cost at call 3 : 7391.389
+85.1 -2.302585 -4.60517 0
+Model cost at call 4 : 7391.389
+85.1 -2.302585 -4.60517 1.490116e-08
+85.06371 -1.77328 -4.250366 0.7698268
+Model cost at call 6 : 2000.127
+85.06375 -1.77328 -4.250366 0.7698268
+85.06371 -1.773322 -4.250366 0.7698268
+85.06371 -1.77328 -4.250408 0.7698268
+85.06371 -1.77328 -4.250366 0.7697847
+85.03542 -0.9608523 -4.11546 1.336361
+Model cost at call 11 : 32.97798
+85.03542 -0.9608523 -4.11546 1.336361
+85.03542 -0.9608526 -4.11546 1.336361
+85.03542 -0.9608523 -4.11546 1.336361
+85.03542 -0.9608523 -4.11546 1.336361
+85.03704 -0.256064 -4.273512 0.6447755
+85.03285 -0.7822828 -4.127513 1.312494
+Model cost at call 17 : 5.348133
+85.03286 -0.7822828 -4.127513 1.312494
+Model cost at call 18 : 5.348132
+85.03285 -0.7822828 -4.127513 1.312494
+Model cost at call 19 : 5.348131
+85.03285 -0.7822828 -4.127513 1.312494
+85.03285 -0.7822828 -4.127513 1.312494
+Model cost at call 21 : 5.348131
+85.02325 -0.74968 -4.059 1.14891
+85.03127 -0.7909068 -4.114802 1.268157
+Model cost at call 23 : 4.704445
+85.03127 -0.7909068 -4.114802 1.268157
+Model cost at call 24 : 4.704444
+85.03127 -0.7909068 -4.114802 1.268157
+85.03127 -0.7909068 -4.1148 1.268157
+Model cost at call 26 : 4.704433
+85.03127 -0.7909068 -4.114802 1.268158
+85.03001 -0.7801506 -4.069435 1.262797
+Model cost at call 28 : 4.421625
+85.03001 -0.7801506 -4.069435 1.262797
+85.03001 -0.7801507 -4.069435 1.262797
+Model cost at call 30 : 4.421624
+85.03001 -0.7801506 -4.069435 1.262797
+85.03001 -0.7801506 -4.069435 1.262797
+85.02878 -0.7900844 -4.023945 1.256918
+85.02964 -0.7857352 -4.054587 1.260236
+Model cost at call 34 : 4.414346
+85.02964 -0.7857352 -4.054587 1.260236
+85.02964 -0.7857351 -4.054587 1.260236
+Model cost at call 36 : 4.414346
+85.02964 -0.7857352 -4.054588 1.260236
+85.02964 -0.7857352 -4.054587 1.260236
+85.02812 -0.7778128 -4.042219 1.25389
+Model cost at call 39 : 4.372463
+85.02812 -0.7778128 -4.042219 1.25389
+85.02812 -0.7778129 -4.042219 1.25389
+Model cost at call 41 : 4.372462
+85.02812 -0.7778128 -4.042219 1.25389
+85.02812 -0.7778128 -4.042219 1.25389
+85.02419 -0.7765144 -4.02942 1.245094
+85.0263 -0.7778419 -4.036021 1.249634
+Model cost at call 45 : 4.369313
+85.0263 -0.7778419 -4.036021 1.249634
+85.0263 -0.7778418 -4.036021 1.249634
+Model cost at call 47 : 4.369313
+85.0263 -0.7778419 -4.036022 1.249634
+85.0263 -0.7778419 -4.036021 1.249634
+Model cost at call 49 : 4.369313
+85.02267 -0.7786811 -4.02967 1.252015
+Model cost at call 50 : 4.365062
