From bc3825ae2d12c18ea3d3caf17eb23c93fef180b8 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Thu, 8 Oct 2020 09:31:35 +0200 Subject: Fix issues for release --- vignettes/FOCUS_D.html | 19 +- vignettes/FOCUS_L.html | 154 ++++---- vignettes/mkin.html | 12 +- vignettes/mkin.rmd | 8 +- vignettes/references.bib | 2 +- vignettes/twa.html | 4 +- vignettes/web_only/NAFTA_examples.html | 629 +++++++++++++++++---------------- vignettes/web_only/NAFTA_examples.rmd | 2 +- vignettes/web_only/mkin_benchmarks.rda | Bin 930 -> 938 bytes 9 files changed, 420 insertions(+), 410 deletions(-) (limited to 'vignettes') diff --git a/vignettes/FOCUS_D.html b/vignettes/FOCUS_D.html index 16bc2084..ea6acdbe 100644 --- a/vignettes/FOCUS_D.html +++ b/vignettes/FOCUS_D.html @@ -11,7 +11,7 @@ - + Example evaluation of FOCUS Example Dataset D @@ -365,7 +365,7 @@ summary {

Example evaluation of FOCUS Example Dataset D

Johannes Ranke

2020-05-26

2020-10-08

@@ -431,6 +431,8 @@ print(FOCUS_2006_D)

fit <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE)

## Warning in mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE): Observations with value
 ## of zero were removed from the data

## Warning in mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE): Shapiro-Wilk test for
+## standardized residuals: p = 0.0165

A plot of the fit including a residual plot for both observed variables is obtained using the plot_sep method for mkinfit objects, which shows separate graphs for all compounds and their residuals.

plot_sep(fit, lpos = c("topright", "bottomright"))

@@ -440,9 +442,9 @@ print(FOCUS_2006_D)

A comprehensive report of the results is obtained using the summary method for mkinfit objects.

summary(fit)

## mkin version used for fitting:    0.9.50.3 
-## R version used for fitting:       4.0.0 
-## Date of fit:     Tue May 26 17:01:07 2020 
-## Date of summary: Tue May 26 17:01:07 2020 
+## R version used for fitting:       4.0.2 
+## Date of fit:     Thu Oct  8 09:06:19 2020 
+## Date of summary: Thu Oct  8 09:06:19 2020 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent * parent
@@ -450,7 +452,7 @@ print(FOCUS_2006_D)

## ## Model predictions using solution type analytical ## -## Fitted using 421 model solutions performed in 0.149 s +## Fitted using 421 model solutions performed in 0.152 s ## ## Error model: Constant variance ## @@ -474,6 +476,11 @@ print(FOCUS_2006_D) ## value type ## m1_0 0 state ## +## +## Warning(s): +## Observations with value of zero were removed from the data +## Shapiro-Wilk test for standardized residuals: p = 0.0165 +## ## Results: ## ## AIC BIC logLik diff --git a/vignettes/FOCUS_L.html b/vignettes/FOCUS_L.html index 7573ef58..c7722f37 100644 --- a/vignettes/FOCUS_L.html +++ b/vignettes/FOCUS_L.html @@ -11,7 +11,7 @@ - + Example evaluation of FOCUS Laboratory Data L1 to L3 @@ -1518,7 +1518,7 @@ div.tocify {

Example evaluation of FOCUS Laboratory Data L1 to L3

Johannes Ranke

2020-05-26

2020-10-08

@@ -1538,30 +1538,30 @@ FOCUS_2006_L1_mkin <- mkin_wide_to_long(FOCUS_2006_L1)

m.L1.SFO <- mkinfit("SFO", FOCUS_2006_L1_mkin, quiet = TRUE)
 summary(m.L1.SFO)

## mkin version used for fitting:    0.9.50.3 
-## R version used for fitting:       4.0.0 
-## Date of fit:     Tue May 26 17:01:08 2020 
-## Date of summary: Tue May 26 17:01:08 2020 
+## R version used for fitting:       4.0.2 
+## Date of fit:     Thu Oct  8 09:06:20 2020 
+## Date of summary: Thu Oct  8 09:06:20 2020 
 ## 
 ## Equations:
-## d_parent/dt = - k_parent_sink * parent
+## d_parent/dt = - k_parent * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 133 model solutions performed in 0.031 s
+## Fitted using 133 model solutions performed in 0.032 s
 ## 
 ## Error model: Constant variance 
 ## 
 ## Error model algorithm: OLS 
 ## 
 ## Starting values for parameters to be optimised:
-##               value   type
-## parent_0      89.85  state
-## k_parent_sink  0.10 deparm
+##          value   type
+## parent_0 89.85  state
+## k_parent  0.10 deparm
 ## 
 ## Starting values for the transformed parameters actually optimised:
-##                       value lower upper
-## parent_0          89.850000  -Inf   Inf
-## log_k_parent_sink -2.302585  -Inf   Inf
+##                  value lower upper
+## parent_0     89.850000  -Inf   Inf
+## log_k_parent -2.302585  -Inf   Inf
 ## 
 ## Fixed parameter values:
 ## None
@@ -1572,25 +1572,25 @@ summary(m.L1.SFO)

## 93.88778 96.5589 -43.94389 ## ## Optimised, transformed parameters with symmetric confidence intervals: -## Estimate Std. Error Lower Upper -## parent_0 92.470 1.28200 89.740 95.200 -## log_k_parent_sink -2.347 0.03763 -2.428 -2.267 -## sigma 2.780 0.46330 1.792 3.767 +## Estimate Std. Error Lower Upper +## parent_0 92.470 1.28200 89.740 95.200 +## log_k_parent -2.347 0.03763 -2.428 -2.267 +## sigma 2.780 0.46330 1.792 3.767 ## ## Parameter correlation: -## parent_0 log_k_parent_sink sigma -## parent_0 1.000e+00 6.186e-01 -1.516e-09 -## log_k_parent_sink 6.186e-01 1.000e+00 -3.124e-09 -## sigma -1.516e-09 -3.124e-09 1.000e+00 +## parent_0 log_k_parent sigma +## parent_0 1.000e+00 6.186e-01 -1.516e-09 +## log_k_parent 6.186e-01 1.000e+00 -3.124e-09 +## sigma -1.516e-09 -3.124e-09 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. ## t-test (unrealistically) based on the assumption of normal distribution ## for estimators of untransformed parameters. -## Estimate t value Pr(>t) Lower Upper -## parent_0 92.47000 72.13 8.824e-21 89.74000 95.2000 -## k_parent_sink 0.09561 26.57 2.487e-14 0.08824 0.1036 -## sigma 2.78000 6.00 1.216e-05 1.79200 3.7670 +## Estimate t value Pr(>t) Lower Upper +## parent_0 92.47000 72.13 8.824e-21 89.74000 95.2000 +## k_parent 0.09561 26.57 2.487e-14 0.08824 0.1036 +## sigma 2.78000 6.00 1.216e-05 1.79200 3.7670 ## ## FOCUS Chi2 error levels in percent: ## err.min n.optim df @@ -1639,21 +1639,16 @@ summary(m.L1.SFO)

## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is
 ## doubtful

## mkin version used for fitting:    0.9.50.3 
-## R version used for fitting:       4.0.0 
-## Date of fit:     Tue May 26 17:01:09 2020 
-## Date of summary: Tue May 26 17:01:09 2020 
-## 
-## 
-## Warning: Optimisation did not converge:
-## false convergence (8) 
-## 
+## R version used for fitting:       4.0.2 
+## Date of fit:     Thu Oct  8 09:06:21 2020 
+## Date of summary: Thu Oct  8 09:06:21 2020 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 380 model solutions performed in 0.08 s
+## Fitted using 380 model solutions performed in 0.088 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -1674,6 +1669,11 @@ summary(m.L1.SFO)

## Fixed parameter values: ## None ## +## +## Warning(s): +## Optimisation did not converge: +## false convergence (8) +## ## Results: ## ## AIC BIC logLik @@ -1744,16 +1744,16 @@ plot(m.L2.FOMC, show_residuals = TRUE,

summary(m.L2.FOMC, data = FALSE)

## mkin version used for fitting:    0.9.50.3 
-## R version used for fitting:       4.0.0 
-## Date of fit:     Tue May 26 17:01:09 2020 
-## Date of summary: Tue May 26 17:01:09 2020 
+## R version used for fitting:       4.0.2 
+## Date of fit:     Thu Oct  8 09:06:21 2020 
+## Date of summary: Thu Oct  8 09:06:21 2020 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 239 model solutions performed in 0.047 s
+## Fitted using 239 model solutions performed in 0.05 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -1822,9 +1822,9 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE,
 
 summary(m.L2.DFOP, data = FALSE)
 ## mkin version used for fitting:    0.9.50.3 
-## R version used for fitting:       4.0.0 
-## Date of fit:     Tue May 26 17:01:09 2020 
-## Date of summary: Tue May 26 17:01:09 2020 
+## R version used for fitting:       4.0.2 
+## Date of fit:     Thu Oct  8 09:06:21 2020 
+## Date of summary: Thu Oct  8 09:06:21 2020 
 ## 
 ## Equations:
 ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -1833,7 +1833,7 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE,
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 572 model solutions performed in 0.13 s
+## Fitted using 572 model solutions performed in 0.136 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -1894,8 +1894,8 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE,
 ## parent      2.53       4  2
 ## 
 ## Estimated disappearance times:
-##          DT50  DT90 DT50_k1 DT50_k2
-## parent 0.5335 5.311 0.03009   2.058
+##          DT50  DT90 DT50back DT50_k1 DT50_k2
+## parent 0.5335 5.311    1.599 0.03009   2.058

Here, the DFOP model is clearly the best-fit model for dataset L2 based on the chi^2 error level criterion. However, the failure to calculate the covariance matrix indicates that the parameter estimates correlate excessively. Therefore, the FOMC model may be preferred for this dataset.

