Example evaluation of FOCUS Example Dataset D

Johannes Ranke

2019-07-09

2019-09-18

FOCUS_D.Rmd

@@ -98,8 +100,8 @@

This is just a very simple vignette showing how to fit a degradation model for a parent compound with one transformation product using mkin. After loading the library we look at the data. We have observed concentrations in the column named value at the times specified in column time for the two observed variables named parent and m1.

library(mkin, quietly = TRUE)
-print(FOCUS_2006_D)

library(mkin, quietly = TRUE)
+print(FOCUS_2006_D)

##      name time  value
 ## 1  parent    0  99.46
 ## 2  parent    0 102.04
@@ -149,7 +151,7 @@
 The call to mkinmod returns a degradation model. The differential equations represented in R code can be found in the character vector $diffs of the mkinmod object. If a C compiler (gcc) is installed and functional, the differential equation model will be compiled from auto-generated C code.
 SFO_SFO <- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"))
 ## Successfully compiled differential equation model from auto-generated C code.
-print(SFO_SFO$diffs)
+print(SFO_SFO$diffs)
 ##                                                       parent 
 ## "d_parent = - k_parent_sink * parent - k_parent_m1 * parent" 
 ##                                                           m1 
@@ -159,17 +161,17 @@
 ## Warning in mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE): Observations with
 ## value of zero were removed from the data
 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"))
+plot_sep(fit, lpos = c("topright", "bottomright"))
 
 Confidence intervals for the parameter estimates are obtained using the mkinparplot function.
 mkinparplot(fit)
 
 A comprehensive report of the results is obtained using the summary method for mkinfit objects.
-summary(fit)
+summary(fit)
 ## mkin version used for fitting:    0.9.49.6 
-## R version used for fitting:       3.6.0 
-## Date of fit:     Tue Jul  9 09:00:18 2019 
-## Date of summary: Tue Jul  9 09:00:18 2019 
+## R version used for fitting:       3.6.1 
+## Date of fit:     Wed Sep 18 12:57:33 2019 
+## Date of summary: Wed Sep 18 12:57:33 2019 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent_sink * parent - k_parent_m1 * parent
@@ -177,7 +179,7 @@
 ## 
 ## Model predictions using solution type deSolve 
 ## 
-## Fitted using 389 model solutions performed in 1.003 s
+## Fitted using 389 model solutions performed in 1.01 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -301,17 +303,20 @@


 
 
+
       
   Developed by Johannes Ranke.
 
 
 
-  Site built with pkgdown 1.3.0.
+  Site built with pkgdown 1.4.1.
 
+


 
   
 
+
   
 
diff --git a/docs/articles/FOCUS_L.html b/docs/articles/FOCUS_L.html
index 779635cd..81659030 100644
--- a/docs/articles/FOCUS_L.html
+++ b/docs/articles/FOCUS_L.html
@@ -7,8 +7,9 @@
 
 Example evaluation of FOCUS Laboratory Data L1 to L3 • mkin
 
-
-
+
+
+
 
 
 
@@ -82,13 +83,14 @@
 
 
       
+
       
   
     
       Example evaluation of FOCUS Laboratory Data L1 to L3
                         Johannes Ranke
             
-            2019-07-09
+            2019-09-18
       
       
       FOCUS_L.Rmd
@@ -101,28 +103,28 @@
 
 Laboratory Data L1
 The following code defines example dataset L1 from the FOCUS kinetics report, p. 284:
-library("mkin", quietly = TRUE)
-FOCUS_2006_L1 = data.frame(
-  t = rep(c(0, 1, 2, 3, 5, 7, 14, 21, 30), each = 2),
-  parent = c(88.3, 91.4, 85.6, 84.5, 78.9, 77.6,
+library("mkin", quietly = TRUE)
+FOCUS_2006_L1 = data.frame(
+  t = rep(c(0, 1, 2, 3, 5, 7, 14, 21, 30), each = 2),
+  parent = c(88.3, 91.4, 85.6, 84.5, 78.9, 77.6,
              72.0, 71.9, 50.3, 59.4, 47.0, 45.1,
              27.7, 27.3, 10.0, 10.4, 2.9, 4.0))
 FOCUS_2006_L1_mkin <- mkin_wide_to_long(FOCUS_2006_L1)
 Here we use the assumptions of simple first order (SFO), the case of declining rate constant over time (FOMC) and the case of two different phases of the kinetics (DFOP). For a more detailed discussion of the models, please see the FOCUS kinetics report.
 Since mkin version 0.9-32 (July 2014), we can use shorthand notation like "SFO" for parent only degradation models. The following two lines fit the model and produce the summary report of the model fit. This covers the numerical analysis given in the FOCUS report.
 m.L1.SFO <- mkinfit("SFO", FOCUS_2006_L1_mkin, quiet = TRUE)
-summary(m.L1.SFO)
+summary(m.L1.SFO)
 ## mkin version used for fitting:    0.9.49.6 
-## R version used for fitting:       3.6.0 
-## Date of fit:     Tue Jul  9 09:00:20 2019 
-## Date of summary: Tue Jul  9 09:00:20 2019 
+## R version used for fitting:       3.6.1 
+## Date of fit:     Wed Sep 18 12:57:35 2019 
+## Date of summary: Wed Sep 18 12:57:35 2019 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent_sink * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 133 model solutions performed in 0.282 s
+## Fitted using 133 model solutions performed in 0.287 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -198,7 +200,7 @@
 ##    30   parent      2.9     5.251  -2.3513
 ##    30   parent      4.0     5.251  -1.2513
 A plot of the fit is obtained with the plot function for mkinfit objects.
-plot(m.L1.SFO, show_errmin = TRUE, main = "FOCUS L1 - SFO")
+plot(m.L1.SFO, show_errmin = TRUE, main = "FOCUS L1 - SFO")
 
 The residual plot can be easily obtained by
 mkinresplot(m.L1.SFO, ylab = "Observed", xlab = "Time")
@@ -207,17 +209,17 @@
 m.L1.FOMC <- mkinfit("FOMC", FOCUS_2006_L1_mkin, quiet=TRUE)
 ## Warning in mkinfit("FOMC", FOCUS_2006_L1_mkin, quiet = TRUE): Optimisation did not converge:
 ## false convergence (8)
-plot(m.L1.FOMC, show_errmin = TRUE, main = "FOCUS L1 - FOMC")
+plot(m.L1.FOMC, show_errmin = TRUE, main = "FOCUS L1 - FOMC")
 