+85.02268 -0.7786811 -4.02967 1.252015
+85.02267 -0.7786812 -4.02967 1.252015
+85.02267 -0.7786811 -4.02967 1.252015
+Model cost at call 53 : 4.365062
+85.02267 -0.7786811 -4.02967 1.252015
+85.01633 -0.7763163 -4.027611 1.248897
+Model cost at call 55 : 4.364078
+85.01633 -0.7763163 -4.027611 1.248897
+Model cost at call 56 : 4.364078
+85.01633 -0.7763164 -4.027611 1.248897
+Model cost at call 57 : 4.364077
+85.01633 -0.7763163 -4.027611 1.248897
+85.01633 -0.7763163 -4.027611 1.248897
+85.00894 -0.7777917 -4.026307 1.24772
+Model cost at call 60 : 4.364052
+85.00894 -0.7777917 -4.026307 1.24772
+Model cost at call 61 : 4.364052
+85.00894 -0.7777917 -4.026307 1.24772
+Model cost at call 62 : 4.364052
+85.00894 -0.7777917 -4.026307 1.24772
+85.00894 -0.7777917 -4.026307 1.24772
+Model cost at call 64 : 4.364052
+85.00518 -0.7773082 -4.026004 1.248453
+Model cost at call 65 : 4.362751
+85.00519 -0.7773082 -4.026004 1.248453
+85.00518 -0.7773083 -4.026004 1.248453
+Model cost at call 67 : 4.362751
+85.00518 -0.7773082 -4.026005 1.248453
+85.00518 -0.7773082 -4.026004 1.248453
+85.00134 -0.7776046 -4.025878 1.248775
+Model cost at call 70 : 4.362721
+85.00135 -0.7776046 -4.025878 1.248775
+Model cost at call 71 : 4.362721
+85.00134 -0.7776046 -4.025878 1.248775
+Model cost at call 72 : 4.362721
+85.00134 -0.7776046 -4.025878 1.248775
+85.00134 -0.7776046 -4.025878 1.248775
+85.0032 -0.7774734 -4.0257 1.248643
+Model cost at call 75 : 4.362715
+85.0032 -0.7774734 -4.0257 1.248643
+85.0032 -0.7774734 -4.0257 1.248643
+Model cost at call 77 : 4.362715
+85.0032 -0.7774735 -4.0257 1.248643
+85.0032 -0.7774734 -4.0257 1.248643
+85.0032 -0.7774734 -4.0257 1.248643
+85.0032 -0.7774734 -4.0257 1.248643
+85.00249 -0.7774909 -4.025911 1.248679
+Model cost at call 82 : 4.362715
+85.0025 -0.7774909 -4.025911 1.248679
+Model cost at call 83 : 4.362715
+85.00249 -0.7774909 -4.025911 1.248679
+85.00249 -0.7774905 -4.025911 1.248679
+85.00249 -0.7774914 -4.025911 1.248679
+Model cost at call 86 : 4.362715
+85.00249 -0.7774909 -4.025911 1.248679
+85.00249 -0.7774909 -4.025911 1.248679
+85.00249 -0.7774909 -4.025911 1.248679
+85.00249 -0.7774909 -4.025911 1.248679
+85.00274 -0.7774922 -4.025821 1.248672
+Model cost at call 91 : 4.362714
+85.00274 -0.7774922 -4.025821 1.248672
+85.00274 -0.7774922 -4.025821 1.248672
+Model cost at call 93 : 4.362714
+85.00274 -0.7774921 -4.025821 1.248672
+Model cost at call 94 : 4.362714
+85.00274 -0.7774922 -4.025821 1.248672
+85.00274 -0.7774922 -4.025821 1.248672
+85.00274 -0.7774922 -4.025821 1.248672