@@ -1922,9 +1922,9 @@ plot(mm.L3)

We can extract the summary and plot for e.g. the DFOP fit, using square brackets for indexing which will result in the use of the summary and plot functions working on mkinfit objects.

summary(mm.L3[["DFOP", 1]])

## mkin version used for fitting:    0.9.50.3 
-## R version used for fitting:       4.0.0 
-## Date of fit:     Tue May 26 17:01:10 2020 
-## Date of summary: Tue May 26 17:01:10 2020 
+## R version used for fitting:       4.0.2 
+## Date of fit:     Thu Oct  8 09:06:22 2020 
+## Date of summary: Thu Oct  8 09:06:22 2020 
 ## 
 ## Equations:
 ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -1933,7 +1933,7 @@ plot(mm.L3)

## ## Model predictions using solution type analytical ## -## Fitted using 373 model solutions performed in 0.083 s +## Fitted using 373 model solutions performed in 0.085 s ## ## Error model: Constant variance ## @@ -1994,8 +1994,8 @@ plot(mm.L3) ## parent 2.225 4 4 ## ## Estimated disappearance times: -## DT50 DT90 DT50_k1 DT50_k2 -## parent 7.464 123 1.343 50.37 +## DT50 DT90 DT50back DT50_k1 DT50_k2 +## parent 7.464 123 37.03 1.343 50.37 ## ## Data: ## time variable observed predicted residual @@ -2030,30 +2030,30 @@ plot(mm.L4)

The χ² error level of 3.3% as well as the plot suggest that the SFO model fits very well. The error level at which the χ² test passes is slightly lower for the FOMC model. However, the difference appears negligible.

summary(mm.L4[["SFO", 1]], data = FALSE)

## mkin version used for fitting:    0.9.50.3 
-## R version used for fitting:       4.0.0 
-## Date of fit:     Tue May 26 17:01:10 2020 
-## Date of summary: Tue May 26 17:01:10 2020 
+## R version used for fitting:       4.0.2 
+## Date of fit:     Thu Oct  8 09:06:22 2020 
+## Date of summary: Thu Oct  8 09:06:22 2020 
 ## 
 ## Equations:
-## d_parent/dt = - k_parent_sink * parent
+## d_parent/dt = - k_parent * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 142 model solutions performed in 0.029 s
+## Fitted using 142 model solutions performed in 0.03 s
 ## 
 ## Error model: Constant variance 
 ## 
 ## Error model algorithm: OLS 
 ## 
 ## Starting values for parameters to be optimised:
-##               value   type
-## parent_0       96.6  state
-## k_parent_sink   0.1 deparm
+##          value   type
+## parent_0  96.6  state
+## k_parent   0.1 deparm
 ## 
 ## Starting values for the transformed parameters actually optimised:
-##                       value lower upper
-## parent_0          96.600000  -Inf   Inf
-## log_k_parent_sink -2.302585  -Inf   Inf
+##                  value lower upper
+## parent_0     96.600000  -Inf   Inf
+## log_k_parent -2.302585  -Inf   Inf
 ## 
 ## Fixed parameter values:
 ## None
@@ -2064,25 +2064,25 @@ plot(mm.L4)

## 47.12133 47.35966 -20.56067 ## ## Optimised, transformed parameters with symmetric confidence intervals: -## Estimate Std. Error Lower Upper -## parent_0 96.440 1.69900 92.070 100.800 -## log_k_parent_sink -5.030 0.07059 -5.211 -4.848 -## sigma 3.162 0.79050 1.130 5.194 +## Estimate Std. Error Lower Upper +## parent_0 96.440 1.69900 92.070 100.800 +## log_k_parent -5.030 0.07059 -5.211 -4.848 +## sigma 3.162 0.79050 1.130 5.194 ## ## Parameter correlation: -## parent_0 log_k_parent_sink sigma -## parent_0 1.000e+00 5.938e-01 3.387e-07 -## log_k_parent_sink 5.938e-01 1.000e+00 5.830e-07 -## sigma 3.387e-07 5.830e-07 1.000e+00 +## parent_0 log_k_parent sigma +## parent_0 1.000e+00 5.938e-01 3.387e-07 +## log_k_parent 5.938e-01 1.000e+00 5.830e-07 +## sigma 3.387e-07 5.830e-07 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. ## t-test (unrealistically) based on the assumption of normal distribution ## for estimators of untransformed parameters. -## Estimate t value Pr(>t) Lower Upper -## parent_0 96.440000 56.77 1.604e-08 92.070000 1.008e+02 -## k_parent_sink 0.006541 14.17 1.578e-05 0.005455 7.842e-03 -## sigma 3.162000 4.00 5.162e-03 1.130000 5.194e+00 +## Estimate t value Pr(>t) Lower Upper +## parent_0 96.440000 56.77 1.604e-08 92.070000 1.008e+02 +## k_parent 0.006541 14.17 1.578e-05 0.005455 7.842e-03 +## sigma 3.162000 4.00 5.162e-03 1.130000 5.194e+00 ## ## FOCUS Chi2 error levels in percent: ## err.min n.optim df @@ -2094,16 +2094,16 @@ plot(mm.L4) ## parent 106 352

summary(mm.L4[["FOMC", 1]], data = FALSE)

## mkin version used for fitting:    0.9.50.3 
-## R version used for fitting:       4.0.0 
-## Date of fit:     Tue May 26 17:01:10 2020 
-## Date of summary: Tue May 26 17:01:10 2020 
+## R version used for fitting:       4.0.2 
+## Date of fit:     Thu Oct  8 09:06:22 2020 
+## Date of summary: Thu Oct  8 09:06:22 2020 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 224 model solutions performed in 0.044 s
+## Fitted using 224 model solutions performed in 0.046 s
 ## 
 ## Error model: Constant variance 
 ## 
diff --git a/vignettes/mkin.html b/vignettes/mkin.html
index e14cb374..8d9989a2 100644
--- a/vignettes/mkin.html
+++ b/vignettes/mkin.html
@@ -11,7 +11,7 @@
 
 
 
-
+
 
 Introduction to mkin
 
@@ -1583,12 +1583,12 @@ div.tocify {
 
 Introduction to mkin
 Johannes Ranke
-2020-05-26
+2020-10-08
 
 
 
 
-Wissenschaftlicher Berater, Kronacher Str. 12, 79639 Grenzach-Wyhlen, Germany
 Privatdozent at the University of Bremen
+Wissenschaftlicher Berater, Kronacher Str. 12, 79639 Grenzach-Wyhlen, Germany
 Privatdozent at the University of Bremen
 