-summary(m.L1.FOMC, data = FALSE)
+summary(m.L1.FOMC, data = FALSE)
 ## 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
 ## mkin version used for fitting:    0.9.49.6 
-## R version used for fitting:       3.6.0 
-## Date of fit:     Tue Jul  9 09:00:22 2019 
-## Date of summary: Tue Jul  9 09:00:22 2019 
+## R version used for fitting:       3.6.1 
+## Date of fit:     Wed Sep 18 12:57:37 2019 
+## Date of summary: Wed Sep 18 12:57:37 2019 
 ## 
 ## 
 ## Warning: Optimisation did not converge:
@@ -229,7 +231,7 @@
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 899 model solutions performed in 1.881 s
+## Fitted using 899 model solutions performed in 1.921 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -292,9 +294,9 @@
 
 Laboratory Data L2
 The following code defines example dataset L2 from the FOCUS kinetics report, p. 287:
-FOCUS_2006_L2 = data.frame(
-  t = rep(c(0, 1, 3, 7, 14, 28), each = 2),
-  parent = c(96.1, 91.8, 41.4, 38.7,
+FOCUS_2006_L2 = data.frame(
+  t = rep(c(0, 1, 3, 7, 14, 28), each = 2),
+  parent = c(96.1, 91.8, 41.4, 38.7,
              19.3, 22.3, 4.6, 4.6,
              2.6, 1.2, 0.3, 0.6))
 FOCUS_2006_L2_mkin <- mkin_wide_to_long(FOCUS_2006_L2)
@@ -303,7 +305,7 @@
 SFO fit for L2
 Again, the SFO model is fitted and the result is plotted. The residual plot can be obtained simply by adding the argument show_residuals to the plot command.
 m.L2.SFO <- mkinfit("SFO", FOCUS_2006_L2_mkin, quiet=TRUE)
-plot(m.L2.SFO, show_residuals = TRUE, show_errmin = TRUE,
+plot(m.L2.SFO, show_residuals = TRUE, show_errmin = TRUE,
      main = "FOCUS L2 - SFO")
 
 The \(\chi^2\) error level of 14% suggests that the model does not fit very well. This is also obvious from the plots of the fit, in which we have included the residual plot.
@@ -315,21 +317,21 @@
 FOMC fit for L2
 For comparison, the FOMC model is fitted as well, and the \(\chi^2\) error level is checked.
 m.L2.FOMC <- mkinfit("FOMC", FOCUS_2006_L2_mkin, quiet = TRUE)
-plot(m.L2.FOMC, show_residuals = TRUE,
+plot(m.L2.FOMC, show_residuals = TRUE,
      main = "FOCUS L2 - FOMC")
 
-summary(m.L2.FOMC, data = FALSE)
+summary(m.L2.FOMC, data = FALSE)
 ## mkin version used for fitting:    0.9.49.6 
-## R version used for fitting:       3.6.0 
-## Date of fit:     Tue Jul  9 09:00:23 2019 
-## Date of summary: Tue Jul  9 09:00:23 2019 
+## R version used for fitting:       3.6.1 
+## Date of fit:     Wed Sep 18 12:57:38 2019 
+## Date of summary: Wed Sep 18 12:57:38 2019 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 239 model solutions performed in 0.495 s
+## Fitted using 239 model solutions performed in 0.49 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -391,14 +393,14 @@
 DFOP fit for L2
 Fitting the four parameter DFOP model further reduces the \(\chi^2\) error level.
 m.L2.DFOP <- mkinfit("DFOP", FOCUS_2006_L2_mkin, quiet = TRUE)
-plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE,
+plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE,
      main = "FOCUS L2 - DFOP")
 
-summary(m.L2.DFOP, data = FALSE)
+summary(m.L2.DFOP, data = FALSE)
 ## mkin version used for fitting:    0.9.49.6 
-## R version used for fitting:       3.6.0 
-## Date of fit:     Tue Jul  9 09:00:24 2019 
-## Date of summary: Tue Jul  9 09:00:25 2019 
+## R version used for fitting:       3.6.1 
+## Date of fit:     Wed Sep 18 12:57:40 2019 
+## Date of summary: Wed Sep 18 12:57:40 2019 
 ## 
 ## Equations:
 ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) *
@@ -407,7 +409,7 @@
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 572 model solutions performed in 1.193 s
+## Fitted using 572 model solutions performed in 1.209 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -474,18 +476,18 @@
 
 Laboratory Data L3
 The following code defines example dataset L3 from the FOCUS kinetics report, p. 290.
-FOCUS_2006_L3 = data.frame(
-  t = c(0, 3, 7, 14, 30, 60, 91, 120),
-  parent = c(97.8, 60, 51, 43, 35, 22, 15, 12))
+FOCUS_2006_L3 = data.frame(
+  t = c(0, 3, 7, 14, 30, 60, 91, 120),
+  parent = c(97.8, 60, 51, 43, 35, 22, 15, 12))
 FOCUS_2006_L3_mkin <- mkin_wide_to_long(FOCUS_2006_L3)
 
 
 Fit multiple models
 As of mkin version 0.9-39 (June 2015), we can fit several models to one or more datasets in one call to the function mmkin. The datasets have to be passed in a list, in this case a named list holding only the L3 dataset prepared above.
 # Only use one core here, not to offend the CRAN checks
-mm.L3 <- mmkin(c("SFO", "FOMC", "DFOP"), cores = 1,
-               list("FOCUS L3" = FOCUS_2006_L3_mkin), quiet = TRUE)
-plot(mm.L3)
+mm.L3 <- mmkin(c("SFO", "FOMC", "DFOP"), cores = 1,
+               list("FOCUS L3" = FOCUS_2006_L3_mkin), quiet = TRUE)
+plot(mm.L3)
 
 The \(\chi^2\) error level of 21% as well as the plot suggest that the SFO model does not fit very well. The FOMC model performs better, with an error level at which the \(\chi^2\) test passes of 7%. Fitting the four parameter DFOP model further reduces the \(\chi^2\) error level considerably.
 