+85.00274 -0.7774922 -4.025821 1.248672
+85.00274 -0.7774922 -4.025821 1.248672
+85.00273 -0.7774912 -4.025817 1.24867
+Model cost at call 100 : 4.362714
+85.00275 -0.7774912 -4.025817 1.24867
+85.00271 -0.7774912 -4.025817 1.24867
+85.00273 -0.7774905 -4.025817 1.24867
+85.00273 -0.7774919 -4.025817 1.24867
+85.00273 -0.7774912 -4.025814 1.24867
+85.00273 -0.7774912 -4.025821 1.24867
+85.00273 -0.7774912 -4.025817 1.248671
+85.00273 -0.7774912 -4.025817 1.248669
+85.00274 -0.7774913 -4.025819 1.248671
+Model cost at call 109 : 4.362714
+85.00276 -0.7774913 -4.025819 1.248671
+85.00272 -0.7774913 -4.025819 1.248671
+85.00274 -0.7774904 -4.025819 1.248671
+85.00274 -0.7774922 -4.025819 1.248671
+85.00274 -0.7774913 -4.025815 1.248671
+85.00274 -0.7774913 -4.025822 1.248671
+85.00274 -0.7774913 -4.025819 1.248672
+85.00274 -0.7774913 -4.025819 1.248669
+85.00274 -0.7774913 -4.025819 1.248671
+85.00274 -0.7774913 -4.025819 1.248671
+85.00274 -0.7774913 -4.025819 1.248671
+85.00274 -0.7774913 -4.025819 1.248671
+85.00274 -0.7774913 -4.025819 1.248671
+85.00274 -0.7774913 -4.025819 1.248671
+Model cost at call 123 : 4.362714
+85.00274 -0.7774913 -4.025819 1.248671
+85.00274 -0.7774913 -4.025819 1.248671
+85.00274 -0.7774913 -4.025819 1.248671
+85.00274 -0.7774913 -4.025819 1.248671
+85.00274 -0.7774913 -4.025819 1.248671
+IRLS based on variance estimates according to the two component error model
+Initial variance components are:
+sigma_low rsd_high
+ 1.08984 0.00000
+85.00274 -0.7774913 -4.025819 1.248671
+Model cost at call 129 : 3.673061
+85.00274 -0.7774913 -4.025819 1.248671
+85.00274 -0.7774913 -4.025819 1.248671
+85.00274 -0.7774913 -4.025819 1.248671
+Model cost at call 132 : 3.673061
+85.00274 -0.7774913 -4.025819 1.248671
+85.00273 -0.7775309 -4.025818 1.24866
+85.00274 -0.7774953 -4.025819 1.24867
+85.00274 -0.7774917 -4.025819 1.248671
+85.00274 -0.7774914 -4.025819 1.248671
+85.00274 -0.7774913 -4.025819 1.248671
+Model cost at call 138 : 3.673061
+85.00277 -0.7774913 -4.025819 1.248671
+85.0027 -0.7774913 -4.025819 1.248671
+85.00274 -0.7774744 -4.025819 1.248671
+85.00274 -0.7775083 -4.025819 1.248671
+85.00274 -0.7774913 -4.025779 1.248671
+85.00274 -0.7774913 -4.025858 1.248671
+85.00274 -0.7774913 -4.025819 1.248702
+85.00274 -0.7774913 -4.025819 1.248639
+85.00274 -0.7774913 -4.025819 1.248671
+Model cost at call 147 : 3.673061
+85.00276 -0.7774913 -4.025819 1.248671
+85.00271 -0.7774913 -4.025819 1.248671
+85.00274 -0.7774906 -4.025819 1.248671
+85.00274 -0.7774921 -4.025819 1.248671