 Abstract
 In the regulatory evaluation of chemical substances like plant protection products (pesticides), biocides and other chemicals, degradation data play an important role. For the evaluation of pesticide degradation experiments, detailed guidance has been developed, based on nonlinear optimisation. The R add-on package mkin implements fitting some of the models recommended in this guidance from within R and calculates some statistical measures for data series within one or more compartments, for parent and metabolites.
@@ -1627,7 +1627,7 @@ plot_sep(f_SFO_SFO_SFO, lpos = c("topright", "bottomright",
 Many approaches are possible regarding the evaluation of chemical degradation data.
 The mkin package (Ranke 2019) implements the approach recommended in the kinetics report provided by the FOrum for Co-ordination of pesticide fate models and their USe (FOCUS Work Group on Degradation Kinetics 2006, 2014) for simple decline data series, data series with transformation products, commonly termed metabolites, and for data series for more than one compartment. It is also possible to include back reactions, so equilibrium reactions and equilibrium partitioning can be specified, although this oftentimes leads to an overparameterisation of the model.
 When the first mkin code was published in 2010, the most commonly used tools for fitting more complex kinetic degradation models to experimental data were KinGUI (Schäfer et al. 2007), a MATLAB based tool with a graphical user interface that was specifically tailored to the task and included some output as proposed by the FOCUS Kinetics Workgroup, and ModelMaker, a general purpose compartment based tool providing infrastructure for fitting dynamic simulation models based on differential equations to data.
-The code was first uploaded to the BerliOS platform. When this was taken down, the version control history was imported into the R-Forge site (see e.g. the initial commit on 11 May 2010), where the code is still occasionally updated.
+The code was first uploaded to the BerliOS platform. When this was taken down, the version control history was imported into the R-Forge site (see e.g. the initial commit on 11 May 2010), where the code is still occasionally updated.
 At that time, the R package FME (Flexible Modelling Environment) (Soetaert and Petzoldt 2010) was already available, and provided a good basis for developing a package specifically tailored to the task. The remaining challenge was to make it as easy as possible for the users (including the author of this vignette) to specify the system of differential equations and to include the output requested by the FOCUS guidance, such as the relative standard deviation that has to be assumed for the residuals, such that the \(\chi^2\) goodness-of-fit test as defined by the FOCUS kinetics workgroup would pass using an significance level \(\alpha\) of 0.05. This relative error, expressed as a percentage, is often termed \(\chi^2\) error level or similar.
 Also, mkin introduced using analytical solutions for parent only kinetics for improved optimization speed. Later, Eigenvalue based solutions were introduced to mkin for the case of linear differential equations (i.e. where the FOMC or DFOP models were not used for the parent compound), greatly improving the optimization speed for these cases. This, however, has become somehow obsolete, as the use of compiled code described below gives even smaller execution times.
 The possibility to specify back-reactions and a biphasic model (SFORB) for metabolites were present in mkin from the very beginning.
@@ -1636,7 +1636,7 @@ plot_sep(f_SFO_SFO_SFO, lpos = c("topright", "bottomright",
 Soon after the publication of mkin, two derived tools were published, namely KinGUII (available from Bayer Crop Science) and CAKE (commissioned to Tessella by Syngenta), which added a graphical user interface (GUI), and added fitting by iteratively reweighted least squares (IRLS) and characterisation of likely parameter distributions by Markov Chain Monte Carlo (MCMC) sampling.
 CAKE focuses on a smooth use experience, sacrificing some flexibility in the model definition, originally allowing only two primary metabolites in parallel. The current version 3.3 of CAKE release in March 2016 uses a basic scheme for up to six metabolites in a flexible arrangement, but does not support back-reactions (non-instantaneous equilibria) or biphasic kinetics for metabolites.
 KinGUI offers an even more flexible widget for specifying complex kinetic models. Back-reactions (non-instantaneous equilibria) were supported early on, but until 2014, only simple first-order models could be specified for transformation products. Starting with KinGUII version 2.1, biphasic modelling of metabolites was also available in KinGUII.
-A further graphical user interface (GUI) that has recently been brought to a decent degree of maturity is the browser based GUI named gmkin. Please see its documentation page and manual for further information.
+A further graphical user interface (GUI) that has recently been brought to a decent degree of maturity is the browser based GUI named gmkin. Please see its documentation page and manual for further information.
 A comparison of scope, usability and numerical results obtained with these tools has been recently been published by Ranke, Wöltjen, and Meinecke (2018).
 
 
@@ -1697,7 +1697,7 @@ plot_sep(f_SFO_SFO_SFO, lpos = c("topright", "bottomright",
 Schäfer, D., B. Mikolasch, P. Rainbird, and B. Harvey. 2007. “KinGUI: A New Kinetic Software Tool for Evaluations According to FOCUS Degradation Kinetics.” In Proceedings of the Xiii Symposium Pesticide Chemistry, edited by Del Re A. A. M., Capri E., Fragoulis G., and Trevisan M., 916–23. Piacenza.
 
 
-Soetaert, Karline, and Thomas Petzoldt. 2010. “Inverse Modelling, Sensitivity and Monte Carlo Analysis in R Using Package FME.” Journal of Statistical Software 33 (3): 1–28. http://www.jstatsoft.org/v33/i03/.
+Soetaert, Karline, and Thomas Petzoldt. 2010. “Inverse Modelling, Sensitivity and Monte Carlo Analysis in R Using Package FME.” Journal of Statistical Software 33 (3): 1–28. https://www.jstatsoft.org/v33/i03/.
 
 
 
diff --git a/vignettes/mkin.rmd b/vignettes/mkin.rmd
index acca0e44..a672f2a6 100644
--- a/vignettes/mkin.rmd
+++ b/vignettes/mkin.rmd
@@ -15,8 +15,8 @@ vignette: >
   %\VignetteEncoding{UTF-8}
 ---
 
-[Wissenschaftlicher Berater, Kronacher Str. 12, 79639 Grenzach-Wyhlen, Germany](http://www.jrwb.de)

-[Privatdozent at the University of Bremen](http://chem.uft.uni-bremen.de/ranke)
+[Wissenschaftlicher Berater, Kronacher Str. 12, 79639 Grenzach-Wyhlen, Germany](https://www.jrwb.de)

