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-rw-r--r--R/mkinfit.R14
-rw-r--r--test.log41
-rw-r--r--tests/testthat/DFOP_FOCUS_C_messages.txt90
-rw-r--r--tests/testthat/FOCUS_2006_D.csf2
-rw-r--r--tests/testthat/summary_DFOP_FOCUS_C.txt6
-rw-r--r--tests/testthat/test_FOCUS_D_UBA_expertise.R19
-rw-r--r--tests/testthat/test_logistic.R10
7 files changed, 70 insertions, 112 deletions
diff --git a/R/mkinfit.R b/R/mkinfit.R
index 55b57aa6..754e72b8 100644
--- a/R/mkinfit.R
+++ b/R/mkinfit.R
@@ -327,7 +327,8 @@ mkinfit <- function(mkinmod, observed,
if (nlogLik < nlogLik.current) {
assign("nlogLik.current", nlogLik, inherits = TRUE)
- if (!quiet) cat("Negative log-likelihood at call ", calls, ": ", nlogLik.current, "\n", sep = "")
+ if (!quiet) cat(ifelse(OLS, "Sum of squared residuals", "Negative log-likelihood"),
+ " at call ", calls, ": ", nlogLik.current, "\n", sep = "")
}
return(nlogLik)
}
@@ -593,6 +594,7 @@ summary.mkinfit <- function(object, data = TRUE, distimes = TRUE, alpha = 0.05,
use_of_ff = object$mkinmod$use_of_ff,
df = c(p, rdf),
cov.unscaled = covar,
+ err_mod = object$err_mod,
#cov.scaled = covar * resvar,
niter = object$iterations,
calls = object$calls,
@@ -657,11 +659,13 @@ print.summary.mkinfit <- function(x, digits = max(3, getOption("digits") - 3), .
cat("\nModel predictions using solution type", x$solution_type, "\n")
- cat("\nFitted with method", x$method.modFit,
- "using", x$calls, "model solutions performed in", x$time[["elapsed"]], "s\n")
+ cat("\nFitted using", x$calls, "model solutions performed in", x$time[["elapsed"]], "s\n")
cat("\nError model:\n")
- print(x$err_mod)
+ cat(switch(x$err_mod,
+ const = "Constant variance",
+ obs = "Variance unique to each observed variable",
+ tc = "Two-component variance function"), "\n")
cat("\nStarting values for parameters to be optimised:\n")
print(x$start)
@@ -696,7 +700,7 @@ print.summary.mkinfit <- function(x, digits = max(3, getOption("digits") - 3), .
print(signif(x$bpar[, c(1, 3, 4, 5, 6)], digits = digits))
}
- cat("\nChi2 error levels in percent:\n")
+ cat("\nFOCUS Chi2 error levels in percent:\n")
x$errmin$err.min <- 100 * x$errmin$err.min
print(x$errmin, digits=digits,...)
diff --git a/test.log b/test.log
index 58c819f4..bcdb550a 100644
--- a/test.log
+++ b/test.log
@@ -2,45 +2,28 @@ Loading mkin
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 | Error model fitting ⠋ | 1 | Error model fitting ⠙ | 2 | Error model fitting ⠹ | 3 | Error model fitting ⠸ | 4 | Error model fitting ⠼ | 5 | Error model fitting ⠴ | 6 | Error model fitting ⠦ | 7 | Error model fitting ⠧ | 8 | Error model fitting ⠇ | 9 | Error model fitting ⠏ | 9 1 | Error model fitting ⠋ | 10 1 | Error model fitting ⠙ | 10 2 | Error model fitting ⠙ | 10 2 | Error model fitting ⠙ | 10 2 | Error model fitting ⠙ | 10 2 | Error model fitting ⠙ | 10 2 | Error model fitting ⠙ | 10 2 | Error model fitting ⠙ | 10 2 | Error model fitting ⠙ | 10 2 | Error model fitting ⠙ | 10 2 | Error model fitting ⠙ | 10 2 | Error model fitting ⠙ | 10 2 | Error model fitting ⠙ | 10 2 | Error model fitting ⠙ | 10 2 | Error model fitting ⠙ | 10 2 ⠙ | 10 | Error model fitting2 | Error model fitting ⠙ | 11 1 | Error model fitting ⠹ | 12 1 | Error model fitting ✔ | 12 1 | Error model fitting [163.7 s]
-────────────────────────────────────────────────────────────────────────────────
-test_error_models.R:148: warning: Reweighting method 'tc' produces reasonable variance estimates
-Observations with value of zero were removed from the data
-────────────────────────────────────────────────────────────────────────────────