+85.00274 -0.7774913 -4.025812 1.248671
+85.00274 -0.7774913 -4.025825 1.248671
+85.00274 -0.7774913 -4.025819 1.248672
+85.00274 -0.7774913 -4.025819 1.248669
+85.00274 -0.7774913 -4.025819 1.248671
+85.00274 -0.7774913 -4.025819 1.248671
+85.00274 -0.7774913 -4.025819 1.248671
+85.00274 -0.7774913 -4.025819 1.248671
+Model cost at call 159 : 3.673061
+85.00274 -0.7774913 -4.025819 1.248671
+85.00274 -0.7774913 -4.025819 1.248671
+85.00274 -0.7774913 -4.025819 1.248671
+85.00274 -0.7774913 -4.025819 1.248671
+85.00274 -0.7774913 -4.025819 1.248671
+85.00274 -0.7774913 -4.025819 1.248671
+85.00274 -0.7774913 -4.025819 1.248671
+Iteration 1 yields variance estimates:
+sigma_low rsd_high
+0.7434091 0.0000000
+Sum of squared differences to last variance (component) estimates: 0.12
+85.00274 -0.7774913 -4.025819 1.248671
+85.00274 -0.7774913 -4.025819 1.248671
+85.00274 -0.7774913 -4.025819 1.248671
+85.00274 -0.7774913 -4.025819 1.248671
+85.00274 -0.7774913 -4.025819 1.248671
+85.00273 -0.7775366 -4.025821 1.248652
+85.00274 -0.7774959 -4.025819 1.248669
+85.00274 -0.7774918 -4.025819 1.24867
+85.00274 -0.7774914 -4.025819 1.248671
+85.00274 -0.7774913 -4.025819 1.248671
+85.00274 -0.7774913 -4.025819 1.248671
+85.00279 -0.7774913 -4.025819 1.248671
+85.00268 -0.7774913 -4.025819 1.248671
+85.00274 -0.7774621 -4.025819 1.248671
+85.00274 -0.7775206 -4.025819 1.248671
+85.00274 -0.7774913 -4.025761 1.248671
+85.00274 -0.7774913 -4.025876 1.248671
+85.00274 -0.7774913 -4.025819 1.248714
+85.00274 -0.7774913 -4.025819 1.248627
+85.00274 -0.7774913 -4.025819 1.248671
+85.00274 -0.7774913 -4.025819 1.248671
+85.00273 -0.7774913 -4.025819 1.248671
+85.00274 -0.7774908 -4.025819 1.248671
+85.00274 -0.7774919 -4.025819 1.248671
+85.00274 -0.7774913 -4.025816 1.248671
+85.00274 -0.7774913 -4.025822 1.248671
+85.00274 -0.7774913 -4.025819 1.248672
+85.00274 -0.7774913 -4.025819 1.24867
+85.00274 -0.7774913 -4.025819 1.248671
+85.00274 -0.7774913 -4.025819 1.248671
+85.00274 -0.7774913 -4.025819 1.248671
+85.00274 -0.7774913 -4.025819 1.248671
+85.00274 -0.7774913 -4.025819 1.248671
+85.00274 -0.7774913 -4.025819 1.248671
+85.00274 -0.7774913 -4.025819 1.248671
+85.00274 -0.7774913 -4.025819 1.248671
+85.00274 -0.7774913 -4.025819 1.248671
+85.00274 -0.7774913 -4.025819 1.248671
+85.00274 -0.7774913 -4.025819 1.248671
+Iteration 2 yields variance estimates:
+sigma_low rsd_high
+0.7434091 0.0000000
+Sum of squared differences to last variance (component) estimates: 2.9e-16
+Optimisation by method Port successfully terminated.