@@ -494,11 +496,11 @@
 Accessing mmkin objects
 The objects returned by mmkin are arranged like a matrix, with models as a row index and datasets as a column index.
 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]])
+summary(mm.L3[["DFOP", 1]])
 ## mkin version used for fitting:    0.9.49.6 
-## R version used for fitting:       3.6.0 
-## Date of fit:     Tue Jul  9 09:00:26 2019 
-## Date of summary: Tue Jul  9 09:00:27 2019 
+## R version used for fitting:       3.6.1 
+## Date of fit:     Wed Sep 18 12:57:42 2019 
+## Date of summary: Wed Sep 18 12:57:42 2019 
 ## 
 ## Equations:
 ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) *
@@ -507,7 +509,7 @@
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 373 model solutions performed in 0.797 s
+## Fitted using 373 model solutions performed in 0.783 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -578,7 +580,7 @@
 ##    60   parent     22.0     23.26 -1.25919
 ##    91   parent     15.0     15.18 -0.18181
 ##   120   parent     12.0     10.19  1.81395
-plot(mm.L3[["DFOP", 1]], show_errmin = TRUE)
+plot(mm.L3[["DFOP", 1]], show_errmin = TRUE)
 
 Here, a look to the model plot, the confidence intervals of the parameters and the correlation matrix suggest that the parameter estimates are reliable, and the DFOP model can be used as the best-fit model based on the \(\chi^2\) error level criterion for laboratory data L3.
 This is also an example where the standard t-test for the parameter g_ilr is misleading, as it tests for a significant difference from zero. In this case, zero appears to be the correct value for this parameter, and the confidence interval for the backtransformed parameter g is quite narrow.
@@ -588,30 +590,30 @@
 
 Laboratory Data L4
 The following code defines example dataset L4 from the FOCUS kinetics report, p. 293:
-FOCUS_2006_L4 = data.frame(
-  t = c(0, 3, 7, 14, 30, 60, 91, 120),
-  parent = c(96.6, 96.3, 94.3, 88.8, 74.9, 59.9, 53.5, 49.0))
+FOCUS_2006_L4 = data.frame(
+  t = c(0, 3, 7, 14, 30, 60, 91, 120),
+  parent = c(96.6, 96.3, 94.3, 88.8, 74.9, 59.9, 53.5, 49.0))
 FOCUS_2006_L4_mkin <- mkin_wide_to_long(FOCUS_2006_L4)
 Fits of the SFO and FOMC models, plots and summaries are produced below:
 # Only use one core here, not to offend the CRAN checks
-mm.L4 <- mmkin(c("SFO", "FOMC"), cores = 1,
-               list("FOCUS L4" = FOCUS_2006_L4_mkin),
+mm.L4 <- mmkin(c("SFO", "FOMC"), cores = 1,
+               list("FOCUS L4" = FOCUS_2006_L4_mkin),
                quiet = TRUE)
-plot(mm.L4)
+plot(mm.L4)
 
 The \(\chi^2\) error level of 3.3% as well as the plot suggest that the SFO model fits very well. The error level at which the \(\chi^2\) test passes is slightly lower for the FOMC model. However, the difference appears negligible.
-summary(mm.L4[["SFO", 1]], data = FALSE)
+summary(mm.L4[["SFO", 1]], data = FALSE)
 ## mkin version used for fitting:    0.9.49.6 
-## R version used for fitting:       3.6.0 
-## Date of fit:     Tue Jul  9 09:00:27 2019 
-## Date of summary: Tue Jul  9 09:00:28 2019 
+## R version used for fitting:       3.6.1 
+## Date of fit:     Wed Sep 18 12:57:42 2019 
+## Date of summary: Wed Sep 18 12:57:43 2019 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent_sink * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 142 model solutions performed in 0.299 s
+## Fitted using 142 model solutions performed in 0.288 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -665,18 +667,18 @@
 ## Estimated disappearance times:
 ##        DT50 DT90
 ## parent  106  352
-summary(mm.L4[["FOMC", 1]], data = FALSE)
+summary(mm.L4[["FOMC", 1]], data = FALSE)
 ## mkin version used for fitting:    0.9.49.6 
-## R version used for fitting:       3.6.0 
-## Date of fit:     Tue Jul  9 09:00:28 2019 
-## Date of summary: Tue Jul  9 09:00:28 2019 
+## R version used for fitting:       3.6.1 
+## Date of fit:     Wed Sep 18 12:57:43 2019 
+## Date of summary: Wed Sep 18 12:57:43 2019 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 224 model solutions performed in 0.478 s
+## Fitted using 224 model solutions performed in 0.463 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -771,17 +773,20 @@
 

 
 
+
       
   Developed by Johannes Ranke.
 
 
 
-  Site built with pkgdown 1.3.0.
+  Site built with pkgdown 1.4.1.
 
+
       
 

 
   
 
+
   
 
diff --git a/docs/articles/index.html b/docs/articles/index.html
index 95828a82..cc136fdc 100644
--- a/docs/articles/index.html
+++ b/docs/articles/index.html
@@ -16,13 +16,15 @@
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -44,6 +46,7 @@
 
 
 
+
   
 
   
@@ -113,6 +116,7 @@
 

 
       
+
       
 
 
@@ -139,19 +143,23 @@

Introduction to mkin

Johannes Ranke

2019-07-09

2019-09-18

mkin.Rmd

@@ -102,7 +104,7 @@

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.

library("mkin", quietly = TRUE)
+library("mkin", quietly = TRUE)
 # Define the kinetic model
 m_SFO_SFO_SFO <- mkinmod(parent = mkinsub("SFO", "M1"),
                          M1 = mkinsub("SFO", "M2"),
@@ -111,12 +113,12 @@
 
 
 # Produce model predictions using some arbitrary parameters
-sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
+sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
 d_SFO_SFO_SFO <- mkinpredict(m_SFO_SFO_SFO,
-  c(k_parent = 0.03,
-    f_parent_to_M1 = 0.5, k_M1 = log(2)/100,
-    f_M1_to_M2 = 0.9, k_M2 = log(2)/50),
-  c(parent = 100, M1 = 0, M2 = 0),
+  c(k_parent = 0.03,
+    f_parent_to_M1 = 0.5, k_M1 = log(2)/100,
+    f_M1_to_M2 = 0.9, k_M2 = log(2)/50),
+  c(parent = 100, M1 = 0, M2 = 0),
   sampling_times)
 