+[Privatdozent at the University of Bremen](http://chem.uft.uni-bremen.de/ranke/)
 
 ```{r, include = FALSE}
 require(knitr)
@@ -88,7 +88,7 @@ models based on differential equations to data.
 
 The code was first uploaded to the BerliOS platform. When this was taken down,
 the version control history was imported into the R-Forge site (see *e.g.*
-[the initial commit on 11 May 2010](http://cgit.jrwb.de/mkin/commit/?id=30cbb4092f6d2d3beff5800603374a0d009ad770)),
+[the initial commit on 11 May 2010](https://cgit.jrwb.de/mkin/commit/?id=30cbb4092f6d2d3beff5800603374a0d009ad770)),
 where the code is still occasionally updated.
 
 At that time, the R package `FME` (Flexible Modelling Environment)
@@ -135,7 +135,7 @@ of metabolites was also available in KinGUII.
 
 A further graphical user interface (GUI) that has recently been brought to a decent
 degree of maturity is the browser based GUI named `gmkin`. Please see its
-[documentation page](https://pkgdown.jrwb.de/gmkin) and
+[documentation page](https://pkgdown.jrwb.de/gmkin/) and
 [manual](https://pkgdown.jrwb.de/gmkin/articles/gmkin_manual.html)
 for further information.
 
diff --git a/vignettes/references.bib b/vignettes/references.bib
index a18922c9..69ef74a7 100644
--- a/vignettes/references.bib
+++ b/vignettes/references.bib
@@ -76,7 +76,7 @@
   volume = {33},
   pages = {1--28},
   number = {3},
-  url = {http://www.jstatsoft.org/v33/i03/}
+  url = {https://www.jstatsoft.org/v33/i03/}
 }
 
 @Inproceedings{   ranke2012,
diff --git a/vignettes/twa.html b/vignettes/twa.html
index 80272eef..663625bf 100644
--- a/vignettes/twa.html
+++ b/vignettes/twa.html
@@ -12,7 +12,7 @@
 
 
 
-
+
 
 Calculation of time weighted average concentrations with mkin
 
@@ -215,7 +215,7 @@ code > span.er { color: #a61717; background-color: #e3d2d2; }
 
 Calculation of time weighted average concentrations with mkin
 Johannes Ranke
-2020-05-26
+2020-10-08
 
 
 
diff --git a/vignettes/web_only/NAFTA_examples.html b/vignettes/web_only/NAFTA_examples.html
index f1b3fa03..dda93242 100644
--- a/vignettes/web_only/NAFTA_examples.html
+++ b/vignettes/web_only/NAFTA_examples.html
@@ -1,17 +1,17 @@
 
 
-
+
 
 
 
 
-
 
+
 
 
 
 
-
+
 
 Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance
 
@@ -69,8 +69,6 @@ overflow: auto;
 margin-left: 2%;
 position: fixed;
 border: 1px solid #ccc;
-webkit-border-radius: 6px;
-moz-border-radius: 6px;
 border-radius: 6px;
 }
 
@@ -98,10 +96,15 @@ font-size: 12px;
 .tocify-subheader .tocify-subheader {
 text-indent: 30px;
 }
-
 .tocify-subheader .tocify-subheader .tocify-subheader {
 text-indent: 40px;
 }
+.tocify-subheader .tocify-subheader .tocify-subheader .tocify-subheader {
+text-indent: 50px;
+}
+.tocify-subheader .tocify-subheader .tocify-subheader .tocify-subheader .tocify-subheader {
+text-indent: 60px;
+}
 
 .tocify .tocify-item > a, .tocify .nav-list .nav-header {
 margin: 0px;
@@ -504,13 +507,13 @@ float: none;
 
           item.append($("", {
 
-            "text": self.text()
+            "html": self.html()
 
           }));
 
         } else {
 
-          item.text(self.text());
+          item.html(self.html());
 
         }
 
@@ -1327,9 +1330,7 @@ h6 {
 
 
 
-
 
-
 
 
 
-
-
 
 
 
 
-
-
 
 
+
+
+
+
+
+
+
+
+
+
 
 
 
@@ -1543,8 +1517,8 @@ div.tocify {
 
 
 Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance
-Johannes Ranke
-2019-04-10
+Johannes Ranke
+2020-10-08
 
 
 
@@ -1563,7 +1537,7 @@ div.tocify {
 ## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c
 ## The half-life obtained from the IORE model may be used
 plot(p5a)
-
+
 print(p5a)
 ## Sums of squares:
 ##       SFO      IORE      DFOP 
@@ -1574,23 +1548,23 @@ div.tocify {
 ## 
 ## Parameters:
 ## $SFO
-##               Estimate   Pr(>t)  Lower   Upper
-## parent_0       95.8401 4.67e-21 92.245 99.4357
-## k_parent_sink   0.0102 3.92e-12  0.009  0.0117
-## sigma           4.8230 3.81e-06  3.214  6.4318
+##          Estimate   Pr(>t)  Lower   Upper
+## parent_0  95.8401 4.67e-21 92.245 99.4357
+## k_parent   0.0102 3.92e-12  0.009  0.0117
+## sigma      4.8230 3.81e-06  3.214  6.4318
 ## 
 ## $IORE
-##                     Estimate Pr(>t)    Lower    Upper
-## parent_0            1.01e+02     NA 9.91e+01 1.02e+02
-## k__iore_parent_sink 1.54e-05     NA 4.08e-06 5.84e-05
-## N_parent            2.57e+00     NA 2.25e+00 2.89e+00
-## sigma               1.68e+00     NA 1.12e+00 2.24e+00
+##                Estimate Pr(>t)    Lower    Upper
+## parent_0       1.01e+02     NA 9.91e+01 1.02e+02
+## k__iore_parent 1.54e-05     NA 4.08e-06 5.84e-05
+## N_parent       2.57e+00     NA 2.25e+00 2.89e+00
+## sigma          1.68e+00     NA 1.12e+00 2.24e+00
 ## 
 ## $DFOP
 ##          Estimate   Pr(>t)   Lower    Upper
 ## parent_0 9.99e+01 1.41e-26 98.8116 101.0810
 ## k1       2.67e-02 5.05e-06  0.0243   0.0295
-## k2       3.41e-12 5.00e-01  0.0000      Inf
+## k2       2.17e-12 5.00e-01  0.0000      Inf
 ## g        6.47e-01 3.67e-06  0.6248   0.6677
 ## sigma    1.27e+00 8.91e-06  0.8395   1.6929
 ## 
@@ -1599,10 +1573,10 @@ div.tocify {
 ##      DT50     DT90 DT50_rep
 ## SFO  67.7 2.25e+02 6.77e+01
 ## IORE 58.2 1.07e+03 3.22e+02
-## DFOP 55.5 3.70e+11 2.03e+11
+## DFOP 55.5 5.83e+11 3.20e+11
 ## 
 ## Representative half-life:
-## [1] 321.5119
+## [1] 321.51

Example on page 5, lower panel

@@ -1610,7 +1584,7 @@ div.tocify {

## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c

## The half-life obtained from the IORE model may be used

plot(p5b)

print(p5b)

## Sums of squares:
 ##      SFO     IORE     DFOP 
@@ -1621,23 +1595,23 @@ div.tocify {
 ## 
 ## Parameters:
 ## $SFO
-##               Estimate   Pr(>t)    Lower    Upper
-## parent_0        96.497 2.32e-24 94.85271 98.14155
-## k_parent_sink    0.008 3.42e-14  0.00737  0.00869
-## sigma            2.295 1.22e-05  1.47976  3.11036
+##          Estimate   Pr(>t)    Lower    Upper
+## parent_0   96.497 2.32e-24 94.85271 98.14155
+## k_parent    0.008 3.42e-14  0.00737  0.00869
+## sigma       2.295 1.22e-05  1.47976  3.11036
 ## 
 ## $IORE
-##                     Estimate   Pr(>t)    Lower    Upper
-## parent_0            9.85e+01 1.17e-28 9.79e+01 9.92e+01
-## k__iore_parent_sink 1.53e-04 6.50e-03 7.21e-05 3.26e-04
-## N_parent            1.94e+00 5.88e-13 1.76e+00 2.12e+00
-## sigma               7.49e-01 1.63e-05 4.82e-01 1.02e+00
+##                Estimate   Pr(>t)    Lower    Upper
+## parent_0       9.85e+01 1.17e-28 9.79e+01 9.92e+01
+## k__iore_parent 1.53e-04 6.50e-03 7.21e-05 3.26e-04
+## N_parent       1.94e+00 5.88e-13 1.76e+00 2.12e+00
+## sigma          7.49e-01 1.63e-05 4.82e-01 1.02e+00
 ## 
 ## $DFOP
 ##          Estimate   Pr(>t)   Lower   Upper
 ## parent_0 9.84e+01 1.24e-27 97.8078 98.9187
 ## k1       1.55e-02 4.10e-04  0.0143  0.0167
-## k2       1.09e-11 5.00e-01  0.0000     Inf
+## k2       1.04e-11 5.00e-01  0.0000     Inf
 ## g        6.89e-01 2.92e-03  0.6626  0.7142
 ## sigma    6.48e-01 2.38e-05  0.4147  0.8813
 ## 
@@ -1646,10 +1620,10 @@ div.tocify {
 ##      DT50     DT90 DT50_rep
 ## SFO  86.6 2.88e+02 8.66e+01
 ## IORE 85.5 7.17e+02 2.16e+02
-## DFOP 83.6 1.04e+11 6.34e+10
+## DFOP 83.6 1.09e+11 6.67e+10
 ## 
 ## Representative half-life:
-## [1] 215.8655

+## [1] 215.87

Example on page 6

@@ -1657,7 +1631,7 @@ div.tocify {

## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c

## The half-life obtained from the IORE model may be used

plot(p6)

print(p6)

## Sums of squares:
 ##       SFO      IORE      DFOP 
@@ -1668,17 +1642,17 @@ div.tocify {
 ## 
 ## Parameters:
 ## $SFO
-##               Estimate   Pr(>t)   Lower   Upper
-## parent_0       94.7759 7.29e-24 92.3479 97.2039
-## k_parent_sink   0.0179 8.02e-16  0.0166  0.0194
-## sigma           3.0696 3.81e-06  2.0456  4.0936
+##          Estimate   Pr(>t)   Lower   Upper
+## parent_0  94.7759 7.29e-24 92.3478 97.2039
+## k_parent   0.0179 8.02e-16  0.0166  0.0194