- ⠏ | 0 | Calculation of FOCUS chi2 error levels ⠋ | 1 | Calculation of FOCUS chi2 error levels ⠙ | 2 | Calculation of FOCUS chi2 error levels ⠹ | 3 | Calculation of FOCUS chi2 error levels ✔ | 3 | Calculation of FOCUS chi2 error levels [2.4 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 1 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ✔ | 13 1 | Results for FOCUS D established in expertise for UBA (Ranke 2014) [8.3 s]
-────────────────────────────────────────────────────────────────────────────────
-test_FOCUS_D_UBA_expertise.R:89: skip: The t-value for fits using internal transformations corresponds with results from FME, synthetic data
-Hessian matrices and df calculations differ from those in FME
-────────────────────────────────────────────────────────────────────────────────
+ ⠏ | 0 | Error model fitting ⠋ | 1 | Error model fitting ⠙ | 2 | Error model fitting ⠹ | 3 | Error model fitting ⠸ | 4 | Error model fitting ⠼ | 5 | Error model fitting ⠴ | 6 | Error model fitting ⠦ | 7 | Error model fitting ⠧ | 8 | Error model fitting ⠇ | 9 | Error model fitting ⠏ | 10 | Error model fitting ⠋ | 11 | Error model fitting ⠙ | 12 | Error model fitting ✔ | 12 | Error model fitting [164.6 s]
+ ⠏ | 0 | Calculation of FOCUS chi2 error levels ⠋ | 1 | Calculation of FOCUS chi2 error levels ⠙ | 2 | Calculation of FOCUS chi2 error levels ⠹ | 3 | Calculation of FOCUS chi2 error levels ✔ | 3 | 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) ✔ | 13 | Results for FOCUS D established in expertise for UBA (Ranke 2014) [3.8 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 ⠸ | 4 | Test fitting the decline of metabolites from their maximum ⠼ | 5 | Test fitting the decline of metabolites from their maximum ⠴ | 6 | Test fitting the decline of metabolites from their maximum ✔ | 6 | Test fitting the decline of metabolites from their maximum [0.9 s]
- ⠏ | 0 | Fitting the logistic model ⠋ | 1 | Fitting the logistic model ⠙ | 1 1 | Fitting the logistic model ✔ | 1 1 | Fitting the logistic model [0.9 s]
-────────────────────────────────────────────────────────────────────────────────
-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 | Fitting the logistic model ⠋ | 1 | Fitting the logistic model ✔ | 1 | Fitting the logistic model [1.0 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 ⠇ | 8 1 | Special cases of mkinfit calls ⠏ | 9 1 | Special cases of mkinfit calls ⠋ | 10 1 | Special cases of mkinfit calls ⠙ | 11 1 | Special cases of mkinfit calls ⠹ | 12 1 | Special cases of mkinfit calls ✔ | 12 1 | Special cases of mkinfit calls [3.2 s]
-────────────────────────────────────────────────────────────────────────────────
-test_mkinfit_errors.R:59: warning: mkinfit stops early when a low maximum number of iterations is specified
-Optimisation did not converge:
-iteration limit reached without convergence (10)
-────────────────────────────────────────────────────────────────────────────────
+ ⠏ | 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 | Special cases of mkinfit calls [2.8 s]
⠏ | 0 | mkinmod model generation and printing ⠋ | 1 | mkinmod model generation and printing ⠙ | 2 | mkinmod model generation and printing ⠹ | 3 | mkinmod model generation and printing ⠸ | 4 | mkinmod model generation and printing ⠼ | 5 | mkinmod model generation and printing ⠴ | 6 | mkinmod model generation and printing ⠦ | 7 | mkinmod model generation and printing ⠧ | 8 | mkinmod model generation and printing ⠇ | 9 | mkinmod model generation and printing ✔ | 9 | mkinmod model generation and printing [0.2 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 | Model predictions with mkinpredict ⠋ | 1 | Model predictions with mkinpredict ⠙ | 2 | Model predictions with mkinpredict ⠹ | 3 | Model predictions with mkinpredict ✔ | 3 | Model predictions with mkinpredict [0.4 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 ⠏ | 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 ✔ | 16 | Evaluations according to 2015 NAFTA guidance [4.1 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 [58.6 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 [58.1 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 | 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 [5.6 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]