 # Generate a dataset by adding normally distributed errors with
@@ -129,7 +131,7 @@
 f_SFO_SFO_SFO <- mkinfit(m_SFO_SFO_SFO, d_SFO_SFO_SFO_err[[1]], quiet = TRUE)
 
 # Plot the results separately for parent and metabolites
-plot_sep(f_SFO_SFO_SFO, lpos = c("topright", "bottomright", "bottomright"))
+plot_sep(f_SFO_SFO_SFO, lpos = c("topright", "bottomright", "bottomright"))

@@ -246,17 +248,20 @@

+ diff --git a/docs/articles/twa.html b/docs/articles/twa.html index 30d54f9e..b15f1522 100644 --- a/docs/articles/twa.html +++ b/docs/articles/twa.html @@ -7,8 +7,9 @@ Calculation of time weighted average concentrations with mkin • mkin - - + + + @@ -82,13 +83,14 @@ +

Calculation of time weighted average concentrations with mkin

Johannes Ranke

2019-07-09

2019-09-18

twa.Rmd

@@ -108,6 +110,10 @@

\[c_\textrm{twa} = \frac{c_0}{t} \left( \frac{g}{k_1} \left( 1 - e^{- k_1 t} \right) + \frac{1-g}{k_2} \left( 1 - e^{- k_2 t} \right) \right) \]

HS for \(t > t_b\):

\[c_\textrm{twa} = \frac{c_0}{t} \left( + \frac{1}{k_1} \left( 1 - e^{- k_1 t_b} \right) + + \frac{e^{- k_1 t_b}}{k_2} \left( 1 - e^{- k_2 (t - t_b)} \right) \right) \]

Often, the ratio between the time weighted average concentration \(c_\textrm{twa}\) and the initial concentration \(c_0\)

\[f_\textrm{twa} = \frac{c_\textrm{twa}}{c_0}\]

is needed. This can be calculated from the fitted initial concentration \(c_0\) and the time weighted average concentration \(c_\textrm{twa}\), or directly from the model parameters using the following formulas:

@@ -120,7 +126,11 @@

\[f_\textrm{twa} = \frac{1}{t} \left( \frac{g}{k_1} \left( 1 - e^{- k_1 t} \right) + \frac{1-g}{k_2} \left( 1 - e^{- k_2 t} \right) \right) \]

Note that a method for calculating maximum moving window time weighted average concentrations for any model fitted by ‘mkinfit’, and also for metabolites in such models, can be found in the ‘pfm’ package, which is currently not published on CRAN, but available from github and documented at my website.

HS for \(t > t_b\):

\[f_\textrm{twa} = \frac{1}{t} \left( + \frac{1}{k_1} \left( 1 - e^{- k_1 t_b} \right) + + \frac{e^{- k_1 t_b}}{k_2} \left( 1 - e^{- k_2 (t - t_b)} \right) \right) \]

Note that a method for calculating maximum moving window time weighted average concentrations for a model fitted by ‘mkinfit’ is include in the ‘mkin’ package (‘max_twa_parent’. If the same is needed for metabolites, the function ‘max_twa’ can be used.

FOCUS Work Group on Degradation Kinetics. 2014. Generic Guidance for Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in Eu Registration. 1.1 ed. http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics.

@@ -134,17 +144,20 @@

+ diff --git a/docs/articles/web_only/FOCUS_Z.html b/docs/articles/web_only/FOCUS_Z.html index 8f3fe97e..aed1debc 100644 --- a/docs/articles/web_only/FOCUS_Z.html +++ b/docs/articles/web_only/FOCUS_Z.html @@ -7,8 +7,9 @@ Example evaluation of FOCUS dataset Z • mkin - - + + + @@ -82,13 +83,14 @@ +

Example evaluation of FOCUS dataset Z

Johannes Ranke

2019-07-09

2019-09-18

FOCUS_Z.Rmd

@@ -102,18 +104,18 @@

The data

The following code defines the example dataset from Appendix 7 to the FOCUS kinetics report (FOCUS Work Group on Degradation Kinetics 2014, 354).

library(mkin, quietly = TRUE)
+library(mkin, quietly = TRUE)
 LOD = 0.5
-FOCUS_2006_Z = data.frame(
-  t = c(0, 0.04, 0.125, 0.29, 0.54, 1, 2, 3, 4, 7, 10, 14, 21,
+FOCUS_2006_Z = data.frame(
+  t = c(0, 0.04, 0.125, 0.29, 0.54, 1, 2, 3, 4, 7, 10, 14, 21,
         42, 61, 96, 124),
-  Z0 = c(100, 81.7, 70.4, 51.1, 41.2, 6.6, 4.6, 3.9, 4.6, 4.3, 6.8,
+  Z0 = c(100, 81.7, 70.4, 51.1, 41.2, 6.6, 4.6, 3.9, 4.6, 4.3, 6.8,
          2.9, 3.5, 5.3, 4.4, 1.2, 0.7),
-  Z1 = c(0, 18.3, 29.6, 46.3, 55.1, 65.7, 39.1, 36, 15.3, 5.6, 1.1,
+  Z1 = c(0, 18.3, 29.6, 46.3, 55.1, 65.7, 39.1, 36, 15.3, 5.6, 1.1,
          1.6, 0.6, 0.5 * LOD, NA, NA, NA),
-  Z2 = c(0, NA, 0.5 * LOD, 2.6, 3.8, 15.3, 37.2, 31.7, 35.6, 14.5,
+  Z2 = c(0, NA, 0.5 * LOD, 2.6, 3.8, 15.3, 37.2, 31.7, 35.6, 14.5,
          0.8, 2.1, 1.9, 0.5 * LOD, NA, NA, NA),
-  Z3 = c(0, NA, NA, NA, NA, 0.5 * LOD, 9.2, 13.1, 22.3, 28.4, 32.5,
+  Z3 = c(0, NA, NA, NA, NA, 0.5 * LOD, 9.2, 13.1, 22.3, 28.4, 32.5,
          25.2, 17.2, 4.8, 4.5, 2.8, 4.4))
 