+## sigma      3.0696 3.81e-06  2.0456  4.0936
 ## 
 ## $IORE
-##                     Estimate   Pr(>t)    Lower    Upper
-## parent_0            97.12446 2.63e-26 95.62461 98.62431
-## k__iore_parent_sink  0.00252 1.95e-03  0.00134  0.00472
-## N_parent             1.49587 4.07e-13  1.33896  1.65279
-## sigma                1.59698 5.05e-06  1.06169  2.13227
+##                Estimate   Pr(>t)    Lower    Upper
+## parent_0       97.12446 2.63e-26 95.62461 98.62431
+## k__iore_parent  0.00252 1.95e-03  0.00134  0.00472
+## N_parent        1.49587 4.07e-13  1.33896  1.65279
+## sigma           1.59698 5.05e-06  1.06169  2.13227
 ## 
 ## $DFOP
 ##          Estimate   Pr(>t)   Lower   Upper
@@ -1696,7 +1670,7 @@ div.tocify {
 ## DFOP 34.1 8.42e+09 1.79e+10
 ## 
 ## Representative half-life:
-## [1] 53.16582

+## [1] 53.17

Example on page 7

@@ -1704,7 +1678,7 @@ div.tocify {

## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c

## The half-life obtained from the IORE model may be used

plot(p7)

print(p7)

## Sums of squares:
 ##      SFO     IORE     DFOP 
@@ -1715,23 +1689,23 @@ div.tocify {
 ## 
 ## Parameters:
 ## $SFO
-##               Estimate   Pr(>t)    Lower    Upper
-## parent_0      96.41796 4.80e-53 93.32245 99.51347
-## k_parent_sink  0.00735 7.64e-21  0.00641  0.00843
-## sigma          7.94557 1.83e-15  6.46713  9.42401
+##          Estimate   Pr(>t)    Lower    Upper
+## parent_0 96.41796 4.80e-53 93.32245 99.51347
+## k_parent  0.00735 7.64e-21  0.00641  0.00843
+## sigma     7.94557 1.83e-15  6.46713  9.42401
 ## 
 ## $IORE
-##                     Estimate Pr(>t)    Lower    Upper
-## parent_0            9.92e+01     NA 9.55e+01 1.03e+02
-## k__iore_parent_sink 1.60e-05     NA 1.45e-07 1.77e-03
-## N_parent            2.45e+00     NA 1.35e+00 3.54e+00
-## sigma               7.42e+00     NA 6.04e+00 8.80e+00
+##                Estimate Pr(>t)    Lower    Upper
+## parent_0       9.92e+01     NA 9.55e+01 1.03e+02
+## k__iore_parent 1.60e-05     NA 1.45e-07 1.77e-03
+## N_parent       2.45e+00     NA 1.35e+00 3.54e+00
+## sigma          7.42e+00     NA 6.04e+00 8.80e+00
 ## 
 ## $DFOP
 ##          Estimate   Pr(>t)   Lower    Upper
 ## parent_0 9.89e+01 9.44e-49 95.4640 102.2573
 ## k1       1.81e-02 1.75e-01  0.0116   0.0281
-## k2       2.57e-10 5.00e-01  0.0000      Inf
+## k2       2.30e-10 5.00e-01  0.0000      Inf
 ## g        6.06e-01 2.19e-01  0.4826   0.7178
 ## sigma    7.40e+00 2.97e-15  6.0201   8.7754
 ## 
@@ -1740,10 +1714,10 @@ div.tocify {
 ##      DT50     DT90 DT50_rep
 ## SFO  94.3 3.13e+02 9.43e+01
 ## IORE 96.7 1.51e+03 4.55e+02
-## DFOP 96.4 5.32e+09 2.69e+09
+## DFOP 96.4 5.95e+09 3.01e+09
 ## 
 ## Representative half-life:
-## [1] 454.5528

+## [1] 454.55

@@ -1751,16 +1725,11 @@ div.tocify {

Example on page 8

For this dataset, the IORE fit does not converge when the default starting values used by mkin for the IORE model are used. Therefore, a lower value for the rate constant is used here.

p8 <- nafta(NAFTA_SOP_Attachment[["p8"]], parms.ini = c(k__iore_parent_sink = 1e-3))

## Warning in summary.mkinfit(x): Could not estimate covariance matrix;
-## singular system.
-
-## Warning in summary.mkinfit(x): Could not estimate covariance matrix;
-## singular system.

p8 <- nafta(NAFTA_SOP_Attachment[["p8"]], parms.ini = c(k__iore_parent = 1e-3))

## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c

## The half-life obtained from the IORE model may be used

plot(p8)

print(p8)

## Sums of squares:
 ##       SFO      IORE      DFOP 
@@ -1771,27 +1740,25 @@ div.tocify {
 ## 
 ## Parameters:
 ## $SFO
-##                     Estimate Pr(>t) Lower Upper
-## parent_0            88.16549     NA    NA    NA
-## k__iore_parent_sink  0.00100     NA    NA    NA
-## k_parent_sink        0.00803     NA    NA    NA
-## sigma                7.44786     NA    NA    NA
+##          Estimate   Pr(>t)    Lower    Upper
+## parent_0 88.16549 6.53e-29 83.37344 92.95754
+## k_parent  0.00803 1.67e-13  0.00674  0.00957
+## sigma     7.44786 4.17e-10  5.66209  9.23363
 ## 
 ## $IORE
-##                     Estimate   Pr(>t)    Lower    Upper
-## parent_0            9.77e+01 7.03e-35 9.44e+01 1.01e+02
-## k__iore_parent_sink 6.14e-05 3.20e-02 2.12e-05 1.78e-04
-## N_parent            2.27e+00 4.23e-18 2.00e+00 2.54e+00
-## sigma               3.52e+00 5.36e-10 2.67e+00 4.36e+00
+##                Estimate   Pr(>t)    Lower    Upper
+## parent_0       9.77e+01 7.03e-35 9.44e+01 1.01e+02
+## k__iore_parent 6.14e-05 3.20e-02 2.12e-05 1.78e-04
+## N_parent       2.27e+00 4.23e-18 2.00e+00 2.54e+00
+## sigma          3.52e+00 5.36e-10 2.67e+00 4.36e+00
 ## 
 ## $DFOP
-##                     Estimate Pr(>t) Lower Upper
-## parent_0            95.70619     NA    NA    NA
-## k__iore_parent_sink  0.00100     NA    NA    NA
-## k1                   0.02500     NA    NA    NA
-## k2                   0.00273     NA    NA    NA
-## g                    0.58835     NA    NA    NA
-## sigma                3.90001     NA    NA    NA
+##          Estimate   Pr(>t)    Lower    Upper
+## parent_0 95.70619 8.99e-32 91.87941 99.53298
+## k1        0.02500 5.25e-04  0.01422  0.04394
+## k2        0.00273 6.84e-03  0.00125  0.00597
+## g         0.58835 2.84e-06  0.36595  0.77970
+## sigma     3.90001 6.94e-10  2.96260  4.83741
 ## 
 ## 
 ## DTx values:
@@ -1801,7 +1768,7 @@ div.tocify {
 ## DFOP 55.6  517    253.0
 ## 
 ## Representative half-life:
-## [1] 201.0316

+## [1] 201.03

@@ -1812,7 +1779,7 @@ div.tocify {

## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c

## The half-life obtained from the IORE model may be used

plot(p9a)

print(p9a)

## Sums of squares:
 ##       SFO      IORE      DFOP 
@@ -1823,23 +1790,23 @@ div.tocify {
 ## 
 ## Parameters:
 ## $SFO
-##               Estimate   Pr(>t)   Lower   Upper
-## parent_0       88.1933 3.06e-12 79.9447 96.4419
-## k_parent_sink   0.0409 2.07e-07  0.0324  0.0516
-## sigma           7.2429 3.92e-05  4.4768 10.0090
+##          Estimate   Pr(>t)   Lower   Upper
+## parent_0  88.1933 3.06e-12 79.9447 96.4419
+## k_parent   0.0409 2.07e-07  0.0324  0.0516
+## sigma      7.2429 3.92e-05  4.4768 10.0090
 ## 
 ## $IORE
-##                     Estimate   Pr(>t)    Lower    Upper
-## parent_0            9.89e+01 1.12e-16 9.54e+01 1.02e+02
-## k__iore_parent_sink 1.93e-05 1.13e-01 3.49e-06 1.06e-04
-## N_parent            2.91e+00 1.45e-09 2.50e+00 3.32e+00
-## sigma               2.35e+00 5.31e-05 1.45e+00 3.26e+00
+##                Estimate   Pr(>t)    Lower    Upper
+## parent_0       9.89e+01 1.12e-16 9.54e+01 1.02e+02
+## k__iore_parent 1.93e-05 1.13e-01 3.49e-06 1.06e-04
+## N_parent       2.91e+00 1.45e-09 2.50e+00 3.32e+00
+## sigma          2.35e+00 5.31e-05 1.45e+00 3.26e+00
 ## 
 ## $DFOP
 ##          Estimate   Pr(>t)  Lower  Upper
 ## parent_0 9.85e+01 2.54e-20 97.390 99.672
 ## k1       1.38e-01 3.52e-05  0.131  0.146
-## k2       5.75e-13 5.00e-01  0.000    Inf
+## k2       6.69e-13 5.00e-01  0.000    Inf
 ## g        6.52e-01 8.13e-06  0.642  0.661
 ## sigma    7.88e-01 6.13e-02  0.481  1.095
 ## 
@@ -1848,24 +1815,19 @@ div.tocify {
 ##      DT50     DT90 DT50_rep
 ## SFO  16.9 5.63e+01 1.69e+01
 ## IORE 11.6 3.37e+02 1.01e+02
-## DFOP 10.5 2.17e+12 1.21e+12
+## DFOP 10.5 1.86e+12 1.04e+12
 ## 
 ## Representative half-life:
-## [1] 101.4264

+## [1] 101.43

In this example, the residuals of the SFO indicate a lack of fit of this model, so even if it was an abiotic experiment, the data do not suggest a simple exponential decline.

Example on page 9, lower panel

p9b <- nafta(NAFTA_SOP_Attachment[["p9b"]])

## Warning in sqrt(diag(covar)): NaNs wurden erzeugt

## Warning in sqrt(diag(covar_notrans)): NaNs wurden erzeugt

## Warning in sqrt(1/diag(V)): NaNs wurden erzeugt

## Warning in cov2cor(ans$cov.unscaled): diag(.) had 0 or NA entries; non-
-## finite result is doubtful

## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c

## The half-life obtained from the IORE model may be used

plot(p9b)

print(p9b)

## Sums of squares:
 ##      SFO     IORE     DFOP 
@@ -1876,24 +1838,24 @@ div.tocify {
 ## 
 ## Parameters:
 ## $SFO
-##               Estimate   Pr(>t)  Lower   Upper
-## parent_0       94.7123 2.15e-19 93.178 96.2464
-## k_parent_sink   0.0389 4.47e-14  0.037  0.0408
-## sigma           1.5957 1.28e-04  0.932  2.2595
+##          Estimate   Pr(>t)  Lower   Upper
+## parent_0  94.7123 2.15e-19 93.178 96.2464
+## k_parent   0.0389 4.47e-14  0.037  0.0408
+## sigma      1.5957 1.28e-04  0.932  2.2595
 ## 
 ## $IORE
-##                     Estimate   Pr(>t)   Lower  Upper
-## parent_0              93.863 2.32e-18 92.4565 95.269
-## k__iore_parent_sink    0.127 1.85e-02  0.0504  0.321
-## N_parent               0.711 1.88e-05  0.4843  0.937
-## sigma                  1.288 1.76e-04  0.7456  1.830
+##                Estimate   Pr(>t)   Lower  Upper
+## parent_0         93.863 2.32e-18 92.4565 95.269
+## k__iore_parent    0.127 1.85e-02  0.0504  0.321
+## N_parent          0.711 1.88e-05  0.4843  0.937
+## sigma             1.288 1.76e-04  0.7456  1.830
 ## 
 ## $DFOP
 ##          Estimate   Pr(>t)   Lower   Upper
 ## parent_0  94.7123 1.61e-16 93.1355 96.2891
-## k1         0.0389      NaN  0.0316  0.0478
-## k2         0.0389 1.13e-08  0.0203  0.0743
-## g          0.7599      NaN      NA      NA
+## k1         0.0389 1.43e-06  0.0312  0.0485
+## k2         0.0389 6.67e-03  0.0186  0.0812
+## g          0.7742 5.00e-01  0.0000  1.0000
 ## sigma      1.5957 2.50e-04  0.9135  2.2779
 ## 
 ## 
@@ -1904,7 +1866,7 @@ div.tocify {
 ## DFOP 17.8 59.2     17.8
 ## 
 ## Representative half-life:
-## [1] 14.80013

+## [1] 14.8

Here, mkin gives a longer slow DT50 for the DFOP model (17.8 days) than PestDF (13.5 days). Presumably, this is related to the fact that PestDF gives a negative value for the proportion of the fast degradation which should be between 0 and 1, inclusive. This parameter is called f in PestDF and g in mkin. In mkin, it is restricted to the interval from 0 to 1.

@@ -1913,7 +1875,7 @@ div.tocify {

## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c

## The half-life obtained from the IORE model may be used

plot(p10)

print(p10)

## Sums of squares:
 ##      SFO     IORE     DFOP 
@@ -1924,25 +1886,25 @@ div.tocify {
 ## 
 ## Parameters:
 ## $SFO
-##               Estimate   Pr(>t)   Lower    Upper
-## parent_0      101.7315 6.42e-11 91.9259 111.5371
-## k_parent_sink   0.0495 1.70e-07  0.0404   0.0607
-## sigma           8.0152 1.28e-04  4.6813  11.3491
+##          Estimate   Pr(>t)   Lower    Upper
+## parent_0 101.7315 6.42e-11 91.9259 111.5371
+## k_parent   0.0495 1.70e-07  0.0404   0.0607
+## sigma      8.0152 1.28e-04  4.6813  11.3491
 ## 
 ## $IORE
-##                     Estimate   Pr(>t)  Lower   Upper
-## parent_0               96.86 3.32e-12 90.848 102.863
-## k__iore_parent_sink     2.96 7.91e-02  0.687  12.761
-## N_parent                0.00 5.00e-01 -0.372   0.372
-## sigma                   4.90 1.77e-04  2.837   6.968
+##                Estimate   Pr(>t)  Lower   Upper
+## parent_0          96.86 3.32e-12 90.848 102.863
+## k__iore_parent     2.96 7.91e-02  0.687  12.761
+## N_parent           0.00 5.00e-01 -0.372   0.372
+## sigma              4.90 1.77e-04  2.837   6.968
 ## 
 ## $DFOP
-##          Estimate   Pr(>t)   Lower    Upper
-## parent_0 101.7315 1.41e-09 91.6534 111.8097
-## k1         0.0495 6.41e-04  0.0303   0.0809
-## k2         0.0495 1.66e-02  0.0201   0.1219
-## g          0.6634 5.00e-01  0.0000   1.0000
-## sigma      8.0152 2.50e-04  4.5886  11.4418
+##          Estimate   Pr(>t)   Lower   Upper
+## parent_0 101.7315 1.41e-09 91.6534 111.810
+## k1         0.0495 6.48e-04  0.0303   0.081
+## k2         0.0495 1.67e-02  0.0201   0.122
+## g          0.6634 5.00e-01  0.0000   1.000
+## sigma      8.0152 2.50e-04  4.5886  11.442
 ## 
 ## 
 ## DTx values:
@@ -1952,7 +1914,7 @@ div.tocify {
 ## DFOP 14.0 46.5    14.00
 ## 
 ## Representative half-life:
-## [1] 8.862193

+## [1] 8.86

Here, a value below N is given for the IORE model, because the data suggests a faster decline towards the end of the experiment, which appears physically rather unlikely in the case of a photolysis study. It seems PestDF does not constrain N to values above zero, thus the slight difference in IORE model parameters between PestDF and mkin.

@@ -1964,7 +1926,7 @@ div.tocify {

## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c

## The half-life obtained from the IORE model may be used

plot(p11)

print(p11)

## Sums of squares:
 ##      SFO     IORE     DFOP 
@@ -1975,23 +1937,23 @@ div.tocify {
 ## 
 ## Parameters:
 ## $SFO
-##               Estimate   Pr(>t)    Lower    Upper
-## parent_0      96.15820 4.83e-13 90.24934 1.02e+02
-## k_parent_sink  0.00321 4.71e-05  0.00222 4.64e-03
-## sigma          6.43473 1.28e-04  3.75822 9.11e+00
+##          Estimate   Pr(>t)    Lower    Upper
+## parent_0 96.15820 4.83e-13 90.24934 1.02e+02
+## k_parent  0.00321 4.71e-05  0.00222 4.64e-03
+## sigma     6.43473 1.28e-04  3.75822 9.11e+00
 ## 
 ## $IORE
-##                     Estimate Pr(>t)    Lower    Upper
-## parent_0            1.05e+02     NA 9.90e+01 1.10e+02
-## k__iore_parent_sink 3.11e-17     NA 1.35e-20 7.18e-14
-## N_parent            8.36e+00     NA 6.62e+00 1.01e+01
-## sigma               3.82e+00     NA 2.21e+00 5.44e+00
+##                Estimate Pr(>t)    Lower    Upper
+## parent_0       1.05e+02     NA 9.90e+01 1.10e+02
+## k__iore_parent 3.11e-17     NA 1.35e-20 7.18e-14
+## N_parent       8.36e+00     NA 6.62e+00 1.01e+01
+## sigma          3.82e+00     NA 2.21e+00 5.44e+00
 ## 
 ## $DFOP
 ##          Estimate   Pr(>t)    Lower    Upper
 ## parent_0 1.05e+02 9.47e-13  99.9990 109.1224
 ## k1       4.41e-02 5.95e-03   0.0296   0.0658
-## k2       9.20e-13 5.00e-01   0.0000      Inf
+## k2       7.25e-13 5.00e-01   0.0000      Inf
 ## g        3.22e-01 1.45e-03   0.2814   0.3650
 ## sigma    3.22e+00 3.52e-04   1.8410   4.5906
 ## 
@@ -2000,10 +1962,10 @@ div.tocify {
 ##          DT50     DT90 DT50_rep
 ## SFO  2.16e+02 7.18e+02 2.16e+02
 ## IORE 9.73e+02 1.37e+08 4.11e+07
-## DFOP 3.31e+11 2.08e+12 7.53e+11
+## DFOP 4.21e+11 2.64e+12 9.56e+11
 ## 
 ## Representative half-life:
-## [1] 41148169

+## [1] 41148171

In this case, the DFOP fit reported for PestDF resulted in a negative value for the slower rate constant, which is not possible in mkin. The other results are in agreement.

@@ -2013,17 +1975,12 @@ div.tocify {

Example on page 12, upper panel

p12a <- nafta(NAFTA_SOP_Attachment[["p12a"]])

## Warning in summary.mkinfit(x): Could not estimate covariance matrix;
-## singular system.

## Warning in sqrt(diag(covar)): NaNs wurden erzeugt

## Warning in sqrt(diag(covar_notrans)): NaNs wurden erzeugt

## Warning in sqrt(1/diag(V)): NaNs wurden erzeugt

## Warning in cov2cor(ans$cov.unscaled): diag(.) had 0 or NA entries; non-
-## finite result is doubtful

## Warning in summary.mkinfit(x): Could not calculate correlation; no covariance
+## matrix

## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c

## The half-life obtained from the IORE model may be used

plot(p12a)

print(p12a)

## Sums of squares:
 ##      SFO     IORE     DFOP 
@@ -2034,24 +1991,24 @@ div.tocify {
 ## 
 ## Parameters:
 ## $SFO
-##               Estimate   Pr(>t)  Lower   Upper
-## parent_0       100.521 8.75e-12 92.461 108.581
-## k_parent_sink    0.124 3.61e-08  0.104   0.148
-## sigma            7.048 1.28e-04  4.116   9.980
+##          Estimate   Pr(>t)  Lower   Upper
+## parent_0  100.521 8.75e-12 92.461 108.581
+## k_parent    0.124 3.61e-08  0.104   0.148
+## sigma       7.048 1.28e-04  4.116   9.980