+ ⠏ | 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 [5.5 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) [7.2 s]
══ Results ═════════════════════════════════════════════════════════════════════
-Duration: 265.8 s
+Duration: 261.0 s
OK: 115
Failed: 0
-Warnings: 2
-Skipped: 2
+Warnings: 0
+Skipped: 0
diff --git a/tests/testthat/DFOP_FOCUS_C_messages.txt b/tests/testthat/DFOP_FOCUS_C_messages.txt
index 5327b3f6..9abde683 100644
--- a/tests/testthat/DFOP_FOCUS_C_messages.txt
+++ b/tests/testthat/DFOP_FOCUS_C_messages.txt
@@ -1,148 +1,148 @@
parent_0 log_k1 log_k2 g_ilr sigma
85.1 -2.302585 -4.60517 0
-Negative log-likelihood at call 1: 7391.39
+Sum of squared residuals at call 1: 7391.39
85.1 -2.302585 -4.60517 0
85.1 -2.302585 -4.60517 0
-Negative log-likelihood at call 3: 7391.389
+Sum of squared residuals at call 3: 7391.389
85.1 -2.302585 -4.60517 0
-Negative log-likelihood at call 4: 7391.389
+Sum of squared residuals at call 4: 7391.389
85.1 -2.302585 -4.60517 1.490116e-08
85.06371 -1.77328 -4.250366 0.7698268
-Negative log-likelihood at call 6: 2000.127
+Sum of squared residuals 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
-Negative log-likelihood at call 11: 32.97798
+Sum of squared residuals 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
-Negative log-likelihood at call 17: 5.348133
+Sum of squared residuals at call 17: 5.348133
85.03286 -0.7822828 -4.127513 1.312494
-Negative log-likelihood at call 18: 5.348132
+Sum of squared residuals at call 18: 5.348132
85.03285 -0.7822828 -4.127513 1.312494
-Negative log-likelihood at call 19: 5.348131
+Sum of squared residuals at call 19: 5.348131
85.03285 -0.7822828 -4.127513 1.312494
85.03285 -0.7822828 -4.127513 1.312494
-Negative log-likelihood at call 21: 5.348131
+Sum of squared residuals at call 21: 5.348131
85.02325 -0.74968 -4.059 1.14891
85.03127 -0.7909068 -4.114802 1.268157
-Negative log-likelihood at call 23: 4.704445
+Sum of squared residuals at call 23: 4.704445
85.03127 -0.7909068 -4.114802 1.268157
-Negative log-likelihood at call 24: 4.704444
+Sum of squared residuals at call 24: 4.704444
85.03127 -0.7909068 -4.114802 1.268157
85.03127 -0.7909068 -4.1148 1.268157
-Negative log-likelihood at call 26: 4.704433
+Sum of squared residuals at call 26: 4.704433
85.03127 -0.7909068 -4.114802 1.268158
85.03001 -0.7801506 -4.069435 1.262797
-Negative log-likelihood at call 28: 4.421625
+Sum of squared residuals at call 28: 4.421625
85.03001 -0.7801506 -4.069435 1.262797
85.03001 -0.7801507 -4.069435 1.262797
-Negative log-likelihood at call 30: 4.421624
+Sum of squared residuals 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
-Negative log-likelihood at call 34: 4.414346
+Sum of squared residuals at call 34: 4.414346
85.02964 -0.7857352 -4.054587 1.260236
85.02964 -0.7857351 -4.054587 1.260236
-Negative log-likelihood at call 36: 4.414346
+Sum of squared residuals 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
-Negative log-likelihood at call 39: 4.372463
+Sum of squared residuals at call 39: 4.372463
85.02812 -0.7778128 -4.042219 1.25389
85.02812 -0.7778129 -4.042219 1.25389
-Negative log-likelihood at call 41: 4.372462
+Sum of squared residuals 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