 FOCUS_2006_Z_mkin <- mkin_wide_to_long(FOCUS_2006_Z)
@@ -130,7 +132,7 @@
 ## with value of zero were removed from the data

plot_sep(m.Z.2a)

summary(m.Z.2a, data = FALSE)$bpar

summary(m.Z.2a, data = FALSE)$bpar

##             Estimate se_notrans    t value     Pr(>t)    Lower    Upper
 ## Z0_0      9.7015e+01   3.393176 2.8591e+01 6.4352e-21 91.66556 102.3642
 ## k_Z0_sink 7.2231e-10   0.225254 3.2067e-09 5.0000e-01  0.00000      Inf
@@ -148,7 +150,7 @@
 ## with value of zero were removed from the data

plot_sep(m.Z.2a.ff)

summary(m.Z.2a.ff, data = FALSE)$bpar

summary(m.Z.2a.ff, data = FALSE)$bpar

##            Estimate se_notrans t value     Pr(>t)    Lower    Upper
 ## Z0_0       97.01488   3.301084 29.3888 3.2971e-21 91.66556 102.3642
 ## k_Z0        2.23601   0.207078 10.7979 3.3309e-11  1.95303   2.5600
@@ -166,7 +168,7 @@
 ## value of zero were removed from the data

plot_sep(m.Z.3)

summary(m.Z.3, data = FALSE)$bpar

summary(m.Z.3, data = FALSE)$bpar

##       Estimate se_notrans t value     Pr(>t)    Lower    Upper
 ## Z0_0  97.01488   2.597342  37.352 2.0106e-24 91.67597 102.3538
 ## k_Z0   2.23601   0.146904  15.221 9.1477e-15  1.95354   2.5593
@@ -201,7 +203,7 @@
 ## 5$bparms.ode, : Observations with value of zero were removed from the data

plot_sep(m.Z.FOCUS)

summary(m.Z.FOCUS, data = FALSE)$bpar

summary(m.Z.FOCUS, data = FALSE)$bpar

##             Estimate se_notrans t value     Pr(>t)     Lower      Upper
 ## Z0_0       96.838607   1.994273 48.5584 4.0283e-42 92.826626 100.850589
 ## k_Z0        2.215405   0.118459 18.7018 1.0415e-23  1.989465   2.467003
@@ -241,7 +243,7 @@
 ## with value of zero were removed from the data

plot_sep(m.Z.mkin.1)

summary(m.Z.mkin.1, data = FALSE)$cov.unscaled

summary(m.Z.mkin.1, data = FALSE)$cov.unscaled

##                            Z0_0 log_k_Z0_Z1 log_k_Z1_Z2 log_k_Z2_sink
 ## Z0_0                 3.8375e+00  5.4918e-03  3.0584e-02    1.2969e-01
 ## log_k_Z0_Z1          5.4918e-03  2.7613e-03 -1.8820e-04    2.6634e-04
@@ -312,7 +314,7 @@
 
 The summary view of the backtransformed parameters shows that we get no confidence intervals due to overparameterisation. As the optimized  is excessively small, it seems reasonable to fix it to zero.
 m.Z.mkin.5a <- mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin,
-                       parms.ini = c(m.Z.mkin.5$bparms.ode[1:7],
+                       parms.ini = c(m.Z.mkin.5$bparms.ode[1:7],
                                      k_Z3_bound_free = 0),
                        fixed_parms = "k_Z3_bound_free",
                        quiet = TRUE)
@@ -372,17 +374,20 @@


 
 
+
       
   Developed by Johannes Ranke.
 
 
 
-  Site built with pkgdown 1.3.0.
+  Site built with pkgdown 1.4.1.
 
+


 
   
 
+
   
 
diff --git a/docs/articles/web_only/NAFTA_examples.html b/docs/articles/web_only/NAFTA_examples.html
index b8a5215d..3ca93667 100644
--- a/docs/articles/web_only/NAFTA_examples.html
+++ b/docs/articles/web_only/NAFTA_examples.html
@@ -7,8 +7,9 @@
 
 Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance • mkin
 
-
-
+
+
+
 
 
 
@@ -82,13 +83,14 @@
 
 
       
+
       
   
     
       Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance
                         Johannes Ranke
             
-            2019-07-09
+            2019-09-18
       
       
       NAFTA_examples.Rmd
@@ -113,9 +115,9 @@
 p5a <- nafta(NAFTA_SOP_Attachment[["p5a"]])
 ## 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)
+plot(p5a)
 
-print(p5a)
+print(p5a)
 ## Sums of squares:
 ##       SFO      IORE      DFOP 
 ## 465.21753  56.27506  32.06401 
@@ -161,9 +163,9 @@
 p5b <- nafta(NAFTA_SOP_Attachment[["p5b"]])
 ## 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)
+plot(p5b)
 
-print(p5b)
+print(p5b)
 ## Sums of squares:
 ##      SFO     IORE     DFOP 
 ## 94.81123 10.10936  7.55871 
@@ -209,9 +211,9 @@
 p6 <- nafta(NAFTA_SOP_Attachment[["p6"]])
 ## 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)
+plot(p6)
 
-print(p6)
+print(p6)
 ## Sums of squares:
 ##       SFO      IORE      DFOP 
 ## 188.45361  51.00699  42.46931 
@@ -257,9 +259,9 @@
 p7 <- nafta(NAFTA_SOP_Attachment[["p7"]])
 ## 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)
+plot(p7)
 
-print(p7)
+print(p7)
 ## Sums of squares:
 ##      SFO     IORE     DFOP 
 ## 3661.661 3195.030 3174.145 
@@ -307,12 +309,12 @@
 
 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))
+p8 <- nafta(NAFTA_SOP_Attachment[["p8"]], parms.ini = c(k__iore_parent_sink = 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)
+plot(p8)
 
-print(p8)
+print(p8)
 ## Sums of squares:
 ##       SFO      IORE      DFOP 
 ## 1996.9408  444.9237  547.5616 
@@ -362,9 +364,9 @@
 p9a <- nafta(NAFTA_SOP_Attachment[["p9a"]])
 ## 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)
+plot(p9a)
 
-print(p9a)
+print(p9a)
 ## Sums of squares:
 ##       SFO      IORE      DFOP 
 ## 839.35238  88.57064   9.93363 
@@ -416,9 +418,9 @@
 ## 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)
+plot(p9b)
 