 ## 
 ## $IORE
-##                     Estimate Pr(>t) Lower Upper
-## parent_0              96.823     NA    NA    NA
-## k__iore_parent_sink    2.436     NA    NA    NA
-## N_parent               0.263     NA    NA    NA
-## sigma                  3.965     NA    NA    NA
+##                Estimate Pr(>t) Lower Upper
+## parent_0         96.823     NA    NA    NA
+## k__iore_parent    2.436     NA    NA    NA
+## N_parent          0.263     NA    NA    NA
+## sigma             3.965     NA    NA    NA
 ## 
 ## $DFOP
 ##          Estimate   Pr(>t)   Lower   Upper
 ## parent_0  100.521 2.74e-10 92.2366 108.805
-## k1          0.124 5.43e-06  0.0959   0.161
-## k2          0.124 6.45e-02  0.0315   0.490
-## g           0.880      NaN      NA      NA
+## k1          0.124 5.75e-06  0.0958   0.161
+## k2          0.124 6.72e-02  0.0319   0.484
+## g           0.877 5.00e-01  0.0000   1.000
 ## sigma       7.048 2.50e-04  4.0349  10.061
 ## 
 ## 
@@ -2062,23 +2019,23 @@ div.tocify {
 ## DFOP 5.58 18.5     5.58
 ## 
 ## Representative half-life:
-## [1] 3.987308

+## [1] 3.99

Example on page 12, lower panel

p12b <- nafta(NAFTA_SOP_Attachment[["p12b"]])

## Warning in sqrt(diag(covar)): NaNs wurden erzeugt

## Warning in qt(alpha/2, rdf): NaNs wurden erzeugt

## Warning in qt(1 - alpha/2, rdf): NaNs wurden erzeugt

## Warning in sqrt(diag(covar_notrans)): NaNs wurden erzeugt

## Warning in pt(abs(tval), rdf, lower.tail = FALSE): NaNs wurden erzeugt

## Warning in sqrt(1/diag(V)): NaNs wurden erzeugt

## Warning in cov2cor(ans$cov.unscaled): diag(.) had 0 or NA entries; non-
-## finite result is doubtful

## Warning in sqrt(diag(covar)): NaNs produced

## Warning in qt(alpha/2, rdf): NaNs produced

## Warning in qt(1 - alpha/2, rdf): NaNs produced

## Warning in sqrt(diag(covar_notrans)): NaNs produced

## Warning in pt(abs(tval), rdf, lower.tail = FALSE): NaNs produced

## Warning in sqrt(1/diag(V)): NaNs produced

## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is
+## doubtful

## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c

## The half-life obtained from the IORE model may be used

plot(p12b)

print(p12b)

## Sums of squares:
 ##      SFO     IORE     DFOP 
@@ -2089,24 +2046,24 @@ div.tocify {
 ## 
 ## Parameters:
 ## $SFO
-##               Estimate  Pr(>t)   Lower    Upper
-## parent_0       97.6840 0.00039 85.9388 109.4292
-## k_parent_sink   0.0589 0.00261  0.0431   0.0805
-## sigma           3.4323 0.04356 -1.2377   8.1023
+##          Estimate  Pr(>t)   Lower    Upper
+## parent_0  97.6840 0.00039 85.9388 109.4292
+## k_parent   0.0589 0.00261  0.0431   0.0805
+## sigma      3.4323 0.04356 -1.2377   8.1023
 ## 
 ## $IORE
-##                     Estimate Pr(>t)     Lower  Upper
-## parent_0              95.523 0.0055 74.539157 116.51
-## k__iore_parent_sink    0.333 0.1433  0.000717 154.57
-## N_parent               0.568 0.0677 -0.989464   2.13
-## sigma                  1.953 0.0975 -5.893100   9.80
+##                Estimate Pr(>t)     Lower  Upper
+## parent_0         95.523 0.0055 74.539157 116.51
+## k__iore_parent    0.333 0.1433  0.000717 154.57
+## N_parent          0.568 0.0677 -0.989464   2.13
+## sigma             1.953 0.0975 -5.893100   9.80
 ## 
 ## $DFOP
 ##          Estimate Pr(>t) Lower Upper
 ## parent_0  97.6840    NaN   NaN   NaN
 ## k1         0.0589    NaN    NA    NA
 ## k2         0.0589    NaN    NA    NA
-## g          0.8275    NaN    NA    NA
+## g          0.6902    NaN    NA    NA
 ## sigma      3.4323    NaN   NaN   NaN
 ## 
 ## 
@@ -2117,7 +2074,7 @@ div.tocify {
 ## DFOP 11.8 39.1    11.80
 ## 
 ## Representative half-life:
-## [1] 9.461912

+## [1] 9.46

Example on page 13

@@ -2125,7 +2082,7 @@ div.tocify {

## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c

## The half-life obtained from the IORE model may be used

plot(p13)

print(p13)

## Sums of squares:
 ##      SFO     IORE     DFOP 
@@ -2136,22 +2093,22 @@ div.tocify {
 ## 
 ## Parameters:
 ## $SFO
-##               Estimate   Pr(>t)    Lower    Upper
-## parent_0      92.73500 5.99e-17 89.61936 95.85065
-## k_parent_sink  0.00258 2.42e-09  0.00223  0.00299
-## sigma          3.41172 7.07e-05  2.05455  4.76888
+##          Estimate   Pr(>t)    Lower    Upper
+## parent_0 92.73500 5.99e-17 89.61936 95.85065
+## k_parent  0.00258 2.42e-09  0.00223  0.00299
+## sigma     3.41172 7.07e-05  2.05455  4.76888
 ## 
 ## $IORE
-##                     Estimate   Pr(>t)    Lower  Upper
-## parent_0             91.6016 6.34e-16 88.53086 94.672
-## k__iore_parent_sink   0.0396 2.36e-01  0.00207  0.759
-## N_parent              0.3541 1.46e-01 -0.35153  1.060
-## sigma                 3.0811 9.64e-05  1.84296  4.319
+##                Estimate   Pr(>t)    Lower  Upper
+## parent_0        91.6016 6.34e-16 88.53086 94.672
+## k__iore_parent   0.0396 2.36e-01  0.00207  0.759
+## N_parent         0.3541 1.46e-01 -0.35153  1.060
+## sigma            3.0811 9.64e-05  1.84296  4.319
 ## 
 ## $DFOP
 ##          Estimate   Pr(>t)    Lower    Upper
 ## parent_0 92.73500 9.25e-15 8.95e+01 9.59e+01
-## k1        0.00258 4.28e-01 1.38e-08 4.82e+02
+## k1        0.00258 4.28e-01 1.45e-08 4.61e+02
 ## k2        0.00258 3.69e-08 2.20e-03 3.03e-03
 ## g         0.00442 5.00e-01 0.00e+00 1.00e+00
 ## sigma     3.41172 1.35e-04 2.02e+00 4.80e+00
@@ -2164,20 +2121,20 @@ div.tocify {
 ## DFOP  269  892      269
 ## 
 ## Representative half-life:
-## [1] 168.5123

+## [1] 168.51

DT50 not observed in the study and DFOP problems in PestDF

p14 <- nafta(NAFTA_SOP_Attachment[["p14"]])

## Warning in sqrt(diag(covar)): NaNs wurden erzeugt

## Warning in sqrt(1/diag(V)): NaNs wurden erzeugt

## Warning in cov2cor(ans$cov.unscaled): diag(.) had 0 or NA entries; non-
-## finite result is doubtful

## Warning in sqrt(diag(covar)): NaNs produced

## Warning in sqrt(1/diag(V)): NaNs produced

## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is
+## doubtful

## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c

## The half-life obtained from the IORE model may be used

plot(p14)

print(p14)

## Sums of squares:
 ##      SFO     IORE     DFOP 
@@ -2188,23 +2145,23 @@ div.tocify {
 ## 
 ## Parameters:
 ## $SFO
-##               Estimate   Pr(>t)    Lower    Upper
-## parent_0      99.47124 2.06e-30 98.42254 1.01e+02
-## k_parent_sink  0.00279 3.75e-15  0.00256 3.04e-03
-## sigma          1.55616 3.81e-06  1.03704 2.08e+00
+##          Estimate   Pr(>t)    Lower    Upper
+## parent_0 99.47124 2.06e-30 98.42254 1.01e+02
+## k_parent  0.00279 3.75e-15  0.00256 3.04e-03
+## sigma     1.55616 3.81e-06  1.03704 2.08e+00
 ## 
 ## $IORE
-##                     Estimate Pr(>t) Lower Upper
-## parent_0            1.00e+02     NA   NaN   NaN
-## k__iore_parent_sink 9.44e-08     NA   NaN   NaN
-## N_parent            3.31e+00     NA   NaN   NaN
-## sigma               1.20e+00     NA 0.796   1.6
+##                Estimate Pr(>t) Lower Upper
+## parent_0       1.00e+02     NA   NaN   NaN
+## k__iore_parent 9.44e-08     NA   NaN   NaN
+## N_parent       3.31e+00     NA   NaN   NaN
+## sigma          1.20e+00     NA 0.796   1.6
 ## 
 ## $DFOP
 ##          Estimate   Pr(>t)    Lower    Upper
 ## parent_0 1.00e+02 2.96e-28 99.40280 101.2768
 ## k1       9.53e-03 1.20e-01  0.00638   0.0143
-## k2       6.17e-12 5.00e-01  0.00000      Inf
+## k2       7.70e-12 5.00e-01  0.00000      Inf
 ## g        3.98e-01 2.19e-01  0.30481   0.4998
 ## sigma    1.17e+00 7.68e-06  0.77406   1.5610
 ## 
@@ -2213,24 +2170,23 @@ div.tocify {
 ##          DT50     DT90 DT50_rep
 ## SFO  2.48e+02 8.25e+02 2.48e+02
 ## IORE 4.34e+02 2.22e+04 6.70e+03
-## DFOP 3.00e+10 2.91e+11 1.12e+11
+## DFOP 2.41e+10 2.33e+11 9.00e+10
 ## 
 ## Representative half-life:
-## [1] 6697.437

+## [1] 6697.44

The slower rate constant reported by PestDF is negative, which is not physically realistic, and not possible in mkin. The other fits give the same results in mkin and PestDF.