-Negative log-likelihood at call 45: 4.369313
+Sum of squared residuals at call 45: 4.369313
85.0263 -0.7778419 -4.036021 1.249634
85.0263 -0.7778418 -4.036021 1.249634
-Negative log-likelihood at call 47: 4.369313
+Sum of squared residuals at call 47: 4.369313
85.0263 -0.7778419 -4.036022 1.249634
85.0263 -0.7778419 -4.036021 1.249634
-Negative log-likelihood at call 49: 4.369313
+Sum of squared residuals at call 49: 4.369313
85.02267 -0.7786811 -4.02967 1.252015
-Negative log-likelihood at call 50: 4.365062
+Sum of squared residuals 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
-Negative log-likelihood at call 53: 4.365062
+Sum of squared residuals at call 53: 4.365062
85.02267 -0.7786811 -4.02967 1.252015
85.01633 -0.7763163 -4.027611 1.248897
-Negative log-likelihood at call 55: 4.364078
+Sum of squared residuals at call 55: 4.364078
85.01633 -0.7763163 -4.027611 1.248897
-Negative log-likelihood at call 56: 4.364078
+Sum of squared residuals at call 56: 4.364078
85.01633 -0.7763164 -4.027611 1.248897
-Negative log-likelihood at call 57: 4.364077
+Sum of squared residuals 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
-Negative log-likelihood at call 60: 4.364052
+Sum of squared residuals at call 60: 4.364052
85.00894 -0.7777917 -4.026307 1.24772
-Negative log-likelihood at call 61: 4.364052
+Sum of squared residuals at call 61: 4.364052
85.00894 -0.7777917 -4.026307 1.24772
-Negative log-likelihood at call 62: 4.364052
+Sum of squared residuals at call 62: 4.364052
85.00894 -0.7777917 -4.026307 1.24772
85.00894 -0.7777917 -4.026307 1.24772
-Negative log-likelihood at call 64: 4.364052
+Sum of squared residuals at call 64: 4.364052
85.00518 -0.7773082 -4.026004 1.248453
-Negative log-likelihood at call 65: 4.362751
+Sum of squared residuals at call 65: 4.362751
85.00519 -0.7773082 -4.026004 1.248453
85.00518 -0.7773083 -4.026004 1.248453
-Negative log-likelihood at call 67: 4.362751
+Sum of squared residuals 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
-Negative log-likelihood at call 70: 4.362721
+Sum of squared residuals at call 70: 4.362721
85.00135 -0.7776046 -4.025878 1.248775
-Negative log-likelihood at call 71: 4.362721
+Sum of squared residuals at call 71: 4.362721
85.00134 -0.7776046 -4.025878 1.248775
-Negative log-likelihood at call 72: 4.362721
+Sum of squared residuals 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
-Negative log-likelihood at call 75: 4.362715
+Sum of squared residuals at call 75: 4.362715
85.0032 -0.7774734 -4.0257 1.248643
85.0032 -0.7774734 -4.0257 1.248643
-Negative log-likelihood at call 77: 4.362715
+Sum of squared residuals 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
-Negative log-likelihood at call 82: 4.362715
+Sum of squared residuals at call 82: 4.362715
85.0025 -0.7774909 -4.025911 1.248679
-Negative log-likelihood at call 83: 4.362715
+Sum of squared residuals 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
-Negative log-likelihood at call 86: 4.362715
+Sum of squared residuals 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
-Negative log-likelihood at call 91: 4.362714
+Sum of squared residuals at call 91: 4.362714
85.00274 -0.7774922 -4.025821 1.248672
85.00274 -0.7774922 -4.025821 1.248672
-Negative log-likelihood at call 93: 4.362714
+Sum of squared residuals at call 93: 4.362714
85.00274 -0.7774921 -4.025821 1.248672
-Negative log-likelihood at call 94: 4.362714
+Sum of squared residuals 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
-Negative log-likelihood at call 100: 4.362714
+Sum of squared residuals 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
@@ -152,7 +152,7 @@ Negative log-likelihood at call 100: 4.362714