-print(p9b)
+print(p9b)
 ## Sums of squares:
 ##      SFO     IORE     DFOP 
 ## 35.64867 23.22334 35.64867 
@@ -465,9 +467,9 @@
 p10 <- nafta(NAFTA_SOP_Attachment[["p10"]])
 ## 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)
+plot(p10)
 
-print(p10)
+print(p10)
 ## Sums of squares:
 ##      SFO     IORE     DFOP 
 ## 899.4089 336.4348 899.4089 
@@ -518,9 +520,9 @@
 p11 <- nafta(NAFTA_SOP_Attachment[["p11"]])
 ## 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)
+plot(p11)
 
-print(p11)
+print(p11)
 ## Sums of squares:
 ##      SFO     IORE     DFOP 
 ## 579.6805 204.7932 144.7783 
@@ -574,9 +576,9 @@
 ## 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)
+plot(p12a)
 
-print(p12a)
+print(p12a)
 ## Sums of squares:
 ##      SFO     IORE     DFOP 
 ## 695.4440 220.0685 695.4440 
@@ -630,9 +632,9 @@
 ## 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)
+plot(p12b)
 
-print(p12b)
+print(p12b)
 ## Sums of squares:
 ##      SFO     IORE     DFOP 
 ## 58.90242 19.06353 58.90242 
@@ -682,9 +684,9 @@
 ## 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(p13)
+plot(p13)
 
-print(p13)
+print(p13)
 ## Sums of squares:
 ##      SFO     IORE     DFOP 
 ## 174.5971 142.3951 174.5971 
@@ -735,9 +737,9 @@
 ## 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)
+plot(p14)
 
-print(p14)
+print(p14)
 ## Sums of squares:
 ##      SFO     IORE     DFOP 
 ## 48.43249 28.67746 27.26248 
@@ -789,9 +791,9 @@
 ## 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)
+plot(p15a)
 
-print(p15a)
+print(p15a)
 ## Sums of squares:
 ##      SFO     IORE     DFOP 
 ## 245.5248 135.0132 245.5248 
@@ -837,9 +839,9 @@
 ## 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)
+plot(p15b)
 
-print(p15b)
+print(p15b)
 ## Sums of squares:
 ##       SFO      IORE      DFOP 
 ## 106.91629  68.55574 106.91629 
@@ -888,9 +890,9 @@
 ## The representative half-life of the IORE model is longer than the one corresponding
 ## to the terminal degradation rate found with the DFOP model.
 ## The representative half-life obtained from the DFOP model may be used
-plot(p16)
+plot(p16)
 
-print(p16)
+print(p16)
 ## Sums of squares:
 ##      SFO     IORE     DFOP 
 ## 3831.804 2062.008 1550.980 
@@ -998,17 +1000,20 @@
 

 
 
+
       
   Developed by Johannes Ranke.
 
 
 
-  Site built with pkgdown 1.3.0.
+  Site built with pkgdown 1.4.1.
 
+
       
 

 
   
 
+
   
 
diff --git a/docs/articles/web_only/benchmarks.html b/docs/articles/web_only/benchmarks.html
index f71659be..f1ed1f96 100644
--- a/docs/articles/web_only/benchmarks.html
+++ b/docs/articles/web_only/benchmarks.html
@@ -7,8 +7,9 @@
 
 Benchmark timings for mkin on various systems • mkin
 
-
-
+
+
+
 
 
 
@@ -82,13 +83,14 @@
 
 
       
+
       
   
     
       Benchmark timings for mkin on various systems
                         Johannes Ranke
             
-            2019-07-09
+            2019-09-18
       
       
       benchmarks.Rmd
@@ -101,12 +103,12 @@
 
 Systems
 Each system is characterized by its CPU type, the operating system type and the mkin version.
-cpu_model <- benchmarkme::get_cpu()$model_name
-operating_system <- Sys.info()[["sysname"]]
-mkin_version <- as.character(packageVersion("mkin"))
-system_string <- paste0(operating_system, ", ", cpu_model, ", mkin version ", mkin_version)
-load("~/git/mkin/vignettes/web_only/mkin_benchmarks.rda")
-mkin_benchmarks[system_string, c("CPU", "OS", "mkin")] <- c(cpu_model, operating_system, mkin_version)
+cpu_model <- benchmarkme::get_cpu()$model_name
+operating_system <- Sys.info()[["sysname"]]
+mkin_version <- as.character(packageVersion("mkin"))
+system_string <- paste0(operating_system, ", ", cpu_model, ", mkin version ", mkin_version)
+load("~/git/mkin/vignettes/web_only/mkin_benchmarks.rda")
+mkin_benchmarks[system_string, c("CPU", "OS", "mkin")] <- c(cpu_model, operating_system, mkin_version)
 
 if (mkin_version > "0.9.48.1") {
   mmkin_bench <- function(models, datasets, error_model = "const") mmkin(models, datasets, error_model = error_model, cores = 1, quiet = TRUE)
@@ -114,8 +116,8 @@
   mmkin_bench <- function(models, datasets, error_model = NULL) mmkin(models, datasets, reweight.method = error_model, cores = 1, quiet = TRUE)
 }
 # Parent only
-t1 <- system.time(mmkin_bench(c("SFO", "FOMC", "DFOP", "HS"), list(FOCUS_2006_C, FOCUS_2006_D)))[["elapsed"]]
-t2 <- system.time(mmkin_bench(c("SFO", "FOMC", "DFOP", "HS"), list(FOCUS_2006_C, FOCUS_2006_D), error_model = "tc"))[["elapsed"]]
+t1 <- system.time(mmkin_bench(c("SFO", "FOMC", "DFOP", "HS"), list(FOCUS_2006_C, FOCUS_2006_D)))[["elapsed"]]
+t2 <- system.time(mmkin_bench(c("SFO", "FOMC", "DFOP", "HS"), list(FOCUS_2006_C, FOCUS_2006_D), error_model = "tc"))[["elapsed"]]
 ## Warning in mkinfit(models[[model_index]], datasets[[dataset_index]], ...): Optimisation did not converge:
 ## iteration limit reached without convergence (10)
 # One metabolite
@@ -131,7 +133,7 @@
   parent = mkinsub("FOMC", "m1"),
   m1 = mkinsub("SFO"))
 ## Successfully compiled differential equation model from auto-generated C code.
-t3 <- system.time(mmkin_bench(list(SFO_SFO, FOMC_SFO, DFOP_SFO), list(FOCUS_2006_D)))[["elapsed"]]
+t3 <- system.time(mmkin_bench(list(SFO_SFO, FOMC_SFO, DFOP_SFO), list(FOCUS_2006_D)))[["elapsed"]]
 ## Warning in mkinfit(models[[model_index]], datasets[[dataset_index]], ...):
 ## Observations with value of zero were removed from the data
 ## Warning in mkinfit(models[[model_index]], datasets[[dataset_index]], ...):
@@ -139,8 +141,8 @@
 