diff --git a/tests/testthat/FOCUS_2006_C_mkinds.txt b/tests/testthat/FOCUS_2006_C_mkinds.txt
new file mode 100644
index 00000000..e69de29b
--- /dev/null
+++ b/tests/testthat/FOCUS_2006_C_mkinds.txt
diff --git a/tests/testthat/test_from_max_mean.R b/tests/testthat/test_from_max_mean.R
new file mode 100644
index 00000000..8e5953b9
--- /dev/null
+++ b/tests/testthat/test_from_max_mean.R
@@ -0,0 +1,17 @@
+context("Test fitting the decline of metabolites from their maximum")
+
+test_that("Fitting from maximum mean value works", {
+ SFO_SFO <- mkinmod(parent = mkinsub("SFO", "m1"),
+ m1 = mkinsub("SFO"))
+ expect_error(mkinfit(SFO_SFO, FOCUS_2006_D, from_max_mean = TRUE))
+
+ # We can either explicitly create a model for m1, or subset the data
+ SFO_m1 <- mkinmod(m1 = mkinsub("SFO"))
+ f.1 <- mkinfit(SFO_m1, FOCUS_2006_D, from_max_mean = TRUE, quiet = TRUE)
+ expect_equivalent(endpoints(f.1)$distimes["m1", ], c(170.8, 567.5),
+ scale = 1, tolerance = 0.1)
+
+ f.2 <- mkinfit("SFO", subset(FOCUS_2006_D, name == "m1"), from_max_mean = TRUE, quiet = TRUE)
+ expect_equivalent(endpoints(f.2)$distimes["m1", ], c(170.8, 567.5),
+ scale = 1, tolerance = 0.1)
+})
diff --git a/tests/testthat/test_mkinds.R b/tests/testthat/test_mkinds.R
new file mode 100644
index 00000000..e017301c
--- /dev/null
+++ b/tests/testthat/test_mkinds.R
@@ -0,0 +1,6 @@
+context("Test dataset class mkinds used in gmkin")
+
+test_that("An mkinds object can be created and printed", {
+ testdata <- mkinds$new("FOCUS C", data = FOCUS_2006_C, time_unit = "days", unit = "%AR")
+ expect_known_output(testdata, "FOCUS_2006_C_mkinds.txt")
+})
diff --git a/tests/testthat/test_mkinfit_errors.R b/tests/testthat/test_mkinfit_errors.R
new file mode 100644
index 00000000..50032628
--- /dev/null
+++ b/tests/testthat/test_mkinfit_errors.R
@@ -0,0 +1,71 @@
+library(mkin)
+library(testthat)
+
+context("Special cases of mkinfit calls")
+
+SFO_SFO.ff.nosink <- mkinmod(
+ parent = mkinsub("SFO", "m1", sink = FALSE),
+ m1 = mkinsub("SFO"), quiet = TRUE, use_of_ff = "max")
+
+SFO_SFO.ff <- mkinmod(
+ parent = mkinsub("SFO", "m1"),
+ m1 = mkinsub("SFO"), quiet = TRUE, use_of_ff = "max")
+
+test_that("mkinfit stops to prevent and/or explain user errors", {
+ expect_error(mkinfit("foo", FOCUS_2006_A))
+ expect_error(mkinfit(3, FOCUS_2006_A))
+
+ # We get a warning if we use transform_fractions = FALSE with formation fractions
+ # and an error if any pathway to sink is turned off as well
+ expect_warning(
+ expect_error(
+ mkinfit(SFO_SFO.ff.nosink, FOCUS_2006_D, transform_fractions = FALSE, quiet = TRUE),
+ "turn off pathways to sink"
+ ),
+ "sum of formation fractions")
+
+ expect_error(mkinfit(SFO_SFO.ff, FOCUS_2006_D, transform_fractions = TRUE,
+ parms.ini = c(f_parent_to_m1 = 0.5), fixed_parms = "f_parent_to_m1", quiet = TRUE),
+ "not supported")
+
+ expect_error(mkinfit(SFO_SFO.ff, FOCUS_2006_D,
+ parms.ini = c(f_parent_to_m1 = 1.1), quiet = TRUE),
+ "sum up to more than 1")
+
+ expect_error(mkinfit(SFO_SFO.ff, FOCUS_2006_D, solution_type = "analytical"), "not implemented")
+
+ expect_error(mkinfit("FOMC", FOCUS_2006_A, solution_type = "eigen"), "coefficient matrix not present")
+