85.00273 -0.7774912 -4.025817 1.248671
85.00273 -0.7774912 -4.025817 1.248669
85.00274 -0.7774913 -4.025819 1.248671
-Negative log-likelihood at call 109: 4.362714
+Sum of squared residuals 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
diff --git a/tests/testthat/FOCUS_2006_D.csf b/tests/testthat/FOCUS_2006_D.csf
index 84500b54..9e912a28 100644
--- a/tests/testthat/FOCUS_2006_D.csf
+++ b/tests/testthat/FOCUS_2006_D.csf
@@ -5,7 +5,7 @@ Description:
MeasurementUnits: % AR
TimeUnits: days
Comments: Created using mkin::CAKE_export
-Date: 2019-04-10
+Date: 2019-04-24
Optimiser: IRLS
[Data]
diff --git a/tests/testthat/summary_DFOP_FOCUS_C.txt b/tests/testthat/summary_DFOP_FOCUS_C.txt
index 1d669d43..b0a6bb6d 100644
--- a/tests/testthat/summary_DFOP_FOCUS_C.txt
+++ b/tests/testthat/summary_DFOP_FOCUS_C.txt
@@ -10,10 +10,10 @@ d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
Model predictions using solution type analytical
-Fitted with method using test 0 model solutions performed in test time 0 s
+Fitted using test 0 model solutions performed in test time 0 s
Error model:
-NULL
+Constant variance
Starting values for parameters to be optimised:
value type
@@ -61,7 +61,7 @@ k2 0.01785 7.636 7.901e-04 0.01241 0.02568
g 0.85390 83.310 6.221e-08 0.82310 0.88020
sigma 0.69620 4.243 6.618e-03 0.24060 1.15200
-Chi2 error levels in percent:
+FOCUS Chi2 error levels in percent:
err.min n.optim df
All data 2.661 4 5
parent 2.661 4 5
diff --git a/tests/testthat/test_FOCUS_D_UBA_expertise.R b/tests/testthat/test_FOCUS_D_UBA_expertise.R
index 3a49078c..a282f5e7 100644
--- a/tests/testthat/test_FOCUS_D_UBA_expertise.R
+++ b/tests/testthat/test_FOCUS_D_UBA_expertise.R
@@ -74,22 +74,3 @@ test_that("Fits without internal transformations are correct for FOCUS D", {
# References:
# Ranke (2014) Prüfung und Validierung von Modellierungssoftware als Alternative
# zu ModelMaker 4.0, Umweltbundesamt Projektnummer 27452
-
-test_that("The t-value for fits using internal transformations corresponds with results from FME, synthetic data", {
- skip_on_cran()
- m_synth_DFOP_par.minff <- mkinmod(parent = mkinsub("DFOP", c("M1", "M2")),
- M1 = mkinsub("SFO"),
- M2 = mkinsub("SFO"),
- use_of_ff = "min", quiet = TRUE)
-
- fit_DFOP_par_c_2 <- mkinfit(m_synth_DFOP_par.minff,
- synthetic_data_for_UBA_2014[[12]]$data,
- quiet = TRUE)
-
- skip("Hessian matrices and df calculations differ from those in FME")
- # Note that the k1 and k2 are exchanged in the untransformed fit evaluated with FME for this test
- expect_equal(signif(summary(fit_DFOP_par_c_2)$bpar[1:7, "t value"], 5),
- c(parent_0 = 80.054, k_M1_sink = 12.291, k_M2_sink = 10.588,
- f_parent_to_M1 = 21.4960, f_parent_to_M2 = 24.0890,
- k1 = 16.1450, k2 = 8.1747))
-})
diff --git a/tests/testthat/test_logistic.R b/tests/testthat/test_logistic.R
index 9f50f32e..5a89fbf5 100644
--- a/tests/testthat/test_logistic.R
+++ b/tests/testthat/test_logistic.R
@@ -34,13 +34,3 @@ test_that("The logistic model fit is reproducible", {
dtx <- endpoints(m)$distimes["parent", ]
expect_equivalent(dtx, c(36.865, 62.415, 4297.854, 10.833), tolerance = 0.001)
})
-
-test_that("The logistic fit can be done via differential equation", {
- # This is slow as we did not implement conversion to C
- # because it is unlikely we will use the logistic model with metabolites
- skip("Skip slow fit of logistic model using deSolve without compilation")
- m_2 <- mkinfit("logistic", d_2_1[[1]], solution_type = "deSolve",
- quiet = TRUE)
- dtx_2 <- endpoints(m_2)$distimes["parent", ]
- expect_equivalent(dtx_2, c(36.865, 62.415, 4297.854, 10.833), tolerance = 0.001)
-})

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