 ## Warning in mkinfit(models[[model_index]], datasets[[dataset_index]], ...):
 ## Observations with value of zero were removed from the data
-t4 <- system.time(mmkin_bench(list(SFO_SFO, FOMC_SFO, DFOP_SFO), list(subset(FOCUS_2006_D, value != 0)), error_model = "tc"))[["elapsed"]]
-t5 <- system.time(mmkin_bench(list(SFO_SFO, FOMC_SFO, DFOP_SFO), list(FOCUS_2006_D), error_model = "obs"))[["elapsed"]]
+t4 <- system.time(mmkin_bench(list(SFO_SFO, FOMC_SFO, DFOP_SFO), list(subset(FOCUS_2006_D, value != 0)), error_model = "tc"))[["elapsed"]]
+t5 <- system.time(mmkin_bench(list(SFO_SFO, FOMC_SFO, DFOP_SFO), list(FOCUS_2006_D), error_model = "obs"))[["elapsed"]]
 ## Warning in mkinfit(models[[model_index]], datasets[[dataset_index]], ...):
 ## Observations with value of zero were removed from the data
 
@@ -155,7 +157,7 @@
                            M2 = mkinsub("SFO"),
                            use_of_ff = "max", quiet = TRUE)
 
-m_synth_DFOP_par <- mkinmod(parent = mkinsub("DFOP", c("M1", "M2")),
+m_synth_DFOP_par <- mkinmod(parent = mkinsub("DFOP", c("M1", "M2")),
                            M1 = mkinsub("SFO"),
                            M2 = mkinsub("SFO"),
                            use_of_ff = "max", quiet = TRUE)
@@ -164,16 +166,16 @@
 
 DFOP_par_c <- synthetic_data_for_UBA_2014[[12]]$data
 
-t6 <- system.time(mmkin_bench(list(m_synth_SFO_lin), list(SFO_lin_a)))["elapsed"]
-t7 <- system.time(mmkin_bench(list(m_synth_DFOP_par), list(DFOP_par_c)))["elapsed"]
+t6 <- system.time(mmkin_bench(list(m_synth_SFO_lin), list(SFO_lin_a)))["elapsed"]
+t7 <- system.time(mmkin_bench(list(m_synth_DFOP_par), list(DFOP_par_c)))["elapsed"]
 
-t8 <- system.time(mmkin_bench(list(m_synth_SFO_lin), list(SFO_lin_a), error_model = "tc"))["elapsed"]
-t9 <- system.time(mmkin_bench(list(m_synth_DFOP_par), list(DFOP_par_c), error_model = "tc"))["elapsed"]
+t8 <- system.time(mmkin_bench(list(m_synth_SFO_lin), list(SFO_lin_a), error_model = "tc"))["elapsed"]
+t9 <- system.time(mmkin_bench(list(m_synth_DFOP_par), list(DFOP_par_c), error_model = "tc"))["elapsed"]
 
-t10 <- system.time(mmkin_bench(list(m_synth_SFO_lin), list(SFO_lin_a), error_model = "obs"))["elapsed"]
-t11 <- system.time(mmkin_bench(list(m_synth_DFOP_par), list(DFOP_par_c), error_model = "obs"))["elapsed"]
+t10 <- system.time(mmkin_bench(list(m_synth_SFO_lin), list(SFO_lin_a), error_model = "obs"))["elapsed"]
+t11 <- system.time(mmkin_bench(list(m_synth_DFOP_par), list(DFOP_par_c), error_model = "obs"))["elapsed"]
 
-mkin_benchmarks[system_string, paste0("t", 1:11)] <- c(t1, t2, t3, t4, t5, t6, t7, t8, t9, t10, t11)
+mkin_benchmarks[system_string, paste0("t", 1:11)] <- c(t1, t2, t3, t4, t5, t6, t7, t8, t9, t10, t11)
 mkin_benchmarks
 ##                                                                                                       CPU
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 AMD Ryzen 7 1700 Eight-Core Processor
@@ -202,78 +204,78 @@
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 7.064
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 7.296
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 5.936
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 5.826
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 5.920
 ##                                                                         t2
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 11.019
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 22.889
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 12.558
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 21.239
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 20.545
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 36.053
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 36.932
 ##                                                                        t3
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 3.764
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 4.649
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 4.786
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 4.510
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 4.446
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 4.452
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 4.519
 ##                                                                         t4
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 14.347
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 13.789
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2  8.461
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 13.805
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 15.335
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 30.603
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 31.518
 ##                                                                         t5
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1  9.495
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1  6.395
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2  5.675
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3  7.386
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4  6.002
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 10.465
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 10.619
 ##                                                                        t6
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 2.623
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 2.542
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 2.723
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 2.643
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 2.635
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 2.595
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6  2.61
 ##                                                                        t7
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 4.587
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 4.128
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 4.478
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 4.374
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 4.259
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 4.202
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 4.312
 ##                                                                        t8
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 7.525
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 4.632
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 4.862
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3  7.02
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 4.737
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 7.726
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6  8.07
 ##                                                                         t9
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 16.621
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1  8.171
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2  7.618
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 11.124
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4  7.763
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 23.042
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 16.102
 ##                                                                       t10
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 8.576
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 3.676
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 3.579
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 5.388
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 3.427
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 18.26
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 8.013
 ##                                                                        t11
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 31.267
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1  5.636
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2  5.574
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3  7.365
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4  5.626
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 12.106
-save(mkin_benchmarks, file = "~/git/mkin/vignettes/mkin_benchmarks.rda")
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 10.805
+save(mkin_benchmarks, file = "~/git/mkin/vignettes/mkin_benchmarks.rda")
 
   
 
@@ -290,17 +292,20 @@
 

 
 
+
       
   Developed by Johannes Ranke.
 