N is less than 1 and DFOP fraction parameter is below zero

p15a <- nafta(NAFTA_SOP_Attachment[["p15a"]])

## Warning in sqrt(diag(covar)): NaNs wurden erzeugt

## Warning in sqrt(diag(covar_notrans)): NaNs wurden erzeugt

## Warning in sqrt(1/diag(V)): NaNs wurden erzeugt

## Warning in cov2cor(ans$cov.unscaled): diag(.) had 0 or NA entries; non-
-## finite result is doubtful

## Warning in sqrt(diag(covar)): NaNs produced

## Warning in sqrt(1/diag(V)): NaNs produced

## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is
+## doubtful

## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c

## The half-life obtained from the IORE model may be used

plot(p15a)

print(p15a)

## Sums of squares:
 ##      SFO     IORE     DFOP 
@@ -2241,25 +2197,25 @@ div.tocify {
 ## 
 ## Parameters:
 ## $SFO
-##               Estimate   Pr(>t)    Lower   Upper
-## parent_0      97.96751 2.00e-15 94.32049 101.615
-## k_parent_sink  0.00952 4.93e-09  0.00824   0.011
-## sigma          4.18778 1.28e-04  2.44588   5.930
+##          Estimate   Pr(>t)    Lower   Upper
+## parent_0 97.96751 2.00e-15 94.32049 101.615
+## k_parent  0.00952 4.93e-09  0.00824   0.011
+## sigma     4.18778 1.28e-04  2.44588   5.930
 ## 
 ## $IORE
-##                     Estimate   Pr(>t)  Lower  Upper
-## parent_0              95.874 2.94e-15 92.937 98.811
-## k__iore_parent_sink    0.629 2.11e-01  0.044  8.982
-## N_parent               0.000 5.00e-01 -0.642  0.642
-## sigma                  3.105 1.78e-04  1.795  4.416
+##                Estimate   Pr(>t)  Lower  Upper
+## parent_0         95.874 2.94e-15 92.937 98.811
+## k__iore_parent    0.629 2.11e-01  0.044  8.982
+## N_parent          0.000 5.00e-01 -0.642  0.642
+## sigma             3.105 1.78e-04  1.795  4.416
 ## 
 ## $DFOP
-##          Estimate   Pr(>t)    Lower    Upper
-## parent_0 97.96751 2.85e-13 94.21913 101.7159
-## k1        0.00952 5.68e-02  0.00262   0.0347
-## k2        0.00952 1.52e-04  0.00639   0.0142
-## g         0.22357      NaN       NA       NA
-## sigma     4.18778 2.50e-04  2.39747   5.9781
+##          Estimate Pr(>t)    Lower    Upper
+## parent_0 97.96752     NA 94.21914 101.7159
+## k1        0.00952     NA  0.00241   0.0377
+## k2        0.00952     NA  0.00747   0.0121
+## g         0.17247     NA       NA       NA
+## sigma     4.18778     NA  2.39747   5.9781
 ## 
 ## 
 ## DTx values:
@@ -2269,12 +2225,16 @@ div.tocify {
 ## DFOP 72.8  242     72.8
 ## 
 ## Representative half-life:
-## [1] 41.32749

+## [1] 41.33

p15b <- nafta(NAFTA_SOP_Attachment[["p15b"]])

## Warning in sqrt(diag(covar)): NaNs produced

## Warning in sqrt(1/diag(V)): NaNs produced

## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is
+## doubtful

## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c

## The half-life obtained from the IORE model may be used

plot(p15b)

print(p15b)

## Sums of squares:
 ##       SFO      IORE      DFOP 
@@ -2285,25 +2245,25 @@ div.tocify {
 ## 
 ## Parameters:
 ## $SFO
-##               Estimate   Pr(>t)    Lower    Upper
-## parent_0      1.01e+02 3.06e-17 98.31594 1.03e+02
-## k_parent_sink 4.86e-03 2.48e-10  0.00435 5.42e-03
-## sigma         2.76e+00 1.28e-04  1.61402 3.91e+00
+##          Estimate   Pr(>t)    Lower    Upper
+## parent_0 1.01e+02 3.06e-17 98.31594 1.03e+02
+## k_parent 4.86e-03 2.48e-10  0.00435 5.42e-03
+## sigma    2.76e+00 1.28e-04  1.61402 3.91e+00
 ## 
 ## $IORE
-##                     Estimate   Pr(>t)    Lower  Upper
-## parent_0               99.83 1.81e-16 97.51349 102.14
-## k__iore_parent_sink     0.38 3.22e-01  0.00352  41.05
-## N_parent                0.00 5.00e-01 -1.07695   1.08
-## sigma                   2.21 2.57e-04  1.23245   3.19
+##                Estimate   Pr(>t)    Lower  Upper
+## parent_0          99.83 1.81e-16 97.51348 102.14
+## k__iore_parent     0.38 3.22e-01  0.00352  41.05
+## N_parent           0.00 5.00e-01 -1.07696   1.08
+## sigma              2.21 2.57e-04  1.23245   3.19
 ## 
 ## $DFOP
 ##          Estimate Pr(>t)    Lower    Upper
-## parent_0 1.01e+02     NA 9.82e+01 1.04e+02
-## k1       4.86e-03     NA 6.49e-04 3.64e-02
-## k2       4.86e-03     NA 3.36e-03 7.03e-03
-## g        1.50e-01     NA 0.00e+00 1.00e+00
-## sigma    2.76e+00     NA 1.58e+00 3.94e+00
+## parent_0 1.01e+02     NA 98.24464 1.04e+02
+## k1       4.86e-03     NA  0.00068 3.47e-02
+## k2       4.86e-03     NA  0.00338 6.99e-03
+## g        1.50e-01     NA       NA       NA
+## sigma    2.76e+00     NA  1.58208 3.94e+00
 ## 
 ## 
 ## DTx values:
@@ -2313,7 +2273,7 @@ div.tocify {
 ## DFOP  143  474    143.0
 ## 
 ## Representative half-life:
-## [1] 71.18014

+## [1] 71.18

In mkin, only the IORE fit is affected (deemed unrealistic), as the fraction parameter of the DFOP model is restricted to the interval between 0 and 1 in mkin. The SFO fits give the same results for both mkin and PestDF.

@@ -2324,7 +2284,7 @@ div.tocify {

## to the terminal degradation rate found with the DFOP model.

## The representative half-life obtained from the DFOP model may be used

plot(p16)

print(p16)

## Sums of squares:
 ##      SFO     IORE     DFOP 
@@ -2335,22 +2295,22 @@ div.tocify {
 ## 
 ## Parameters:
 ## $SFO
-##               Estimate   Pr(>t)  Lower Upper
-## parent_0        71.953 2.33e-13 60.509 83.40
-## k_parent_sink    0.159 4.86e-05  0.102  0.25
-## sigma           11.302 1.25e-08  8.308 14.30
+##          Estimate   Pr(>t)  Lower Upper
+## parent_0   71.953 2.33e-13 60.509 83.40
+## k_parent    0.159 4.86e-05  0.102  0.25
+## sigma      11.302 1.25e-08  8.308 14.30
 ## 
 ## $IORE
-##                     Estimate   Pr(>t)    Lower    Upper
-## parent_0            8.74e+01 2.48e-16 7.72e+01 97.52972
-## k__iore_parent_sink 4.55e-04 2.16e-01 3.48e-05  0.00595
-## N_parent            2.70e+00 1.21e-08 1.99e+00  3.40046
-## sigma               8.29e+00 1.61e-08 6.09e+00 10.49062
+##                Estimate   Pr(>t)    Lower    Upper
+## parent_0       8.74e+01 2.48e-16 7.72e+01 97.52972
+## k__iore_parent 4.55e-04 2.16e-01 3.48e-05  0.00595
+## N_parent       2.70e+00 1.21e-08 1.99e+00  3.40046
+## sigma          8.29e+00 1.61e-08 6.09e+00 10.49062
 ## 
 ## $DFOP
 ##          Estimate   Pr(>t)   Lower  Upper
 ## parent_0  88.5333 7.40e-18 79.9836 97.083
-## k1        18.6317 5.00e-01  0.0000    Inf
+## k1        18.5560 5.00e-01  0.0000    Inf
 ## k2         0.0776 1.41e-05  0.0518  0.116
 ## g          0.4733 1.41e-09  0.3674  0.582
 ## sigma      7.1902 2.11e-08  5.2785  9.102
@@ -2363,7 +2323,7 @@ div.tocify {
 ## DFOP 0.67 21.4     8.93
 ## 
 ## Representative half-life:
-## [1] 8.932679

+## [1] 8.93

In PestDF, the DFOP fit seems to have stuck in a local minimum, as mkin finds a solution with a much lower χ² error level. As the half-life from the slower rate constant of the DFOP model is larger than the IORE derived half-life, the NAFTA recommendation obtained with mkin is to use the DFOP representative half-life of 8.9 days.

@@ -2400,6 +2360,49 @@ $(document).ready(function () { + + + + + + + + diff --git a/vignettes/web_only/NAFTA_examples.rmd b/vignettes/web_only/NAFTA_examples.rmd index 26a9240a..d18a3e84 100644 --- a/vignettes/web_only/NAFTA_examples.rmd +++ b/vignettes/web_only/NAFTA_examples.rmd @@ -91,7 +91,7 @@ used by mkin for the IORE model are used. Therefore, a lower value for the rate constant is used here. ```{r p8} -p8 <- nafta(NAFTA_SOP_Attachment[["p8"]], parms.ini = c(k__iore_parent_sink = 1e-3)) +p8 <- nafta(NAFTA_SOP_Attachment[["p8"]], parms.ini = c(k__iore_parent = 1e-3)) plot(p8) print(p8) ``` diff --git a/vignettes/web_only/mkin_benchmarks.rda b/vignettes/web_only/mkin_benchmarks.rda index cc08f7ad..128473e7 100644 Binary files a/vignettes/web_only/mkin_benchmarks.rda and b/vignettes/web_only/mkin_benchmarks.rda differ -- cgit v1.2.1