+ # We suppress a message stemming from the interrupted call to system.time()
+ expect_error(suppressMessages(mkinfit("SFO", FOCUS_2006_A, reweight.method =
+ "foo", quiet = TRUE), "implemented"))
+
+})
+
+test_that("mkinfit stops early when a low maximum number of iterations is specified", {
+ expect_warning(mkinfit("SFO", FOCUS_2006_A, maxit.modFit = 1, quiet = TRUE))
+ expect_warning(mkinfit("SFO", FOCUS_2006_A, maxit.modFit = 1, quiet = TRUE, method.modFit = "Marq"))
+})
+
+test_that("mkinfit warns if the user chooses the SANN method", {
+ expect_warning(mkinfit("SFO", FOCUS_2006_A, method.modFit = "SANN", maxit.modFit = 10, quiet = TRUE))
+ skip("The SANN algorithm takes very long with the default maximum number of iterations of 10000")
+ expect_warning(mkinfit("SFO", FOCUS_2006_A, method.modFit = "SANN"))
+})
+
+test_that("mkinfit warns if a specified initial parameter value is not in the model", {
+ expect_warning(mkinfit("SFO", FOCUS_2006_A, parms.ini = c(k_xy = 0.1), quiet = TRUE))
+})
+
+test_that("We get reproducible output if quiet = FALSE", {
+ # We cannot expect parameter and sum of squares traces to be the same across platforms
+ skip_on_cran()
+ skip_on_travis()
+ expect_known_output(mkinfit("DFOP", FOCUS_2006_C, reweight.method = "tc", trace_parms = TRUE),
+ file = "DFOP_FOCUS_C_messages.txt")
+})
+
+test_that("We get warnings in case of overparameterisation", {
+ expect_warning(f <- mkinfit("FOMC", FOCUS_2006_A, quiet = TRUE), "not converge")
+ s2 <- expect_warning(summary(mkinfit("DFOP", FOCUS_2006_A, quiet = TRUE)), "singular system")
+})
diff --git a/tests/testthat/test_plots_summary_twa.R b/tests/testthat/test_plots_summary_twa.R
index e13111bb..f58ce764 100644
--- a/tests/testthat/test_plots_summary_twa.R
+++ b/tests/testthat/test_plots_summary_twa.R
@@ -28,23 +28,22 @@ test_that("Time weighted average concentrations are correct", {
outtimes_10 <- seq(0, 10, length.out = 10000)
- for (ds in c("FOCUS_C", "FOCUS_D")) {
- for (model in models) {
- fit <- fits[[model, ds]]
- bpar <- summary(fit)$bpar[, "Estimate"]
- pred_10 <- mkinpredict(fit$mkinmod,
- odeparms = bpar[2:length(bpar)],
- odeini = c(parent = bpar[[1]]),
- outtimes = outtimes_10)
- twa_num <- mean(pred_10$parent)
- names(twa_num) <- 10
- twa_ana <- max_twa_parent(fit, 10)
-
- # Test for absolute difference (scale = 1)
- # The tolerance can be reduced if the length of outtimes is increased,
- # but this needs more computing time so we stay with lenght.out = 10k
- expect_equal(twa_num, twa_ana, tolerance = 0.003, scale = 1)
- }
+ ds <- "FOCUS_C"
+ for (model in models) {
+ fit <- fits[[model, ds]]
+ bpar <- summary(fit)$bpar[, "Estimate"]
+ pred_10 <- mkinpredict(fit$mkinmod,
+ odeparms = bpar[2:length(bpar)],
+ odeini = c(parent = bpar[[1]]),
+ outtimes = outtimes_10)
+ twa_num <- mean(pred_10$parent)
+ names(twa_num) <- 10
+ twa_ana <- max_twa_parent(fit, 10)
+
+ # Test for absolute difference (scale = 1)
+ # The tolerance can be reduced if the length of outtimes is increased,
+ # but this needs more computing time so we stay with lenght.out = 10k
+ expect_equal(twa_num, twa_ana, tolerance = 0.003, scale = 1)
}
})

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