 
 
-  Site built with pkgdown 1.3.0.
+  Site built with pkgdown 1.4.1.
 
+


 
   
 
+
   
 
diff --git a/docs/articles/web_only/compiled_models.html b/docs/articles/web_only/compiled_models.html
index 0bb564ad..89977591 100644
--- a/docs/articles/web_only/compiled_models.html
+++ b/docs/articles/web_only/compiled_models.html
@@ -7,8 +7,9 @@
 
 Performance benefit by using compiled model definitions in mkin • mkin
 
-
-
+
+
+
 
 
 
@@ -82,13 +83,14 @@
 
 
       
+
       
   
     
       Performance benefit by using compiled model definitions in mkin
                         Johannes Ranke
             
-            2019-07-09
+            2019-09-18
       
       
       compiled_models.Rmd
@@ -101,18 +103,18 @@
 
 Model that can also be solved with Eigenvalues
 This evaluation is taken from the example section of mkinfit. When using an mkin version equal to or greater than 0.9-36 and a C compiler (gcc) is available, you will see a message that the model is being compiled from autogenerated C code when defining a model using mkinmod. The mkinmod() function checks for presence of the gcc compiler using
-Sys.which("gcc")
+Sys.which("gcc")
 ##            gcc 
 ## "/usr/bin/gcc"
 First, we build a simple degradation model for a parent compound with one metabolite.
-library("mkin", quietly = TRUE)
+library("mkin", quietly = TRUE)
 SFO_SFO <- mkinmod(
   parent = mkinsub("SFO", "m1"),
   m1 = mkinsub("SFO"))
 ## Successfully compiled differential equation model from auto-generated C code.
 We can compare the performance of the Eigenvalue based solution against the compiled version and the R implementation of the differential equations using the benchmark package.
-if (require(rbenchmark)) {
-  b.1 <- benchmark(
+if (require(rbenchmark)) {
+  b.1 <- benchmark(
     "deSolve, not compiled" = mkinfit(SFO_SFO, FOCUS_2006_D,
                                       solution_type = "deSolve",
                                       use_compiled = FALSE, quiet = TRUE),
@@ -121,11 +123,11 @@
     "deSolve, compiled" = mkinfit(SFO_SFO, FOCUS_2006_D,
                                   solution_type = "deSolve", quiet = TRUE),
     replications = 3)
-  print(b.1)
-  factor_SFO_SFO <- round(b.1["1", "relative"])
+  print(b.1)
+  factor_SFO_SFO <- round(b.1["1", "relative"])
 } else {
   factor_SFO_SFO <- NA
-  print("R package rbenchmark is not available")
+  print("R package rbenchmark is not available")
 }
 ## Lade nötiges Paket: rbenchmark
 ## Warning in mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "deSolve",
@@ -163,9 +165,9 @@
 ## Warning in mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "deSolve", quiet
 ## = TRUE): Observations with value of zero were removed from the data
 ##                    test replications elapsed relative user.self sys.self
-## 3     deSolve, compiled            3   3.127    1.000     3.125        0
-## 1 deSolve, not compiled            3  28.337    9.062    28.322        0
-## 2      Eigenvalue based            3   4.320    1.382     4.317        0
+## 3     deSolve, compiled            3   3.124    1.000     3.122        0
+## 1 deSolve, not compiled            3  28.822    9.226    28.807        0
+## 2      Eigenvalue based            3   4.416    1.414     4.413        0
 ##   user.child sys.child
 ## 3          0         0
 ## 1          0         0
@@ -176,21 +178,21 @@
 
 Model that can not be solved with Eigenvalues
 This evaluation is also taken from the example section of mkinfit.
-if (require(rbenchmark)) {
+if (require(rbenchmark)) {
   FOMC_SFO <- mkinmod(
     parent = mkinsub("FOMC", "m1"),
     m1 = mkinsub( "SFO"))
 
-  b.2 <- benchmark(
+  b.2 <- benchmark(
     "deSolve, not compiled" = mkinfit(FOMC_SFO, FOCUS_2006_D,
                                       use_compiled = FALSE, quiet = TRUE),
     "deSolve, compiled" = mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE),
     replications = 3)
-  print(b.2)
-  factor_FOMC_SFO <- round(b.2["1", "relative"])
+  print(b.2)
+  factor_FOMC_SFO <- round(b.2["1", "relative"])
 } else {
   factor_FOMC_SFO <- NA
-  print("R package benchmark is not available")
+  print("R package benchmark is not available")
 }
 ## Successfully compiled differential equation model from auto-generated C code.
 ## Warning in mkinfit(FOMC_SFO, FOCUS_2006_D, use_compiled = FALSE, quiet =
@@ -214,14 +216,14 @@
 ## Warning in mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE): Observations with
 ## value of zero were removed from the data
 ##                    test replications elapsed relative user.self sys.self
-## 2     deSolve, compiled            3   4.783    1.000     4.780        0
-## 1 deSolve, not compiled            3  52.761   11.031    52.733        0
+## 2     deSolve, compiled            3   4.878    1.000     4.875        0
+## 1 deSolve, not compiled            3  53.602   10.989    53.568        0
 ##   user.child sys.child
 ## 2          0         0
 ## 1          0         0
 Here we get a performance benefit of a factor of 11 using the version of the differential equation model compiled from C code!
 This vignette was built with mkin 0.9.49.6 on
-## R version 3.6.0 (2019-04-26)
+## R version 3.6.1 (2019-07-05)
 ## Platform: x86_64-pc-linux-gnu (64-bit)
 ## Running under: Debian GNU/Linux 10 (buster)
 ## CPU model: AMD Ryzen 7 1700 Eight-Core Processor
@@ -242,17 +244,20 @@
 

 
 
+
       
   Developed by Johannes Ranke.
 
 
 
-  Site built with pkgdown 1.3.0.
+  Site built with pkgdown 1.4.1.
 
+
       
 

 
   
 
+
   
 
-- 
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